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Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Backend} from './backend.js';\nimport {InferenceSession} from './inference-session.js';\n\ninterface BackendInfo {\n  backend: Backend;\n  priority: number;\n\n  initPromise?: Promise<void>;\n  initialized?: boolean;\n  aborted?: boolean;\n  error?: string;\n}\n\nconst backends: Map<string, BackendInfo> = new Map();\nconst backendsSortedByPriority: string[] = [];\n\n/**\n * Register a backend.\n *\n * @param name - the name as a key to lookup as an execution provider.\n * @param backend - the backend object.\n * @param priority - an integer indicating the priority of the backend. Higher number means higher priority. if priority\n * < 0, it will be considered as a 'beta' version and will not be used as a fallback backend by default.\n *\n * @ignore\n */\nexport const registerBackend = (name: string, backend: Backend, priority: number): void => {\n  if (backend && typeof backend.init === 'function' && typeof backend.createInferenceSessionHandler === 'function') {\n    const currentBackend = backends.get(name);\n    if (currentBackend === undefined) {\n      backends.set(name, {backend, priority});\n    } else if (currentBackend.priority > priority) {\n      // same name is already registered with a higher priority. skip registeration.\n      return;\n    } else if (currentBackend.priority === priority) {\n      if (currentBackend.backend !== backend) {\n        throw new Error(`cannot register backend \"${name}\" using priority ${priority}`);\n      }\n    }\n\n    if (priority >= 0) {\n      const i = backendsSortedByPriority.indexOf(name);\n      if (i !== -1) {\n        backendsSortedByPriority.splice(i, 1);\n      }\n\n      for (let i = 0; i < backendsSortedByPriority.length; i++) {\n        if (backends.get(backendsSortedByPriority[i])!.priority <= priority) {\n          backendsSortedByPriority.splice(i, 0, name);\n          return;\n        }\n      }\n      backendsSortedByPriority.push(name);\n    }\n    return;\n  }\n\n  throw new TypeError('not a valid backend');\n};\n\n/**\n * Try to resolve and initialize a backend.\n *\n * @param backendName - the name of the backend.\n * @returns the backend instance if resolved and initialized successfully, or an error message if failed.\n */\nconst tryResolveAndInitializeBackend = async(backendName: string): Promise<Backend|string> => {\n  const backendInfo = backends.get(backendName);\n  if (!backendInfo) {\n    return 'backend not found.';\n  }\n\n  if (backendInfo.initialized) {\n    return backendInfo.backend;\n  } else if (backendInfo.aborted) {\n    return backendInfo.error!;\n  } else {\n    const isInitializing = !!backendInfo.initPromise;\n    try {\n      if (!isInitializing) {\n        backendInfo.initPromise = backendInfo.backend.init(backendName);\n      }\n      await backendInfo.initPromise;\n      backendInfo.initialized = true;\n      return backendInfo.backend;\n    } catch (e) {\n      if (!isInitializing) {\n        backendInfo.error = `${e}`;\n        backendInfo.aborted = true;\n      }\n      return backendInfo.error!;\n    } finally {\n      delete backendInfo.initPromise;\n    }\n  }\n};\n\n/**\n * Resolve execution providers from the specific session options.\n *\n * @param options - the session options object.\n * @returns a promise that resolves to a tuple of an initialized backend instance and a session options object with\n * filtered EP list.\n *\n * @ignore\n */\nexport const resolveBackendAndExecutionProviders = async(options: InferenceSession.SessionOptions):\n    Promise<[backend: Backend, options: InferenceSession.SessionOptions]> => {\n      // extract backend hints from session options\n      const eps = options.executionProviders || [];\n      const backendHints = eps.map(i => typeof i === 'string' ? i : i.name);\n      const backendNames = backendHints.length === 0 ? backendsSortedByPriority : backendHints;\n\n      // try to resolve and initialize all requested backends\n      let backend: Backend|undefined;\n      const errors = [];\n      const availableBackendNames = new Set<string>();\n      for (const backendName of backendNames) {\n        const resolveResult = await tryResolveAndInitializeBackend(backendName);\n        if (typeof resolveResult === 'string') {\n          errors.push({name: backendName, err: resolveResult});\n        } else {\n          if (!backend) {\n            backend = resolveResult;\n          }\n          if (backend === resolveResult) {\n            availableBackendNames.add(backendName);\n          }\n        }\n      }\n\n      // if no backend is available, throw error.\n      if (!backend) {\n        throw new Error(`no available backend found. ERR: ${errors.map(e => `[${e.name}] ${e.err}`).join(', ')}`);\n      }\n\n      // for each explicitly requested backend, if it's not available, output warning message.\n      for (const {name, err} of errors) {\n        if (backendHints.includes(name)) {\n          // eslint-disable-next-line no-console\n          console.warn(`removing requested execution provider \"${\n              name}\" from session options because it is not available: ${err}`);\n        }\n      }\n\n      const filteredEps = eps.filter(i => availableBackendNames.has(typeof i === 'string' ? i : i.name));\n\n      return [\n        backend, new Proxy(options, {\n          get: (target, prop) => {\n            if (prop === 'executionProviders') {\n              return filteredEps;\n            }\n            return Reflect.get(target, prop);\n          }\n        })\n      ];\n    };\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {InferenceSession} from './inference-session.js';\nimport {OnnxValue} from './onnx-value.js';\nimport {TrainingSession} from './training-session.js';\n\n/**\n * @ignore\n */\nexport declare namespace SessionHandler {\n  type FeedsType = {[name: string]: OnnxValue};\n  type FetchesType = {[name: string]: OnnxValue | null};\n  type ReturnType = {[name: string]: OnnxValue};\n}\n\n/**\n * Represents shared SessionHandler functionality\n *\n * @ignore\n */\ninterface SessionHandler {\n  dispose(): Promise<void>;\n\n  readonly inputNames: readonly string[];\n  readonly outputNames: readonly string[];\n}\n\n/**\n * Represent a handler instance of an inference session.\n *\n * @ignore\n */\nexport interface InferenceSessionHandler extends SessionHandler {\n  startProfiling(): void;\n  endProfiling(): void;\n\n  run(feeds: SessionHandler.FeedsType, fetches: SessionHandler.FetchesType,\n      options: InferenceSession.RunOptions): Promise<SessionHandler.ReturnType>;\n}\n\n/**\n * Represent a handler instance of a training inference session.\n *\n * @ignore\n */\nexport interface TrainingSessionHandler extends SessionHandler {\n  readonly evalInputNames: readonly string[];\n  readonly evalOutputNames: readonly string[];\n\n  lazyResetGrad(): Promise<void>;\n  runTrainStep(\n      feeds: SessionHandler.FeedsType, fetches: SessionHandler.FetchesType,\n      options: InferenceSession.RunOptions): Promise<SessionHandler.ReturnType>;\n  runOptimizerStep(options: InferenceSession.RunOptions): Promise<void>;\n  runEvalStep(\n      feeds: SessionHandler.FeedsType, fetches: SessionHandler.FetchesType,\n      options: InferenceSession.RunOptions): Promise<SessionHandler.ReturnType>;\n\n  getParametersSize(trainableOnly: boolean): Promise<number>;\n  loadParametersBuffer(buffer: Uint8Array, trainableOnly: boolean): Promise<void>;\n  getContiguousParameters(trainableOnly: boolean): Promise<OnnxValue>;\n}\n\n/**\n * Represent a backend that provides implementation of model inferencing.\n *\n * @ignore\n */\nexport interface Backend {\n  /**\n   * Initialize the backend asynchronously. Should throw when failed.\n   */\n  init(backendName: string): Promise<void>;\n\n  createInferenceSessionHandler(uriOrBuffer: string|Uint8Array, options?: InferenceSession.SessionOptions):\n      Promise<InferenceSessionHandler>;\n\n  createTrainingSessionHandler?\n      (checkpointStateUriOrBuffer: TrainingSession.UriOrBuffer, trainModelUriOrBuffer: TrainingSession.UriOrBuffer,\n       evalModelUriOrBuffer: TrainingSession.UriOrBuffer, optimizerModelUriOrBuffer: TrainingSession.UriOrBuffer,\n       options: InferenceSession.SessionOptions): Promise<TrainingSessionHandler>;\n}\n\nexport {registerBackend} from './backend-impl.js';\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\n// This file is generated by /js/scripts/update-version.ts\n// Do not modify file content manually.\n\nexport const version = '1.19.0';\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Env} from './env.js';\nimport {version} from './version.js';\n\ntype LogLevelType = Env['logLevel'];\n\nlet logLevelValue: Required<LogLevelType> = 'warning';\n\nexport const env: Env = {\n  wasm: {} as Env.WebAssemblyFlags,\n  webgl: {} as Env.WebGLFlags,\n  webgpu: {} as Env.WebGpuFlags,\n  versions: {common: version},\n\n  set logLevel(value: LogLevelType) {\n    if (value === undefined) {\n      return;\n    }\n    if (typeof value !== 'string' || ['verbose', 'info', 'warning', 'error', 'fatal'].indexOf(value) === -1) {\n      throw new Error(`Unsupported logging level: ${value}`);\n    }\n    logLevelValue = value;\n  },\n  get logLevel(): Required<LogLevelType> {\n    return logLevelValue;\n  },\n};\n\n// set property 'logLevel' so that they can be correctly transferred to worker by `postMessage()`.\nObject.defineProperty(env, 'logLevel', {enumerable: true});\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {env as envImpl} from './env-impl.js';\n\nexport declare namespace Env {\n  export type WasmPathPrefix = string;\n  export interface WasmFilePaths {\n    /**\n     * Specify the override path for the main .wasm file.\n     *\n     * This path should be an absolute path.\n     *\n     * If not modified, the filename of the .wasm file is:\n     * - `ort-wasm-simd-threaded.wasm` for default build\n     * - `ort-wasm-simd-threaded.jsep.wasm` for JSEP build (with WebGPU and WebNN)\n     * - `ort-training-wasm-simd-threaded.wasm` for training build\n     */\n    wasm?: URL|string;\n    /**\n     * Specify the override path for the main .mjs file.\n     *\n     * This path should be an absolute path.\n     *\n     * If not modified, the filename of the .mjs file is:\n     * - `ort-wasm-simd-threaded.mjs` for default build\n     * - `ort-wasm-simd-threaded.jsep.mjs` for JSEP build (with WebGPU and WebNN)\n     * - `ort-training-wasm-simd-threaded.mjs` for training build\n     */\n    mjs?: URL|string;\n  }\n  export type WasmPrefixOrFilePaths = WasmPathPrefix|WasmFilePaths;\n  export interface WebAssemblyFlags {\n    /**\n     * set or get number of thread(s). If omitted or set to 0, number of thread(s) will be determined by system. If set\n     * to 1, no worker thread will be spawned.\n     *\n     * This setting is available only when WebAssembly multithread feature is available in current context.\n     *\n     * @defaultValue `0`\n     */\n    numThreads?: number;\n\n    /**\n     * set or get a boolean value indicating whether to enable SIMD. If set to false, SIMD will be forcely disabled.\n     *\n     * This setting is available only when WebAssembly SIMD feature is available in current context.\n     *\n     * @deprecated This property is deprecated. Since SIMD is supported by all major JavaScript engines, non-SIMD\n     * build is no longer provided. This property will be removed in future release.\n     * @defaultValue `true`\n     */\n    simd?: boolean;\n\n    /**\n     * set or get a boolean value indicating whether to enable trace.\n     *\n     * @deprecated Use `env.trace` instead. If `env.trace` is set, this property will be ignored.\n     * @defaultValue `false`\n     */\n    trace?: boolean;\n\n    /**\n     * Set or get a number specifying the timeout for initialization of WebAssembly backend, in milliseconds. A zero\n     * value indicates no timeout is set.\n     *\n     * @defaultValue `0`\n     */\n    initTimeout?: number;\n\n    /**\n     * Set a custom URL prefix to the .wasm/.mjs files, or an object of overrides for both .wasm/.mjs file. The override\n     * path should be an absolute path.\n     */\n    wasmPaths?: WasmPrefixOrFilePaths;\n\n    /**\n     * Set a custom buffer which contains the WebAssembly binary. If this property is set, the `wasmPaths` property will\n     * be ignored.\n     */\n    wasmBinary?: ArrayBufferLike|Uint8Array;\n\n    /**\n     * Set or get a boolean value indicating whether to proxy the execution of main thread to a worker thread.\n     *\n     * @defaultValue `false`\n     */\n    proxy?: boolean;\n  }\n\n  export interface WebGLFlags {\n    /**\n     * Set or get the WebGL Context ID (webgl or webgl2).\n     *\n     * @defaultValue `'webgl2'`\n     */\n    contextId?: 'webgl'|'webgl2';\n    /**\n     * Get the WebGL rendering context.\n     */\n    readonly context: WebGLRenderingContext;\n    /**\n     * Set or get the maximum batch size for matmul. 0 means to disable batching.\n     *\n     * @deprecated\n     */\n    matmulMaxBatchSize?: number;\n    /**\n     * Set or get the texture cache mode.\n     *\n     * @defaultValue `'full'`\n     */\n    textureCacheMode?: 'initializerOnly'|'full';\n    /**\n     * Set or get the packed texture mode\n     *\n     * @defaultValue `false`\n     */\n    pack?: boolean;\n    /**\n     * Set or get whether enable async download.\n     *\n     * @defaultValue `false`\n     */\n    async?: boolean;\n  }\n\n  export interface WebGpuProfilingDataV1TensorMetadata {\n    dims: readonly number[];\n    dataType: string;\n  }\n  export interface WebGpuProfilingDataV1 {\n    version: 1;\n    inputsMetadata: readonly WebGpuProfilingDataV1TensorMetadata[];\n    outputsMetadata: readonly WebGpuProfilingDataV1TensorMetadata[];\n    kernelId: number;\n    kernelType: string;\n    kernelName: string;\n    programName: string;\n    startTime: number;\n    endTime: number;\n  }\n\n  export type WebGpuProfilingData = WebGpuProfilingDataV1;\n\n  export interface WebGpuFlags {\n    /**\n     * Set or get the profiling mode.\n     *\n     * @deprecated Use `env.webgpu.profiling.mode` instead. If `env.webgpu.profiling.mode` is set, this property will be\n     * ignored.\n     */\n    profilingMode?: 'off'|'default';\n    /**\n     * Set or get the profiling configuration.\n     */\n    profiling?: {\n      /**\n       * Set or get the profiling mode.\n       *\n       * @defaultValue `'off'`\n       */\n      mode?: 'off'|'default';\n\n      /**\n       * Set or get a callback function when a profiling data is received. If not set, the profiling data will be\n       * printed to console.\n       */\n      ondata?: (data: WebGpuProfilingData) => void;\n    };\n    /**\n     * Set or get the power preference.\n     *\n     * Setting this property only has effect before the first WebGPU inference session is created. The value will be\n     * used as options for `navigator.gpu.requestAdapter()`.\n     *\n     * See {@link https://gpuweb.github.io/gpuweb/#dictdef-gpurequestadapteroptions} for more details.\n     *\n     * @defaultValue `undefined`\n     */\n    powerPreference?: 'low-power'|'high-performance';\n    /**\n     * Set or get the force fallback adapter flag.\n     *\n     * Setting this property only has effect before the first WebGPU inference session is created. The value will be\n     * used as options for `navigator.gpu.requestAdapter()`.\n     *\n     * See {@link https://gpuweb.github.io/gpuweb/#dictdef-gpurequestadapteroptions} for more details.\n     *\n     * @defaultValue `undefined`\n     */\n    forceFallbackAdapter?: boolean;\n    /**\n     * Set or get the adapter for WebGPU.\n     *\n     * Setting this property only has effect before the first WebGPU inference session is created. The value will be\n     * used as the GPU adapter for the underlying WebGPU backend to create GPU device.\n     *\n     * If this property is not set, it will be available to get after the first WebGPU inference session is created. The\n     * value will be the GPU adapter that created by the underlying WebGPU backend.\n     *\n     * When use with TypeScript, the type of this property is `GPUAdapter` defined in \"@webgpu/types\".\n     * Use `const adapter = env.webgpu.adapter as GPUAdapter;` in TypeScript to access this property with correct type.\n     *\n     * see comments on {@link Tensor.GpuBufferType}\n     */\n    adapter: unknown;\n    /**\n     * Get the device for WebGPU.\n     *\n     * This property is only available after the first WebGPU inference session is created.\n     *\n     * When use with TypeScript, the type of this property is `GPUDevice` defined in \"@webgpu/types\".\n     * Use `const device = env.webgpu.device as GPUDevice;` in TypeScript to access this property with correct type.\n     *\n     * see comments on {@link Tensor.GpuBufferType} for more details about why not use types defined in \"@webgpu/types\".\n     */\n    readonly device: unknown;\n    /**\n     * Set or get whether validate input content.\n     *\n     * @defaultValue `false`\n     */\n    validateInputContent?: boolean;\n  }\n}\n\nexport interface Env {\n  /**\n   * set the severity level for logging.\n   *\n   * @defaultValue `'warning'`\n   */\n  logLevel?: 'verbose'|'info'|'warning'|'error'|'fatal';\n\n  /**\n   * Indicate whether run in debug mode.\n   *\n   * @defaultValue `false`\n   */\n  debug?: boolean;\n\n  /**\n   * set or get a boolean value indicating whether to enable trace.\n   *\n   * @defaultValue `false`\n   */\n  trace?: boolean;\n\n  /**\n   * Get version of the current package.\n   */\n  readonly versions: {\n    readonly common: string;\n    readonly web?: string;\n    readonly node?: string;\n    // eslint-disable-next-line @typescript-eslint/naming-convention\n    readonly 'react-native'?: string;\n  };\n\n  /**\n   * Represent a set of flags for WebAssembly\n   */\n  readonly wasm: Env.WebAssemblyFlags;\n\n  /**\n   * Represent a set of flags for WebGL\n   */\n  readonly webgl: Env.WebGLFlags;\n\n  /**\n   * Represent a set of flags for WebGPU\n   */\n  readonly webgpu: Env.WebGpuFlags;\n\n  [name: string]: unknown;\n}\n\n/**\n * Represent a set of flags as a global singleton.\n */\nexport const env: Env = envImpl;\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {TensorToDataUrlOptions, TensorToImageDataOptions} from './tensor-conversion.js';\nimport {Tensor} from './tensor.js';\n\n/**\n * implementation of Tensor.toDataURL()\n */\nexport const tensorToDataURL = (tensor: Tensor, options?: TensorToDataUrlOptions): string => {\n  const canvas = typeof document !== 'undefined' ? document.createElement('canvas') : (new OffscreenCanvas(1, 1));\n  canvas.width = tensor.dims[3];\n  canvas.height = tensor.dims[2];\n  const pixels2DContext =\n      canvas.getContext('2d') as (CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D | null);\n\n  if (pixels2DContext != null) {\n    // Default values for height and width & format\n    let width: number;\n    let height: number;\n    if (options?.tensorLayout !== undefined && options.tensorLayout === 'NHWC') {\n      width = tensor.dims[2];\n      height = tensor.dims[3];\n    } else {  // Default layout is NCWH\n      width = tensor.dims[3];\n      height = tensor.dims[2];\n    }\n\n    const inputformat = options?.format !== undefined ? options.format : 'RGB';\n\n    const norm = options?.norm;\n    let normMean: [number, number, number, number];\n    let normBias: [number, number, number, number];\n    if (norm === undefined || norm.mean === undefined) {\n      normMean = [255, 255, 255, 255];\n    } else {\n      if (typeof (norm.mean) === 'number') {\n        normMean = [norm.mean, norm.mean, norm.mean, norm.mean];\n      } else {\n        normMean = [norm.mean[0], norm.mean[1], norm.mean[2], 0];\n        if (norm.mean[3] !== undefined) {\n          normMean[3] = norm.mean[3];\n        }\n      }\n    }\n    if (norm === undefined || norm.bias === undefined) {\n      normBias = [0, 0, 0, 0];\n    } else {\n      if (typeof (norm.bias) === 'number') {\n        normBias = [norm.bias, norm.bias, norm.bias, norm.bias];\n      } else {\n        normBias = [norm.bias[0], norm.bias[1], norm.bias[2], 0];\n        if (norm.bias[3] !== undefined) {\n          normBias[3] = norm.bias[3];\n        }\n      }\n    }\n\n    const stride = height * width;\n    // Default pointer assignments\n    let rTensorPointer = 0, gTensorPointer = stride, bTensorPointer = stride * 2, aTensorPointer = -1;\n\n    // Updating the pointer assignments based on the input image format\n    if (inputformat === 'RGBA') {\n      rTensorPointer = 0;\n      gTensorPointer = stride;\n      bTensorPointer = stride * 2;\n      aTensorPointer = stride * 3;\n    } else if (inputformat === 'RGB') {\n      rTensorPointer = 0;\n      gTensorPointer = stride;\n      bTensorPointer = stride * 2;\n    } else if (inputformat === 'RBG') {\n      rTensorPointer = 0;\n      bTensorPointer = stride;\n      gTensorPointer = stride * 2;\n    }\n\n    for (let i = 0; i < height; i++) {\n      for (let j = 0; j < width; j++) {\n        const R = ((tensor.data[rTensorPointer++] as number) - normBias[0]) * normMean[0];  // R value\n        const G = ((tensor.data[gTensorPointer++] as number) - normBias[1]) * normMean[1];  // G value\n        const B = ((tensor.data[bTensorPointer++] as number) - normBias[2]) * normMean[2];  // B value\n        const A = aTensorPointer === -1 ?\n            255 :\n            ((tensor.data[aTensorPointer++] as number) - normBias[3]) * normMean[3];  // A value\n        // eslint-disable-next-line @typescript-eslint/restrict-plus-operands\n        pixels2DContext.fillStyle = 'rgba(' + R + ',' + G + ',' + B + ',' + A + ')';\n        pixels2DContext.fillRect(j, i, 1, 1);\n      }\n    }\n    if ('toDataURL' in canvas) {\n      return canvas.toDataURL();\n    } else {\n      throw new Error('toDataURL is not supported');\n    }\n  } else {\n    throw new Error('Can not access image data');\n  }\n};\n\n/**\n * implementation of Tensor.toImageData()\n */\nexport const tensorToImageData = (tensor: Tensor, options?: TensorToImageDataOptions): ImageData => {\n  const pixels2DContext = typeof document !== 'undefined' ?\n      document.createElement('canvas').getContext('2d') :\n      new OffscreenCanvas(1, 1).getContext('2d') as OffscreenCanvasRenderingContext2D;\n  let image: ImageData;\n  if (pixels2DContext != null) {\n    // Default values for height and width & format\n    let width: number;\n    let height: number;\n    let channels: number;\n    if (options?.tensorLayout !== undefined && options.tensorLayout === 'NHWC') {\n      width = tensor.dims[2];\n      height = tensor.dims[1];\n      channels = tensor.dims[3];\n    } else {  // Default layout is NCWH\n      width = tensor.dims[3];\n      height = tensor.dims[2];\n      channels = tensor.dims[1];\n    }\n    const inputformat = options !== undefined ? (options.format !== undefined ? options.format : 'RGB') : 'RGB';\n\n    const norm = options?.norm;\n    let normMean: [number, number, number, number];\n    let normBias: [number, number, number, number];\n    if (norm === undefined || norm.mean === undefined) {\n      normMean = [255, 255, 255, 255];\n    } else {\n      if (typeof (norm.mean) === 'number') {\n        normMean = [norm.mean, norm.mean, norm.mean, norm.mean];\n      } else {\n        normMean = [norm.mean[0], norm.mean[1], norm.mean[2], 255];\n        if (norm.mean[3] !== undefined) {\n          normMean[3] = norm.mean[3];\n        }\n      }\n    }\n    if (norm === undefined || norm.bias === undefined) {\n      normBias = [0, 0, 0, 0];\n    } else {\n      if (typeof (norm.bias) === 'number') {\n        normBias = [norm.bias, norm.bias, norm.bias, norm.bias];\n      } else {\n        normBias = [norm.bias[0], norm.bias[1], norm.bias[2], 0];\n        if (norm.bias[3] !== undefined) {\n          normBias[3] = norm.bias[3];\n        }\n      }\n    }\n\n    const stride = height * width;\n    if (options !== undefined) {\n      if (options.format !== undefined && (channels === 4 && options.format !== 'RGBA') ||\n          (channels === 3 && (options.format !== 'RGB' && options.format !== 'BGR'))) {\n        throw new Error('Tensor format doesn\\'t match input tensor dims');\n      }\n    }\n\n    // Default pointer assignments\n    const step = 4;\n    let rImagePointer = 0, gImagePointer = 1, bImagePointer = 2, aImagePointer = 3;\n    let rTensorPointer = 0, gTensorPointer = stride, bTensorPointer = stride * 2, aTensorPointer = -1;\n\n    // Updating the pointer assignments based on the input image format\n    if (inputformat === 'RGBA') {\n      rTensorPointer = 0;\n      gTensorPointer = stride;\n      bTensorPointer = stride * 2;\n      aTensorPointer = stride * 3;\n    } else if (inputformat === 'RGB') {\n      rTensorPointer = 0;\n      gTensorPointer = stride;\n      bTensorPointer = stride * 2;\n    } else if (inputformat === 'RBG') {\n      rTensorPointer = 0;\n      bTensorPointer = stride;\n      gTensorPointer = stride * 2;\n    }\n\n    image = pixels2DContext.createImageData(width, height);\n\n    for (let i = 0; i < height * width;\n         rImagePointer += step, gImagePointer += step, bImagePointer += step, aImagePointer += step, i++) {\n      image.data[rImagePointer] = ((tensor.data[rTensorPointer++] as number) - normBias[0]) * normMean[0];  // R value\n      image.data[gImagePointer] = ((tensor.data[gTensorPointer++] as number) - normBias[1]) * normMean[1];  // G value\n      image.data[bImagePointer] = ((tensor.data[bTensorPointer++] as number) - normBias[2]) * normMean[2];  // B value\n      image.data[aImagePointer] = aTensorPointer === -1 ?\n          255 :\n          ((tensor.data[aTensorPointer++] as number) - normBias[3]) * normMean[3];  // A value\n    }\n\n  } else {\n    throw new Error('Can not access image data');\n  }\n  return image;\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {OptionsDimensions, OptionsFormat, OptionsNormalizationParameters, OptionsTensorFormat, OptionsTensorLayout, TensorFromGpuBufferOptions, TensorFromImageBitmapOptions, TensorFromImageDataOptions, TensorFromImageElementOptions, TensorFromTextureOptions, TensorFromUrlOptions} from './tensor-factory.js';\nimport {Tensor} from './tensor-impl.js';\nimport {Tensor as TensorInterface} from './tensor.js';\n\ninterface BufferToTensorOptions extends OptionsDimensions, OptionsTensorLayout, OptionsNormalizationParameters,\n                                        OptionsFormat, OptionsTensorFormat {}\n\n/**\n * Create a new tensor object from image object\n *\n * @param buffer - Extracted image buffer data - assuming RGBA format\n * @param imageFormat - input image configuration - required configurations height, width, format\n * @param tensorFormat - output tensor configuration - Default is RGB format\n */\nexport const bufferToTensor = (buffer: Uint8ClampedArray|undefined, options: BufferToTensorOptions): Tensor => {\n  if (buffer === undefined) {\n    throw new Error('Image buffer must be defined');\n  }\n  if (options.height === undefined || options.width === undefined) {\n    throw new Error('Image height and width must be defined');\n  }\n  if (options.tensorLayout === 'NHWC') {\n    throw new Error('NHWC Tensor layout is not supported yet');\n  }\n\n  const {height, width} = options;\n\n  const norm = options.norm ?? {mean: 255, bias: 0};\n  let normMean: [number, number, number, number];\n  let normBias: [number, number, number, number];\n\n  if (typeof (norm.mean) === 'number') {\n    normMean = [norm.mean, norm.mean, norm.mean, norm.mean];\n  } else {\n    normMean = [norm.mean![0], norm.mean![1], norm.mean![2], norm.mean![3] ?? 255];\n  }\n\n  if (typeof (norm.bias) === 'number') {\n    normBias = [norm.bias, norm.bias, norm.bias, norm.bias];\n  } else {\n    normBias = [norm.bias![0], norm.bias![1], norm.bias![2], norm.bias![3] ?? 0];\n  }\n\n  const inputformat = options.format !== undefined ? options.format : 'RGBA';\n  // default value is RGBA since imagedata and HTMLImageElement uses it\n\n  const outputformat =\n      options.tensorFormat !== undefined ? (options.tensorFormat !== undefined ? options.tensorFormat : 'RGB') : 'RGB';\n  const stride = height * width;\n  const float32Data = outputformat === 'RGBA' ? new Float32Array(stride * 4) : new Float32Array(stride * 3);\n\n  // Default pointer assignments\n  let step = 4, rImagePointer = 0, gImagePointer = 1, bImagePointer = 2, aImagePointer = 3;\n  let rTensorPointer = 0, gTensorPointer = stride, bTensorPointer = stride * 2, aTensorPointer = -1;\n\n  // Updating the pointer assignments based on the input image format\n  if (inputformat === 'RGB') {\n    step = 3;\n    rImagePointer = 0;\n    gImagePointer = 1;\n    bImagePointer = 2;\n    aImagePointer = -1;\n  }\n\n  // Updating the pointer assignments based on the output tensor format\n  if (outputformat === 'RGBA') {\n    aTensorPointer = stride * 3;\n  } else if (outputformat === 'RBG') {\n    rTensorPointer = 0;\n    bTensorPointer = stride;\n    gTensorPointer = stride * 2;\n  } else if (outputformat === 'BGR') {\n    bTensorPointer = 0;\n    gTensorPointer = stride;\n    rTensorPointer = stride * 2;\n  }\n\n  for (let i = 0; i < stride;\n       i++, rImagePointer += step, bImagePointer += step, gImagePointer += step, aImagePointer += step) {\n    float32Data[rTensorPointer++] = (buffer[rImagePointer] + normBias[0]) / normMean[0];\n    float32Data[gTensorPointer++] = (buffer[gImagePointer] + normBias[1]) / normMean[1];\n    float32Data[bTensorPointer++] = (buffer[bImagePointer] + normBias[2]) / normMean[2];\n    if (aTensorPointer !== -1 && aImagePointer !== -1) {\n      float32Data[aTensorPointer++] = (buffer[aImagePointer] + normBias[3]) / normMean[3];\n    }\n  }\n\n  // Float32Array -> ort.Tensor\n  const outputTensor = outputformat === 'RGBA' ? new Tensor('float32', float32Data, [1, 4, height, width]) :\n                                                 new Tensor('float32', float32Data, [1, 3, height, width]);\n  return outputTensor;\n};\n\n/**\n * implementation of Tensor.fromImage().\n */\nexport const tensorFromImage = async(\n    image: ImageData|HTMLImageElement|ImageBitmap|string,\n    options?: TensorFromImageDataOptions|TensorFromImageElementOptions|TensorFromImageBitmapOptions|\n    TensorFromUrlOptions): Promise<Tensor> => {\n  // checking the type of image object\n  const isHTMLImageEle = typeof (HTMLImageElement) !== 'undefined' && image instanceof HTMLImageElement;\n  const isImageDataEle = typeof (ImageData) !== 'undefined' && image instanceof ImageData;\n  const isImageBitmap = typeof (ImageBitmap) !== 'undefined' && image instanceof ImageBitmap;\n  const isString = typeof image === 'string';\n\n  let data: Uint8ClampedArray|undefined;\n  let bufferToTensorOptions: BufferToTensorOptions = options ?? {};\n\n  const createCanvas = () => {\n    if (typeof document !== 'undefined') {\n      return document.createElement('canvas');\n    } else if (typeof OffscreenCanvas !== 'undefined') {\n      return new OffscreenCanvas(1, 1);\n    } else {\n      throw new Error('Canvas is not supported');\n    }\n  };\n  const createCanvasContext = (canvas: HTMLCanvasElement|OffscreenCanvas) => {\n    if (canvas instanceof HTMLCanvasElement) {\n      return canvas.getContext('2d');\n    } else if (canvas instanceof OffscreenCanvas) {\n      return canvas.getContext('2d') as OffscreenCanvasRenderingContext2D;\n    } else {\n      return null;\n    }\n  };\n  // filling and checking image configuration options\n  if (isHTMLImageEle) {\n    // HTMLImageElement - image object - format is RGBA by default\n    const canvas = createCanvas();\n    canvas.width = image.width;\n    canvas.height = image.height;\n    const pixels2DContext = createCanvasContext(canvas);\n\n    if (pixels2DContext != null) {\n      let height = image.height;\n      let width = image.width;\n      if (options !== undefined && options.resizedHeight !== undefined && options.resizedWidth !== undefined) {\n        height = options.resizedHeight;\n        width = options.resizedWidth;\n      }\n\n      if (options !== undefined) {\n        bufferToTensorOptions = options;\n        if (options.tensorFormat !== undefined) {\n          throw new Error('Image input config format must be RGBA for HTMLImageElement');\n        } else {\n          bufferToTensorOptions.tensorFormat = 'RGBA';\n        }\n        bufferToTensorOptions.height = height;\n        bufferToTensorOptions.width = width;\n      } else {\n        bufferToTensorOptions.tensorFormat = 'RGBA';\n        bufferToTensorOptions.height = height;\n        bufferToTensorOptions.width = width;\n      }\n\n      pixels2DContext.drawImage(image, 0, 0);\n      data = pixels2DContext.getImageData(0, 0, width, height).data;\n    } else {\n      throw new Error('Can not access image data');\n    }\n  } else if (isImageDataEle) {\n    let height: number;\n    let width: number;\n\n    if (options !== undefined && options.resizedWidth !== undefined && options.resizedHeight !== undefined) {\n      height = options.resizedHeight;\n      width = options.resizedWidth;\n    } else {\n      height = image.height;\n      width = image.width;\n    }\n\n    if (options !== undefined) {\n      bufferToTensorOptions = options;\n    }\n    bufferToTensorOptions.format = 'RGBA';\n    bufferToTensorOptions.height = height;\n    bufferToTensorOptions.width = width;\n\n    if (options !== undefined) {\n      const tempCanvas = createCanvas();\n\n      tempCanvas.width = width;\n      tempCanvas.height = height;\n\n      const pixels2DContext = createCanvasContext(tempCanvas);\n\n      if (pixels2DContext != null) {\n        pixels2DContext.putImageData(image, 0, 0);\n        data = pixels2DContext.getImageData(0, 0, width, height).data;\n      } else {\n        throw new Error('Can not access image data');\n      }\n    } else {\n      data = image.data;\n    }\n  } else if (isImageBitmap) {\n    // ImageBitmap - image object - format must be provided by user\n    if (options === undefined) {\n      throw new Error('Please provide image config with format for Imagebitmap');\n    }\n\n    const canvas = createCanvas();\n    canvas.width = image.width;\n    canvas.height = image.height;\n    const pixels2DContext = createCanvasContext(canvas);\n\n    if (pixels2DContext != null) {\n      const height = image.height;\n      const width = image.width;\n      pixels2DContext.drawImage(image, 0, 0, width, height);\n      data = pixels2DContext.getImageData(0, 0, width, height).data;\n      bufferToTensorOptions.height = height;\n      bufferToTensorOptions.width = width;\n      return bufferToTensor(data, bufferToTensorOptions);\n    } else {\n      throw new Error('Can not access image data');\n    }\n  } else if (isString) {\n    return new Promise((resolve, reject) => {\n      const canvas = createCanvas();\n      const context = createCanvasContext(canvas);\n      if (!image || !context) {\n        return reject();\n      }\n      const newImage = new Image();\n      newImage.crossOrigin = 'Anonymous';\n      newImage.src = image;\n      newImage.onload = () => {\n        canvas.width = newImage.width;\n        canvas.height = newImage.height;\n        context.drawImage(newImage, 0, 0, canvas.width, canvas.height);\n        const img = context.getImageData(0, 0, canvas.width, canvas.height);\n\n        bufferToTensorOptions.height = canvas.height;\n        bufferToTensorOptions.width = canvas.width;\n        resolve(bufferToTensor(img.data, bufferToTensorOptions));\n      };\n    });\n  } else {\n    throw new Error('Input data provided is not supported - aborted tensor creation');\n  }\n\n  if (data !== undefined) {\n    return bufferToTensor(data, bufferToTensorOptions);\n  } else {\n    throw new Error('Input data provided is not supported - aborted tensor creation');\n  }\n};\n\n/**\n * implementation of Tensor.fromTexture().\n */\nexport const tensorFromTexture = <T extends TensorInterface.TextureDataTypes>(\n    texture: TensorInterface.TextureType, options: TensorFromTextureOptions<T>): Tensor => {\n  const {width, height, download, dispose} = options;\n  // Always assume RGBAF32. TODO: support different texture format\n  const dims = [1, height, width, 4];\n  return new Tensor({location: 'texture', type: 'float32', texture, dims, download, dispose});\n};\n\n/**\n * implementation of Tensor.fromGpuBuffer().\n */\nexport const tensorFromGpuBuffer = <T extends TensorInterface.GpuBufferDataTypes>(\n    gpuBuffer: TensorInterface.GpuBufferType, options: TensorFromGpuBufferOptions<T>): Tensor => {\n  const {dataType, dims, download, dispose} = options;\n  return new Tensor({location: 'gpu-buffer', type: dataType ?? 'float32', gpuBuffer, dims, download, dispose});\n};\n\n/**\n * implementation of Tensor.fromPinnedBuffer().\n */\nexport const tensorFromPinnedBuffer = <T extends TensorInterface.CpuPinnedDataTypes>(\n    type: T, buffer: TensorInterface.DataTypeMap[T], dims?: readonly number[]): Tensor =>\n    new Tensor({location: 'cpu-pinned', type, data: buffer, dims: dims ?? [buffer.length]});\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from './tensor.js';\n\nexport type SupportedTypedArrayConstructors = Float32ArrayConstructor|Uint8ArrayConstructor|Int8ArrayConstructor|\n    Uint16ArrayConstructor|Int16ArrayConstructor|Int32ArrayConstructor|BigInt64ArrayConstructor|Uint8ArrayConstructor|\n    Float64ArrayConstructor|Uint32ArrayConstructor|BigUint64ArrayConstructor;\nexport type SupportedTypedArray = InstanceType<SupportedTypedArrayConstructors>;\n\n// a runtime map that maps type string to TypedArray constructor. Should match Tensor.DataTypeMap.\nexport const NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP = new Map<string, SupportedTypedArrayConstructors>([\n  ['float32', Float32Array],\n  ['uint8', Uint8Array],\n  ['int8', Int8Array],\n  ['uint16', Uint16Array],\n  ['int16', Int16Array],\n  ['int32', Int32Array],\n  ['bool', Uint8Array],\n  ['float64', Float64Array],\n  ['uint32', Uint32Array],\n]);\n\n// a runtime map that maps type string to TypedArray constructor. Should match Tensor.DataTypeMap.\nexport const NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP = new Map<SupportedTypedArrayConstructors, Tensor.Type>([\n  [Float32Array, 'float32'],\n  [Uint8Array, 'uint8'],\n  [Int8Array, 'int8'],\n  [Uint16Array, 'uint16'],\n  [Int16Array, 'int16'],\n  [Int32Array, 'int32'],\n  [Float64Array, 'float64'],\n  [Uint32Array, 'uint32'],\n]);\n\n// a dummy type declaration for Float16Array in case any polyfill is available.\ndeclare global {\n  // eslint-disable-next-line @typescript-eslint/naming-convention, @typescript-eslint/no-explicit-any\n  const Float16Array: any;\n}\n\n// the following code allows delaying execution of BigInt/Float16Array checking. This allows lazy initialization for\n// NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP and NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP, which allows BigInt/Float16Array\n// polyfill if available.\nlet isTypedArrayChecked = false;\nexport const checkTypedArray = () => {\n  if (!isTypedArrayChecked) {\n    isTypedArrayChecked = true;\n    const isBigInt64ArrayAvailable = typeof BigInt64Array !== 'undefined' && BigInt64Array.from;\n    const isBigUint64ArrayAvailable = typeof BigUint64Array !== 'undefined' && BigUint64Array.from;\n    const isFloat16ArrayAvailable = typeof Float16Array !== 'undefined' && Float16Array.from;\n\n    if (isBigInt64ArrayAvailable) {\n      NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('int64', BigInt64Array);\n      NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigInt64Array, 'int64');\n    }\n    if (isBigUint64ArrayAvailable) {\n      NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('uint64', BigUint64Array);\n      NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigUint64Array, 'uint64');\n    }\n    if (isFloat16ArrayAvailable) {\n      NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('float16', Float16Array);\n      NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(Float16Array, 'float16');\n    } else {\n      // if Float16Array is not available, use 'Uint16Array' to store the data.\n      NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('float16', Uint16Array);\n    }\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {CpuPinnedConstructorParameters, GpuBufferConstructorParameters, TextureConstructorParameters} from './tensor-factory.js';\nimport {Tensor} from './tensor-impl.js';\n\n/**\n * calculate size from dims.\n *\n * @param dims the dims array. May be an illegal input.\n */\nexport const calculateSize = (dims: readonly unknown[]): number => {\n  let size = 1;\n  for (let i = 0; i < dims.length; i++) {\n    const dim = dims[i];\n    if (typeof dim !== 'number' || !Number.isSafeInteger(dim)) {\n      throw new TypeError(`dims[${i}] must be an integer, got: ${dim}`);\n    }\n    if (dim < 0) {\n      throw new RangeError(`dims[${i}] must be a non-negative integer, got: ${dim}`);\n    }\n    size *= dim;\n  }\n  return size;\n};\n\n/**\n * implementation of Tensor.reshape()\n */\nexport const tensorReshape = (tensor: Tensor, dims: readonly number[]): Tensor => {\n  switch (tensor.location) {\n    case 'cpu':\n      return new Tensor(tensor.type, tensor.data, dims);\n    case 'cpu-pinned':\n      return new Tensor({\n        location: 'cpu-pinned',\n        data: tensor.data as CpuPinnedConstructorParameters['data'],\n        type: tensor.type as CpuPinnedConstructorParameters['type'],\n        dims,\n      });\n    case 'texture':\n      return new Tensor({\n        location: 'texture',\n        texture: tensor.texture,\n        type: tensor.type as TextureConstructorParameters['type'],\n        dims,\n      });\n    case 'gpu-buffer':\n      return new Tensor({\n        location: 'gpu-buffer',\n        gpuBuffer: tensor.gpuBuffer,\n        type: tensor.type as GpuBufferConstructorParameters['type'],\n        dims,\n      });\n    default:\n      throw new Error(`tensorReshape: tensor location ${tensor.location} is not supported`);\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {tensorToDataURL, tensorToImageData} from './tensor-conversion-impl.js';\nimport {TensorToDataUrlOptions, TensorToImageDataOptions} from './tensor-conversion.js';\nimport {tensorFromGpuBuffer, tensorFromImage, tensorFromPinnedBuffer, tensorFromTexture} from './tensor-factory-impl.js';\nimport {CpuPinnedConstructorParameters, GpuBufferConstructorParameters, TensorFromGpuBufferOptions, TensorFromImageBitmapOptions, TensorFromImageDataOptions, TensorFromImageElementOptions, TensorFromTextureOptions, TensorFromUrlOptions, TextureConstructorParameters} from './tensor-factory.js';\nimport {checkTypedArray, NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP, NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP, SupportedTypedArray, SupportedTypedArrayConstructors} from './tensor-impl-type-mapping.js';\nimport {calculateSize, tensorReshape} from './tensor-utils-impl.js';\nimport {Tensor as TensorInterface} from './tensor.js';\n\n// type aliases for those exported from Tensor interface\n\ntype TensorType = TensorInterface.Type;\ntype TensorDataType = TensorInterface.DataType;\ntype TensorDataLocation = TensorInterface.DataLocation;\ntype TensorTextureType = TensorInterface.TextureType;\ntype TensorGpuBufferType = TensorInterface.GpuBufferType;\n\n/**\n * the implementation of Tensor interface.\n *\n * @ignore\n */\nexport class Tensor implements TensorInterface {\n  // #region constructors\n\n  /**\n   * Construct a new CPU tensor object from the given type, data and dims.\n   */\n  constructor(\n      type: TensorType, data: TensorDataType|readonly string[]|readonly number[]|readonly boolean[],\n      dims?: readonly number[]);\n  /**\n   * Construct a new CPU tensor object from the given data and dims. Type is inferred from data.\n   */\n  constructor(data: TensorDataType|readonly string[]|readonly boolean[], dims?: readonly number[]);\n  /**\n   * Construct a new tensor object from the pinned CPU data with the given type and dims.\n   *\n   * Tensor's location will be set to 'cpu-pinned'.\n   *\n   * @param params - Specify the parameters to construct the tensor.\n   */\n  constructor(params: CpuPinnedConstructorParameters);\n  /**\n   * Construct a new tensor object from the WebGL texture with the given type and dims.\n   *\n   * Tensor's location will be set to 'texture'.\n   *\n   * @param params - Specify the parameters to construct the tensor.\n   */\n  constructor(params: TextureConstructorParameters);\n  /**\n   * Construct a new tensor object from the WebGPU buffer with the given type and dims.\n   *\n   * Tensor's location will be set to 'gpu-buffer'.\n   *\n   * @param params - Specify the parameters to construct the tensor.\n   */\n  constructor(params: GpuBufferConstructorParameters);\n\n  /**\n   * implementation.\n   */\n  constructor(\n      arg0: TensorType|TensorDataType|readonly string[]|readonly boolean[]|CpuPinnedConstructorParameters|\n      TextureConstructorParameters|GpuBufferConstructorParameters,\n      arg1?: TensorDataType|readonly number[]|readonly string[]|readonly boolean[], arg2?: readonly number[]) {\n    // perform one-time check for BigInt/Float16Array support\n    checkTypedArray();\n\n    let type: TensorType;\n    let dims: readonly number[];\n\n    if (typeof arg0 === 'object' && 'location' in arg0) {\n      //\n      // constructing tensor from specific location\n      //\n      this.dataLocation = arg0.location;\n      type = arg0.type;\n      dims = arg0.dims;\n      switch (arg0.location) {\n        case 'cpu-pinned': {\n          const expectedTypedArrayConstructor = NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(type);\n          if (!expectedTypedArrayConstructor) {\n            throw new TypeError(`unsupported type \"${type}\" to create tensor from pinned buffer`);\n          }\n          if (!(arg0.data instanceof expectedTypedArrayConstructor)) {\n            throw new TypeError(`buffer should be of type ${expectedTypedArrayConstructor.name}`);\n          }\n          this.cpuData = arg0.data;\n          break;\n        }\n        case 'texture': {\n          if (type !== 'float32') {\n            throw new TypeError(`unsupported type \"${type}\" to create tensor from texture`);\n          }\n          this.gpuTextureData = arg0.texture;\n          this.downloader = arg0.download;\n          this.disposer = arg0.dispose;\n          break;\n        }\n        case 'gpu-buffer': {\n          if ((type !== 'float32' && type !== 'float16' && type !== 'int32' && type !== 'int64' && type !== 'uint32' &&\n               type !== 'uint8' && type !== 'bool')) {\n            throw new TypeError(`unsupported type \"${type}\" to create tensor from gpu buffer`);\n          }\n          this.gpuBufferData = arg0.gpuBuffer;\n          this.downloader = arg0.download;\n          this.disposer = arg0.dispose;\n          break;\n        }\n        default:\n          throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`);\n      }\n    } else {\n      //\n      // constructing tensor of location 'cpu'\n      //\n      let data: TensorDataType;\n      let maybeDims: typeof arg1|typeof arg2;\n      // check whether arg0 is type or data\n      if (typeof arg0 === 'string') {\n        //\n        // Override: constructor(type, data, ...)\n        //\n        type = arg0;\n        maybeDims = arg2;\n        if (arg0 === 'string') {\n          // string tensor\n          if (!Array.isArray(arg1)) {\n            throw new TypeError('A string tensor\\'s data must be a string array.');\n          }\n          // we don't check whether every element in the array is string; this is too slow. we assume it's correct and\n          // error will be populated at inference\n          data = arg1;\n        } else {\n          // numeric tensor\n          const typedArrayConstructor = NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(arg0);\n          if (typedArrayConstructor === undefined) {\n            throw new TypeError(`Unsupported tensor type: ${arg0}.`);\n          }\n          if (Array.isArray(arg1)) {\n            if (arg0 === 'float16' && typedArrayConstructor === Uint16Array) {\n              // When no Float16Array polyfill is used, we cannot create 'float16' tensor from number array.\n              //\n              // Throw error here because when user try to use number array as data,\n              // e.g. new Tensor('float16', [1, 2, 3, 4], dims)), it will actually call\n              // Uint16Array.from(arg1) which generates wrong data.\n              throw new TypeError(\n                  'Creating a float16 tensor from number array is not supported. Please use Uint16Array as data.');\n            } else if (arg0 === 'uint64' || arg0 === 'int64') {\n              // use 'as any' here because:\n              // 1. TypeScript's check on type of 'Array.isArray()' does not work with readonly arrays.\n              // see https://github.com/microsoft/TypeScript/issues/17002\n              // 2. TypeScript's check on union type of '(BigInt64ArrayConstructor|BigUint64ArrayConstructor).from()'\n              // does not accept parameter mapFn.\n              // 3. parameters of 'SupportedTypedArrayConstructors.from()' does not match the requirement of the union\n              // type.\n\n              // assume 'arg1' is of type \"readonly number[]|readonly bigint[]\" here.\n\n              // eslint-disable-next-line @typescript-eslint/no-explicit-any\n              data = (typedArrayConstructor as any).from(arg1, BigInt);\n            } else {\n              // assume 'arg1' is of type \"readonly number[]\" here.\n              // eslint-disable-next-line @typescript-eslint/no-explicit-any\n              data = (typedArrayConstructor as any).from(arg1);\n            }\n          } else if (arg1 instanceof typedArrayConstructor) {\n            data = arg1;\n          } else {\n            throw new TypeError(`A ${type} tensor's data must be type of ${typedArrayConstructor}`);\n          }\n        }\n      } else {\n        //\n        // Override: constructor(data, ...)\n        //\n        maybeDims = arg1;\n        if (Array.isArray(arg0)) {\n          // only boolean[] and string[] is supported\n          if (arg0.length === 0) {\n            throw new TypeError('Tensor type cannot be inferred from an empty array.');\n          }\n          const firstElementType = typeof arg0[0];\n          if (firstElementType === 'string') {\n            type = 'string';\n            data = arg0;\n          } else if (firstElementType === 'boolean') {\n            type = 'bool';\n            // 'arg0' is of type 'boolean[]'. Uint8Array.from(boolean[]) actually works, but typescript thinks this is\n            // wrong type. We use 'as any' to make it happy.\n            // eslint-disable-next-line @typescript-eslint/no-explicit-any\n            data = Uint8Array.from(arg0 as any[]);\n          } else {\n            throw new TypeError(`Invalid element type of data array: ${firstElementType}.`);\n          }\n        } else {\n          // get tensor type from TypedArray\n          const mappedType =\n              NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.get(arg0.constructor as SupportedTypedArrayConstructors);\n          if (mappedType === undefined) {\n            throw new TypeError(`Unsupported type for tensor data: ${arg0.constructor}.`);\n          }\n          type = mappedType;\n          data = arg0 as SupportedTypedArray;\n        }\n      }\n\n      // type and data is processed, now processing dims\n      if (maybeDims === undefined) {\n        // assume 1-D tensor if dims omitted\n        maybeDims = [data.length];\n      } else if (!Array.isArray(maybeDims)) {\n        throw new TypeError('A tensor\\'s dims must be a number array');\n      }\n      dims = maybeDims as readonly number[];\n\n      this.cpuData = data;\n      this.dataLocation = 'cpu';\n    }\n\n    // perform check on dims\n    const size = calculateSize(dims);\n    // if data is on CPU, check whether data length matches tensor size\n    if (this.cpuData && size !== this.cpuData.length) {\n      throw new Error(`Tensor's size(${size}) does not match data length(${this.cpuData.length}).`);\n    }\n\n    this.type = type;\n    this.dims = dims;\n    this.size = size;\n  }\n  // #endregion\n\n  // #region factory\n  static async fromImage(\n      image: ImageData|HTMLImageElement|ImageBitmap|string,\n      options?: TensorFromImageDataOptions|TensorFromImageElementOptions|TensorFromImageBitmapOptions|\n      TensorFromUrlOptions): Promise<TensorInterface> {\n    return tensorFromImage(image, options);\n  }\n\n  static fromTexture<T extends TensorInterface.TextureDataTypes>(\n      texture: TensorTextureType, options: TensorFromTextureOptions<T>): TensorInterface {\n    return tensorFromTexture(texture, options);\n  }\n\n  static fromGpuBuffer<T extends TensorInterface.GpuBufferDataTypes>(\n      gpuBuffer: TensorGpuBufferType, options: TensorFromGpuBufferOptions<T>): TensorInterface {\n    return tensorFromGpuBuffer(gpuBuffer, options);\n  }\n\n  static fromPinnedBuffer<T extends TensorInterface.CpuPinnedDataTypes>(\n      type: T, buffer: TensorInterface.DataTypeMap[T], dims?: readonly number[]): Tensor {\n    return tensorFromPinnedBuffer(type, buffer, dims);\n  }\n\n  // #endregion\n\n  // #region conversions\n  toDataURL(options?: TensorToDataUrlOptions): string {\n    return tensorToDataURL(this, options);\n  }\n\n  toImageData(options?: TensorToImageDataOptions): ImageData {\n    return tensorToImageData(this, options);\n  }\n  // #endregion\n\n  // #region public fields\n  readonly dims: readonly number[];\n  readonly type: TensorType;\n  readonly size: number;\n  // #endregion\n\n  // #region private fields\n\n  /**\n   * stores the location of the data.\n   */\n  private dataLocation: TensorDataLocation;\n\n  /**\n   * stores the data on CPU, if location is 'cpu' or 'cpu-pinned'. otherwise empty.\n   */\n  private cpuData?: TensorDataType;\n\n  /**\n   * stores the underlying texture when location is 'texture'. otherwise empty.\n   */\n  private gpuTextureData?: TensorTextureType;\n\n  /**\n   * stores the underlying GPU buffer when location is 'gpu-buffer'. otherwise empty.\n   */\n  private gpuBufferData?: TensorGpuBufferType;\n\n  /**\n   * stores an optional downloader function to download data from GPU to CPU.\n   */\n  private downloader?(): Promise<TensorDataType>;\n\n  /**\n   * a flag indicating whether the data is being downloaded from GPU to CPU.\n   */\n  private isDownloading?: boolean;\n\n  /**\n   * stores an optional disposer function to dispose the underlying data.\n   */\n  private disposer?(): void;\n  // #endregion\n\n  // #region properties\n  get data(): TensorDataType {\n    this.ensureValid();\n    if (!this.cpuData) {\n      throw new Error(\n          'The data is not on CPU. Use `getData()` to download GPU data to CPU, ' +\n          'or use `texture` or `gpuBuffer` property to access the GPU data directly.');\n    }\n    return this.cpuData;\n  }\n\n  get location(): TensorDataLocation {\n    return this.dataLocation;\n  }\n\n  get texture(): TensorTextureType {\n    this.ensureValid();\n    if (!this.gpuTextureData) {\n      throw new Error('The data is not stored as a WebGL texture.');\n    }\n    return this.gpuTextureData;\n  }\n\n  get gpuBuffer(): TensorGpuBufferType {\n    this.ensureValid();\n    if (!this.gpuBufferData) {\n      throw new Error('The data is not stored as a WebGPU buffer.');\n    }\n    return this.gpuBufferData;\n  }\n  // #endregion\n\n  // #region methods\n\n  async getData(releaseData?: boolean): Promise<TensorDataType> {\n    this.ensureValid();\n    switch (this.dataLocation) {\n      case 'cpu':\n      case 'cpu-pinned':\n        return this.data;\n      case 'texture':\n      case 'gpu-buffer': {\n        if (!this.downloader) {\n          throw new Error('The current tensor is not created with a specified data downloader.');\n        }\n        if (this.isDownloading) {\n          throw new Error('The current tensor is being downloaded.');\n        }\n        try {\n          this.isDownloading = true;\n          const data = await this.downloader();\n          this.downloader = undefined;\n          this.dataLocation = 'cpu';\n          this.cpuData = data;\n\n          if (releaseData && this.disposer) {\n            this.disposer();\n            this.disposer = undefined;\n          }\n\n          return data;\n\n        } finally {\n          this.isDownloading = false;\n        }\n      }\n      default:\n        throw new Error(`cannot get data from location: ${this.dataLocation}`);\n    }\n  }\n\n  dispose(): void {\n    if (this.isDownloading) {\n      throw new Error('The current tensor is being downloaded.');\n    }\n\n    if (this.disposer) {\n      this.disposer();\n      this.disposer = undefined;\n    }\n    this.cpuData = undefined;\n    this.gpuTextureData = undefined;\n    this.gpuBufferData = undefined;\n    this.downloader = undefined;\n    this.isDownloading = undefined;\n\n    this.dataLocation = 'none';\n  }\n\n  // #endregion\n\n  // #region tensor utilities\n  private ensureValid(): void {\n    if (this.dataLocation === 'none') {\n      throw new Error('The tensor is disposed.');\n    }\n  }\n\n  reshape(dims: readonly number[]): TensorInterface {\n    this.ensureValid();\n    if (this.downloader || this.disposer) {\n      throw new Error('Cannot reshape a tensor that owns GPU resource.');\n    }\n    return tensorReshape(this, dims);\n  }\n  // #endregion\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {TensorFactory} from './tensor-factory.js';\nimport {Tensor as TensorImpl} from './tensor-impl.js';\nimport {TypedTensorUtils} from './tensor-utils.js';\n\n/* eslint-disable @typescript-eslint/no-redeclare */\n\n/**\n * represent a basic tensor with specified dimensions and data type.\n */\ninterface TypedTensorBase<T extends Tensor.Type> {\n  /**\n   * Get the dimensions of the tensor.\n   */\n  readonly dims: readonly number[];\n  /**\n   * Get the data type of the tensor.\n   */\n  readonly type: T;\n  /**\n   * Get the buffer data of the tensor.\n   *\n   * If the data is not on CPU (eg. it's in the form of WebGL texture or WebGPU buffer), throw error.\n   */\n  readonly data: Tensor.DataTypeMap[T];\n  /**\n   * Get the location of the data.\n   */\n  readonly location: Tensor.DataLocation;\n  /**\n   * Get the WebGL texture that holds the tensor data.\n   *\n   * If the data is not on GPU as WebGL texture, throw error.\n   */\n  readonly texture: Tensor.TextureType;\n  /**\n   * Get the WebGPU buffer that holds the tensor data.\n   *\n   * If the data is not on GPU as WebGPU buffer, throw error.\n   */\n  readonly gpuBuffer: Tensor.GpuBufferType;\n\n  /**\n   * Get the buffer data of the tensor.\n   *\n   * If the data is on CPU, returns the data immediately.\n   * If the data is on GPU, downloads the data and returns the promise.\n   *\n   * @param releaseData - whether release the data on GPU. Ignore if data is already on CPU.\n   */\n  getData(releaseData?: boolean): Promise<Tensor.DataTypeMap[T]>;\n\n  /**\n   * Dispose the tensor data.\n   *\n   * If the data is on CPU, remove its internal reference to the underlying data.\n   * If the data is on GPU, release the data on GPU.\n   *\n   * After calling this function, the tensor is considered no longer valid. Its location will be set to 'none'.\n   */\n  dispose(): void;\n}\n\nexport declare namespace Tensor {\n  interface DataTypeMap {\n    float32: Float32Array;\n    uint8: Uint8Array;\n    int8: Int8Array;\n    uint16: Uint16Array;\n    int16: Int16Array;\n    int32: Int32Array;\n    int64: BigInt64Array;\n    string: string[];\n    bool: Uint8Array;\n    float16: Uint16Array;  // Keep using Uint16Array until we have a concrete solution for float 16.\n    float64: Float64Array;\n    uint32: Uint32Array;\n    uint64: BigUint64Array;\n    // complex64: never;\n    // complex128: never;\n    // bfloat16: never;\n  }\n\n  interface ElementTypeMap {\n    float32: number;\n    uint8: number;\n    int8: number;\n    uint16: number;\n    int16: number;\n    int32: number;\n    int64: bigint;\n    string: string;\n    bool: boolean;\n    float16: number;  // Keep using Uint16Array until we have a concrete solution for float 16.\n    float64: number;\n    uint32: number;\n    uint64: bigint;\n    // complex64: never;\n    // complex128: never;\n    // bfloat16: never;\n  }\n\n  type DataType = DataTypeMap[Type];\n  type ElementType = ElementTypeMap[Type];\n\n  /**\n   * supported data types for constructing a tensor from a pinned CPU buffer\n   */\n  export type CpuPinnedDataTypes = Exclude<Tensor.Type, 'string'>;\n\n  /**\n   * type alias for WebGL texture\n   */\n  export type TextureType = WebGLTexture;\n\n  /**\n   * supported data types for constructing a tensor from a WebGL texture\n   */\n  export type TextureDataTypes = 'float32';\n\n  /**\n   * type alias for WebGPU buffer\n   *\n   * The reason why we don't use type \"GPUBuffer\" defined in webgpu.d.ts from @webgpu/types is because \"@webgpu/types\"\n   * requires \"@types/dom-webcodecs\" as peer dependency when using TypeScript < v5.1 and its version need to be chosen\n   * carefully according to the TypeScript version being used. This means so far there is not a way to keep every\n   * TypeScript version happy. It turns out that we will easily broke users on some TypeScript version.\n   *\n   * for more info see https://github.com/gpuweb/types/issues/127\n   */\n  export type GpuBufferType = {size: number; mapState: 'unmapped' | 'pending' | 'mapped'};\n\n  /**\n   * supported data types for constructing a tensor from a WebGPU buffer\n   */\n  export type GpuBufferDataTypes = 'float32'|'float16'|'int32'|'int64'|'uint32'|'uint8'|'bool';\n\n  /**\n   * represent where the tensor data is stored\n   */\n  export type DataLocation = 'none'|'cpu'|'cpu-pinned'|'texture'|'gpu-buffer';\n\n  /**\n   * represent the data type of a tensor\n   */\n  export type Type = keyof DataTypeMap;\n}\n\n/**\n * Represent multi-dimensional arrays to feed to or fetch from model inferencing.\n */\nexport interface TypedTensor<T extends Tensor.Type> extends TypedTensorBase<T>, TypedTensorUtils<T> {}\n/**\n * Represent multi-dimensional arrays to feed to or fetch from model inferencing.\n */\nexport interface Tensor extends TypedTensorBase<Tensor.Type>, TypedTensorUtils<Tensor.Type> {}\n\n/**\n * type TensorConstructor defines the constructors of 'Tensor' to create CPU tensor instances.\n */\nexport interface TensorConstructor extends TensorFactory {\n  // #region CPU tensor - specify element type\n  /**\n   * Construct a new string tensor object from the given type, data and dims.\n   *\n   * @param type - Specify the element type.\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(type: 'string', data: Tensor.DataTypeMap['string']|readonly string[],\n      dims?: readonly number[]): TypedTensor<'string'>;\n\n  /**\n   * Construct a new bool tensor object from the given type, data and dims.\n   *\n   * @param type - Specify the element type.\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(type: 'bool', data: Tensor.DataTypeMap['bool']|readonly boolean[], dims?: readonly number[]): TypedTensor<'bool'>;\n\n  /**\n   * Construct a new 64-bit integer typed tensor object from the given type, data and dims.\n   *\n   * @param type - Specify the element type.\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new<T extends 'uint64'|'int64'>(\n      type: T, data: Tensor.DataTypeMap[T]|readonly bigint[]|readonly number[],\n      dims?: readonly number[]): TypedTensor<T>;\n\n  /**\n   * Construct a new numeric tensor object from the given type, data and dims.\n   *\n   * @param type - Specify the element type.\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new<T extends Exclude<Tensor.Type, 'string'|'bool'|'uint64'|'int64'>>(\n      type: T, data: Tensor.DataTypeMap[T]|readonly number[], dims?: readonly number[]): TypedTensor<T>;\n  // #endregion\n\n  // #region CPU tensor - infer element types\n\n  /**\n   * Construct a new float32 tensor object from the given data and dims.\n   *\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(data: Float32Array, dims?: readonly number[]): TypedTensor<'float32'>;\n\n  /**\n   * Construct a new int8 tensor object from the given data and dims.\n   *\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(data: Int8Array, dims?: readonly number[]): TypedTensor<'int8'>;\n\n  /**\n   * Construct a new uint8 tensor object from the given data and dims.\n   *\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(data: Uint8Array, dims?: readonly number[]): TypedTensor<'uint8'>;\n\n  /**\n   * Construct a new uint16 tensor object from the given data and dims.\n   *\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(data: Uint16Array, dims?: readonly number[]): TypedTensor<'uint16'>;\n\n  /**\n   * Construct a new int16 tensor object from the given data and dims.\n   *\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(data: Int16Array, dims?: readonly number[]): TypedTensor<'int16'>;\n\n  /**\n   * Construct a new int32 tensor object from the given data and dims.\n   *\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(data: Int32Array, dims?: readonly number[]): TypedTensor<'int32'>;\n\n  /**\n   * Construct a new int64 tensor object from the given data and dims.\n   *\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(data: BigInt64Array, dims?: readonly number[]): TypedTensor<'int64'>;\n\n  /**\n   * Construct a new string tensor object from the given data and dims.\n   *\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(data: readonly string[], dims?: readonly number[]): TypedTensor<'string'>;\n\n  /**\n   * Construct a new bool tensor object from the given data and dims.\n   *\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(data: readonly boolean[], dims?: readonly number[]): TypedTensor<'bool'>;\n\n  /**\n   * Construct a new float64 tensor object from the given data and dims.\n   *\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(data: Float64Array, dims?: readonly number[]): TypedTensor<'float64'>;\n\n  /**\n   * Construct a new uint32 tensor object from the given data and dims.\n   *\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(data: Uint32Array, dims?: readonly number[]): TypedTensor<'uint32'>;\n\n  /**\n   * Construct a new uint64 tensor object from the given data and dims.\n   *\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(data: BigUint64Array, dims?: readonly number[]): TypedTensor<'uint64'>;\n\n  // #endregion\n\n  // #region CPU tensor - fall back to non-generic tensor type declaration\n\n  /**\n   * Construct a new tensor object from the given type, data and dims.\n   *\n   * @param type - Specify the element type.\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(type: Tensor.Type, data: Tensor.DataType|readonly number[]|readonly string[]|readonly bigint[]|readonly boolean[],\n      dims?: readonly number[]): Tensor;\n\n  /**\n   * Construct a new tensor object from the given data and dims.\n   *\n   * @param data - Specify the CPU tensor data.\n   * @param dims - Specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   */\n  new(data: Tensor.DataType, dims?: readonly number[]): Tensor;\n  // #endregion\n}\n\n// eslint-disable-next-line @typescript-eslint/naming-convention\nexport const Tensor = TensorImpl as TensorConstructor;\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {env} from './env-impl.js';\n\n/**\n * @ignore\n */\nexport const TRACE = (deviceType: string, label: string) => {\n  if (typeof env.trace === 'undefined' ? !env.wasm.trace : !env.trace) {\n    return;\n  }\n  // eslint-disable-next-line no-console\n  console.timeStamp(`${deviceType}::ORT::${label}`);\n};\n\nconst TRACE_FUNC = (msg: string, extraMsg?: string) => {\n  const stack = new Error().stack?.split(/\\r\\n|\\r|\\n/g) || [];\n  let hasTraceFunc = false;\n  for (let i = 0; i < stack.length; i++) {\n    if (hasTraceFunc && !stack[i].includes('TRACE_FUNC')) {\n      let label = `FUNC_${msg}::${stack[i].trim().split(' ')[1]}`;\n      if (extraMsg) {\n        label += `::${extraMsg}`;\n      }\n      TRACE('CPU', label);\n      return;\n    }\n    if (stack[i].includes('TRACE_FUNC')) {\n      hasTraceFunc = true;\n    }\n  }\n};\n\n/**\n * @ignore\n */\nexport const TRACE_FUNC_BEGIN = (extraMsg?: string) => {\n  if (typeof env.trace === 'undefined' ? !env.wasm.trace : !env.trace) {\n    return;\n  }\n  TRACE_FUNC('BEGIN', extraMsg);\n};\n\n/**\n * @ignore\n */\nexport const TRACE_FUNC_END = (extraMsg?: string) => {\n  if (typeof env.trace === 'undefined' ? !env.wasm.trace : !env.trace) {\n    return;\n  }\n  TRACE_FUNC('END', extraMsg);\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {resolveBackendAndExecutionProviders} from './backend-impl.js';\nimport {InferenceSessionHandler} from './backend.js';\nimport {InferenceSession as InferenceSessionInterface} from './inference-session.js';\nimport {OnnxValue} from './onnx-value.js';\nimport {Tensor} from './tensor.js';\nimport {TRACE_FUNC_BEGIN, TRACE_FUNC_END} from './trace.js';\n\ntype SessionOptions = InferenceSessionInterface.SessionOptions;\ntype RunOptions = InferenceSessionInterface.RunOptions;\ntype FeedsType = InferenceSessionInterface.FeedsType;\ntype FetchesType = InferenceSessionInterface.FetchesType;\ntype ReturnType = InferenceSessionInterface.ReturnType;\n\nexport class InferenceSession implements InferenceSessionInterface {\n  private constructor(handler: InferenceSessionHandler) {\n    this.handler = handler;\n  }\n  run(feeds: FeedsType, options?: RunOptions): Promise<ReturnType>;\n  run(feeds: FeedsType, fetches: FetchesType, options?: RunOptions): Promise<ReturnType>;\n  async run(feeds: FeedsType, arg1?: FetchesType|RunOptions, arg2?: RunOptions): Promise<ReturnType> {\n    TRACE_FUNC_BEGIN();\n    const fetches: {[name: string]: OnnxValue|null} = {};\n    let options: RunOptions = {};\n    // check inputs\n    if (typeof feeds !== 'object' || feeds === null || feeds instanceof Tensor || Array.isArray(feeds)) {\n      throw new TypeError(\n          '\\'feeds\\' must be an object that use input names as keys and OnnxValue as corresponding values.');\n    }\n\n    let isFetchesEmpty = true;\n    // determine which override is being used\n    if (typeof arg1 === 'object') {\n      if (arg1 === null) {\n        throw new TypeError('Unexpected argument[1]: cannot be null.');\n      }\n      if (arg1 instanceof Tensor) {\n        throw new TypeError('\\'fetches\\' cannot be a Tensor');\n      }\n\n      if (Array.isArray(arg1)) {\n        if (arg1.length === 0) {\n          throw new TypeError('\\'fetches\\' cannot be an empty array.');\n        }\n        isFetchesEmpty = false;\n        // output names\n        for (const name of arg1) {\n          if (typeof name !== 'string') {\n            throw new TypeError('\\'fetches\\' must be a string array or an object.');\n          }\n          if (this.outputNames.indexOf(name) === -1) {\n            throw new RangeError(`'fetches' contains invalid output name: ${name}.`);\n          }\n          fetches[name] = null;\n        }\n\n        if (typeof arg2 === 'object' && arg2 !== null) {\n          options = arg2;\n        } else if (typeof arg2 !== 'undefined') {\n          throw new TypeError('\\'options\\' must be an object.');\n        }\n      } else {\n        // decide whether arg1 is fetches or options\n        // if any output name is present and its value is valid OnnxValue, we consider it fetches\n        let isFetches = false;\n        const arg1Keys = Object.getOwnPropertyNames(arg1);\n        for (const name of this.outputNames) {\n          if (arg1Keys.indexOf(name) !== -1) {\n            const v = (arg1 as InferenceSessionInterface.NullableOnnxValueMapType)[name];\n            if (v === null || v instanceof Tensor) {\n              isFetches = true;\n              isFetchesEmpty = false;\n              fetches[name] = v;\n            }\n          }\n        }\n\n        if (isFetches) {\n          if (typeof arg2 === 'object' && arg2 !== null) {\n            options = arg2;\n          } else if (typeof arg2 !== 'undefined') {\n            throw new TypeError('\\'options\\' must be an object.');\n          }\n        } else {\n          options = arg1 as RunOptions;\n        }\n      }\n    } else if (typeof arg1 !== 'undefined') {\n      throw new TypeError('Unexpected argument[1]: must be \\'fetches\\' or \\'options\\'.');\n    }\n\n    // check if all inputs are in feed\n    for (const name of this.inputNames) {\n      if (typeof feeds[name] === 'undefined') {\n        throw new Error(`input '${name}' is missing in 'feeds'.`);\n      }\n    }\n\n    // if no fetches is specified, we use the full output names list\n    if (isFetchesEmpty) {\n      for (const name of this.outputNames) {\n        fetches[name] = null;\n      }\n    }\n\n    // feeds, fetches and options are prepared\n\n    const results = await this.handler.run(feeds, fetches, options);\n    const returnValue: {[name: string]: OnnxValue} = {};\n    for (const key in results) {\n      if (Object.hasOwnProperty.call(results, key)) {\n        const result = results[key];\n        if (result instanceof Tensor) {\n          returnValue[key] = result;\n        } else {\n          returnValue[key] = new Tensor(result.type, result.data, result.dims);\n        }\n      }\n    }\n    TRACE_FUNC_END();\n    return returnValue;\n  }\n\n  async release(): Promise<void> {\n    return this.handler.dispose();\n  }\n\n  static create(path: string, options?: SessionOptions): Promise<InferenceSessionInterface>;\n  static create(buffer: ArrayBufferLike, options?: SessionOptions): Promise<InferenceSessionInterface>;\n  static create(buffer: ArrayBufferLike, byteOffset: number, byteLength?: number, options?: SessionOptions):\n      Promise<InferenceSessionInterface>;\n  static create(buffer: Uint8Array, options?: SessionOptions): Promise<InferenceSessionInterface>;\n  static async create(\n      arg0: string|ArrayBufferLike|Uint8Array, arg1?: SessionOptions|number, arg2?: number,\n      arg3?: SessionOptions): Promise<InferenceSessionInterface> {\n    TRACE_FUNC_BEGIN();\n    // either load from a file or buffer\n    let filePathOrUint8Array: string|Uint8Array;\n    let options: SessionOptions = {};\n\n    if (typeof arg0 === 'string') {\n      filePathOrUint8Array = arg0;\n      if (typeof arg1 === 'object' && arg1 !== null) {\n        options = arg1;\n      } else if (typeof arg1 !== 'undefined') {\n        throw new TypeError('\\'options\\' must be an object.');\n      }\n    } else if (arg0 instanceof Uint8Array) {\n      filePathOrUint8Array = arg0;\n      if (typeof arg1 === 'object' && arg1 !== null) {\n        options = arg1;\n      } else if (typeof arg1 !== 'undefined') {\n        throw new TypeError('\\'options\\' must be an object.');\n      }\n    } else if (\n        arg0 instanceof ArrayBuffer ||\n        (typeof SharedArrayBuffer !== 'undefined' && arg0 instanceof SharedArrayBuffer)) {\n      const buffer = arg0;\n      let byteOffset = 0;\n      let byteLength = arg0.byteLength;\n      if (typeof arg1 === 'object' && arg1 !== null) {\n        options = arg1;\n      } else if (typeof arg1 === 'number') {\n        byteOffset = arg1;\n        if (!Number.isSafeInteger(byteOffset)) {\n          throw new RangeError('\\'byteOffset\\' must be an integer.');\n        }\n        if (byteOffset < 0 || byteOffset >= buffer.byteLength) {\n          throw new RangeError(`'byteOffset' is out of range [0, ${buffer.byteLength}).`);\n        }\n        byteLength = arg0.byteLength - byteOffset;\n        if (typeof arg2 === 'number') {\n          byteLength = arg2;\n          if (!Number.isSafeInteger(byteLength)) {\n            throw new RangeError('\\'byteLength\\' must be an integer.');\n          }\n          if (byteLength <= 0 || byteOffset + byteLength > buffer.byteLength) {\n            throw new RangeError(`'byteLength' is out of range (0, ${buffer.byteLength - byteOffset}].`);\n          }\n          if (typeof arg3 === 'object' && arg3 !== null) {\n            options = arg3;\n          } else if (typeof arg3 !== 'undefined') {\n            throw new TypeError('\\'options\\' must be an object.');\n          }\n        } else if (typeof arg2 !== 'undefined') {\n          throw new TypeError('\\'byteLength\\' must be a number.');\n        }\n      } else if (typeof arg1 !== 'undefined') {\n        throw new TypeError('\\'options\\' must be an object.');\n      }\n      filePathOrUint8Array = new Uint8Array(buffer, byteOffset, byteLength);\n    } else {\n      throw new TypeError('Unexpected argument[0]: must be \\'path\\' or \\'buffer\\'.');\n    }\n\n    // resolve backend, update session options with validated EPs, and create session handler\n    const [backend, optionsWithValidatedEPs] = await resolveBackendAndExecutionProviders(options);\n    const handler = await backend.createInferenceSessionHandler(filePathOrUint8Array, optionsWithValidatedEPs);\n    TRACE_FUNC_END();\n    return new InferenceSession(handler);\n  }\n\n  startProfiling(): void {\n    this.handler.startProfiling();\n  }\n  endProfiling(): void {\n    this.handler.endProfiling();\n  }\n\n  get inputNames(): readonly string[] {\n    return this.handler.inputNames;\n  }\n  get outputNames(): readonly string[] {\n    return this.handler.outputNames;\n  }\n\n  private handler: InferenceSessionHandler;\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {InferenceSession as InferenceSessionImpl} from './inference-session-impl.js';\nimport {OnnxModelOptions} from './onnx-model.js';\nimport {OnnxValue, OnnxValueDataLocation} from './onnx-value.js';\n\n/* eslint-disable @typescript-eslint/no-redeclare */\n\nexport declare namespace InferenceSession {\n  // #region input/output types\n\n  type OnnxValueMapType = {readonly [name: string]: OnnxValue};\n  type NullableOnnxValueMapType = {readonly [name: string]: OnnxValue | null};\n\n  /**\n   * A feeds (model inputs) is an object that uses input names as keys and OnnxValue as corresponding values.\n   */\n  type FeedsType = OnnxValueMapType;\n\n  /**\n   * A fetches (model outputs) could be one of the following:\n   *\n   * - Omitted. Use model's output names definition.\n   * - An array of string indicating the output names.\n   * - An object that use output names as keys and OnnxValue or null as corresponding values.\n   *\n   * @remark\n   * different from input argument, in output, OnnxValue is optional. If an OnnxValue is present it will be\n   * used as a pre-allocated value by the inference engine; if omitted, inference engine will allocate buffer\n   * internally.\n   */\n  type FetchesType = readonly string[]|NullableOnnxValueMapType;\n\n  /**\n   * A inferencing return type is an object that uses output names as keys and OnnxValue as corresponding values.\n   */\n  type ReturnType = OnnxValueMapType;\n\n  // #endregion\n\n  // #region session options\n\n  /**\n   * A set of configurations for session behavior.\n   */\n  export interface SessionOptions extends OnnxModelOptions {\n    /**\n     * An array of execution provider options.\n     *\n     * An execution provider option can be a string indicating the name of the execution provider,\n     * or an object of corresponding type.\n     */\n    executionProviders?: readonly ExecutionProviderConfig[];\n\n    /**\n     * The intra OP threads number.\n     *\n     * This setting is available only in ONNXRuntime (Node.js binding and react-native).\n     */\n    intraOpNumThreads?: number;\n\n    /**\n     * The inter OP threads number.\n     *\n     * This setting is available only in ONNXRuntime (Node.js binding and react-native).\n     */\n    interOpNumThreads?: number;\n\n    /**\n     * The free dimension override.\n     *\n     * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n     */\n    freeDimensionOverrides?: {readonly [dimensionName: string]: number};\n\n    /**\n     * The optimization level.\n     *\n     * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n     */\n    graphOptimizationLevel?: 'disabled'|'basic'|'extended'|'all';\n\n    /**\n     * Whether enable CPU memory arena.\n     *\n     * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n     */\n    enableCpuMemArena?: boolean;\n\n    /**\n     * Whether enable memory pattern.\n     *\n     * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n     */\n    enableMemPattern?: boolean;\n\n    /**\n     * Execution mode.\n     *\n     * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n     */\n    executionMode?: 'sequential'|'parallel';\n\n    /**\n     * Optimized model file path.\n     *\n     * If this setting is specified, the optimized model will be dumped. In browser, a blob will be created\n     * with a pop-up window.\n     */\n    optimizedModelFilePath?: string;\n\n    /**\n     * Whether enable profiling.\n     *\n     * This setting is a placeholder for a future use.\n     */\n    enableProfiling?: boolean;\n\n    /**\n     * File prefix for profiling.\n     *\n     * This setting is a placeholder for a future use.\n     */\n    profileFilePrefix?: string;\n\n    /**\n     * Log ID.\n     *\n     * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n     */\n    logId?: string;\n\n    /**\n     * Log severity level. See\n     * https://github.com/microsoft/onnxruntime/blob/main/include/onnxruntime/core/common/logging/severity.h\n     *\n     * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n     */\n    logSeverityLevel?: 0|1|2|3|4;\n\n    /**\n     * Log verbosity level.\n     *\n     * This setting is available only in WebAssembly backend. Will support Node.js binding and react-native later\n     */\n    logVerbosityLevel?: number;\n\n    /**\n     * Specify string as a preferred data location for all outputs, or an object that use output names as keys and a\n     * preferred data location as corresponding values.\n     *\n     * This setting is available only in ONNXRuntime Web for WebGL and WebGPU EP.\n     */\n    preferredOutputLocation?: OnnxValueDataLocation|{readonly [outputName: string]: OnnxValueDataLocation};\n\n    /**\n     * Whether enable graph capture.\n     * This setting is available only in ONNXRuntime Web for WebGPU EP.\n     */\n    enableGraphCapture?: boolean;\n\n    /**\n     * Store configurations for a session. See\n     * https://github.com/microsoft/onnxruntime/blob/main/include/onnxruntime/core/session/\n     * onnxruntime_session_options_config_keys.h\n     *\n     * This setting is available only in WebAssembly backend. Will support Node.js binding and react-native later\n     *\n     * @example\n     * ```js\n     * extra: {\n     *   session: {\n     *     set_denormal_as_zero: \"1\",\n     *     disable_prepacking: \"1\"\n     *   },\n     *   optimization: {\n     *     enable_gelu_approximation: \"1\"\n     *   }\n     * }\n     * ```\n     */\n    extra?: Record<string, unknown>;\n  }\n\n  // #region execution providers\n\n  // Currently, we have the following backends to support execution providers:\n  // Backend Node.js binding: supports 'cpu', 'dml' (win32), 'coreml' (macOS) and 'cuda' (linux).\n  // Backend WebAssembly: supports 'cpu', 'wasm', 'webgpu' and 'webnn'.\n  // Backend ONNX.js: supports 'webgl'.\n  // Backend React Native: supports 'cpu', 'xnnpack', 'coreml' (iOS), 'nnapi' (Android).\n  interface ExecutionProviderOptionMap {\n    coreml: CoreMLExecutionProviderOption;\n    cpu: CpuExecutionProviderOption;\n    cuda: CudaExecutionProviderOption;\n    dml: DmlExecutionProviderOption;\n    nnapi: NnapiExecutionProviderOption;\n    tensorrt: TensorRtExecutionProviderOption;\n    wasm: WebAssemblyExecutionProviderOption;\n    webgl: WebGLExecutionProviderOption;\n    webgpu: WebGpuExecutionProviderOption;\n    webnn: WebNNExecutionProviderOption;\n    qnn: QnnExecutionProviderOption;\n    xnnpack: XnnpackExecutionProviderOption;\n  }\n\n  type ExecutionProviderName = keyof ExecutionProviderOptionMap;\n  type ExecutionProviderConfig =\n      ExecutionProviderOptionMap[ExecutionProviderName]|ExecutionProviderOption|ExecutionProviderName|string;\n\n  export interface ExecutionProviderOption {\n    readonly name: string;\n  }\n  export interface CpuExecutionProviderOption extends ExecutionProviderOption {\n    readonly name: 'cpu';\n    useArena?: boolean;\n  }\n  export interface CudaExecutionProviderOption extends ExecutionProviderOption {\n    readonly name: 'cuda';\n    deviceId?: number;\n  }\n  export interface DmlExecutionProviderOption extends ExecutionProviderOption {\n    readonly name: 'dml';\n    deviceId?: number;\n  }\n  export interface TensorRtExecutionProviderOption extends ExecutionProviderOption {\n    readonly name: 'tensorrt';\n    deviceId?: number;\n  }\n  export interface WebAssemblyExecutionProviderOption extends ExecutionProviderOption {\n    readonly name: 'wasm';\n  }\n  export interface WebGLExecutionProviderOption extends ExecutionProviderOption {\n    readonly name: 'webgl';\n    // TODO: add flags\n  }\n  export interface XnnpackExecutionProviderOption extends ExecutionProviderOption {\n    readonly name: 'xnnpack';\n  }\n  export interface WebGpuExecutionProviderOption extends ExecutionProviderOption {\n    readonly name: 'webgpu';\n    preferredLayout?: 'NCHW'|'NHWC';\n  }\n\n  // #region WebNN options\n\n  interface WebNNExecutionProviderName extends ExecutionProviderOption {\n    readonly name: 'webnn';\n  }\n\n  /**\n   * Represents a set of options for creating a WebNN MLContext.\n   *\n   * @see https://www.w3.org/TR/webnn/#dictdef-mlcontextoptions\n   */\n  export interface WebNNContextOptions {\n    deviceType?: 'cpu'|'gpu'|'npu';\n    numThreads?: number;\n    powerPreference?: 'default'|'low-power'|'high-performance';\n  }\n\n  /**\n   * Represents a set of options for WebNN execution provider without MLContext.\n   */\n  export interface WebNNOptionsWithoutMLContext extends WebNNExecutionProviderName, WebNNContextOptions {\n    context?: never;\n  }\n\n  /**\n   * Represents a set of options for WebNN execution provider with MLContext.\n   *\n   * When MLContext is provided, the deviceType is also required so that the WebNN EP can determine the preferred\n   * channel layout.\n   *\n   * @see https://www.w3.org/TR/webnn/#dom-ml-createcontext\n   */\n  export interface WebNNOptionsWithMLContext extends WebNNExecutionProviderName,\n                                                     Omit<WebNNContextOptions, 'deviceType'>,\n                                                     Required<Pick<WebNNContextOptions, 'deviceType'>> {\n    context: unknown /* MLContext */;\n  }\n\n  /**\n   * Represents a set of options for WebNN execution provider with MLContext which is created from GPUDevice.\n   *\n   * @see https://www.w3.org/TR/webnn/#dom-ml-createcontext-gpudevice\n   */\n  export interface WebNNOptionsWebGpu extends WebNNExecutionProviderName {\n    context: unknown /* MLContext */;\n    gpuDevice: unknown /* GPUDevice */;\n  }\n\n  /**\n   * Options for WebNN execution provider.\n   */\n  export type WebNNExecutionProviderOption = WebNNOptionsWithoutMLContext|WebNNOptionsWithMLContext|WebNNOptionsWebGpu;\n\n  // #endregion\n\n  export interface QnnExecutionProviderOption extends ExecutionProviderOption {\n    readonly name: 'qnn';\n    // TODO add flags\n  }\n  export interface CoreMLExecutionProviderOption extends ExecutionProviderOption {\n    readonly name: 'coreml';\n    /**\n     * The bit flags for CoreML execution provider.\n     *\n     * ```\n     * COREML_FLAG_USE_CPU_ONLY = 0x001\n     * COREML_FLAG_ENABLE_ON_SUBGRAPH = 0x002\n     * COREML_FLAG_ONLY_ENABLE_DEVICE_WITH_ANE = 0x004\n     * COREML_FLAG_ONLY_ALLOW_STATIC_INPUT_SHAPES = 0x008\n     * COREML_FLAG_CREATE_MLPROGRAM = 0x010\n     * ```\n     *\n     * See include/onnxruntime/core/providers/coreml/coreml_provider_factory.h for more details.\n     *\n     * This flag is available only in ONNXRuntime (Node.js binding).\n     */\n    coreMlFlags?: number;\n    /**\n     * Specify whether to use CPU only in CoreML EP.\n     *\n     * This setting is available only in ONNXRuntime (react-native).\n     */\n    useCPUOnly?: boolean;\n    /**\n     * Specify whether to enable CoreML EP on subgraph.\n     *\n     * This setting is available only in ONNXRuntime (react-native).\n     */\n    enableOnSubgraph?: boolean;\n    /**\n     * Specify whether to only enable CoreML EP for Apple devices with ANE (Apple Neural Engine).\n     *\n     * This setting is available only in ONNXRuntime (react-native).\n     */\n    onlyEnableDeviceWithANE?: boolean;\n  }\n  export interface NnapiExecutionProviderOption extends ExecutionProviderOption {\n    readonly name: 'nnapi';\n    useFP16?: boolean;\n    useNCHW?: boolean;\n    cpuDisabled?: boolean;\n    cpuOnly?: boolean;\n  }\n  // #endregion\n\n  // #endregion\n\n  // #region run options\n\n  /**\n   * A set of configurations for inference run behavior\n   */\n  export interface RunOptions {\n    /**\n     * Log severity level. See\n     * https://github.com/microsoft/onnxruntime/blob/main/include/onnxruntime/core/common/logging/severity.h\n     *\n     * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n     */\n    logSeverityLevel?: 0|1|2|3|4;\n\n    /**\n     * Log verbosity level.\n     *\n     * This setting is available only in WebAssembly backend. Will support Node.js binding and react-native later\n     */\n    logVerbosityLevel?: number;\n\n    /**\n     * Terminate all incomplete OrtRun calls as soon as possible if true\n     *\n     * This setting is available only in WebAssembly backend. Will support Node.js binding and react-native later\n     */\n    terminate?: boolean;\n\n    /**\n     * A tag for the Run() calls using this\n     *\n     * This setting is available only in ONNXRuntime (Node.js binding and react-native) or WebAssembly backend\n     */\n    tag?: string;\n\n    /**\n     * Set a single run configuration entry. See\n     * https://github.com/microsoft/onnxruntime/blob/main/include/onnxruntime/core/session/\n     * onnxruntime_run_options_config_keys.h\n     *\n     * This setting is available only in WebAssembly backend. Will support Node.js binding and react-native later\n     *\n     * @example\n     *\n     * ```js\n     * extra: {\n     *   memory: {\n     *     enable_memory_arena_shrinkage: \"1\",\n     *   }\n     * }\n     * ```\n     */\n    extra?: Record<string, unknown>;\n  }\n\n  // #endregion\n\n  // #region value metadata\n\n  // eslint-disable-next-line @typescript-eslint/no-empty-interface\n  interface ValueMetadata {\n    // TBD\n  }\n\n  // #endregion\n}\n\n/**\n * Represent a runtime instance of an ONNX model.\n */\nexport interface InferenceSession {\n  // #region run()\n\n  /**\n   * Execute the model asynchronously with the given feeds and options.\n   *\n   * @param feeds - Representation of the model input. See type description of `InferenceSession.InputType` for detail.\n   * @param options - Optional. A set of options that controls the behavior of model inference.\n   * @returns A promise that resolves to a map, which uses output names as keys and OnnxValue as corresponding values.\n   */\n  run(feeds: InferenceSession.FeedsType, options?: InferenceSession.RunOptions): Promise<InferenceSession.ReturnType>;\n\n  /**\n   * Execute the model asynchronously with the given feeds, fetches and options.\n   *\n   * @param feeds - Representation of the model input. See type description of `InferenceSession.InputType` for detail.\n   * @param fetches - Representation of the model output. See type description of `InferenceSession.OutputType` for\n   * detail.\n   * @param options - Optional. A set of options that controls the behavior of model inference.\n   * @returns A promise that resolves to a map, which uses output names as keys and OnnxValue as corresponding values.\n   */\n  run(feeds: InferenceSession.FeedsType, fetches: InferenceSession.FetchesType,\n      options?: InferenceSession.RunOptions): Promise<InferenceSession.ReturnType>;\n\n  // #endregion\n\n  // #region release()\n\n  /**\n   * Release the inference session and the underlying resources.\n   */\n  release(): Promise<void>;\n\n  // #endregion\n\n  // #region profiling\n\n  /**\n   * Start profiling.\n   */\n  startProfiling(): void;\n\n  /**\n   * End profiling.\n   */\n  endProfiling(): void;\n\n  // #endregion\n\n  // #region metadata\n\n  /**\n   * Get input names of the loaded model.\n   */\n  readonly inputNames: readonly string[];\n\n  /**\n   * Get output names of the loaded model.\n   */\n  readonly outputNames: readonly string[];\n\n  // /**\n  //  * Get input metadata of the loaded model.\n  //  */\n  // readonly inputMetadata: ReadonlyArray<Readonly<InferenceSession.ValueMetadata>>;\n\n  // /**\n  //  * Get output metadata of the loaded model.\n  //  */\n  // readonly outputMetadata: ReadonlyArray<Readonly<InferenceSession.ValueMetadata>>;\n\n  // #endregion\n}\n\nexport interface InferenceSessionFactory {\n  // #region create()\n\n  /**\n   * Create a new inference session and load model asynchronously from an ONNX model file.\n   *\n   * @param uri - The URI or file path of the model to load.\n   * @param options - specify configuration for creating a new inference session.\n   * @returns A promise that resolves to an InferenceSession object.\n   */\n  create(uri: string, options?: InferenceSession.SessionOptions): Promise<InferenceSession>;\n\n  /**\n   * Create a new inference session and load model asynchronously from an array bufer.\n   *\n   * @param buffer - An ArrayBuffer representation of an ONNX model.\n   * @param options - specify configuration for creating a new inference session.\n   * @returns A promise that resolves to an InferenceSession object.\n   */\n  create(buffer: ArrayBufferLike, options?: InferenceSession.SessionOptions): Promise<InferenceSession>;\n\n  /**\n   * Create a new inference session and load model asynchronously from segment of an array bufer.\n   *\n   * @param buffer - An ArrayBuffer representation of an ONNX model.\n   * @param byteOffset - The beginning of the specified portion of the array buffer.\n   * @param byteLength - The length in bytes of the array buffer.\n   * @param options - specify configuration for creating a new inference session.\n   * @returns A promise that resolves to an InferenceSession object.\n   */\n  create(buffer: ArrayBufferLike, byteOffset: number, byteLength?: number, options?: InferenceSession.SessionOptions):\n      Promise<InferenceSession>;\n\n  /**\n   * Create a new inference session and load model asynchronously from a Uint8Array.\n   *\n   * @param buffer - A Uint8Array representation of an ONNX model.\n   * @param options - specify configuration for creating a new inference session.\n   * @returns A promise that resolves to an InferenceSession object.\n   */\n  create(buffer: Uint8Array, options?: InferenceSession.SessionOptions): Promise<InferenceSession>;\n\n  // #endregion\n}\n\n// eslint-disable-next-line @typescript-eslint/naming-convention\nexport const InferenceSession: InferenceSessionFactory = InferenceSessionImpl;\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {OptionsFormat, OptionsNormalizationParameters, OptionsTensorLayout} from './tensor-factory.js';\n\nexport interface TensorToDataUrlOptions extends OptionsTensorLayout, OptionsFormat, OptionsNormalizationParameters {}\n\nexport interface TensorToImageDataOptions extends OptionsTensorLayout, OptionsFormat, OptionsNormalizationParameters {}\n\nexport interface ConversionUtils {\n  /**\n   * creates a DataURL instance from tensor\n   *\n   * @param options - An optional object representing options for creating a DataURL instance from the tensor.\n   *\n   * The following default settings will be applied:\n   * - `format`: `'RGB'`\n   * - `tensorLayout`: `'NCHW'`\n   * @returns a DataURL string representing the image converted from tensor data\n   */\n  toDataURL(options?: TensorToDataUrlOptions): string;\n\n  /**\n   * creates an ImageData instance from tensor\n   *\n   * @param options - An optional object representing options for creating an ImageData instance from the tensor.\n   *\n   * The following default settings will be applied:\n   * - `format`: `'RGB'`\n   * - `tensorLayout`: `'NCHW'`\n   * @returns an ImageData instance representing the image converted from tensor data\n   */\n  toImageData(options?: TensorToImageDataOptions): ImageData;\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor, TypedTensor} from './tensor.js';\n\nexport type ImageFormat = 'RGB'|'RGBA'|'BGR'|'RBG';\nexport type ImageTensorLayout = 'NHWC'|'NCHW';\n\n// the following region contains type definitions for constructing tensor from a specific location.\n\n// #region types for constructing a tensor from a specific location\n\n/**\n * represent common properties of the parameter for constructing a tensor from a specific location.\n */\ninterface CommonConstructorParameters<T> extends Pick<Tensor, 'dims'> {\n  /**\n   * Specify the data type of the tensor.\n   */\n  readonly type: T;\n}\n\n/**\n * represent the parameter for constructing a tensor from a GPU resource.\n */\ninterface GpuResourceConstructorParameters<T extends Tensor.Type> {\n  /**\n   * an optional callback function to download data from GPU to CPU.\n   *\n   * If not provided, the tensor treat the GPU data as external resource.\n   */\n  download?(): Promise<Tensor.DataTypeMap[T]>;\n\n  /**\n   * an optional callback function that will be called when the tensor is disposed.\n   *\n   * If not provided, the tensor treat the GPU data as external resource.\n   */\n  dispose?(): void;\n}\n\n/**\n * represent the parameter for constructing a tensor from a pinned CPU buffer\n */\nexport interface CpuPinnedConstructorParameters<T extends Tensor.CpuPinnedDataTypes = Tensor.CpuPinnedDataTypes> extends\n    CommonConstructorParameters<T> {\n  /**\n   * Specify the location of the data to be 'cpu-pinned'.\n   */\n  readonly location: 'cpu-pinned';\n  /**\n   * Specify the CPU pinned buffer that holds the tensor data.\n   */\n  readonly data: Tensor.DataTypeMap[T];\n}\n\n/**\n * represent the parameter for constructing a tensor from a WebGL texture\n */\nexport interface TextureConstructorParameters<T extends Tensor.TextureDataTypes = Tensor.TextureDataTypes> extends\n    CommonConstructorParameters<T>, GpuResourceConstructorParameters<T> {\n  /**\n   * Specify the location of the data to be 'texture'.\n   */\n  readonly location: 'texture';\n  /**\n   * Specify the WebGL texture that holds the tensor data.\n   */\n  readonly texture: Tensor.TextureType;\n}\n\n/**\n * represent the parameter for constructing a tensor from a WebGPU buffer\n */\nexport interface GpuBufferConstructorParameters<T extends Tensor.GpuBufferDataTypes = Tensor.GpuBufferDataTypes> extends\n    CommonConstructorParameters<T>, GpuResourceConstructorParameters<T> {\n  /**\n   * Specify the location of the data to be 'gpu-buffer'.\n   */\n  readonly location: 'gpu-buffer';\n  /**\n   * Specify the WebGPU buffer that holds the tensor data.\n   */\n  readonly gpuBuffer: Tensor.GpuBufferType;\n}\n\n// #endregion\n\n// the following region contains type definitions of each individual options.\n// the tensor factory functions use a composition of those options as the parameter type.\n\n// #region Options fields\n\nexport interface OptionsFormat {\n  /**\n   * Describes the image format represented in RGBA color space.\n   */\n  format?: ImageFormat;\n}\n\nexport interface OptionsTensorFormat {\n  /**\n   * Describes the image format of the tensor.\n   *\n   * NOTE: this is different from option 'format'. While option 'format' represents the original image, 'tensorFormat'\n   * represents the target format of the tensor. A transpose will be performed if they are different.\n   */\n  tensorFormat?: ImageFormat;\n}\n\nexport interface OptionsTensorDataType {\n  /**\n   * Describes the data type of the tensor.\n   */\n  dataType?: 'float32'|'uint8';\n}\n\nexport interface OptionsTensorLayout {\n  /**\n   * Describes the tensor layout when representing data of one or more image(s).\n   */\n  tensorLayout?: ImageTensorLayout;\n}\n\nexport interface OptionsDimensions {\n  /**\n   * Describes the image height in pixel\n   */\n  height?: number;\n  /**\n   * Describes the image width in pixel\n   */\n  width?: number;\n}\n\nexport interface OptionResizedDimensions {\n  /**\n   * Describes the resized height. If omitted, original height will be used.\n   */\n  resizedHeight?: number;\n  /**\n   * Describes resized width - can be accessed via tensor dimensions as well\n   */\n  resizedWidth?: number;\n}\n\nexport interface OptionsNormalizationParameters {\n  /**\n   * Describes normalization parameters when preprocessing the image as model input.\n   *\n   * Data element are ranged from 0 to 255.\n   */\n  norm?: {\n    /**\n     * The 'bias' value for image normalization.\n     * - If omitted, use default value 0.\n     * - If it's a single number, apply to each channel\n     * - If it's an array of 3 or 4 numbers, apply element-wise. Number of elements need to match the number of channels\n     * for the corresponding image format\n     */\n    bias?: number|[number, number, number]|[number, number, number, number];\n    /**\n     * The 'mean' value for image normalization.\n     * - If omitted, use default value 255.\n     * - If it's a single number, apply to each channel\n     * - If it's an array of 3 or 4 numbers, apply element-wise. Number of elements need to match the number of channels\n     * for the corresponding image format\n     */\n    mean?: number | [number, number, number] | [number, number, number, number];\n  };\n}\n\n// #endregion\n\n// #region Options composition\n\nexport interface TensorFromImageDataOptions extends OptionResizedDimensions, OptionsTensorFormat, OptionsTensorLayout,\n                                                    OptionsTensorDataType, OptionsNormalizationParameters {}\n\nexport interface TensorFromImageElementOptions extends OptionResizedDimensions, OptionsTensorFormat,\n                                                       OptionsTensorLayout, OptionsTensorDataType,\n                                                       OptionsNormalizationParameters {}\n\nexport interface TensorFromUrlOptions extends OptionsDimensions, OptionResizedDimensions, OptionsTensorFormat,\n                                              OptionsTensorLayout, OptionsTensorDataType,\n                                              OptionsNormalizationParameters {}\n\nexport interface TensorFromImageBitmapOptions extends OptionResizedDimensions, OptionsTensorFormat, OptionsTensorLayout,\n                                                      OptionsTensorDataType, OptionsNormalizationParameters {}\n\nexport interface TensorFromTextureOptions<T extends Tensor.TextureDataTypes> extends\n    Required<OptionsDimensions>, OptionsFormat, GpuResourceConstructorParameters<T>/* TODO: add more */ {}\n\nexport interface TensorFromGpuBufferOptions<T extends Tensor.GpuBufferDataTypes> extends\n    Pick<Tensor, 'dims'>, GpuResourceConstructorParameters<T> {\n  /**\n   * Describes the data type of the tensor.\n   */\n  dataType?: T;\n}\n\n// #endregion\n\n/**\n * type TensorFactory defines the factory functions of 'Tensor' to create tensor instances from existing data or\n * resources.\n */\nexport interface TensorFactory {\n  /**\n   * create a tensor from an ImageData object\n   *\n   * @param imageData - the ImageData object to create tensor from\n   * @param options - An optional object representing options for creating tensor from ImageData.\n   *\n   * The following default settings will be applied:\n   * - `tensorFormat`: `'RGB'`\n   * - `tensorLayout`: `'NCHW'`\n   * - `dataType`: `'float32'`\n   * @returns A promise that resolves to a tensor object\n   */\n  fromImage(imageData: ImageData, options?: TensorFromImageDataOptions):\n      Promise<TypedTensor<'float32'>|TypedTensor<'uint8'>>;\n\n  /**\n   * create a tensor from a HTMLImageElement object\n   *\n   * @param imageElement - the HTMLImageElement object to create tensor from\n   * @param options - An optional object representing options for creating tensor from HTMLImageElement.\n   *\n   * The following default settings will be applied:\n   * - `tensorFormat`: `'RGB'`\n   * - `tensorLayout`: `'NCHW'`\n   * - `dataType`: `'float32'`\n   * @returns A promise that resolves to a tensor object\n   */\n  fromImage(imageElement: HTMLImageElement, options?: TensorFromImageElementOptions):\n      Promise<TypedTensor<'float32'>|TypedTensor<'uint8'>>;\n\n  /**\n   * create a tensor from URL\n   *\n   * @param urlSource - a string as a URL to the image or a data URL containing the image data.\n   * @param options - An optional object representing options for creating tensor from URL.\n   *\n   * The following default settings will be applied:\n   * - `tensorFormat`: `'RGB'`\n   * - `tensorLayout`: `'NCHW'`\n   * - `dataType`: `'float32'`\n   * @returns A promise that resolves to a tensor object\n   */\n  fromImage(urlSource: string, options?: TensorFromUrlOptions): Promise<TypedTensor<'float32'>|TypedTensor<'uint8'>>;\n\n  /**\n   * create a tensor from an ImageBitmap object\n   *\n   * @param bitmap - the ImageBitmap object to create tensor from\n   * @param options - An optional object representing options for creating tensor from URL.\n   *\n   * The following default settings will be applied:\n   * - `tensorFormat`: `'RGB'`\n   * - `tensorLayout`: `'NCHW'`\n   * - `dataType`: `'float32'`\n   * @returns A promise that resolves to a tensor object\n   */\n  fromImage(bitmap: ImageBitmap, options: TensorFromImageBitmapOptions):\n      Promise<TypedTensor<'float32'>|TypedTensor<'uint8'>>;\n\n  /**\n   * create a tensor from a WebGL texture\n   *\n   * @param texture - the WebGLTexture object to create tensor from\n   * @param options - An optional object representing options for creating tensor from WebGL texture.\n   *\n   * The options include following properties:\n   * - `width`: the width of the texture. Required.\n   * - `height`: the height of the texture. Required.\n   * - `format`: the format of the texture. If omitted, assume 'RGBA'.\n   * - `download`: an optional function to download the tensor data from GPU to CPU. If omitted, the GPU data\n   * will not be able to download. Usually, this is provided by a GPU backend for the inference outputs. Users don't\n   * need to provide this function.\n   * - `dispose`: an optional function to dispose the tensor data on GPU. If omitted, the GPU data will not be disposed.\n   * Usually, this is provided by a GPU backend for the inference outputs. Users don't need to provide this function.\n   *\n   * @returns a tensor object\n   */\n  fromTexture<T extends Tensor.TextureDataTypes = 'float32'>(\n      texture: Tensor.TextureType, options: TensorFromTextureOptions<T>): TypedTensor<'float32'>;\n\n  /**\n   * create a tensor from a WebGPU buffer\n   *\n   * @param buffer - the GPUBuffer object to create tensor from\n   * @param options - An optional object representing options for creating tensor from WebGPU buffer.\n   *\n   * The options include following properties:\n   * - `dataType`: the data type of the tensor. If omitted, assume 'float32'.\n   * - `dims`: the dimension of the tensor. Required.\n   * - `download`: an optional function to download the tensor data from GPU to CPU. If omitted, the GPU data\n   * will not be able to download. Usually, this is provided by a GPU backend for the inference outputs. Users don't\n   * need to provide this function.\n   * - `dispose`: an optional function to dispose the tensor data on GPU. If omitted, the GPU data will not be disposed.\n   * Usually, this is provided by a GPU backend for the inference outputs. Users don't need to provide this function.\n   *\n   * @returns a tensor object\n   */\n  fromGpuBuffer<T extends Tensor.GpuBufferDataTypes>(\n      buffer: Tensor.GpuBufferType, options: TensorFromGpuBufferOptions<T>): TypedTensor<T>;\n\n  /**\n   * create a tensor from a pre-allocated buffer. The buffer will be used as a pinned buffer.\n   *\n   * @param type - the tensor element type.\n   * @param buffer - a TypedArray corresponding to the type.\n   * @param dims - specify the dimension of the tensor. If omitted, a 1-D tensor is assumed.\n   *\n   * @returns a tensor object\n   */\n  fromPinnedBuffer<T extends Exclude<Tensor.Type, 'string'>>(\n      type: T, buffer: Tensor.DataTypeMap[T], dims?: readonly number[]): TypedTensor<T>;\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\n/**\n * A string that represents a file's URL or path.\n *\n * Path is vailable only in onnxruntime-node or onnxruntime-web running in Node.js.\n */\nexport type FileUrlOrPath = string;\n\n/**\n * A Blob object that represents a file.\n */\nexport type FileBlob = Blob;\n\n/**\n * A Uint8Array, ArrayBuffer or SharedArrayBuffer object that represents a file content.\n *\n * When it is an ArrayBuffer or SharedArrayBuffer, the whole buffer is assumed to be the file content.\n */\nexport type FileData = Uint8Array|ArrayBufferLike;\n\n/**\n * Represents a file that can be loaded by the ONNX Runtime JavaScript API.\n */\nexport type FileType = FileUrlOrPath|FileBlob|FileData;\n\n/**\n * Represents an external data file.\n */\nexport interface ExternalDataFileDescription {\n  /**\n   * Specify the external data file.\n   */\n  data: FileType;\n  /**\n   * Specify the file path.\n   */\n  path: string;\n}\n\n/**\n * Represents an external data file.\n *\n * When using a string, it should be a file URL or path that in the same directory as the model file.\n */\nexport type ExternalDataFileType = ExternalDataFileDescription|FileUrlOrPath;\n\n/**\n * Options for model loading.\n */\nexport interface OnnxModelOptions {\n  /**\n   * Specifying a list of files that represents the external data.\n   */\n  externalData?: readonly ExternalDataFileType[];\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from './tensor.js';\n\nexport type NonTensorType = never;\n\n/**\n * Type OnnxValue Represents both tensors and non-tensors value for model's inputs/outputs.\n *\n * NOTE: currently not support non-tensor\n */\nexport type OnnxValue = Tensor|NonTensorType;\n\n/**\n * Type OnnxValueDataLocation represents the location of the data of an OnnxValue.\n */\nexport type OnnxValueDataLocation = Tensor.DataLocation;\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {resolveBackendAndExecutionProviders} from './backend-impl.js';\nimport {SessionHandler, TrainingSessionHandler} from './backend.js';\nimport {InferenceSession as InferenceSession} from './inference-session.js';\nimport {OnnxValue} from './onnx-value.js';\nimport {Tensor} from './tensor.js';\nimport {TrainingSession as TrainingSessionInterface, TrainingSessionCreateOptions} from './training-session.js';\n\ntype SessionOptions = InferenceSession.SessionOptions;\ntype FeedsType = InferenceSession.FeedsType;\ntype FetchesType = InferenceSession.FetchesType;\ntype ReturnType = InferenceSession.ReturnType;\ntype RunOptions = InferenceSession.RunOptions;\n\nconst noBackendErrMsg: string = 'Training backend could not be resolved. ' +\n    'Make sure you\\'re using the correct configuration & WebAssembly files.';\n\nexport class TrainingSession implements TrainingSessionInterface {\n  private constructor(handler: TrainingSessionHandler, hasOptimizerModel: boolean, hasEvalModel: boolean) {\n    this.handler = handler;\n    this.hasOptimizerModel = hasOptimizerModel;\n    this.hasEvalModel = hasEvalModel;\n  }\n  private handler: TrainingSessionHandler;\n  private hasOptimizerModel: boolean;\n  private hasEvalModel: boolean;\n\n  get trainingInputNames(): readonly string[] {\n    return this.handler.inputNames;\n  }\n  get trainingOutputNames(): readonly string[] {\n    return this.handler.outputNames;\n  }\n\n  get evalInputNames(): readonly string[] {\n    if (this.hasEvalModel) {\n      return this.handler.evalInputNames;\n    } else {\n      throw new Error('This training session has no evalModel loaded.');\n    }\n  }\n  get evalOutputNames(): readonly string[] {\n    if (this.hasEvalModel) {\n      return this.handler.evalOutputNames;\n    } else {\n      throw new Error('This training session has no evalModel loaded.');\n    }\n  }\n\n  static async create(trainingOptions: TrainingSessionCreateOptions, sessionOptions?: SessionOptions):\n      Promise<TrainingSession> {\n    const evalModel: string|Uint8Array = trainingOptions.evalModel || '';\n    const optimizerModel: string|Uint8Array = trainingOptions.optimizerModel || '';\n    const options: SessionOptions = sessionOptions || {};\n\n    // resolve backend, update session options with validated EPs, and create session handler\n    const [backend, optionsWithValidatedEPs] = await resolveBackendAndExecutionProviders(options);\n    if (backend.createTrainingSessionHandler) {\n      const handler = await backend.createTrainingSessionHandler(\n          trainingOptions.checkpointState, trainingOptions.trainModel, evalModel, optimizerModel,\n          optionsWithValidatedEPs);\n      return new TrainingSession(handler, !!trainingOptions.optimizerModel, !!trainingOptions.evalModel);\n    } else {\n      throw new Error(noBackendErrMsg);\n    }\n  }\n\n  /**\n   * Helper function for runTrainStep and future runStep methods that handles the type-narrowing conversion from\n   * the given parameters to SessionHandler.FetchesType and RunOptions.\n   *\n   * @param inputNames the feeds object is checked that they contain all input names in the provided list of input\n   * names.\n   * @param outputNames the fetches object is checked that their keys match up with valid names in the list of output\n   * names.\n   * @param feeds the required input\n   * @param arg1 narrowed & converted into the SessionHandler.FetchesType or RunOptions object\n   * @param arg2 optional RunOptions object.\n   * @returns\n   */\n  typeNarrowingForRunStep(\n      inputNames: readonly string[], outputNames: readonly string[], feeds: FeedsType, arg1?: FetchesType|RunOptions,\n      arg2?: RunOptions): [SessionHandler.FetchesType, RunOptions] {\n    const fetches: {[name: string]: OnnxValue|null} = {};\n    let options: RunOptions = {};\n    // check inputs\n    if (typeof feeds !== 'object' || feeds === null || feeds instanceof Tensor || Array.isArray(feeds)) {\n      throw new TypeError(\n          '\\'feeds\\' must be an object that use input names as keys and OnnxValue as corresponding values.');\n    }\n\n    let isFetchesEmpty = true;\n    // determine which override is being used\n    if (typeof arg1 === 'object') {\n      if (arg1 === null) {\n        throw new TypeError('Unexpected argument[1]: cannot be null.');\n      }\n      if (arg1 instanceof Tensor) {\n        throw new TypeError('\\'fetches\\' cannot be a Tensor');\n      }\n\n      if (Array.isArray(arg1)) {\n        if (arg1.length === 0) {\n          throw new TypeError('\\'fetches\\' cannot be an empty array.');\n        }\n        isFetchesEmpty = false;\n        // output names\n        for (const name of arg1) {\n          if (typeof name !== 'string') {\n            throw new TypeError('\\'fetches\\' must be a string array or an object.');\n          }\n          if (outputNames.indexOf(name) === -1) {\n            throw new RangeError(`'fetches' contains invalid output name: ${name}.`);\n          }\n          fetches[name] = null;\n        }\n\n        if (typeof arg2 === 'object' && arg2 !== null) {\n          options = arg2;\n        } else if (typeof arg2 !== 'undefined') {\n          throw new TypeError('\\'options\\' must be an object.');\n        }\n      } else {\n        // decide whether arg1 is fetches or options\n        // if any output name is present and its value is valid OnnxValue, we consider it fetches\n        let isFetches = false;\n        const arg1Keys = Object.getOwnPropertyNames(arg1);\n        for (const name of outputNames) {\n          if (arg1Keys.indexOf(name) !== -1) {\n            const v = (arg1 as InferenceSession.NullableOnnxValueMapType)[name];\n            if (v === null || v instanceof Tensor) {\n              isFetches = true;\n              isFetchesEmpty = false;\n              fetches[name] = v;\n            }\n          }\n        }\n\n        if (isFetches) {\n          if (typeof arg2 === 'object' && arg2 !== null) {\n            options = arg2;\n          } else if (typeof arg2 !== 'undefined') {\n            throw new TypeError('\\'options\\' must be an object.');\n          }\n        } else {\n          options = arg1 as RunOptions;\n        }\n      }\n    } else if (typeof arg1 !== 'undefined') {\n      throw new TypeError('Unexpected argument[1]: must be \\'fetches\\' or \\'options\\'.');\n    }\n\n    // check if all inputs are in feed\n    for (const name of inputNames) {\n      if (typeof feeds[name] === 'undefined') {\n        throw new Error(`input '${name}' is missing in 'feeds'.`);\n      }\n    }\n\n    // if no fetches is specified, we use the full output names list\n    if (isFetchesEmpty) {\n      for (const name of outputNames) {\n        fetches[name] = null;\n      }\n    }\n\n    return [fetches, options];\n  }\n\n  /**\n   * Helper method for runTrainStep and any other runStep methods. Takes the ReturnType result from the SessionHandler\n   * and changes it into a map of Tensors.\n   *\n   * @param results\n   * @returns\n   */\n  convertHandlerReturnTypeToMapOfTensors(results: SessionHandler.ReturnType): ReturnType {\n    const returnValue: {[name: string]: OnnxValue} = {};\n    for (const key in results) {\n      if (Object.hasOwnProperty.call(results, key)) {\n        const result = results[key];\n        if (result instanceof Tensor) {\n          returnValue[key] = result;\n        } else {\n          returnValue[key] = new Tensor(result.type, result.data, result.dims);\n        }\n      }\n    }\n    return returnValue;\n  }\n\n  async lazyResetGrad(): Promise<void> {\n    await this.handler.lazyResetGrad();\n  }\n\n  runTrainStep(feeds: FeedsType, options?: RunOptions): Promise<ReturnType>;\n  runTrainStep(feeds: FeedsType, fetches: FetchesType, options?: RunOptions): Promise<ReturnType>;\n  async runTrainStep(feeds: FeedsType, arg1?: FetchesType|RunOptions, arg2?: RunOptions): Promise<ReturnType> {\n    const [fetches, options] =\n        this.typeNarrowingForRunStep(this.trainingInputNames, this.trainingOutputNames, feeds, arg1, arg2);\n    const results = await this.handler.runTrainStep(feeds, fetches, options);\n    return this.convertHandlerReturnTypeToMapOfTensors(results);\n  }\n\n  async runOptimizerStep(options?: InferenceSession.RunOptions|undefined): Promise<void> {\n    if (this.hasOptimizerModel) {\n      await this.handler.runOptimizerStep(options || {});\n    } else {\n      throw new Error('This TrainingSession has no OptimizerModel loaded.');\n    }\n  }\n\n  runEvalStep(feeds: FeedsType, options?: RunOptions|undefined): Promise<ReturnType>;\n  runEvalStep(feeds: FeedsType, fetches: FetchesType, options?: RunOptions|undefined): Promise<ReturnType>;\n  async runEvalStep(feeds: FeedsType, arg1?: FetchesType|RunOptions, arg2?: RunOptions): Promise<ReturnType> {\n    if (this.hasEvalModel) {\n      const [fetches, options] =\n          this.typeNarrowingForRunStep(this.evalInputNames, this.evalOutputNames, feeds, arg1, arg2);\n      const results = await this.handler.runEvalStep(feeds, fetches, options);\n      return this.convertHandlerReturnTypeToMapOfTensors(results);\n    } else {\n      throw new Error('This TrainingSession has no EvalModel loaded.');\n    }\n  }\n\n  async getParametersSize(trainableOnly = true): Promise<number> {\n    return this.handler.getParametersSize(trainableOnly);\n  }\n\n  async loadParametersBuffer(array: Uint8Array, trainableOnly = true): Promise<void> {\n    const paramsSize = await this.getParametersSize(trainableOnly);\n    // checking that the size of the Uint8Array is equivalent to the byte length of a Float32Array of the number\n    // of parameters\n    if (array.length !== 4 * paramsSize) {\n      throw new Error(\n          'Size of the buffer passed into loadParametersBuffer must match the number of parameters in ' +\n          'the model. Please use getParametersSize method to check.');\n    }\n    return this.handler.loadParametersBuffer(array, trainableOnly);\n  }\n\n  async getContiguousParameters(trainableOnly = true): Promise<OnnxValue> {\n    return this.handler.getContiguousParameters(trainableOnly);\n  }\n\n  async release(): Promise<void> {\n    return this.handler.dispose();\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {InferenceSession} from './inference-session.js';\nimport {OnnxValue} from './onnx-value.js';\nimport {TrainingSession as TrainingSessionImpl} from './training-session-impl.js';\n\n/* eslint-disable @typescript-eslint/no-redeclare */\n\nexport declare namespace TrainingSession {\n  /**\n   * Either URI file path (string) or Uint8Array containing model or checkpoint information.\n   */\n  type UriOrBuffer = string|Uint8Array;\n}\n\n/**\n * Represent a runtime instance of an ONNX training session,\n * which contains a model that can be trained, and, optionally,\n * an eval and optimizer model.\n */\nexport interface TrainingSession {\n  // #region run()\n\n  /**\n   * Lazily resets the gradients of all trainable parameters to zero. Should happen after the invocation of\n   * runOptimizerStep.\n   */\n  lazyResetGrad(): Promise<void>;\n\n  /**\n   * Run TrainStep asynchronously with the given feeds and options.\n   *\n   * @param feeds - Representation of the model input. See type description of `InferenceSession.InputType` for\n   detail.\n   * @param options - Optional. A set of options that controls the behavior of model training.\n   * @returns A promise that resolves to a map, which uses output names as keys and OnnxValue as corresponding values.\n   */\n  runTrainStep(feeds: InferenceSession.FeedsType, options?: InferenceSession.RunOptions):\n      Promise<InferenceSession.ReturnType>;\n\n  /**\n   * Run a single train step with the given inputs and options.\n   *\n   * @param feeds - Representation of the model input.\n   * @param fetches - Representation of the model output.\n   * detail.\n   * @param options - Optional. A set of options that controls the behavior of model training.\n   * @returns A promise that resolves to a map, which uses output names as keys and OnnxValue as corresponding\n   values.\n   */\n  runTrainStep(\n      feeds: InferenceSession.FeedsType, fetches: InferenceSession.FetchesType,\n      options?: InferenceSession.RunOptions): Promise<InferenceSession.ReturnType>;\n\n  /**\n   * Runs a single optimizer step, which performs weight updates for the trainable parameters using the optimizer model.\n   *\n   * @param options - Optional. A set of options that controls the behavior of model optimizing.\n   */\n  runOptimizerStep(options?: InferenceSession.RunOptions): Promise<void>;\n\n  /**\n   * Run a single eval step with the given inputs and options using the eval model.\n   *\n   * @param feeds - Representation of the model input.\n   * @param options - Optional. A set of options that controls the behavior of model eval step.\n   * @returns A promise that resolves to a map, which uses output names as keys and OnnxValue as corresponding\n   values.\n   */\n  runEvalStep(feeds: InferenceSession.FeedsType, options?: InferenceSession.RunOptions):\n      Promise<InferenceSession.ReturnType>;\n\n  /**\n   * Run a single eval step with the given inputs and options using the eval model.\n   *\n   * @param feeds - Representation of the model input.\n   * @param fetches - Representation of the model output.\n   * detail.\n   * @param options - Optional. A set of options that controls the behavior of model eval step.\n   * @returns A promise that resolves to a map, which uses output names as keys and OnnxValue as corresponding\n   values.\n   */\n  runEvalStep(\n      feeds: InferenceSession.FeedsType, fetches: InferenceSession.FetchesType,\n      options?: InferenceSession.RunOptions): Promise<InferenceSession.ReturnType>;\n\n  // #endregion\n\n  // #region copy parameters\n\n  /**\n   * Retrieves the size of all parameters for the training state. Calculates the total number of primitive (datatype of\n   * the parameters) elements of all the parameters in the training state.\n   *\n   * @param trainableOnly - When set to true, the size is calculated for trainable params only. Default value is true.\n   */\n  getParametersSize(trainableOnly: boolean): Promise<number>;\n\n  /**\n   * Copies parameter values from the given buffer to the training state. Currently, only supporting models with\n   * parameters of type Float32.\n   *\n   * @param buffer - A Uint8Array representation of Float32 parameters.\n   * @param trainableOnly - True if trainable parameters only to be modified, false otherwise. Default value is true.\n   */\n  loadParametersBuffer(buffer: Uint8Array, trainableOnly: boolean): Promise<void>;\n\n  /**\n   * Copies the model parameters to a contiguous buffer. Usually used in the context of Federated Learning.\n   * Currently, only supporting models with parameters of type Float32.\n   *\n   * @param trainableOnly - When set to true, only trainable parameters are copied. Trainable parameters are parameters\n   * for which requires_grad is set to true. Default value is true.\n   * @returns A promise that resolves to a Float32 OnnxValue of the requested parameters.\n   */\n  getContiguousParameters(trainableOnly: boolean): Promise<OnnxValue>;\n  // #endregion\n\n  // #region release()\n\n  /**\n   * Release the inference session and the underlying resources.\n   */\n  release(): Promise<void>;\n  // #endregion\n\n  // #region metadata\n\n  /**\n   * Get input names of the loaded training model.\n   */\n  readonly trainingInputNames: readonly string[];\n\n  /**\n   * Get output names of the loaded training model.\n   */\n  readonly trainingOutputNames: readonly string[];\n\n  /**\n   * Get input names of the loaded eval model. Is an empty array if no eval model is loaded.\n   */\n  readonly evalInputNames: readonly string[];\n\n  /**\n   * Get output names of the loaded eval model. Is an empty array if no eval model is loaded.\n   */\n  readonly evalOutputNames: readonly string[];\n\n  // #endregion\n}\n\n/**\n * Represents the optional parameters that can be passed into the TrainingSessionFactory.\n */\nexport interface TrainingSessionCreateOptions {\n  /**\n   * URI or buffer for a .ckpt file that contains the checkpoint for the training model.\n   */\n  checkpointState: TrainingSession.UriOrBuffer;\n  /**\n   * URI or buffer for the .onnx training file.\n   */\n  trainModel: TrainingSession.UriOrBuffer;\n  /**\n   * Optional. URI or buffer for the .onnx optimizer model file.\n   */\n  optimizerModel?: TrainingSession.UriOrBuffer;\n  /**\n   * Optional. URI or buffer for the .onnx eval model file.\n   */\n  evalModel?: TrainingSession.UriOrBuffer;\n}\n\n/**\n * Defines method overload possibilities for creating a TrainingSession.\n */\nexport interface TrainingSessionFactory {\n  // #region create()\n\n  /**\n   * Creates a new TrainingSession and asynchronously loads any models passed in through trainingOptions\n   *\n   * @param trainingOptions specify models and checkpoints to load into the Training Session\n   * @param sessionOptions specify configuration for training session behavior\n   *\n   * @returns Promise that resolves to a TrainingSession object\n   */\n  create(trainingOptions: TrainingSessionCreateOptions, sessionOptions?: InferenceSession.SessionOptions):\n      Promise<TrainingSession>;\n\n  // #endregion\n}\n\n// eslint-disable-next-line @typescript-eslint/naming-convention\nexport const TrainingSession: TrainingSessionFactory = TrainingSessionImpl;\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\n/**\n * # ONNX Runtime JavaScript API\n *\n * ONNX Runtime JavaScript API is a unified API for all JavaScript usages, including the following NPM packages:\n *\n * - [onnxruntime-node](https://www.npmjs.com/package/onnxruntime-node)\n * - [onnxruntime-web](https://www.npmjs.com/package/onnxruntime-web)\n * - [onnxruntime-react-native](https://www.npmjs.com/package/onnxruntime-react-native)\n *\n * See also:\n * - [Get Started](https://onnxruntime.ai/docs/get-started/with-javascript/)\n * - [Inference examples](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/js)\n *\n * @packageDocumentation\n */\n\nexport * from './backend.js';\nexport * from './env.js';\nexport * from './inference-session.js';\nexport * from './tensor.js';\nexport * from './tensor-conversion.js';\nexport * from './tensor-factory.js';\nexport * from './trace.js';\nexport * from './onnx-model.js';\nexport * from './onnx-value.js';\nexport * from './training-session.js';\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Env} from 'onnxruntime-common';\n\nimport {WebGLContext} from './backends/webgl/webgl-context';\n\nexport declare namespace Logger {\n  export interface SeverityTypeMap {\n    verbose: 'v';\n    info: 'i';\n    warning: 'w';\n    error: 'e';\n    fatal: 'f';\n  }\n\n  export type Severity = keyof SeverityTypeMap;\n\n  export type Provider = 'none'|'console';\n\n  /**\n   * Logging config that used to control the behavior of logger\n   */\n  export interface Config {\n    /**\n     * Specify the logging provider. 'console' by default\n     */\n    provider?: Provider;\n    /**\n     * Specify the minimal logger serverity. 'warning' by default\n     */\n    minimalSeverity?: Logger.Severity;\n    /**\n     * Whether to output date time in log. true by default\n     */\n    logDateTime?: boolean;\n    /**\n     * Whether to output source information (Not yet supported). false by default\n     */\n    logSourceLocation?: boolean;\n  }\n\n  export interface CategorizedLogger {\n    verbose(content: string): void;\n    info(content: string): void;\n    warning(content: string): void;\n    error(content: string): void;\n    fatal(content: string): void;\n  }\n}\n\n// eslint-disable-next-line @typescript-eslint/no-redeclare\nexport interface Logger {\n  (category: string): Logger.CategorizedLogger;\n\n  verbose(content: string): void;\n  verbose(category: string, content: string): void;\n  info(content: string): void;\n  info(category: string, content: string): void;\n  warning(content: string): void;\n  warning(category: string, content: string): void;\n  error(content: string): void;\n  error(category: string, content: string): void;\n  fatal(content: string): void;\n  fatal(category: string, content: string): void;\n\n  /**\n   * Reset the logger configuration.\n   * @param config specify an optional default config\n   */\n  reset(config?: Logger.Config): void;\n  /**\n   * Set the logger's behavior on the given category\n   * @param category specify a category string. If '*' is specified, all previous configuration will be overwritten. If\n   * '' is specified, the default behavior will be updated.\n   * @param config the config object to indicate the logger's behavior\n   */\n  set(category: string, config: Logger.Config): void;\n\n  /**\n   * Set the logger's behavior from ort-common env\n   * @param env the env used to set logger. Currently only setting loglevel is supported through Env.\n   */\n  setWithEnv(env: Env): void;\n}\n\ninterface LoggerProvider {\n  log(severity: Logger.Severity, content: string, category?: string): void;\n}\nclass NoOpLoggerProvider implements LoggerProvider {\n  log(_severity: Logger.Severity, _content: string, _category?: string) {\n    // do nothing\n  }\n}\nclass ConsoleLoggerProvider implements LoggerProvider {\n  log(severity: Logger.Severity, content: string, category?: string) {\n    // eslint-disable-next-line no-console\n    console.log(`${this.color(severity)} ${category ? '\\x1b[35m' + category + '\\x1b[0m ' : ''}${content}`);\n  }\n\n  private color(severity: Logger.Severity) {\n    switch (severity) {\n      case 'verbose':\n        return '\\x1b[34;40mv\\x1b[0m';\n      case 'info':\n        return '\\x1b[32mi\\x1b[0m';\n      case 'warning':\n        return '\\x1b[30;43mw\\x1b[0m';\n      case 'error':\n        return '\\x1b[31;40me\\x1b[0m';\n      case 'fatal':\n        return '\\x1b[101mf\\x1b[0m';\n      default:\n        throw new Error(`unsupported severity: ${severity}`);\n    }\n  }\n}\n\nconst SEVERITY_VALUE = {\n  verbose: 1000,\n  info: 2000,\n  warning: 4000,\n  error: 5000,\n  fatal: 6000\n};\n\nconst LOGGER_PROVIDER_MAP: {readonly [provider: string]: Readonly<LoggerProvider>} = {\n  ['none']: new NoOpLoggerProvider(),\n  ['console']: new ConsoleLoggerProvider()\n};\nconst LOGGER_DEFAULT_CONFIG = {\n  provider: 'console',\n  minimalSeverity: 'warning',\n  logDateTime: true,\n  logSourceLocation: false\n};\nlet LOGGER_CONFIG_MAP:\n    {[category: string]: Readonly<Required<Logger.Config>>} = {['']: LOGGER_DEFAULT_CONFIG as Required<Logger.Config>};\n\nfunction log(category: string): Logger.CategorizedLogger;\nfunction log(severity: Logger.Severity, content: string): void;\nfunction log(severity: Logger.Severity, category: string, content: string): void;\nfunction log(severity: Logger.Severity, arg1: string, arg2?: string): void;\nfunction log(\n    arg0: string|Logger.Severity, arg1?: string, arg2?: string|number, arg3?: number): Logger.CategorizedLogger|void {\n  if (arg1 === undefined) {\n    // log(category: string): Logger.CategorizedLogger;\n    return createCategorizedLogger(arg0);\n  } else if (arg2 === undefined) {\n    // log(severity, content);\n    logInternal(arg0 as Logger.Severity, arg1, 1);\n  } else if (typeof arg2 === 'number' && arg3 === undefined) {\n    // log(severity, content, stack)\n    logInternal(arg0 as Logger.Severity, arg1, arg2);\n  } else if (typeof arg2 === 'string' && arg3 === undefined) {\n    // log(severity, category, content)\n    logInternal(arg0 as Logger.Severity, arg2, 1, arg1);\n  } else if (typeof arg2 === 'string' && typeof arg3 === 'number') {\n    // log(severity, category, content, stack)\n    logInternal(arg0 as Logger.Severity, arg2, arg3, arg1);\n  } else {\n    throw new TypeError('input is valid');\n  }\n}\n\nfunction createCategorizedLogger(category: string): Logger.CategorizedLogger {\n  return {\n    verbose: log.verbose.bind(null, category),\n    info: log.info.bind(null, category),\n    warning: log.warning.bind(null, category),\n    error: log.error.bind(null, category),\n    fatal: log.fatal.bind(null, category)\n  };\n}\n\n// NOTE: argument 'category' is put the last parameter beacause typescript\n// doesn't allow optional argument put in front of required argument. This\n// order is different from a usual logging API.\nfunction logInternal(severity: Logger.Severity, content: string, _stack: number, category?: string) {\n  const config = LOGGER_CONFIG_MAP[category || ''] || LOGGER_CONFIG_MAP[''];\n  if (SEVERITY_VALUE[severity] < SEVERITY_VALUE[config.minimalSeverity]) {\n    return;\n  }\n\n  if (config.logDateTime) {\n    content = `${new Date().toISOString()}|${content}`;\n  }\n\n  if (config.logSourceLocation) {\n    // TODO: calculate source location from 'stack'\n  }\n\n  LOGGER_PROVIDER_MAP[config.provider].log(severity, content, category);\n}\n\n// eslint-disable-next-line @typescript-eslint/no-namespace\nnamespace log {\n  export function verbose(content: string): void;\n  export function verbose(category: string, content: string): void;\n  export function verbose(arg0: string, arg1?: string) {\n    log('verbose', arg0, arg1);\n  }\n  export function info(content: string): void;\n  export function info(category: string, content: string): void;\n  export function info(arg0: string, arg1?: string) {\n    log('info', arg0, arg1);\n  }\n  export function warning(content: string): void;\n  export function warning(category: string, content: string): void;\n  export function warning(arg0: string, arg1?: string) {\n    log('warning', arg0, arg1);\n  }\n  export function error(content: string): void;\n  export function error(category: string, content: string): void;\n  export function error(arg0: string, arg1?: string) {\n    log('error', arg0, arg1);\n  }\n  export function fatal(content: string): void;\n  export function fatal(category: string, content: string): void;\n  export function fatal(arg0: string, arg1?: string) {\n    log('fatal', arg0, arg1);\n  }\n\n  export function reset(config?: Logger.Config): void {\n    LOGGER_CONFIG_MAP = {};\n    set('', config || {});\n  }\n  export function set(category: string, config: Logger.Config): void {\n    if (category === '*') {\n      reset(config);\n    } else {\n      const previousConfig = LOGGER_CONFIG_MAP[category] || LOGGER_DEFAULT_CONFIG;\n      LOGGER_CONFIG_MAP[category] = {\n        provider: config.provider || previousConfig.provider,\n        minimalSeverity: config.minimalSeverity || previousConfig.minimalSeverity,\n        logDateTime: (config.logDateTime === undefined) ? previousConfig.logDateTime : config.logDateTime,\n        logSourceLocation: (config.logSourceLocation === undefined) ? previousConfig.logSourceLocation :\n                                                                      config.logSourceLocation\n      };\n    }\n\n    // TODO: we want to support wildcard or regex?\n  }\n\n  export function setWithEnv(env: Env): void {\n    const config: Logger.Config = {};\n    if (env.logLevel) {\n      config.minimalSeverity = env.logLevel as Logger.Severity;\n    }\n    set('', config);\n  }\n}\n\n// eslint-disable-next-line @typescript-eslint/no-redeclare, @typescript-eslint/naming-convention\nexport const Logger: Logger = log;\n\nexport declare namespace Profiler {\n  export interface Config {\n    maxNumberEvents?: number;\n    flushBatchSize?: number;\n    flushIntervalInMilliseconds?: number;\n  }\n\n  export type EventCategory = 'session'|'node'|'op'|'backend';\n\n  export interface Event {\n    end(): void|Promise<void>;\n  }\n}\n// TODO\n// class WebGLEvent implements Profiler.Event {}\n\nclass Event implements Profiler.Event {\n  constructor(\n      public category: Profiler.EventCategory, public name: string, public startTime: number,\n      private endCallback: (e: Event) => void|Promise<void>, public timer?: WebGLQuery, public ctx?: WebGLContext) {}\n\n  async end() {\n    return this.endCallback(this);\n  }\n\n  async checkTimer(): Promise<number> {\n    if (this.ctx === undefined || this.timer === undefined) {\n      throw new Error('No webgl timer found');\n    } else {\n      this.ctx.endTimer();\n      return this.ctx.waitForQueryAndGetTime(this.timer);\n    }\n  }\n}\n\nclass EventRecord {\n  constructor(\n      public category: Profiler.EventCategory, public name: string, public startTime: number, public endTime: number) {}\n}\n\nexport class Profiler {\n  static create(config?: Profiler.Config): Profiler {\n    if (config === undefined) {\n      return new this();\n    }\n    return new this(config.maxNumberEvents, config.flushBatchSize, config.flushIntervalInMilliseconds);\n  }\n\n  private constructor(maxNumberEvents?: number, flushBatchSize?: number, flushIntervalInMilliseconds?: number) {\n    this._started = false;\n    this._maxNumberEvents = maxNumberEvents === undefined ? 10000 : maxNumberEvents;\n    this._flushBatchSize = flushBatchSize === undefined ? 10 : flushBatchSize;\n    this._flushIntervalInMilliseconds = flushIntervalInMilliseconds === undefined ? 5000 : flushIntervalInMilliseconds;\n  }\n\n  // start profiling\n  start() {\n    this._started = true;\n    this._timingEvents = [];\n    this._flushTime = now();\n    this._flushPointer = 0;\n  }\n\n  // stop profiling\n  stop() {\n    this._started = false;\n    for (; this._flushPointer < this._timingEvents.length; this._flushPointer++) {\n      this.logOneEvent(this._timingEvents[this._flushPointer]);\n    }\n  }\n\n  // create an event scope for the specific function\n  event<T>(category: Profiler.EventCategory, name: string, func: () => T, ctx?: WebGLContext): T;\n  event<T>(category: Profiler.EventCategory, name: string, func: () => Promise<T>, ctx?: WebGLContext): Promise<T>;\n\n  event<T>(category: Profiler.EventCategory, name: string, func: () => T | Promise<T>, ctx?: WebGLContext): T\n      |Promise<T> {\n    const event = this._started ? this.begin(category, name, ctx) : undefined;\n    let isPromise = false;\n\n    const res = func();\n\n    // we consider a then-able object is a promise\n    if (res && typeof (res as Promise<T>).then === 'function') {\n      isPromise = true;\n      return new Promise<T>((resolve, reject) => {\n        (res as Promise<T>)\n            .then(\n                async value => {  // fulfilled\n                  if (event) {\n                    await event.end();\n                  }\n                  resolve(value);\n                },\n                async reason => {  // rejected\n                  if (event) {\n                    await event.end();\n                  }\n                  reject(reason);\n                });\n      });\n    }\n    if (!isPromise && event) {\n      const eventRes = event.end();\n      if (eventRes && typeof eventRes.then === 'function') {\n        return new Promise<T>((resolve, reject) => {\n          (eventRes).then(\n              () => {  // fulfilled\n                resolve(res);\n              },\n              (reason) => {  // rejected\n                reject(reason);\n              });\n        });\n      }\n    }\n    return res;\n  }\n\n  // begin an event\n  begin(category: Profiler.EventCategory, name: string, ctx?: WebGLContext): Event {\n    if (!this._started) {\n      throw new Error('profiler is not started yet');\n    }\n    if (ctx === undefined) {\n      const startTime = now();\n      this.flush(startTime);\n      return new Event(category, name, startTime, e => this.endSync(e));\n    } else {\n      const timer: WebGLQuery = ctx.beginTimer();\n      return new Event(category, name, 0, async e => this.end(e), timer, ctx);\n    }\n  }\n\n  // end the specific event\n  private async end(event: Event): Promise<void> {\n    const endTime: number = await event.checkTimer();\n    if (this._timingEvents.length < this._maxNumberEvents) {\n      this._timingEvents.push(new EventRecord(event.category, event.name, event.startTime, endTime));\n      this.flush(endTime);\n    }\n  }\n\n  private endSync(event: Event): void {\n    const endTime: number = now();\n    if (this._timingEvents.length < this._maxNumberEvents) {\n      this._timingEvents.push(new EventRecord(event.category, event.name, event.startTime, endTime));\n      this.flush(endTime);\n    }\n  }\n\n  private logOneEvent(event: EventRecord) {\n    Logger.verbose(\n        `Profiler.${event.category}`,\n        `${(event.endTime - event.startTime).toFixed(2)}ms on event '${event.name}' at ${event.endTime.toFixed(2)}`);\n  }\n\n  private flush(currentTime: number) {\n    if (this._timingEvents.length - this._flushPointer >= this._flushBatchSize ||\n        currentTime - this._flushTime >= this._flushIntervalInMilliseconds) {\n      // should flush when either batch size accumlated or interval elepsed\n\n      for (const previousPointer = this._flushPointer; this._flushPointer < previousPointer + this._flushBatchSize &&\n           this._flushPointer < this._timingEvents.length;\n           this._flushPointer++) {\n        this.logOneEvent(this._timingEvents[this._flushPointer]);\n      }\n\n      this._flushTime = now();\n    }\n  }\n\n  get started() {\n    return this._started;\n  }\n  private _started = false;\n  private _timingEvents: EventRecord[];\n\n  private readonly _maxNumberEvents: number;\n\n  private readonly _flushBatchSize: number;\n  private readonly _flushIntervalInMilliseconds: number;\n\n  private _flushTime: number;\n  private _flushPointer = 0;\n}\n\n/**\n * returns a number to represent the current timestamp in a resolution as high as possible.\n */\nexport const now = (typeof performance !== 'undefined' && performance.now) ? () => performance.now() : Date.now;\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Graph} from './graph';\nimport {OperatorImplementation, OperatorInitialization} from './operators';\n\nexport interface OpSet {\n  domain: string;\n  version: number;\n}\nexport declare namespace OpSet {\n  /**\n   * Domain of an opset, it can be an empty string(default value, represent for ai.onnx), or 'ai.onnx.ml'\n   */\n  type Domain = ''|'ai.onnx.ml'|'com.microsoft';\n  /**\n   * A resolve rule consists of 4 or 5 items: opType, opSetDomain, versionSelector, operatorImplementation and\n   * operatorInitialization (optional)\n   */\n  type ResolveRule = [\n    string, Domain, string, OperatorImplementation<Graph.Node>\n  ]|[string, Domain, string, OperatorImplementation<unknown>, OperatorInitialization<unknown>];\n}\n\nexport function resolveOperator(node: Graph.Node, opsets: readonly OpSet[], rules: readonly OpSet.ResolveRule[]) {\n  for (const rule of rules) {\n    const opType = rule[0];\n    const domain = rule[1];\n    const versionSelector = rule[2];\n    const opImpl = rule[3];\n    const opInit = rule[4];\n\n    if (node.opType === opType) {  // operator type matches\n      for (const opset of opsets) {\n        // opset '' and 'ai.onnx' are considered the same.\n        if (opset.domain === domain || (opset.domain === 'ai.onnx' && domain === '')) {  // opset domain found\n          if (matchSelector(opset.version, versionSelector)) {\n            return {opImpl, opInit};\n          }\n        }\n      }\n    }\n  }\n\n  throw new TypeError(`cannot resolve operator '${node.opType}' with opsets: ${\n      opsets.map(set => `${set.domain || 'ai.onnx'} v${set.version}`).join(', ')}`);\n}\n\nfunction matchSelector(version: number, selector: string): boolean {\n  if (selector.endsWith('+')) {\n    // minimum version match ('7+' expects version>=7)\n    const rangeStart = Number.parseInt(selector.substring(0, selector.length - 1), 10);\n    return !isNaN(rangeStart) && rangeStart <= version;\n  } else if (selector.split('-').length === 2) {\n    // range match ('6-8' expects 6<=version<=8)\n    const pair = selector.split('-');\n    const rangeStart = Number.parseInt(pair[0], 10);\n    const rangeEnd = Number.parseInt(pair[1], 10);\n    return !isNaN(rangeStart) && !isNaN(rangeEnd) && rangeStart <= version && version <= rangeEnd;\n  } else {\n    // exact match ('7' expects version===7)\n    return Number.parseInt(selector, 10) === version;\n  }\n}\n","\"use strict\";\r\nexports.__esModule = true;\r\nvar Guid = /** @class */ (function () {\r\n    function Guid(guid) {\r\n        if (!guid) {\r\n            throw new TypeError(\"Invalid argument; `value` has no value.\");\r\n        }\r\n        this.value = Guid.EMPTY;\r\n        if (guid && Guid.isGuid(guid)) {\r\n            this.value = guid;\r\n        }\r\n    }\r\n    Guid.isGuid = function (guid) {\r\n        var value = guid.toString();\r\n        return guid && (guid instanceof Guid || Guid.validator.test(value));\r\n    };\r\n    Guid.create = function () {\r\n        return new Guid([Guid.gen(2), Guid.gen(1), Guid.gen(1), Guid.gen(1), Guid.gen(3)].join(\"-\"));\r\n    };\r\n    Guid.createEmpty = function () {\r\n        return new Guid(\"emptyguid\");\r\n    };\r\n    Guid.parse = function (guid) {\r\n        return new Guid(guid);\r\n    };\r\n    Guid.raw = function () {\r\n        return [Guid.gen(2), Guid.gen(1), Guid.gen(1), Guid.gen(1), Guid.gen(3)].join(\"-\");\r\n    };\r\n    Guid.gen = function (count) {\r\n        var out = \"\";\r\n        for (var i = 0; i < count; i++) {\r\n            // tslint:disable-next-line:no-bitwise\r\n            out += (((1 + Math.random()) * 0x10000) | 0).toString(16).substring(1);\r\n        }\r\n        return out;\r\n    };\r\n    Guid.prototype.equals = function (other) {\r\n        // Comparing string `value` against provided `guid` will auto-call\r\n        // toString on `guid` for comparison\r\n        return Guid.isGuid(other) && this.value === other.toString();\r\n    };\r\n    Guid.prototype.isEmpty = function () {\r\n        return this.value === Guid.EMPTY;\r\n    };\r\n    Guid.prototype.toString = function () {\r\n        return this.value;\r\n    };\r\n    Guid.prototype.toJSON = function () {\r\n        return {\r\n            value: this.value\r\n        };\r\n    };\r\n    Guid.validator = new RegExp(\"^[a-z0-9]{8}-[a-z0-9]{4}-[a-z0-9]{4}-[a-z0-9]{4}-[a-z0-9]{12}$\", \"i\");\r\n    Guid.EMPTY = \"00000000-0000-0000-0000-000000000000\";\r\n    return Guid;\r\n}());\r\nexports.Guid = Guid;\r\n","/**\n * @license\n * Copyright 2009 The Closure Library Authors\n * Copyright 2020 Daniel Wirtz / The long.js Authors.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *     http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * SPDX-License-Identifier: Apache-2.0\n */\n\n// WebAssembly optimizations to do native i64 multiplication and divide\nvar wasm = null;\ntry {\n  wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([\n    0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 0, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 126, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 127, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 128, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11\n  ])), {}).exports;\n} catch (e) {\n  // no wasm support :(\n}\n\n/**\n * Constructs a 64 bit two's-complement integer, given its low and high 32 bit values as *signed* integers.\n *  See the from* functions below for more convenient ways of constructing Longs.\n * @exports Long\n * @class A Long class for representing a 64 bit two's-complement integer value.\n * @param {number} low The low (signed) 32 bits of the long\n * @param {number} high The high (signed) 32 bits of the long\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\n * @constructor\n */\nfunction Long(low, high, unsigned) {\n\n  /**\n   * The low 32 bits as a signed value.\n   * @type {number}\n   */\n  this.low = low | 0;\n\n  /**\n   * The high 32 bits as a signed value.\n   * @type {number}\n   */\n  this.high = high | 0;\n\n  /**\n   * Whether unsigned or not.\n   * @type {boolean}\n   */\n  this.unsigned = !!unsigned;\n}\n\n// The internal representation of a long is the two given signed, 32-bit values.\n// We use 32-bit pieces because these are the size of integers on which\n// Javascript performs bit-operations.  For operations like addition and\n// multiplication, we split each number into 16 bit pieces, which can easily be\n// multiplied within Javascript's floating-point representation without overflow\n// or change in sign.\n//\n// In the algorithms below, we frequently reduce the negative case to the\n// positive case by negating the input(s) and then post-processing the result.\n// Note that we must ALWAYS check specially whether those values are MIN_VALUE\n// (-2^63) because -MIN_VALUE == MIN_VALUE (since 2^63 cannot be represented as\n// a positive number, it overflows back into a negative).  Not handling this\n// case would often result in infinite recursion.\n//\n// Common constant values ZERO, ONE, NEG_ONE, etc. are defined below the from*\n// methods on which they depend.\n\n/**\n * An indicator used to reliably determine if an object is a Long or not.\n * @type {boolean}\n * @const\n * @private\n */\nLong.prototype.__isLong__;\n\nObject.defineProperty(Long.prototype, \"__isLong__\", { value: true });\n\n/**\n * @function\n * @param {*} obj Object\n * @returns {boolean}\n * @inner\n */\nfunction isLong(obj) {\n  return (obj && obj[\"__isLong__\"]) === true;\n}\n\n/**\n * @function\n * @param {*} value number\n * @returns {number}\n * @inner\n */\nfunction ctz32(value) {\n  var c = Math.clz32(value & -value);\n  return value ? 31 - c : c;\n}\n\n/**\n * Tests if the specified object is a Long.\n * @function\n * @param {*} obj Object\n * @returns {boolean}\n */\nLong.isLong = isLong;\n\n/**\n * A cache of the Long representations of small integer values.\n * @type {!Object}\n * @inner\n */\nvar INT_CACHE = {};\n\n/**\n * A cache of the Long representations of small unsigned integer values.\n * @type {!Object}\n * @inner\n */\nvar UINT_CACHE = {};\n\n/**\n * @param {number} value\n * @param {boolean=} unsigned\n * @returns {!Long}\n * @inner\n */\nfunction fromInt(value, unsigned) {\n  var obj, cachedObj, cache;\n  if (unsigned) {\n    value >>>= 0;\n    if (cache = (0 <= value && value < 256)) {\n      cachedObj = UINT_CACHE[value];\n      if (cachedObj)\n        return cachedObj;\n    }\n    obj = fromBits(value, 0, true);\n    if (cache)\n      UINT_CACHE[value] = obj;\n    return obj;\n  } else {\n    value |= 0;\n    if (cache = (-128 <= value && value < 128)) {\n      cachedObj = INT_CACHE[value];\n      if (cachedObj)\n        return cachedObj;\n    }\n    obj = fromBits(value, value < 0 ? -1 : 0, false);\n    if (cache)\n      INT_CACHE[value] = obj;\n    return obj;\n  }\n}\n\n/**\n * Returns a Long representing the given 32 bit integer value.\n * @function\n * @param {number} value The 32 bit integer in question\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\n * @returns {!Long} The corresponding Long value\n */\nLong.fromInt = fromInt;\n\n/**\n * @param {number} value\n * @param {boolean=} unsigned\n * @returns {!Long}\n * @inner\n */\nfunction fromNumber(value, unsigned) {\n  if (isNaN(value))\n    return unsigned ? UZERO : ZERO;\n  if (unsigned) {\n    if (value < 0)\n      return UZERO;\n    if (value >= TWO_PWR_64_DBL)\n      return MAX_UNSIGNED_VALUE;\n  } else {\n    if (value <= -TWO_PWR_63_DBL)\n      return MIN_VALUE;\n    if (value + 1 >= TWO_PWR_63_DBL)\n      return MAX_VALUE;\n  }\n  if (value < 0)\n    return fromNumber(-value, unsigned).neg();\n  return fromBits((value % TWO_PWR_32_DBL) | 0, (value / TWO_PWR_32_DBL) | 0, unsigned);\n}\n\n/**\n * Returns a Long representing the given value, provided that it is a finite number. Otherwise, zero is returned.\n * @function\n * @param {number} value The number in question\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\n * @returns {!Long} The corresponding Long value\n */\nLong.fromNumber = fromNumber;\n\n/**\n * @param {number} lowBits\n * @param {number} highBits\n * @param {boolean=} unsigned\n * @returns {!Long}\n * @inner\n */\nfunction fromBits(lowBits, highBits, unsigned) {\n  return new Long(lowBits, highBits, unsigned);\n}\n\n/**\n * Returns a Long representing the 64 bit integer that comes by concatenating the given low and high bits. Each is\n *  assumed to use 32 bits.\n * @function\n * @param {number} lowBits The low 32 bits\n * @param {number} highBits The high 32 bits\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\n * @returns {!Long} The corresponding Long value\n */\nLong.fromBits = fromBits;\n\n/**\n * @function\n * @param {number} base\n * @param {number} exponent\n * @returns {number}\n * @inner\n */\nvar pow_dbl = Math.pow; // Used 4 times (4*8 to 15+4)\n\n/**\n * @param {string} str\n * @param {(boolean|number)=} unsigned\n * @param {number=} radix\n * @returns {!Long}\n * @inner\n */\nfunction fromString(str, unsigned, radix) {\n  if (str.length === 0)\n    throw Error('empty string');\n  if (typeof unsigned === 'number') {\n    // For goog.math.long compatibility\n    radix = unsigned;\n    unsigned = false;\n  } else {\n    unsigned = !!unsigned;\n  }\n  if (str === \"NaN\" || str === \"Infinity\" || str === \"+Infinity\" || str === \"-Infinity\")\n    return unsigned ? UZERO : ZERO;\n  radix = radix || 10;\n  if (radix < 2 || 36 < radix)\n    throw RangeError('radix');\n\n  var p;\n  if ((p = str.indexOf('-')) > 0)\n    throw Error('interior hyphen');\n  else if (p === 0) {\n    return fromString(str.substring(1), unsigned, radix).neg();\n  }\n\n  // Do several (8) digits each time through the loop, so as to\n  // minimize the calls to the very expensive emulated div.\n  var radixToPower = fromNumber(pow_dbl(radix, 8));\n\n  var result = ZERO;\n  for (var i = 0; i < str.length; i += 8) {\n    var size = Math.min(8, str.length - i),\n      value = parseInt(str.substring(i, i + size), radix);\n    if (size < 8) {\n      var power = fromNumber(pow_dbl(radix, size));\n      result = result.mul(power).add(fromNumber(value));\n    } else {\n      result = result.mul(radixToPower);\n      result = result.add(fromNumber(value));\n    }\n  }\n  result.unsigned = unsigned;\n  return result;\n}\n\n/**\n * Returns a Long representation of the given string, written using the specified radix.\n * @function\n * @param {string} str The textual representation of the Long\n * @param {(boolean|number)=} unsigned Whether unsigned or not, defaults to signed\n * @param {number=} radix The radix in which the text is written (2-36), defaults to 10\n * @returns {!Long} The corresponding Long value\n */\nLong.fromString = fromString;\n\n/**\n * @function\n * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val\n * @param {boolean=} unsigned\n * @returns {!Long}\n * @inner\n */\nfunction fromValue(val, unsigned) {\n  if (typeof val === 'number')\n    return fromNumber(val, unsigned);\n  if (typeof val === 'string')\n    return fromString(val, unsigned);\n  // Throws for non-objects, converts non-instanceof Long:\n  return fromBits(val.low, val.high, typeof unsigned === 'boolean' ? unsigned : val.unsigned);\n}\n\n/**\n * Converts the specified value to a Long using the appropriate from* function for its type.\n * @function\n * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val Value\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\n * @returns {!Long}\n */\nLong.fromValue = fromValue;\n\n// NOTE: the compiler should inline these constant values below and then remove these variables, so there should be\n// no runtime penalty for these.\n\n/**\n * @type {number}\n * @const\n * @inner\n */\nvar TWO_PWR_16_DBL = 1 << 16;\n\n/**\n * @type {number}\n * @const\n * @inner\n */\nvar TWO_PWR_24_DBL = 1 << 24;\n\n/**\n * @type {number}\n * @const\n * @inner\n */\nvar TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL;\n\n/**\n * @type {number}\n * @const\n * @inner\n */\nvar TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL;\n\n/**\n * @type {number}\n * @const\n * @inner\n */\nvar TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2;\n\n/**\n * @type {!Long}\n * @const\n * @inner\n */\nvar TWO_PWR_24 = fromInt(TWO_PWR_24_DBL);\n\n/**\n * @type {!Long}\n * @inner\n */\nvar ZERO = fromInt(0);\n\n/**\n * Signed zero.\n * @type {!Long}\n */\nLong.ZERO = ZERO;\n\n/**\n * @type {!Long}\n * @inner\n */\nvar UZERO = fromInt(0, true);\n\n/**\n * Unsigned zero.\n * @type {!Long}\n */\nLong.UZERO = UZERO;\n\n/**\n * @type {!Long}\n * @inner\n */\nvar ONE = fromInt(1);\n\n/**\n * Signed one.\n * @type {!Long}\n */\nLong.ONE = ONE;\n\n/**\n * @type {!Long}\n * @inner\n */\nvar UONE = fromInt(1, true);\n\n/**\n * Unsigned one.\n * @type {!Long}\n */\nLong.UONE = UONE;\n\n/**\n * @type {!Long}\n * @inner\n */\nvar NEG_ONE = fromInt(-1);\n\n/**\n * Signed negative one.\n * @type {!Long}\n */\nLong.NEG_ONE = NEG_ONE;\n\n/**\n * @type {!Long}\n * @inner\n */\nvar MAX_VALUE = fromBits(0xFFFFFFFF | 0, 0x7FFFFFFF | 0, false);\n\n/**\n * Maximum signed value.\n * @type {!Long}\n */\nLong.MAX_VALUE = MAX_VALUE;\n\n/**\n * @type {!Long}\n * @inner\n */\nvar MAX_UNSIGNED_VALUE = fromBits(0xFFFFFFFF | 0, 0xFFFFFFFF | 0, true);\n\n/**\n * Maximum unsigned value.\n * @type {!Long}\n */\nLong.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE;\n\n/**\n * @type {!Long}\n * @inner\n */\nvar MIN_VALUE = fromBits(0, 0x80000000 | 0, false);\n\n/**\n * Minimum signed value.\n * @type {!Long}\n */\nLong.MIN_VALUE = MIN_VALUE;\n\n/**\n * @alias Long.prototype\n * @inner\n */\nvar LongPrototype = Long.prototype;\n\n/**\n * Converts the Long to a 32 bit integer, assuming it is a 32 bit integer.\n * @this {!Long}\n * @returns {number}\n */\nLongPrototype.toInt = function toInt() {\n  return this.unsigned ? this.low >>> 0 : this.low;\n};\n\n/**\n * Converts the Long to a the nearest floating-point representation of this value (double, 53 bit mantissa).\n * @this {!Long}\n * @returns {number}\n */\nLongPrototype.toNumber = function toNumber() {\n  if (this.unsigned)\n    return ((this.high >>> 0) * TWO_PWR_32_DBL) + (this.low >>> 0);\n  return this.high * TWO_PWR_32_DBL + (this.low >>> 0);\n};\n\n/**\n * Converts the Long to a string written in the specified radix.\n * @this {!Long}\n * @param {number=} radix Radix (2-36), defaults to 10\n * @returns {string}\n * @override\n * @throws {RangeError} If `radix` is out of range\n */\nLongPrototype.toString = function toString(radix) {\n  radix = radix || 10;\n  if (radix < 2 || 36 < radix)\n    throw RangeError('radix');\n  if (this.isZero())\n    return '0';\n  if (this.isNegative()) { // Unsigned Longs are never negative\n    if (this.eq(MIN_VALUE)) {\n      // We need to change the Long value before it can be negated, so we remove\n      // the bottom-most digit in this base and then recurse to do the rest.\n      var radixLong = fromNumber(radix),\n        div = this.div(radixLong),\n        rem1 = div.mul(radixLong).sub(this);\n      return div.toString(radix) + rem1.toInt().toString(radix);\n    } else\n      return '-' + this.neg().toString(radix);\n  }\n\n  // Do several (6) digits each time through the loop, so as to\n  // minimize the calls to the very expensive emulated div.\n  var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned),\n    rem = this;\n  var result = '';\n  while (true) {\n    var remDiv = rem.div(radixToPower),\n      intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0,\n      digits = intval.toString(radix);\n    rem = remDiv;\n    if (rem.isZero())\n      return digits + result;\n    else {\n      while (digits.length < 6)\n        digits = '0' + digits;\n      result = '' + digits + result;\n    }\n  }\n};\n\n/**\n * Gets the high 32 bits as a signed integer.\n * @this {!Long}\n * @returns {number} Signed high bits\n */\nLongPrototype.getHighBits = function getHighBits() {\n  return this.high;\n};\n\n/**\n * Gets the high 32 bits as an unsigned integer.\n * @this {!Long}\n * @returns {number} Unsigned high bits\n */\nLongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() {\n  return this.high >>> 0;\n};\n\n/**\n * Gets the low 32 bits as a signed integer.\n * @this {!Long}\n * @returns {number} Signed low bits\n */\nLongPrototype.getLowBits = function getLowBits() {\n  return this.low;\n};\n\n/**\n * Gets the low 32 bits as an unsigned integer.\n * @this {!Long}\n * @returns {number} Unsigned low bits\n */\nLongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() {\n  return this.low >>> 0;\n};\n\n/**\n * Gets the number of bits needed to represent the absolute value of this Long.\n * @this {!Long}\n * @returns {number}\n */\nLongPrototype.getNumBitsAbs = function getNumBitsAbs() {\n  if (this.isNegative()) // Unsigned Longs are never negative\n    return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs();\n  var val = this.high != 0 ? this.high : this.low;\n  for (var bit = 31; bit > 0; bit--)\n    if ((val & (1 << bit)) != 0)\n      break;\n  return this.high != 0 ? bit + 33 : bit + 1;\n};\n\n/**\n * Tests if this Long's value equals zero.\n * @this {!Long}\n * @returns {boolean}\n */\nLongPrototype.isZero = function isZero() {\n  return this.high === 0 && this.low === 0;\n};\n\n/**\n * Tests if this Long's value equals zero. This is an alias of {@link Long#isZero}.\n * @returns {boolean}\n */\nLongPrototype.eqz = LongPrototype.isZero;\n\n/**\n * Tests if this Long's value is negative.\n * @this {!Long}\n * @returns {boolean}\n */\nLongPrototype.isNegative = function isNegative() {\n  return !this.unsigned && this.high < 0;\n};\n\n/**\n * Tests if this Long's value is positive or zero.\n * @this {!Long}\n * @returns {boolean}\n */\nLongPrototype.isPositive = function isPositive() {\n  return this.unsigned || this.high >= 0;\n};\n\n/**\n * Tests if this Long's value is odd.\n * @this {!Long}\n * @returns {boolean}\n */\nLongPrototype.isOdd = function isOdd() {\n  return (this.low & 1) === 1;\n};\n\n/**\n * Tests if this Long's value is even.\n * @this {!Long}\n * @returns {boolean}\n */\nLongPrototype.isEven = function isEven() {\n  return (this.low & 1) === 0;\n};\n\n/**\n * Tests if this Long's value equals the specified's.\n * @this {!Long}\n * @param {!Long|number|string} other Other value\n * @returns {boolean}\n */\nLongPrototype.equals = function equals(other) {\n  if (!isLong(other))\n    other = fromValue(other);\n  if (this.unsigned !== other.unsigned && (this.high >>> 31) === 1 && (other.high >>> 31) === 1)\n    return false;\n  return this.high === other.high && this.low === other.low;\n};\n\n/**\n * Tests if this Long's value equals the specified's. This is an alias of {@link Long#equals}.\n * @function\n * @param {!Long|number|string} other Other value\n * @returns {boolean}\n */\nLongPrototype.eq = LongPrototype.equals;\n\n/**\n * Tests if this Long's value differs from the specified's.\n * @this {!Long}\n * @param {!Long|number|string} other Other value\n * @returns {boolean}\n */\nLongPrototype.notEquals = function notEquals(other) {\n  return !this.eq(/* validates */ other);\n};\n\n/**\n * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}.\n * @function\n * @param {!Long|number|string} other Other value\n * @returns {boolean}\n */\nLongPrototype.neq = LongPrototype.notEquals;\n\n/**\n * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}.\n * @function\n * @param {!Long|number|string} other Other value\n * @returns {boolean}\n */\nLongPrototype.ne = LongPrototype.notEquals;\n\n/**\n * Tests if this Long's value is less than the specified's.\n * @this {!Long}\n * @param {!Long|number|string} other Other value\n * @returns {boolean}\n */\nLongPrototype.lessThan = function lessThan(other) {\n  return this.comp(/* validates */ other) < 0;\n};\n\n/**\n * Tests if this Long's value is less than the specified's. This is an alias of {@link Long#lessThan}.\n * @function\n * @param {!Long|number|string} other Other value\n * @returns {boolean}\n */\nLongPrototype.lt = LongPrototype.lessThan;\n\n/**\n * Tests if this Long's value is less than or equal the specified's.\n * @this {!Long}\n * @param {!Long|number|string} other Other value\n * @returns {boolean}\n */\nLongPrototype.lessThanOrEqual = function lessThanOrEqual(other) {\n  return this.comp(/* validates */ other) <= 0;\n};\n\n/**\n * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}.\n * @function\n * @param {!Long|number|string} other Other value\n * @returns {boolean}\n */\nLongPrototype.lte = LongPrototype.lessThanOrEqual;\n\n/**\n * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}.\n * @function\n * @param {!Long|number|string} other Other value\n * @returns {boolean}\n */\nLongPrototype.le = LongPrototype.lessThanOrEqual;\n\n/**\n * Tests if this Long's value is greater than the specified's.\n * @this {!Long}\n * @param {!Long|number|string} other Other value\n * @returns {boolean}\n */\nLongPrototype.greaterThan = function greaterThan(other) {\n  return this.comp(/* validates */ other) > 0;\n};\n\n/**\n * Tests if this Long's value is greater than the specified's. This is an alias of {@link Long#greaterThan}.\n * @function\n * @param {!Long|number|string} other Other value\n * @returns {boolean}\n */\nLongPrototype.gt = LongPrototype.greaterThan;\n\n/**\n * Tests if this Long's value is greater than or equal the specified's.\n * @this {!Long}\n * @param {!Long|number|string} other Other value\n * @returns {boolean}\n */\nLongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) {\n  return this.comp(/* validates */ other) >= 0;\n};\n\n/**\n * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}.\n * @function\n * @param {!Long|number|string} other Other value\n * @returns {boolean}\n */\nLongPrototype.gte = LongPrototype.greaterThanOrEqual;\n\n/**\n * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}.\n * @function\n * @param {!Long|number|string} other Other value\n * @returns {boolean}\n */\nLongPrototype.ge = LongPrototype.greaterThanOrEqual;\n\n/**\n * Compares this Long's value with the specified's.\n * @this {!Long}\n * @param {!Long|number|string} other Other value\n * @returns {number} 0 if they are the same, 1 if the this is greater and -1\n *  if the given one is greater\n */\nLongPrototype.compare = function compare(other) {\n  if (!isLong(other))\n    other = fromValue(other);\n  if (this.eq(other))\n    return 0;\n  var thisNeg = this.isNegative(),\n    otherNeg = other.isNegative();\n  if (thisNeg && !otherNeg)\n    return -1;\n  if (!thisNeg && otherNeg)\n    return 1;\n  // At this point the sign bits are the same\n  if (!this.unsigned)\n    return this.sub(other).isNegative() ? -1 : 1;\n  // Both are positive if at least one is unsigned\n  return (other.high >>> 0) > (this.high >>> 0) || (other.high === this.high && (other.low >>> 0) > (this.low >>> 0)) ? -1 : 1;\n};\n\n/**\n * Compares this Long's value with the specified's. This is an alias of {@link Long#compare}.\n * @function\n * @param {!Long|number|string} other Other value\n * @returns {number} 0 if they are the same, 1 if the this is greater and -1\n *  if the given one is greater\n */\nLongPrototype.comp = LongPrototype.compare;\n\n/**\n * Negates this Long's value.\n * @this {!Long}\n * @returns {!Long} Negated Long\n */\nLongPrototype.negate = function negate() {\n  if (!this.unsigned && this.eq(MIN_VALUE))\n    return MIN_VALUE;\n  return this.not().add(ONE);\n};\n\n/**\n * Negates this Long's value. This is an alias of {@link Long#negate}.\n * @function\n * @returns {!Long} Negated Long\n */\nLongPrototype.neg = LongPrototype.negate;\n\n/**\n * Returns the sum of this and the specified Long.\n * @this {!Long}\n * @param {!Long|number|string} addend Addend\n * @returns {!Long} Sum\n */\nLongPrototype.add = function add(addend) {\n  if (!isLong(addend))\n    addend = fromValue(addend);\n\n  // Divide each number into 4 chunks of 16 bits, and then sum the chunks.\n\n  var a48 = this.high >>> 16;\n  var a32 = this.high & 0xFFFF;\n  var a16 = this.low >>> 16;\n  var a00 = this.low & 0xFFFF;\n\n  var b48 = addend.high >>> 16;\n  var b32 = addend.high & 0xFFFF;\n  var b16 = addend.low >>> 16;\n  var b00 = addend.low & 0xFFFF;\n\n  var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\n  c00 += a00 + b00;\n  c16 += c00 >>> 16;\n  c00 &= 0xFFFF;\n  c16 += a16 + b16;\n  c32 += c16 >>> 16;\n  c16 &= 0xFFFF;\n  c32 += a32 + b32;\n  c48 += c32 >>> 16;\n  c32 &= 0xFFFF;\n  c48 += a48 + b48;\n  c48 &= 0xFFFF;\n  return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned);\n};\n\n/**\n * Returns the difference of this and the specified Long.\n * @this {!Long}\n * @param {!Long|number|string} subtrahend Subtrahend\n * @returns {!Long} Difference\n */\nLongPrototype.subtract = function subtract(subtrahend) {\n  if (!isLong(subtrahend))\n    subtrahend = fromValue(subtrahend);\n  return this.add(subtrahend.neg());\n};\n\n/**\n * Returns the difference of this and the specified Long. This is an alias of {@link Long#subtract}.\n * @function\n * @param {!Long|number|string} subtrahend Subtrahend\n * @returns {!Long} Difference\n */\nLongPrototype.sub = LongPrototype.subtract;\n\n/**\n * Returns the product of this and the specified Long.\n * @this {!Long}\n * @param {!Long|number|string} multiplier Multiplier\n * @returns {!Long} Product\n */\nLongPrototype.multiply = function multiply(multiplier) {\n  if (this.isZero())\n    return this;\n  if (!isLong(multiplier))\n    multiplier = fromValue(multiplier);\n\n  // use wasm support if present\n  if (wasm) {\n    var low = wasm[\"mul\"](this.low,\n      this.high,\n      multiplier.low,\n      multiplier.high);\n    return fromBits(low, wasm[\"get_high\"](), this.unsigned);\n  }\n\n  if (multiplier.isZero())\n    return this.unsigned ? UZERO : ZERO;\n  if (this.eq(MIN_VALUE))\n    return multiplier.isOdd() ? MIN_VALUE : ZERO;\n  if (multiplier.eq(MIN_VALUE))\n    return this.isOdd() ? MIN_VALUE : ZERO;\n\n  if (this.isNegative()) {\n    if (multiplier.isNegative())\n      return this.neg().mul(multiplier.neg());\n    else\n      return this.neg().mul(multiplier).neg();\n  } else if (multiplier.isNegative())\n    return this.mul(multiplier.neg()).neg();\n\n  // If both longs are small, use float multiplication\n  if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24))\n    return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned);\n\n  // Divide each long into 4 chunks of 16 bits, and then add up 4x4 products.\n  // We can skip products that would overflow.\n\n  var a48 = this.high >>> 16;\n  var a32 = this.high & 0xFFFF;\n  var a16 = this.low >>> 16;\n  var a00 = this.low & 0xFFFF;\n\n  var b48 = multiplier.high >>> 16;\n  var b32 = multiplier.high & 0xFFFF;\n  var b16 = multiplier.low >>> 16;\n  var b00 = multiplier.low & 0xFFFF;\n\n  var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\n  c00 += a00 * b00;\n  c16 += c00 >>> 16;\n  c00 &= 0xFFFF;\n  c16 += a16 * b00;\n  c32 += c16 >>> 16;\n  c16 &= 0xFFFF;\n  c16 += a00 * b16;\n  c32 += c16 >>> 16;\n  c16 &= 0xFFFF;\n  c32 += a32 * b00;\n  c48 += c32 >>> 16;\n  c32 &= 0xFFFF;\n  c32 += a16 * b16;\n  c48 += c32 >>> 16;\n  c32 &= 0xFFFF;\n  c32 += a00 * b32;\n  c48 += c32 >>> 16;\n  c32 &= 0xFFFF;\n  c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48;\n  c48 &= 0xFFFF;\n  return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned);\n};\n\n/**\n * Returns the product of this and the specified Long. This is an alias of {@link Long#multiply}.\n * @function\n * @param {!Long|number|string} multiplier Multiplier\n * @returns {!Long} Product\n */\nLongPrototype.mul = LongPrototype.multiply;\n\n/**\n * Returns this Long divided by the specified. The result is signed if this Long is signed or\n *  unsigned if this Long is unsigned.\n * @this {!Long}\n * @param {!Long|number|string} divisor Divisor\n * @returns {!Long} Quotient\n */\nLongPrototype.divide = function divide(divisor) {\n  if (!isLong(divisor))\n    divisor = fromValue(divisor);\n  if (divisor.isZero())\n    throw Error('division by zero');\n\n  // use wasm support if present\n  if (wasm) {\n    // guard against signed division overflow: the largest\n    // negative number / -1 would be 1 larger than the largest\n    // positive number, due to two's complement.\n    if (!this.unsigned &&\n      this.high === -0x80000000 &&\n      divisor.low === -1 && divisor.high === -1) {\n      // be consistent with non-wasm code path\n      return this;\n    }\n    var low = (this.unsigned ? wasm[\"div_u\"] : wasm[\"div_s\"])(\n      this.low,\n      this.high,\n      divisor.low,\n      divisor.high\n    );\n    return fromBits(low, wasm[\"get_high\"](), this.unsigned);\n  }\n\n  if (this.isZero())\n    return this.unsigned ? UZERO : ZERO;\n  var approx, rem, res;\n  if (!this.unsigned) {\n    // This section is only relevant for signed longs and is derived from the\n    // closure library as a whole.\n    if (this.eq(MIN_VALUE)) {\n      if (divisor.eq(ONE) || divisor.eq(NEG_ONE))\n        return MIN_VALUE;  // recall that -MIN_VALUE == MIN_VALUE\n      else if (divisor.eq(MIN_VALUE))\n        return ONE;\n      else {\n        // At this point, we have |other| >= 2, so |this/other| < |MIN_VALUE|.\n        var halfThis = this.shr(1);\n        approx = halfThis.div(divisor).shl(1);\n        if (approx.eq(ZERO)) {\n          return divisor.isNegative() ? ONE : NEG_ONE;\n        } else {\n          rem = this.sub(divisor.mul(approx));\n          res = approx.add(rem.div(divisor));\n          return res;\n        }\n      }\n    } else if (divisor.eq(MIN_VALUE))\n      return this.unsigned ? UZERO : ZERO;\n    if (this.isNegative()) {\n      if (divisor.isNegative())\n        return this.neg().div(divisor.neg());\n      return this.neg().div(divisor).neg();\n    } else if (divisor.isNegative())\n      return this.div(divisor.neg()).neg();\n    res = ZERO;\n  } else {\n    // The algorithm below has not been made for unsigned longs. It's therefore\n    // required to take special care of the MSB prior to running it.\n    if (!divisor.unsigned)\n      divisor = divisor.toUnsigned();\n    if (divisor.gt(this))\n      return UZERO;\n    if (divisor.gt(this.shru(1))) // 15 >>> 1 = 7 ; with divisor = 8 ; true\n      return UONE;\n    res = UZERO;\n  }\n\n  // Repeat the following until the remainder is less than other:  find a\n  // floating-point that approximates remainder / other *from below*, add this\n  // into the result, and subtract it from the remainder.  It is critical that\n  // the approximate value is less than or equal to the real value so that the\n  // remainder never becomes negative.\n  rem = this;\n  while (rem.gte(divisor)) {\n    // Approximate the result of division. This may be a little greater or\n    // smaller than the actual value.\n    approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber()));\n\n    // We will tweak the approximate result by changing it in the 48-th digit or\n    // the smallest non-fractional digit, whichever is larger.\n    var log2 = Math.ceil(Math.log(approx) / Math.LN2),\n      delta = (log2 <= 48) ? 1 : pow_dbl(2, log2 - 48),\n\n      // Decrease the approximation until it is smaller than the remainder.  Note\n      // that if it is too large, the product overflows and is negative.\n      approxRes = fromNumber(approx),\n      approxRem = approxRes.mul(divisor);\n    while (approxRem.isNegative() || approxRem.gt(rem)) {\n      approx -= delta;\n      approxRes = fromNumber(approx, this.unsigned);\n      approxRem = approxRes.mul(divisor);\n    }\n\n    // We know the answer can't be zero... and actually, zero would cause\n    // infinite recursion since we would make no progress.\n    if (approxRes.isZero())\n      approxRes = ONE;\n\n    res = res.add(approxRes);\n    rem = rem.sub(approxRem);\n  }\n  return res;\n};\n\n/**\n * Returns this Long divided by the specified. This is an alias of {@link Long#divide}.\n * @function\n * @param {!Long|number|string} divisor Divisor\n * @returns {!Long} Quotient\n */\nLongPrototype.div = LongPrototype.divide;\n\n/**\n * Returns this Long modulo the specified.\n * @this {!Long}\n * @param {!Long|number|string} divisor Divisor\n * @returns {!Long} Remainder\n */\nLongPrototype.modulo = function modulo(divisor) {\n  if (!isLong(divisor))\n    divisor = fromValue(divisor);\n\n  // use wasm support if present\n  if (wasm) {\n    var low = (this.unsigned ? wasm[\"rem_u\"] : wasm[\"rem_s\"])(\n      this.low,\n      this.high,\n      divisor.low,\n      divisor.high\n    );\n    return fromBits(low, wasm[\"get_high\"](), this.unsigned);\n  }\n\n  return this.sub(this.div(divisor).mul(divisor));\n};\n\n/**\n * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}.\n * @function\n * @param {!Long|number|string} divisor Divisor\n * @returns {!Long} Remainder\n */\nLongPrototype.mod = LongPrototype.modulo;\n\n/**\n * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}.\n * @function\n * @param {!Long|number|string} divisor Divisor\n * @returns {!Long} Remainder\n */\nLongPrototype.rem = LongPrototype.modulo;\n\n/**\n * Returns the bitwise NOT of this Long.\n * @this {!Long}\n * @returns {!Long}\n */\nLongPrototype.not = function not() {\n  return fromBits(~this.low, ~this.high, this.unsigned);\n};\n\n/**\n * Returns count leading zeros of this Long.\n * @this {!Long}\n * @returns {!number}\n */\nLongPrototype.countLeadingZeros = function countLeadingZeros() {\n  return this.high ? Math.clz32(this.high) : Math.clz32(this.low) + 32;\n};\n\n/**\n * Returns count leading zeros. This is an alias of {@link Long#countLeadingZeros}.\n * @function\n * @param {!Long}\n * @returns {!number}\n */\nLongPrototype.clz = LongPrototype.countLeadingZeros;\n\n/**\n * Returns count trailing zeros of this Long.\n * @this {!Long}\n * @returns {!number}\n */\nLongPrototype.countTrailingZeros = function countTrailingZeros() {\n  return this.low ? ctz32(this.low) : ctz32(this.high) + 32;\n};\n\n/**\n * Returns count trailing zeros. This is an alias of {@link Long#countTrailingZeros}.\n * @function\n * @param {!Long}\n * @returns {!number}\n */\nLongPrototype.ctz = LongPrototype.countTrailingZeros;\n\n/**\n * Returns the bitwise AND of this Long and the specified.\n * @this {!Long}\n * @param {!Long|number|string} other Other Long\n * @returns {!Long}\n */\nLongPrototype.and = function and(other) {\n  if (!isLong(other))\n    other = fromValue(other);\n  return fromBits(this.low & other.low, this.high & other.high, this.unsigned);\n};\n\n/**\n * Returns the bitwise OR of this Long and the specified.\n * @this {!Long}\n * @param {!Long|number|string} other Other Long\n * @returns {!Long}\n */\nLongPrototype.or = function or(other) {\n  if (!isLong(other))\n    other = fromValue(other);\n  return fromBits(this.low | other.low, this.high | other.high, this.unsigned);\n};\n\n/**\n * Returns the bitwise XOR of this Long and the given one.\n * @this {!Long}\n * @param {!Long|number|string} other Other Long\n * @returns {!Long}\n */\nLongPrototype.xor = function xor(other) {\n  if (!isLong(other))\n    other = fromValue(other);\n  return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned);\n};\n\n/**\n * Returns this Long with bits shifted to the left by the given amount.\n * @this {!Long}\n * @param {number|!Long} numBits Number of bits\n * @returns {!Long} Shifted Long\n */\nLongPrototype.shiftLeft = function shiftLeft(numBits) {\n  if (isLong(numBits))\n    numBits = numBits.toInt();\n  if ((numBits &= 63) === 0)\n    return this;\n  else if (numBits < 32)\n    return fromBits(this.low << numBits, (this.high << numBits) | (this.low >>> (32 - numBits)), this.unsigned);\n  else\n    return fromBits(0, this.low << (numBits - 32), this.unsigned);\n};\n\n/**\n * Returns this Long with bits shifted to the left by the given amount. This is an alias of {@link Long#shiftLeft}.\n * @function\n * @param {number|!Long} numBits Number of bits\n * @returns {!Long} Shifted Long\n */\nLongPrototype.shl = LongPrototype.shiftLeft;\n\n/**\n * Returns this Long with bits arithmetically shifted to the right by the given amount.\n * @this {!Long}\n * @param {number|!Long} numBits Number of bits\n * @returns {!Long} Shifted Long\n */\nLongPrototype.shiftRight = function shiftRight(numBits) {\n  if (isLong(numBits))\n    numBits = numBits.toInt();\n  if ((numBits &= 63) === 0)\n    return this;\n  else if (numBits < 32)\n    return fromBits((this.low >>> numBits) | (this.high << (32 - numBits)), this.high >> numBits, this.unsigned);\n  else\n    return fromBits(this.high >> (numBits - 32), this.high >= 0 ? 0 : -1, this.unsigned);\n};\n\n/**\n * Returns this Long with bits arithmetically shifted to the right by the given amount. This is an alias of {@link Long#shiftRight}.\n * @function\n * @param {number|!Long} numBits Number of bits\n * @returns {!Long} Shifted Long\n */\nLongPrototype.shr = LongPrototype.shiftRight;\n\n/**\n * Returns this Long with bits logically shifted to the right by the given amount.\n * @this {!Long}\n * @param {number|!Long} numBits Number of bits\n * @returns {!Long} Shifted Long\n */\nLongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) {\n  if (isLong(numBits)) numBits = numBits.toInt();\n  if ((numBits &= 63) === 0) return this;\n  if (numBits < 32) return fromBits((this.low >>> numBits) | (this.high << (32 - numBits)), this.high >>> numBits, this.unsigned);\n  if (numBits === 32) return fromBits(this.high, 0, this.unsigned);\n  return fromBits(this.high >>> (numBits - 32), 0, this.unsigned);\n};\n\n/**\n * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}.\n * @function\n * @param {number|!Long} numBits Number of bits\n * @returns {!Long} Shifted Long\n */\nLongPrototype.shru = LongPrototype.shiftRightUnsigned;\n\n/**\n * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}.\n * @function\n * @param {number|!Long} numBits Number of bits\n * @returns {!Long} Shifted Long\n */\nLongPrototype.shr_u = LongPrototype.shiftRightUnsigned;\n\n/**\n * Returns this Long with bits rotated to the left by the given amount.\n * @this {!Long}\n * @param {number|!Long} numBits Number of bits\n * @returns {!Long} Rotated Long\n */\nLongPrototype.rotateLeft = function rotateLeft(numBits) {\n  var b;\n  if (isLong(numBits)) numBits = numBits.toInt();\n  if ((numBits &= 63) === 0) return this;\n  if (numBits === 32) return fromBits(this.high, this.low, this.unsigned);\n  if (numBits < 32) {\n    b = (32 - numBits);\n    return fromBits(((this.low << numBits) | (this.high >>> b)), ((this.high << numBits) | (this.low >>> b)), this.unsigned);\n  }\n  numBits -= 32;\n  b = (32 - numBits);\n  return fromBits(((this.high << numBits) | (this.low >>> b)), ((this.low << numBits) | (this.high >>> b)), this.unsigned);\n}\n/**\n * Returns this Long with bits rotated to the left by the given amount. This is an alias of {@link Long#rotateLeft}.\n * @function\n * @param {number|!Long} numBits Number of bits\n * @returns {!Long} Rotated Long\n */\nLongPrototype.rotl = LongPrototype.rotateLeft;\n\n/**\n * Returns this Long with bits rotated to the right by the given amount.\n * @this {!Long}\n * @param {number|!Long} numBits Number of bits\n * @returns {!Long} Rotated Long\n */\nLongPrototype.rotateRight = function rotateRight(numBits) {\n  var b;\n  if (isLong(numBits)) numBits = numBits.toInt();\n  if ((numBits &= 63) === 0) return this;\n  if (numBits === 32) return fromBits(this.high, this.low, this.unsigned);\n  if (numBits < 32) {\n    b = (32 - numBits);\n    return fromBits(((this.high << b) | (this.low >>> numBits)), ((this.low << b) | (this.high >>> numBits)), this.unsigned);\n  }\n  numBits -= 32;\n  b = (32 - numBits);\n  return fromBits(((this.low << b) | (this.high >>> numBits)), ((this.high << b) | (this.low >>> numBits)), this.unsigned);\n}\n/**\n * Returns this Long with bits rotated to the right by the given amount. This is an alias of {@link Long#rotateRight}.\n * @function\n * @param {number|!Long} numBits Number of bits\n * @returns {!Long} Rotated Long\n */\nLongPrototype.rotr = LongPrototype.rotateRight;\n\n/**\n * Converts this Long to signed.\n * @this {!Long}\n * @returns {!Long} Signed long\n */\nLongPrototype.toSigned = function toSigned() {\n  if (!this.unsigned)\n    return this;\n  return fromBits(this.low, this.high, false);\n};\n\n/**\n * Converts this Long to unsigned.\n * @this {!Long}\n * @returns {!Long} Unsigned long\n */\nLongPrototype.toUnsigned = function toUnsigned() {\n  if (this.unsigned)\n    return this;\n  return fromBits(this.low, this.high, true);\n};\n\n/**\n * Converts this Long to its byte representation.\n * @param {boolean=} le Whether little or big endian, defaults to big endian\n * @this {!Long}\n * @returns {!Array.<number>} Byte representation\n */\nLongPrototype.toBytes = function toBytes(le) {\n  return le ? this.toBytesLE() : this.toBytesBE();\n};\n\n/**\n * Converts this Long to its little endian byte representation.\n * @this {!Long}\n * @returns {!Array.<number>} Little endian byte representation\n */\nLongPrototype.toBytesLE = function toBytesLE() {\n  var hi = this.high,\n    lo = this.low;\n  return [\n    lo & 0xff,\n    lo >>> 8 & 0xff,\n    lo >>> 16 & 0xff,\n    lo >>> 24,\n    hi & 0xff,\n    hi >>> 8 & 0xff,\n    hi >>> 16 & 0xff,\n    hi >>> 24\n  ];\n};\n\n/**\n * Converts this Long to its big endian byte representation.\n * @this {!Long}\n * @returns {!Array.<number>} Big endian byte representation\n */\nLongPrototype.toBytesBE = function toBytesBE() {\n  var hi = this.high,\n    lo = this.low;\n  return [\n    hi >>> 24,\n    hi >>> 16 & 0xff,\n    hi >>> 8 & 0xff,\n    hi & 0xff,\n    lo >>> 24,\n    lo >>> 16 & 0xff,\n    lo >>> 8 & 0xff,\n    lo & 0xff\n  ];\n};\n\n/**\n * Creates a Long from its byte representation.\n * @param {!Array.<number>} bytes Byte representation\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\n * @param {boolean=} le Whether little or big endian, defaults to big endian\n * @returns {Long} The corresponding Long value\n */\nLong.fromBytes = function fromBytes(bytes, unsigned, le) {\n  return le ? Long.fromBytesLE(bytes, unsigned) : Long.fromBytesBE(bytes, unsigned);\n};\n\n/**\n * Creates a Long from its little endian byte representation.\n * @param {!Array.<number>} bytes Little endian byte representation\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\n * @returns {Long} The corresponding Long value\n */\nLong.fromBytesLE = function fromBytesLE(bytes, unsigned) {\n  return new Long(\n    bytes[0] |\n    bytes[1] << 8 |\n    bytes[2] << 16 |\n    bytes[3] << 24,\n    bytes[4] |\n    bytes[5] << 8 |\n    bytes[6] << 16 |\n    bytes[7] << 24,\n    unsigned\n  );\n};\n\n/**\n * Creates a Long from its big endian byte representation.\n * @param {!Array.<number>} bytes Big endian byte representation\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\n * @returns {Long} The corresponding Long value\n */\nLong.fromBytesBE = function fromBytesBE(bytes, unsigned) {\n  return new Long(\n    bytes[4] << 24 |\n    bytes[5] << 16 |\n    bytes[6] << 8 |\n    bytes[7],\n    bytes[0] << 24 |\n    bytes[1] << 16 |\n    bytes[2] << 8 |\n    bytes[3],\n    unsigned\n  );\n};\n\nexport default Long;\n","/// @file\n/// @addtogroup flatbuffers_javascript_api\n/// @{\n/// @cond FLATBUFFERS_INTERNAL\n\n/**\n * @fileoverview\n *\n * Need to suppress 'global this' error so the Node.js export line doesn't cause\n * closure compile to error out.\n * @suppress {globalThis}\n */\n\n/**\n * @const\n * @namespace\n */\nvar flatbuffers = {};\n\n/**\n * @typedef {number}\n */\nflatbuffers.Offset;\n\n/**\n * @typedef {{\n *   bb: flatbuffers.ByteBuffer,\n *   bb_pos: number\n * }}\n */\nflatbuffers.Table;\n\n/**\n * @type {number}\n * @const\n */\nflatbuffers.SIZEOF_SHORT = 2;\n\n/**\n * @type {number}\n * @const\n */\nflatbuffers.SIZEOF_INT = 4;\n\n/**\n * @type {number}\n * @const\n */\nflatbuffers.FILE_IDENTIFIER_LENGTH = 4;\n\n/**\n * @type {number}\n * @const\n */\nflatbuffers.SIZE_PREFIX_LENGTH = 4;\n\n/**\n * @enum {number}\n */\nflatbuffers.Encoding = {\n  UTF8_BYTES: 1,\n  UTF16_STRING: 2\n};\n\n/**\n * @type {Int32Array}\n * @const\n */\nflatbuffers.int32 = new Int32Array(2);\n\n/**\n * @type {Float32Array}\n * @const\n */\nflatbuffers.float32 = new Float32Array(flatbuffers.int32.buffer);\n\n/**\n * @type {Float64Array}\n * @const\n */\nflatbuffers.float64 = new Float64Array(flatbuffers.int32.buffer);\n\n/**\n * @type {boolean}\n * @const\n */\nflatbuffers.isLittleEndian = new Uint16Array(new Uint8Array([1, 0]).buffer)[0] === 1;\n\n////////////////////////////////////////////////////////////////////////////////\n\n/**\n * @constructor\n * @param {number} low\n * @param {number} high\n */\nflatbuffers.Long = function(low, high) {\n  /**\n   * @type {number}\n   * @const\n   */\n  this.low = low | 0;\n\n  /**\n   * @type {number}\n   * @const\n   */\n  this.high = high | 0;\n};\n\n/**\n * @param {number} low\n * @param {number} high\n * @returns {!flatbuffers.Long}\n */\nflatbuffers.Long.create = function(low, high) {\n  // Special-case zero to avoid GC overhead for default values\n  return low == 0 && high == 0 ? flatbuffers.Long.ZERO : new flatbuffers.Long(low, high);\n};\n\n/**\n * @returns {number}\n */\nflatbuffers.Long.prototype.toFloat64 = function() {\n  return (this.low >>> 0) + this.high * 0x100000000;\n};\n\n/**\n * @param {flatbuffers.Long} other\n * @returns {boolean}\n */\nflatbuffers.Long.prototype.equals = function(other) {\n  return this.low == other.low && this.high == other.high;\n};\n\n/**\n * @type {!flatbuffers.Long}\n * @const\n */\nflatbuffers.Long.ZERO = new flatbuffers.Long(0, 0);\n\n/// @endcond\n////////////////////////////////////////////////////////////////////////////////\n/**\n * Create a FlatBufferBuilder.\n *\n * @constructor\n * @param {number=} opt_initial_size\n */\nflatbuffers.Builder = function(opt_initial_size) {\n  if (!opt_initial_size) {\n    var initial_size = 1024;\n  } else {\n    var initial_size = opt_initial_size;\n  }\n\n  /**\n   * @type {flatbuffers.ByteBuffer}\n   * @private\n   */\n  this.bb = flatbuffers.ByteBuffer.allocate(initial_size);\n\n  /**\n   * Remaining space in the ByteBuffer.\n   *\n   * @type {number}\n   * @private\n   */\n  this.space = initial_size;\n\n  /**\n   * Minimum alignment encountered so far.\n   *\n   * @type {number}\n   * @private\n   */\n  this.minalign = 1;\n\n  /**\n   * The vtable for the current table.\n   *\n   * @type {Array.<number>}\n   * @private\n   */\n  this.vtable = null;\n\n  /**\n   * The amount of fields we're actually using.\n   *\n   * @type {number}\n   * @private\n   */\n  this.vtable_in_use = 0;\n\n  /**\n   * Whether we are currently serializing a table.\n   *\n   * @type {boolean}\n   * @private\n   */\n  this.isNested = false;\n\n  /**\n   * Starting offset of the current struct/table.\n   *\n   * @type {number}\n   * @private\n   */\n  this.object_start = 0;\n\n  /**\n   * List of offsets of all vtables.\n   *\n   * @type {Array.<number>}\n   * @private\n   */\n  this.vtables = [];\n\n  /**\n   * For the current vector being built.\n   *\n   * @type {number}\n   * @private\n   */\n  this.vector_num_elems = 0;\n\n  /**\n   * False omits default values from the serialized data\n   *\n   * @type {boolean}\n   * @private\n   */\n  this.force_defaults = false;\n};\n\nflatbuffers.Builder.prototype.clear = function() {\n  this.bb.clear();\n  this.space = this.bb.capacity();\n  this.minalign = 1;\n  this.vtable = null;\n  this.vtable_in_use = 0;\n  this.isNested = false;\n  this.object_start = 0;\n  this.vtables = [];\n  this.vector_num_elems = 0;\n  this.force_defaults = false;\n};\n\n/**\n * In order to save space, fields that are set to their default value\n * don't get serialized into the buffer. Forcing defaults provides a\n * way to manually disable this optimization.\n *\n * @param {boolean} forceDefaults true always serializes default values\n */\nflatbuffers.Builder.prototype.forceDefaults = function(forceDefaults) {\n  this.force_defaults = forceDefaults;\n};\n\n/**\n * Get the ByteBuffer representing the FlatBuffer. Only call this after you've\n * called finish(). The actual data starts at the ByteBuffer's current position,\n * not necessarily at 0.\n *\n * @returns {flatbuffers.ByteBuffer}\n */\nflatbuffers.Builder.prototype.dataBuffer = function() {\n  return this.bb;\n};\n\n/**\n * Get the bytes representing the FlatBuffer. Only call this after you've\n * called finish().\n *\n * @returns {!Uint8Array}\n */\nflatbuffers.Builder.prototype.asUint8Array = function() {\n  return this.bb.bytes().subarray(this.bb.position(), this.bb.position() + this.offset());\n};\n\n/// @cond FLATBUFFERS_INTERNAL\n/**\n * Prepare to write an element of `size` after `additional_bytes` have been\n * written, e.g. if you write a string, you need to align such the int length\n * field is aligned to 4 bytes, and the string data follows it directly. If all\n * you need to do is alignment, `additional_bytes` will be 0.\n *\n * @param {number} size This is the of the new element to write\n * @param {number} additional_bytes The padding size\n */\nflatbuffers.Builder.prototype.prep = function(size, additional_bytes) {\n  // Track the biggest thing we've ever aligned to.\n  if (size > this.minalign) {\n    this.minalign = size;\n  }\n\n  // Find the amount of alignment needed such that `size` is properly\n  // aligned after `additional_bytes`\n  var align_size = ((~(this.bb.capacity() - this.space + additional_bytes)) + 1) & (size - 1);\n\n  // Reallocate the buffer if needed.\n  while (this.space < align_size + size + additional_bytes) {\n    var old_buf_size = this.bb.capacity();\n    this.bb = flatbuffers.Builder.growByteBuffer(this.bb);\n    this.space += this.bb.capacity() - old_buf_size;\n  }\n\n  this.pad(align_size);\n};\n\n/**\n * @param {number} byte_size\n */\nflatbuffers.Builder.prototype.pad = function(byte_size) {\n  for (var i = 0; i < byte_size; i++) {\n    this.bb.writeInt8(--this.space, 0);\n  }\n};\n\n/**\n * @param {number} value\n */\nflatbuffers.Builder.prototype.writeInt8 = function(value) {\n  this.bb.writeInt8(this.space -= 1, value);\n};\n\n/**\n * @param {number} value\n */\nflatbuffers.Builder.prototype.writeInt16 = function(value) {\n  this.bb.writeInt16(this.space -= 2, value);\n};\n\n/**\n * @param {number} value\n */\nflatbuffers.Builder.prototype.writeInt32 = function(value) {\n  this.bb.writeInt32(this.space -= 4, value);\n};\n\n/**\n * @param {flatbuffers.Long} value\n */\nflatbuffers.Builder.prototype.writeInt64 = function(value) {\n  this.bb.writeInt64(this.space -= 8, value);\n};\n\n/**\n * @param {number} value\n */\nflatbuffers.Builder.prototype.writeFloat32 = function(value) {\n  this.bb.writeFloat32(this.space -= 4, value);\n};\n\n/**\n * @param {number} value\n */\nflatbuffers.Builder.prototype.writeFloat64 = function(value) {\n  this.bb.writeFloat64(this.space -= 8, value);\n};\n/// @endcond\n\n/**\n * Add an `int8` to the buffer, properly aligned, and grows the buffer (if necessary).\n * @param {number} value The `int8` to add the the buffer.\n */\nflatbuffers.Builder.prototype.addInt8 = function(value) {\n  this.prep(1, 0);\n  this.writeInt8(value);\n};\n\n/**\n * Add an `int16` to the buffer, properly aligned, and grows the buffer (if necessary).\n * @param {number} value The `int16` to add the the buffer.\n */\nflatbuffers.Builder.prototype.addInt16 = function(value) {\n  this.prep(2, 0);\n  this.writeInt16(value);\n};\n\n/**\n * Add an `int32` to the buffer, properly aligned, and grows the buffer (if necessary).\n * @param {number} value The `int32` to add the the buffer.\n */\nflatbuffers.Builder.prototype.addInt32 = function(value) {\n  this.prep(4, 0);\n  this.writeInt32(value);\n};\n\n/**\n * Add an `int64` to the buffer, properly aligned, and grows the buffer (if necessary).\n * @param {flatbuffers.Long} value The `int64` to add the the buffer.\n */\nflatbuffers.Builder.prototype.addInt64 = function(value) {\n  this.prep(8, 0);\n  this.writeInt64(value);\n};\n\n/**\n * Add a `float32` to the buffer, properly aligned, and grows the buffer (if necessary).\n * @param {number} value The `float32` to add the the buffer.\n */\nflatbuffers.Builder.prototype.addFloat32 = function(value) {\n  this.prep(4, 0);\n  this.writeFloat32(value);\n};\n\n/**\n * Add a `float64` to the buffer, properly aligned, and grows the buffer (if necessary).\n * @param {number} value The `float64` to add the the buffer.\n */\nflatbuffers.Builder.prototype.addFloat64 = function(value) {\n  this.prep(8, 0);\n  this.writeFloat64(value);\n};\n\n/// @cond FLATBUFFERS_INTERNAL\n/**\n * @param {number} voffset\n * @param {number} value\n * @param {number} defaultValue\n */\nflatbuffers.Builder.prototype.addFieldInt8 = function(voffset, value, defaultValue) {\n  if (this.force_defaults || value != defaultValue) {\n    this.addInt8(value);\n    this.slot(voffset);\n  }\n};\n\n/**\n * @param {number} voffset\n * @param {number} value\n * @param {number} defaultValue\n */\nflatbuffers.Builder.prototype.addFieldInt16 = function(voffset, value, defaultValue) {\n  if (this.force_defaults || value != defaultValue) {\n    this.addInt16(value);\n    this.slot(voffset);\n  }\n};\n\n/**\n * @param {number} voffset\n * @param {number} value\n * @param {number} defaultValue\n */\nflatbuffers.Builder.prototype.addFieldInt32 = function(voffset, value, defaultValue) {\n  if (this.force_defaults || value != defaultValue) {\n    this.addInt32(value);\n    this.slot(voffset);\n  }\n};\n\n/**\n * @param {number} voffset\n * @param {flatbuffers.Long} value\n * @param {flatbuffers.Long} defaultValue\n */\nflatbuffers.Builder.prototype.addFieldInt64 = function(voffset, value, defaultValue) {\n  if (this.force_defaults || !value.equals(defaultValue)) {\n    this.addInt64(value);\n    this.slot(voffset);\n  }\n};\n\n/**\n * @param {number} voffset\n * @param {number} value\n * @param {number} defaultValue\n */\nflatbuffers.Builder.prototype.addFieldFloat32 = function(voffset, value, defaultValue) {\n  if (this.force_defaults || value != defaultValue) {\n    this.addFloat32(value);\n    this.slot(voffset);\n  }\n};\n\n/**\n * @param {number} voffset\n * @param {number} value\n * @param {number} defaultValue\n */\nflatbuffers.Builder.prototype.addFieldFloat64 = function(voffset, value, defaultValue) {\n  if (this.force_defaults || value != defaultValue) {\n    this.addFloat64(value);\n    this.slot(voffset);\n  }\n};\n\n/**\n * @param {number} voffset\n * @param {flatbuffers.Offset} value\n * @param {flatbuffers.Offset} defaultValue\n */\nflatbuffers.Builder.prototype.addFieldOffset = function(voffset, value, defaultValue) {\n  if (this.force_defaults || value != defaultValue) {\n    this.addOffset(value);\n    this.slot(voffset);\n  }\n};\n\n/**\n * Structs are stored inline, so nothing additional is being added. `d` is always 0.\n *\n * @param {number} voffset\n * @param {flatbuffers.Offset} value\n * @param {flatbuffers.Offset} defaultValue\n */\nflatbuffers.Builder.prototype.addFieldStruct = function(voffset, value, defaultValue) {\n  if (value != defaultValue) {\n    this.nested(value);\n    this.slot(voffset);\n  }\n};\n\n/**\n * Structures are always stored inline, they need to be created right\n * where they're used.  You'll get this assertion failure if you\n * created it elsewhere.\n *\n * @param {flatbuffers.Offset} obj The offset of the created object\n */\nflatbuffers.Builder.prototype.nested = function(obj) {\n  if (obj != this.offset()) {\n    throw new Error('FlatBuffers: struct must be serialized inline.');\n  }\n};\n\n/**\n * Should not be creating any other object, string or vector\n * while an object is being constructed\n */\nflatbuffers.Builder.prototype.notNested = function() {\n  if (this.isNested) {\n    throw new Error('FlatBuffers: object serialization must not be nested.');\n  }\n};\n\n/**\n * Set the current vtable at `voffset` to the current location in the buffer.\n *\n * @param {number} voffset\n */\nflatbuffers.Builder.prototype.slot = function(voffset) {\n  this.vtable[voffset] = this.offset();\n};\n\n/**\n * @returns {flatbuffers.Offset} Offset relative to the end of the buffer.\n */\nflatbuffers.Builder.prototype.offset = function() {\n  return this.bb.capacity() - this.space;\n};\n\n/**\n * Doubles the size of the backing ByteBuffer and copies the old data towards\n * the end of the new buffer (since we build the buffer backwards).\n *\n * @param {flatbuffers.ByteBuffer} bb The current buffer with the existing data\n * @returns {!flatbuffers.ByteBuffer} A new byte buffer with the old data copied\n * to it. The data is located at the end of the buffer.\n *\n * uint8Array.set() formally takes {Array<number>|ArrayBufferView}, so to pass\n * it a uint8Array we need to suppress the type check:\n * @suppress {checkTypes}\n */\nflatbuffers.Builder.growByteBuffer = function(bb) {\n  var old_buf_size = bb.capacity();\n\n  // Ensure we don't grow beyond what fits in an int.\n  if (old_buf_size & 0xC0000000) {\n    throw new Error('FlatBuffers: cannot grow buffer beyond 2 gigabytes.');\n  }\n\n  var new_buf_size = old_buf_size << 1;\n  var nbb = flatbuffers.ByteBuffer.allocate(new_buf_size);\n  nbb.setPosition(new_buf_size - old_buf_size);\n  nbb.bytes().set(bb.bytes(), new_buf_size - old_buf_size);\n  return nbb;\n};\n/// @endcond\n\n/**\n * Adds on offset, relative to where it will be written.\n *\n * @param {flatbuffers.Offset} offset The offset to add.\n */\nflatbuffers.Builder.prototype.addOffset = function(offset) {\n  this.prep(flatbuffers.SIZEOF_INT, 0); // Ensure alignment is already done.\n  this.writeInt32(this.offset() - offset + flatbuffers.SIZEOF_INT);\n};\n\n/// @cond FLATBUFFERS_INTERNAL\n/**\n * Start encoding a new object in the buffer.  Users will not usually need to\n * call this directly. The FlatBuffers compiler will generate helper methods\n * that call this method internally.\n *\n * @param {number} numfields\n */\nflatbuffers.Builder.prototype.startObject = function(numfields) {\n  this.notNested();\n  if (this.vtable == null) {\n    this.vtable = [];\n  }\n  this.vtable_in_use = numfields;\n  for (var i = 0; i < numfields; i++) {\n    this.vtable[i] = 0; // This will push additional elements as needed\n  }\n  this.isNested = true;\n  this.object_start = this.offset();\n};\n\n/**\n * Finish off writing the object that is under construction.\n *\n * @returns {flatbuffers.Offset} The offset to the object inside `dataBuffer`\n */\nflatbuffers.Builder.prototype.endObject = function() {\n  if (this.vtable == null || !this.isNested) {\n    throw new Error('FlatBuffers: endObject called without startObject');\n  }\n\n  this.addInt32(0);\n  var vtableloc = this.offset();\n\n  // Trim trailing zeroes.\n  var i = this.vtable_in_use - 1;\n  for (; i >= 0 && this.vtable[i] == 0; i--) {}\n  var trimmed_size = i + 1;\n\n  // Write out the current vtable.\n  for (; i >= 0; i--) {\n    // Offset relative to the start of the table.\n    this.addInt16(this.vtable[i] != 0 ? vtableloc - this.vtable[i] : 0);\n  }\n\n  var standard_fields = 2; // The fields below:\n  this.addInt16(vtableloc - this.object_start);\n  var len = (trimmed_size + standard_fields) * flatbuffers.SIZEOF_SHORT;\n  this.addInt16(len);\n\n  // Search for an existing vtable that matches the current one.\n  var existing_vtable = 0;\n  var vt1 = this.space;\nouter_loop:\n  for (i = 0; i < this.vtables.length; i++) {\n    var vt2 = this.bb.capacity() - this.vtables[i];\n    if (len == this.bb.readInt16(vt2)) {\n      for (var j = flatbuffers.SIZEOF_SHORT; j < len; j += flatbuffers.SIZEOF_SHORT) {\n        if (this.bb.readInt16(vt1 + j) != this.bb.readInt16(vt2 + j)) {\n          continue outer_loop;\n        }\n      }\n      existing_vtable = this.vtables[i];\n      break;\n    }\n  }\n\n  if (existing_vtable) {\n    // Found a match:\n    // Remove the current vtable.\n    this.space = this.bb.capacity() - vtableloc;\n\n    // Point table to existing vtable.\n    this.bb.writeInt32(this.space, existing_vtable - vtableloc);\n  } else {\n    // No match:\n    // Add the location of the current vtable to the list of vtables.\n    this.vtables.push(this.offset());\n\n    // Point table to current vtable.\n    this.bb.writeInt32(this.bb.capacity() - vtableloc, this.offset() - vtableloc);\n  }\n\n  this.isNested = false;\n  return vtableloc;\n};\n/// @endcond\n\n/**\n * Finalize a buffer, poiting to the given `root_table`.\n *\n * @param {flatbuffers.Offset} root_table\n * @param {string=} opt_file_identifier\n * @param {boolean=} opt_size_prefix\n */\nflatbuffers.Builder.prototype.finish = function(root_table, opt_file_identifier, opt_size_prefix) {\n  var size_prefix = opt_size_prefix ? flatbuffers.SIZE_PREFIX_LENGTH : 0;\n  if (opt_file_identifier) {\n    var file_identifier = opt_file_identifier;\n    this.prep(this.minalign, flatbuffers.SIZEOF_INT +\n      flatbuffers.FILE_IDENTIFIER_LENGTH + size_prefix);\n    if (file_identifier.length != flatbuffers.FILE_IDENTIFIER_LENGTH) {\n      throw new Error('FlatBuffers: file identifier must be length ' +\n        flatbuffers.FILE_IDENTIFIER_LENGTH);\n    }\n    for (var i = flatbuffers.FILE_IDENTIFIER_LENGTH - 1; i >= 0; i--) {\n      this.writeInt8(file_identifier.charCodeAt(i));\n    }\n  }\n  this.prep(this.minalign, flatbuffers.SIZEOF_INT + size_prefix);\n  this.addOffset(root_table);\n  if (size_prefix) {\n    this.addInt32(this.bb.capacity() - this.space);\n  }\n  this.bb.setPosition(this.space);\n};\n\n/**\n * Finalize a size prefixed buffer, pointing to the given `root_table`.\n *\n * @param {flatbuffers.Offset} root_table\n * @param {string=} opt_file_identifier\n */\nflatbuffers.Builder.prototype.finishSizePrefixed = function (root_table, opt_file_identifier) {\n  this.finish(root_table, opt_file_identifier, true);\n};\n\n/// @cond FLATBUFFERS_INTERNAL\n/**\n * This checks a required field has been set in a given table that has\n * just been constructed.\n *\n * @param {flatbuffers.Offset} table\n * @param {number} field\n */\nflatbuffers.Builder.prototype.requiredField = function(table, field) {\n  var table_start = this.bb.capacity() - table;\n  var vtable_start = table_start - this.bb.readInt32(table_start);\n  var ok = this.bb.readInt16(vtable_start + field) != 0;\n\n  // If this fails, the caller will show what field needs to be set.\n  if (!ok) {\n    throw new Error('FlatBuffers: field ' + field + ' must be set');\n  }\n};\n\n/**\n * Start a new array/vector of objects.  Users usually will not call\n * this directly. The FlatBuffers compiler will create a start/end\n * method for vector types in generated code.\n *\n * @param {number} elem_size The size of each element in the array\n * @param {number} num_elems The number of elements in the array\n * @param {number} alignment The alignment of the array\n */\nflatbuffers.Builder.prototype.startVector = function(elem_size, num_elems, alignment) {\n  this.notNested();\n  this.vector_num_elems = num_elems;\n  this.prep(flatbuffers.SIZEOF_INT, elem_size * num_elems);\n  this.prep(alignment, elem_size * num_elems); // Just in case alignment > int.\n};\n\n/**\n * Finish off the creation of an array and all its elements. The array must be\n * created with `startVector`.\n *\n * @returns {flatbuffers.Offset} The offset at which the newly created array\n * starts.\n */\nflatbuffers.Builder.prototype.endVector = function() {\n  this.writeInt32(this.vector_num_elems);\n  return this.offset();\n};\n/// @endcond\n\n/**\n * Encode the string `s` in the buffer using UTF-8. If a Uint8Array is passed\n * instead of a string, it is assumed to contain valid UTF-8 encoded data.\n *\n * @param {string|Uint8Array} s The string to encode\n * @return {flatbuffers.Offset} The offset in the buffer where the encoded string starts\n */\nflatbuffers.Builder.prototype.createString = function(s) {\n  if (s instanceof Uint8Array) {\n    var utf8 = s;\n  } else {\n    var utf8 = [];\n    var i = 0;\n\n    while (i < s.length) {\n      var codePoint;\n\n      // Decode UTF-16\n      var a = s.charCodeAt(i++);\n      if (a < 0xD800 || a >= 0xDC00) {\n        codePoint = a;\n      } else {\n        var b = s.charCodeAt(i++);\n        codePoint = (a << 10) + b + (0x10000 - (0xD800 << 10) - 0xDC00);\n      }\n\n      // Encode UTF-8\n      if (codePoint < 0x80) {\n        utf8.push(codePoint);\n      } else {\n        if (codePoint < 0x800) {\n          utf8.push(((codePoint >> 6) & 0x1F) | 0xC0);\n        } else {\n          if (codePoint < 0x10000) {\n            utf8.push(((codePoint >> 12) & 0x0F) | 0xE0);\n          } else {\n            utf8.push(\n              ((codePoint >> 18) & 0x07) | 0xF0,\n              ((codePoint >> 12) & 0x3F) | 0x80);\n          }\n          utf8.push(((codePoint >> 6) & 0x3F) | 0x80);\n        }\n        utf8.push((codePoint & 0x3F) | 0x80);\n      }\n    }\n  }\n\n  this.addInt8(0);\n  this.startVector(1, utf8.length, 1);\n  this.bb.setPosition(this.space -= utf8.length);\n  for (var i = 0, offset = this.space, bytes = this.bb.bytes(); i < utf8.length; i++) {\n    bytes[offset++] = utf8[i];\n  }\n  return this.endVector();\n};\n\n/**\n * A helper function to avoid generated code depending on this file directly.\n *\n * @param {number} low\n * @param {number} high\n * @returns {!flatbuffers.Long}\n */\nflatbuffers.Builder.prototype.createLong = function(low, high) {\n  return flatbuffers.Long.create(low, high);\n};\n////////////////////////////////////////////////////////////////////////////////\n/// @cond FLATBUFFERS_INTERNAL\n/**\n * Create a new ByteBuffer with a given array of bytes (`Uint8Array`).\n *\n * @constructor\n * @param {Uint8Array} bytes\n */\nflatbuffers.ByteBuffer = function(bytes) {\n  /**\n   * @type {Uint8Array}\n   * @private\n   */\n  this.bytes_ = bytes;\n\n  /**\n   * @type {number}\n   * @private\n   */\n  this.position_ = 0;\n};\n\n/**\n * Create and allocate a new ByteBuffer with a given size.\n *\n * @param {number} byte_size\n * @returns {!flatbuffers.ByteBuffer}\n */\nflatbuffers.ByteBuffer.allocate = function(byte_size) {\n  return new flatbuffers.ByteBuffer(new Uint8Array(byte_size));\n};\n\nflatbuffers.ByteBuffer.prototype.clear = function() {\n  this.position_ = 0;\n};\n\n/**\n * Get the underlying `Uint8Array`.\n *\n * @returns {Uint8Array}\n */\nflatbuffers.ByteBuffer.prototype.bytes = function() {\n  return this.bytes_;\n};\n\n/**\n * Get the buffer's position.\n *\n * @returns {number}\n */\nflatbuffers.ByteBuffer.prototype.position = function() {\n  return this.position_;\n};\n\n/**\n * Set the buffer's position.\n *\n * @param {number} position\n */\nflatbuffers.ByteBuffer.prototype.setPosition = function(position) {\n  this.position_ = position;\n};\n\n/**\n * Get the buffer's capacity.\n *\n * @returns {number}\n */\nflatbuffers.ByteBuffer.prototype.capacity = function() {\n  return this.bytes_.length;\n};\n\n/**\n * @param {number} offset\n * @returns {number}\n */\nflatbuffers.ByteBuffer.prototype.readInt8 = function(offset) {\n  return this.readUint8(offset) << 24 >> 24;\n};\n\n/**\n * @param {number} offset\n * @returns {number}\n */\nflatbuffers.ByteBuffer.prototype.readUint8 = function(offset) {\n  return this.bytes_[offset];\n};\n\n/**\n * @param {number} offset\n * @returns {number}\n */\nflatbuffers.ByteBuffer.prototype.readInt16 = function(offset) {\n  return this.readUint16(offset) << 16 >> 16;\n};\n\n/**\n * @param {number} offset\n * @returns {number}\n */\nflatbuffers.ByteBuffer.prototype.readUint16 = function(offset) {\n  return this.bytes_[offset] | this.bytes_[offset + 1] << 8;\n};\n\n/**\n * @param {number} offset\n * @returns {number}\n */\nflatbuffers.ByteBuffer.prototype.readInt32 = function(offset) {\n  return this.bytes_[offset] | this.bytes_[offset + 1] << 8 | this.bytes_[offset + 2] << 16 | this.bytes_[offset + 3] << 24;\n};\n\n/**\n * @param {number} offset\n * @returns {number}\n */\nflatbuffers.ByteBuffer.prototype.readUint32 = function(offset) {\n  return this.readInt32(offset) >>> 0;\n};\n\n/**\n * @param {number} offset\n * @returns {!flatbuffers.Long}\n */\nflatbuffers.ByteBuffer.prototype.readInt64 = function(offset) {\n  return new flatbuffers.Long(this.readInt32(offset), this.readInt32(offset + 4));\n};\n\n/**\n * @param {number} offset\n * @returns {!flatbuffers.Long}\n */\nflatbuffers.ByteBuffer.prototype.readUint64 = function(offset) {\n  return new flatbuffers.Long(this.readUint32(offset), this.readUint32(offset + 4));\n};\n\n/**\n * @param {number} offset\n * @returns {number}\n */\nflatbuffers.ByteBuffer.prototype.readFloat32 = function(offset) {\n  flatbuffers.int32[0] = this.readInt32(offset);\n  return flatbuffers.float32[0];\n};\n\n/**\n * @param {number} offset\n * @returns {number}\n */\nflatbuffers.ByteBuffer.prototype.readFloat64 = function(offset) {\n  flatbuffers.int32[flatbuffers.isLittleEndian ? 0 : 1] = this.readInt32(offset);\n  flatbuffers.int32[flatbuffers.isLittleEndian ? 1 : 0] = this.readInt32(offset + 4);\n  return flatbuffers.float64[0];\n};\n\n/**\n * @param {number} offset\n * @param {number|boolean} value\n */\nflatbuffers.ByteBuffer.prototype.writeInt8 = function(offset, value) {\n  this.bytes_[offset] = /** @type {number} */(value);\n};\n\n/**\n * @param {number} offset\n * @param {number} value\n */\nflatbuffers.ByteBuffer.prototype.writeUint8 = function(offset, value) {\n  this.bytes_[offset] = value;\n};\n\n/**\n * @param {number} offset\n * @param {number} value\n */\nflatbuffers.ByteBuffer.prototype.writeInt16 = function(offset, value) {\n  this.bytes_[offset] = value;\n  this.bytes_[offset + 1] = value >> 8;\n};\n\n/**\n * @param {number} offset\n * @param {number} value\n */\nflatbuffers.ByteBuffer.prototype.writeUint16 = function(offset, value) {\n    this.bytes_[offset] = value;\n    this.bytes_[offset + 1] = value >> 8;\n};\n\n/**\n * @param {number} offset\n * @param {number} value\n */\nflatbuffers.ByteBuffer.prototype.writeInt32 = function(offset, value) {\n  this.bytes_[offset] = value;\n  this.bytes_[offset + 1] = value >> 8;\n  this.bytes_[offset + 2] = value >> 16;\n  this.bytes_[offset + 3] = value >> 24;\n};\n\n/**\n * @param {number} offset\n * @param {number} value\n */\nflatbuffers.ByteBuffer.prototype.writeUint32 = function(offset, value) {\n    this.bytes_[offset] = value;\n    this.bytes_[offset + 1] = value >> 8;\n    this.bytes_[offset + 2] = value >> 16;\n    this.bytes_[offset + 3] = value >> 24;\n};\n\n/**\n * @param {number} offset\n * @param {flatbuffers.Long} value\n */\nflatbuffers.ByteBuffer.prototype.writeInt64 = function(offset, value) {\n  this.writeInt32(offset, value.low);\n  this.writeInt32(offset + 4, value.high);\n};\n\n/**\n * @param {number} offset\n * @param {flatbuffers.Long} value\n */\nflatbuffers.ByteBuffer.prototype.writeUint64 = function(offset, value) {\n    this.writeUint32(offset, value.low);\n    this.writeUint32(offset + 4, value.high);\n};\n\n/**\n * @param {number} offset\n * @param {number} value\n */\nflatbuffers.ByteBuffer.prototype.writeFloat32 = function(offset, value) {\n  flatbuffers.float32[0] = value;\n  this.writeInt32(offset, flatbuffers.int32[0]);\n};\n\n/**\n * @param {number} offset\n * @param {number} value\n */\nflatbuffers.ByteBuffer.prototype.writeFloat64 = function(offset, value) {\n  flatbuffers.float64[0] = value;\n  this.writeInt32(offset, flatbuffers.int32[flatbuffers.isLittleEndian ? 0 : 1]);\n  this.writeInt32(offset + 4, flatbuffers.int32[flatbuffers.isLittleEndian ? 1 : 0]);\n};\n\n/**\n * Return the file identifier.   Behavior is undefined for FlatBuffers whose\n * schema does not include a file_identifier (likely points at padding or the\n * start of a the root vtable).\n * @returns {string}\n */\nflatbuffers.ByteBuffer.prototype.getBufferIdentifier = function() {\n  if (this.bytes_.length < this.position_ + flatbuffers.SIZEOF_INT +\n      flatbuffers.FILE_IDENTIFIER_LENGTH) {\n    throw new Error(\n        'FlatBuffers: ByteBuffer is too short to contain an identifier.');\n  }\n  var result = \"\";\n  for (var i = 0; i < flatbuffers.FILE_IDENTIFIER_LENGTH; i++) {\n    result += String.fromCharCode(\n        this.readInt8(this.position_ + flatbuffers.SIZEOF_INT + i));\n  }\n  return result;\n};\n\n/**\n * Look up a field in the vtable, return an offset into the object, or 0 if the\n * field is not present.\n *\n * @param {number} bb_pos\n * @param {number} vtable_offset\n * @returns {number}\n */\nflatbuffers.ByteBuffer.prototype.__offset = function(bb_pos, vtable_offset) {\n  var vtable = bb_pos - this.readInt32(bb_pos);\n  return vtable_offset < this.readInt16(vtable) ? this.readInt16(vtable + vtable_offset) : 0;\n};\n\n/**\n * Initialize any Table-derived type to point to the union at the given offset.\n *\n * @param {flatbuffers.Table} t\n * @param {number} offset\n * @returns {flatbuffers.Table}\n */\nflatbuffers.ByteBuffer.prototype.__union = function(t, offset) {\n  t.bb_pos = offset + this.readInt32(offset);\n  t.bb = this;\n  return t;\n};\n\n/**\n * Create a JavaScript string from UTF-8 data stored inside the FlatBuffer.\n * This allocates a new string and converts to wide chars upon each access.\n *\n * To avoid the conversion to UTF-16, pass flatbuffers.Encoding.UTF8_BYTES as\n * the \"optionalEncoding\" argument. This is useful for avoiding conversion to\n * and from UTF-16 when the data will just be packaged back up in another\n * FlatBuffer later on.\n *\n * @param {number} offset\n * @param {flatbuffers.Encoding=} opt_encoding Defaults to UTF16_STRING\n * @returns {string|!Uint8Array}\n */\nflatbuffers.ByteBuffer.prototype.__string = function(offset, opt_encoding) {\n  offset += this.readInt32(offset);\n\n  var length = this.readInt32(offset);\n  var result = '';\n  var i = 0;\n\n  offset += flatbuffers.SIZEOF_INT;\n\n  if (opt_encoding === flatbuffers.Encoding.UTF8_BYTES) {\n    return this.bytes_.subarray(offset, offset + length);\n  }\n\n  while (i < length) {\n    var codePoint;\n\n    // Decode UTF-8\n    var a = this.readUint8(offset + i++);\n    if (a < 0xC0) {\n      codePoint = a;\n    } else {\n      var b = this.readUint8(offset + i++);\n      if (a < 0xE0) {\n        codePoint =\n          ((a & 0x1F) << 6) |\n          (b & 0x3F);\n      } else {\n        var c = this.readUint8(offset + i++);\n        if (a < 0xF0) {\n          codePoint =\n            ((a & 0x0F) << 12) |\n            ((b & 0x3F) << 6) |\n            (c & 0x3F);\n        } else {\n          var d = this.readUint8(offset + i++);\n          codePoint =\n            ((a & 0x07) << 18) |\n            ((b & 0x3F) << 12) |\n            ((c & 0x3F) << 6) |\n            (d & 0x3F);\n        }\n      }\n    }\n\n    // Encode UTF-16\n    if (codePoint < 0x10000) {\n      result += String.fromCharCode(codePoint);\n    } else {\n      codePoint -= 0x10000;\n      result += String.fromCharCode(\n        (codePoint >> 10) + 0xD800,\n        (codePoint & ((1 << 10) - 1)) + 0xDC00);\n    }\n  }\n\n  return result;\n};\n\n/**\n * Retrieve the relative offset stored at \"offset\"\n * @param {number} offset\n * @returns {number}\n */\nflatbuffers.ByteBuffer.prototype.__indirect = function(offset) {\n  return offset + this.readInt32(offset);\n};\n\n/**\n * Get the start of data of a vector whose offset is stored at \"offset\" in this object.\n *\n * @param {number} offset\n * @returns {number}\n */\nflatbuffers.ByteBuffer.prototype.__vector = function(offset) {\n  return offset + this.readInt32(offset) + flatbuffers.SIZEOF_INT; // data starts after the length\n};\n\n/**\n * Get the length of a vector whose offset is stored at \"offset\" in this object.\n *\n * @param {number} offset\n * @returns {number}\n */\nflatbuffers.ByteBuffer.prototype.__vector_len = function(offset) {\n  return this.readInt32(offset + this.readInt32(offset));\n};\n\n/**\n * @param {string} ident\n * @returns {boolean}\n */\nflatbuffers.ByteBuffer.prototype.__has_identifier = function(ident) {\n  if (ident.length != flatbuffers.FILE_IDENTIFIER_LENGTH) {\n    throw new Error('FlatBuffers: file identifier must be length ' +\n                    flatbuffers.FILE_IDENTIFIER_LENGTH);\n  }\n  for (var i = 0; i < flatbuffers.FILE_IDENTIFIER_LENGTH; i++) {\n    if (ident.charCodeAt(i) != this.readInt8(this.position_ + flatbuffers.SIZEOF_INT + i)) {\n      return false;\n    }\n  }\n  return true;\n};\n\n/**\n * A helper function to avoid generated code depending on this file directly.\n *\n * @param {number} low\n * @param {number} high\n * @returns {!flatbuffers.Long}\n */\nflatbuffers.ByteBuffer.prototype.createLong = function(low, high) {\n  return flatbuffers.Long.create(low, high);\n};\n\n// Exports for Node.js and RequireJS\nexport { flatbuffers };\n\n/// @endcond\n/// @}\n","// automatically generated by the FlatBuffers compiler, do not modify\n/* eslint-disable */\n\nimport {flatbuffers} from 'flatbuffers';\n\n/**\n * @enum {number}\n */\nexport namespace onnxruntime.experimental.fbs {\n  export enum AttributeType {\n    UNDEFINED = 0,\n    FLOAT = 1,\n    INT = 2,\n    STRING = 3,\n    TENSOR = 4,\n    GRAPH = 5,\n    FLOATS = 6,\n    INTS = 7,\n    STRINGS = 8,\n    TENSORS = 9,\n    GRAPHS = 10,\n    SPARSE_TENSOR = 11,\n    SPARSE_TENSORS = 12\n  }\n}\n\n/**\n * @enum {number}\n */\nexport namespace onnxruntime.experimental.fbs {\n  export enum DimensionValueType {UNKNOWN = 0, VALUE = 1, PARAM = 2}\n}\n\n/**\n * @enum {number}\n */\nexport namespace onnxruntime.experimental.fbs {\n  export enum TensorDataType {\n    UNDEFINED = 0,\n    FLOAT = 1,\n    UINT8 = 2,\n    INT8 = 3,\n    UINT16 = 4,\n    INT16 = 5,\n    INT32 = 6,\n    INT64 = 7,\n    STRING = 8,\n    BOOL = 9,\n    FLOAT16 = 10,\n    DOUBLE = 11,\n    UINT32 = 12,\n    UINT64 = 13,\n    COMPLEX64 = 14,\n    COMPLEX128 = 15,\n    BFLOAT16 = 16,\n    FLOAT8E4M3FN = 17,\n    FLOAT8E4M3FNUZ = 18,\n    FLOAT8E5M2 = 19,\n    FLOAT8E5M2FNUZ = 20,\n  }\n}\n\n/**\n * @enum {number}\n */\nexport namespace onnxruntime.experimental.fbs {\n  export enum NodeType {Primitive = 0, Fused = 1}\n}\n\n/**\n * @enum {number}\n */\nexport namespace onnxruntime.experimental.fbs {\n  export enum TypeInfoValue {NONE = 0, tensor_type = 1, sequence_type = 2, map_type = 3}\n}\n\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class Shape {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns Shape\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): Shape {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param Shape= obj\n     * @returns Shape\n     */\n    static getRootAsShape(bb: flatbuffers.ByteBuffer, obj?: Shape): Shape {\n      return (obj || new Shape()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param Shape= obj\n     * @returns Shape\n     */\n    static getSizePrefixedRootAsShape(bb: flatbuffers.ByteBuffer, obj?: Shape): Shape {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new Shape()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param number index\n     * @param onnxruntime.experimental.fbs.Dimension= obj\n     * @returns onnxruntime.experimental.fbs.Dimension\n     */\n    dim(index: number, obj?: onnxruntime.experimental.fbs.Dimension): onnxruntime.experimental.fbs.Dimension|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? (obj || new onnxruntime.experimental.fbs.Dimension())\n                          .__init(this.bb!.__indirect(this.bb!.__vector(this.bb_pos + offset) + index * 4), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @returns number\n     */\n    dimLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startShape(builder: flatbuffers.Builder) {\n      builder.startObject(1);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset dimOffset\n     */\n    static addDim(builder: flatbuffers.Builder, dimOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(0, dimOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createDimVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startDimVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endShape(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createShape(builder: flatbuffers.Builder, dimOffset: flatbuffers.Offset): flatbuffers.Offset {\n      Shape.startShape(builder);\n      Shape.addDim(builder, dimOffset);\n      return Shape.endShape(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class Dimension {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns Dimension\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): Dimension {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param Dimension= obj\n     * @returns Dimension\n     */\n    static getRootAsDimension(bb: flatbuffers.ByteBuffer, obj?: Dimension): Dimension {\n      return (obj || new Dimension()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param Dimension= obj\n     * @returns Dimension\n     */\n    static getSizePrefixedRootAsDimension(bb: flatbuffers.ByteBuffer, obj?: Dimension): Dimension {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new Dimension()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param onnxruntime.experimental.fbs.DimensionValue= obj\n     * @returns onnxruntime.experimental.fbs.DimensionValue|null\n     */\n    value(obj?: onnxruntime.experimental.fbs.DimensionValue): onnxruntime.experimental.fbs.DimensionValue|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? (obj || new onnxruntime.experimental.fbs.DimensionValue())\n                          .__init(this.bb!.__indirect(this.bb_pos + offset), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    denotation(): string|null;\n    denotation(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    denotation(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startDimension(builder: flatbuffers.Builder) {\n      builder.startObject(2);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset valueOffset\n     */\n    static addValue(builder: flatbuffers.Builder, valueOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(0, valueOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset denotationOffset\n     */\n    static addDenotation(builder: flatbuffers.Builder, denotationOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(1, denotationOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endDimension(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createDimension(\n        builder: flatbuffers.Builder, valueOffset: flatbuffers.Offset,\n        denotationOffset: flatbuffers.Offset): flatbuffers.Offset {\n      Dimension.startDimension(builder);\n      Dimension.addValue(builder, valueOffset);\n      Dimension.addDenotation(builder, denotationOffset);\n      return Dimension.endDimension(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class DimensionValue {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns DimensionValue\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): DimensionValue {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param DimensionValue= obj\n     * @returns DimensionValue\n     */\n    static getRootAsDimensionValue(bb: flatbuffers.ByteBuffer, obj?: DimensionValue): DimensionValue {\n      return (obj || new DimensionValue()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param DimensionValue= obj\n     * @returns DimensionValue\n     */\n    static getSizePrefixedRootAsDimensionValue(bb: flatbuffers.ByteBuffer, obj?: DimensionValue): DimensionValue {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new DimensionValue()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @returns onnxruntime.experimental.fbs.DimensionValueType\n     */\n    dimType(): onnxruntime.experimental.fbs.DimensionValueType {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? /**  */ (this.bb!.readInt8(this.bb_pos + offset)) :\n                      onnxruntime.experimental.fbs.DimensionValueType.UNKNOWN;\n    }\n\n    /**\n     * @returns flatbuffers.Long\n     */\n    dimValue(): flatbuffers.Long {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? this.bb!.readInt64(this.bb_pos + offset) : this.bb!.createLong(0, 0);\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    dimParam(): string|null;\n    dimParam(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    dimParam(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 8);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startDimensionValue(builder: flatbuffers.Builder) {\n      builder.startObject(3);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param onnxruntime.experimental.fbs.DimensionValueType dimType\n     */\n    static addDimType(builder: flatbuffers.Builder, dimType: onnxruntime.experimental.fbs.DimensionValueType) {\n      builder.addFieldInt8(0, dimType, onnxruntime.experimental.fbs.DimensionValueType.UNKNOWN);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Long dimValue\n     */\n    static addDimValue(builder: flatbuffers.Builder, dimValue: flatbuffers.Long) {\n      builder.addFieldInt64(1, dimValue, builder.createLong(0, 0));\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset dimParamOffset\n     */\n    static addDimParam(builder: flatbuffers.Builder, dimParamOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(2, dimParamOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endDimensionValue(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createDimensionValue(\n        builder: flatbuffers.Builder, dimType: onnxruntime.experimental.fbs.DimensionValueType,\n        dimValue: flatbuffers.Long, dimParamOffset: flatbuffers.Offset): flatbuffers.Offset {\n      DimensionValue.startDimensionValue(builder);\n      DimensionValue.addDimType(builder, dimType);\n      DimensionValue.addDimValue(builder, dimValue);\n      DimensionValue.addDimParam(builder, dimParamOffset);\n      return DimensionValue.endDimensionValue(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class TensorTypeAndShape {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns TensorTypeAndShape\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): TensorTypeAndShape {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param TensorTypeAndShape= obj\n     * @returns TensorTypeAndShape\n     */\n    static getRootAsTensorTypeAndShape(bb: flatbuffers.ByteBuffer, obj?: TensorTypeAndShape): TensorTypeAndShape {\n      return (obj || new TensorTypeAndShape()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param TensorTypeAndShape= obj\n     * @returns TensorTypeAndShape\n     */\n    static getSizePrefixedRootAsTensorTypeAndShape(bb: flatbuffers.ByteBuffer, obj?: TensorTypeAndShape):\n        TensorTypeAndShape {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new TensorTypeAndShape()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @returns onnxruntime.experimental.fbs.TensorDataType\n     */\n    elemType(): onnxruntime.experimental.fbs.TensorDataType {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? /**  */ (this.bb!.readInt32(this.bb_pos + offset)) :\n                      onnxruntime.experimental.fbs.TensorDataType.UNDEFINED;\n    }\n\n    /**\n     * @param onnxruntime.experimental.fbs.Shape= obj\n     * @returns onnxruntime.experimental.fbs.Shape|null\n     */\n    shape(obj?: onnxruntime.experimental.fbs.Shape): onnxruntime.experimental.fbs.Shape|null {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? (obj || new onnxruntime.experimental.fbs.Shape())\n                          .__init(this.bb!.__indirect(this.bb_pos + offset), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startTensorTypeAndShape(builder: flatbuffers.Builder) {\n      builder.startObject(2);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param onnxruntime.experimental.fbs.TensorDataType elemType\n     */\n    static addElemType(builder: flatbuffers.Builder, elemType: onnxruntime.experimental.fbs.TensorDataType) {\n      builder.addFieldInt32(0, elemType, onnxruntime.experimental.fbs.TensorDataType.UNDEFINED);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset shapeOffset\n     */\n    static addShape(builder: flatbuffers.Builder, shapeOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(1, shapeOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endTensorTypeAndShape(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createTensorTypeAndShape(\n        builder: flatbuffers.Builder, elemType: onnxruntime.experimental.fbs.TensorDataType,\n        shapeOffset: flatbuffers.Offset): flatbuffers.Offset {\n      TensorTypeAndShape.startTensorTypeAndShape(builder);\n      TensorTypeAndShape.addElemType(builder, elemType);\n      TensorTypeAndShape.addShape(builder, shapeOffset);\n      return TensorTypeAndShape.endTensorTypeAndShape(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class MapType {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns MapType\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): MapType {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param MapType= obj\n     * @returns MapType\n     */\n    static getRootAsMapType(bb: flatbuffers.ByteBuffer, obj?: MapType): MapType {\n      return (obj || new MapType()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param MapType= obj\n     * @returns MapType\n     */\n    static getSizePrefixedRootAsMapType(bb: flatbuffers.ByteBuffer, obj?: MapType): MapType {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new MapType()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @returns onnxruntime.experimental.fbs.TensorDataType\n     */\n    keyType(): onnxruntime.experimental.fbs.TensorDataType {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? /**  */ (this.bb!.readInt32(this.bb_pos + offset)) :\n                      onnxruntime.experimental.fbs.TensorDataType.UNDEFINED;\n    }\n\n    /**\n     * @param onnxruntime.experimental.fbs.TypeInfo= obj\n     * @returns onnxruntime.experimental.fbs.TypeInfo|null\n     */\n    valueType(obj?: onnxruntime.experimental.fbs.TypeInfo): onnxruntime.experimental.fbs.TypeInfo|null {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? (obj || new onnxruntime.experimental.fbs.TypeInfo())\n                          .__init(this.bb!.__indirect(this.bb_pos + offset), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startMapType(builder: flatbuffers.Builder) {\n      builder.startObject(2);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param onnxruntime.experimental.fbs.TensorDataType keyType\n     */\n    static addKeyType(builder: flatbuffers.Builder, keyType: onnxruntime.experimental.fbs.TensorDataType) {\n      builder.addFieldInt32(0, keyType, onnxruntime.experimental.fbs.TensorDataType.UNDEFINED);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset valueTypeOffset\n     */\n    static addValueType(builder: flatbuffers.Builder, valueTypeOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(1, valueTypeOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endMapType(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createMapType(\n        builder: flatbuffers.Builder, keyType: onnxruntime.experimental.fbs.TensorDataType,\n        valueTypeOffset: flatbuffers.Offset): flatbuffers.Offset {\n      MapType.startMapType(builder);\n      MapType.addKeyType(builder, keyType);\n      MapType.addValueType(builder, valueTypeOffset);\n      return MapType.endMapType(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class SequenceType {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns SequenceType\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): SequenceType {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param SequenceType= obj\n     * @returns SequenceType\n     */\n    static getRootAsSequenceType(bb: flatbuffers.ByteBuffer, obj?: SequenceType): SequenceType {\n      return (obj || new SequenceType()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param SequenceType= obj\n     * @returns SequenceType\n     */\n    static getSizePrefixedRootAsSequenceType(bb: flatbuffers.ByteBuffer, obj?: SequenceType): SequenceType {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new SequenceType()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param onnxruntime.experimental.fbs.TypeInfo= obj\n     * @returns onnxruntime.experimental.fbs.TypeInfo|null\n     */\n    elemType(obj?: onnxruntime.experimental.fbs.TypeInfo): onnxruntime.experimental.fbs.TypeInfo|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? (obj || new onnxruntime.experimental.fbs.TypeInfo())\n                          .__init(this.bb!.__indirect(this.bb_pos + offset), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startSequenceType(builder: flatbuffers.Builder) {\n      builder.startObject(1);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset elemTypeOffset\n     */\n    static addElemType(builder: flatbuffers.Builder, elemTypeOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(0, elemTypeOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endSequenceType(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createSequenceType(builder: flatbuffers.Builder, elemTypeOffset: flatbuffers.Offset): flatbuffers.Offset {\n      SequenceType.startSequenceType(builder);\n      SequenceType.addElemType(builder, elemTypeOffset);\n      return SequenceType.endSequenceType(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class EdgeEnd {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns EdgeEnd\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): EdgeEnd {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @returns number\n     */\n    nodeIndex(): number {\n      return this.bb!.readUint32(this.bb_pos);\n    }\n\n    /**\n     * @returns number\n     */\n    srcArgIndex(): number {\n      return this.bb!.readInt32(this.bb_pos + 4);\n    }\n\n    /**\n     * @returns number\n     */\n    dstArgIndex(): number {\n      return this.bb!.readInt32(this.bb_pos + 8);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number node_index\n     * @param number src_arg_index\n     * @param number dst_arg_index\n     * @returns flatbuffers.Offset\n     */\n    static createEdgeEnd(\n        builder: flatbuffers.Builder, node_index: number, src_arg_index: number,\n        dst_arg_index: number): flatbuffers.Offset {\n      builder.prep(4, 12);\n      builder.writeInt32(dst_arg_index);\n      builder.writeInt32(src_arg_index);\n      builder.writeInt32(node_index);\n      return builder.offset();\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class NodeEdge {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns NodeEdge\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): NodeEdge {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param NodeEdge= obj\n     * @returns NodeEdge\n     */\n    static getRootAsNodeEdge(bb: flatbuffers.ByteBuffer, obj?: NodeEdge): NodeEdge {\n      return (obj || new NodeEdge()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param NodeEdge= obj\n     * @returns NodeEdge\n     */\n    static getSizePrefixedRootAsNodeEdge(bb: flatbuffers.ByteBuffer, obj?: NodeEdge): NodeEdge {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new NodeEdge()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @returns number\n     */\n    nodeIndex(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? this.bb!.readUint32(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param number index\n     * @param onnxruntime.experimental.fbs.EdgeEnd= obj\n     * @returns onnxruntime.experimental.fbs.EdgeEnd\n     */\n    inputEdges(index: number, obj?: onnxruntime.experimental.fbs.EdgeEnd): onnxruntime.experimental.fbs.EdgeEnd|null {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? (obj || new onnxruntime.experimental.fbs.EdgeEnd())\n                          .__init(this.bb!.__vector(this.bb_pos + offset) + index * 12, this.bb!) :\n                      null;\n    }\n\n    /**\n     * @returns number\n     */\n    inputEdgesLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param number index\n     * @param onnxruntime.experimental.fbs.EdgeEnd= obj\n     * @returns onnxruntime.experimental.fbs.EdgeEnd\n     */\n    outputEdges(index: number, obj?: onnxruntime.experimental.fbs.EdgeEnd): onnxruntime.experimental.fbs.EdgeEnd|null {\n      let offset = this.bb!.__offset(this.bb_pos, 8);\n      return offset ? (obj || new onnxruntime.experimental.fbs.EdgeEnd())\n                          .__init(this.bb!.__vector(this.bb_pos + offset) + index * 12, this.bb!) :\n                      null;\n    }\n\n    /**\n     * @returns number\n     */\n    outputEdgesLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 8);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startNodeEdge(builder: flatbuffers.Builder) {\n      builder.startObject(3);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number nodeIndex\n     */\n    static addNodeIndex(builder: flatbuffers.Builder, nodeIndex: number) {\n      builder.addFieldInt32(0, nodeIndex, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset inputEdgesOffset\n     */\n    static addInputEdges(builder: flatbuffers.Builder, inputEdgesOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(1, inputEdgesOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startInputEdgesVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(12, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset outputEdgesOffset\n     */\n    static addOutputEdges(builder: flatbuffers.Builder, outputEdgesOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(2, outputEdgesOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startOutputEdgesVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(12, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endNodeEdge(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createNodeEdge(\n        builder: flatbuffers.Builder, nodeIndex: number, inputEdgesOffset: flatbuffers.Offset,\n        outputEdgesOffset: flatbuffers.Offset): flatbuffers.Offset {\n      NodeEdge.startNodeEdge(builder);\n      NodeEdge.addNodeIndex(builder, nodeIndex);\n      NodeEdge.addInputEdges(builder, inputEdgesOffset);\n      NodeEdge.addOutputEdges(builder, outputEdgesOffset);\n      return NodeEdge.endNodeEdge(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class Node {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns Node\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): Node {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param Node= obj\n     * @returns Node\n     */\n    static getRootAsNode(bb: flatbuffers.ByteBuffer, obj?: Node): Node {\n      return (obj || new Node()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param Node= obj\n     * @returns Node\n     */\n    static getSizePrefixedRootAsNode(bb: flatbuffers.ByteBuffer, obj?: Node): Node {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new Node()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    name(): string|null;\n    name(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    name(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    docString(): string|null;\n    docString(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    docString(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    domain(): string|null;\n    domain(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    domain(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 8);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @returns number\n     */\n    sinceVersion(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 10);\n      return offset ? this.bb!.readInt32(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @returns number\n     */\n    index(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 12);\n      return offset ? this.bb!.readUint32(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    opType(): string|null;\n    opType(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    opType(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 14);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @returns onnxruntime.experimental.fbs.NodeType\n     */\n    type(): onnxruntime.experimental.fbs.NodeType {\n      let offset = this.bb!.__offset(this.bb_pos, 16);\n      return offset ? /**  */ (this.bb!.readInt32(this.bb_pos + offset)) :\n                      onnxruntime.experimental.fbs.NodeType.Primitive;\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    executionProviderType(): string|null;\n    executionProviderType(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    executionProviderType(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 18);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param number index\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array\n     */\n    inputs(index: number): string;\n    inputs(index: number, optionalEncoding: flatbuffers.Encoding): string|Uint8Array;\n    inputs(index: number, optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 20);\n      return offset ? this.bb!.__string(this.bb!.__vector(this.bb_pos + offset) + index * 4, optionalEncoding) : null;\n    }\n\n    /**\n     * @returns number\n     */\n    inputsLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 20);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param number index\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array\n     */\n    outputs(index: number): string;\n    outputs(index: number, optionalEncoding: flatbuffers.Encoding): string|Uint8Array;\n    outputs(index: number, optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 22);\n      return offset ? this.bb!.__string(this.bb!.__vector(this.bb_pos + offset) + index * 4, optionalEncoding) : null;\n    }\n\n    /**\n     * @returns number\n     */\n    outputsLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 22);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param number index\n     * @param onnxruntime.experimental.fbs.Attribute= obj\n     * @returns onnxruntime.experimental.fbs.Attribute\n     */\n    attributes(index: number, obj?: onnxruntime.experimental.fbs.Attribute): onnxruntime.experimental.fbs.Attribute\n        |null {\n      let offset = this.bb!.__offset(this.bb_pos, 24);\n      return offset ? (obj || new onnxruntime.experimental.fbs.Attribute())\n                          .__init(this.bb!.__indirect(this.bb!.__vector(this.bb_pos + offset) + index * 4), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @returns number\n     */\n    attributesLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 24);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param number index\n     * @returns number\n     */\n    inputArgCounts(index: number): number|null {\n      let offset = this.bb!.__offset(this.bb_pos, 26);\n      return offset ? this.bb!.readInt32(this.bb!.__vector(this.bb_pos + offset) + index * 4) : 0;\n    }\n\n    /**\n     * @returns number\n     */\n    inputArgCountsLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 26);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @returns Int32Array\n     */\n    inputArgCountsArray(): Int32Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 26);\n      return offset ?\n          new Int32Array(\n              this.bb!.bytes().buffer, this.bb!.bytes().byteOffset + this.bb!.__vector(this.bb_pos + offset),\n              this.bb!.__vector_len(this.bb_pos + offset)) :\n          null;\n    }\n\n    /**\n     * @param number index\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array\n     */\n    implicitInputs(index: number): string;\n    implicitInputs(index: number, optionalEncoding: flatbuffers.Encoding): string|Uint8Array;\n    implicitInputs(index: number, optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 28);\n      return offset ? this.bb!.__string(this.bb!.__vector(this.bb_pos + offset) + index * 4, optionalEncoding) : null;\n    }\n\n    /**\n     * @returns number\n     */\n    implicitInputsLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 28);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startNode(builder: flatbuffers.Builder) {\n      builder.startObject(13);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset nameOffset\n     */\n    static addName(builder: flatbuffers.Builder, nameOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(0, nameOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset docStringOffset\n     */\n    static addDocString(builder: flatbuffers.Builder, docStringOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(1, docStringOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset domainOffset\n     */\n    static addDomain(builder: flatbuffers.Builder, domainOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(2, domainOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number sinceVersion\n     */\n    static addSinceVersion(builder: flatbuffers.Builder, sinceVersion: number) {\n      builder.addFieldInt32(3, sinceVersion, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number index\n     */\n    static addIndex(builder: flatbuffers.Builder, index: number) {\n      builder.addFieldInt32(4, index, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset opTypeOffset\n     */\n    static addOpType(builder: flatbuffers.Builder, opTypeOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(5, opTypeOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param onnxruntime.experimental.fbs.NodeType type\n     */\n    static addType(builder: flatbuffers.Builder, type: onnxruntime.experimental.fbs.NodeType) {\n      builder.addFieldInt32(6, type, onnxruntime.experimental.fbs.NodeType.Primitive);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset executionProviderTypeOffset\n     */\n    static addExecutionProviderType(builder: flatbuffers.Builder, executionProviderTypeOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(7, executionProviderTypeOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset inputsOffset\n     */\n    static addInputs(builder: flatbuffers.Builder, inputsOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(8, inputsOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createInputsVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startInputsVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset outputsOffset\n     */\n    static addOutputs(builder: flatbuffers.Builder, outputsOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(9, outputsOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createOutputsVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startOutputsVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset attributesOffset\n     */\n    static addAttributes(builder: flatbuffers.Builder, attributesOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(10, attributesOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createAttributesVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startAttributesVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset inputArgCountsOffset\n     */\n    static addInputArgCounts(builder: flatbuffers.Builder, inputArgCountsOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(11, inputArgCountsOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<number> data\n     * @returns flatbuffers.Offset\n     */\n    static createInputArgCountsVector(builder: flatbuffers.Builder, data: number[]|Uint8Array): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addInt32(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startInputArgCountsVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset implicitInputsOffset\n     */\n    static addImplicitInputs(builder: flatbuffers.Builder, implicitInputsOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(12, implicitInputsOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createImplicitInputsVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startImplicitInputsVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endNode(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createNode(\n        builder: flatbuffers.Builder, nameOffset: flatbuffers.Offset, docStringOffset: flatbuffers.Offset,\n        domainOffset: flatbuffers.Offset, sinceVersion: number, index: number, opTypeOffset: flatbuffers.Offset,\n        type: onnxruntime.experimental.fbs.NodeType, executionProviderTypeOffset: flatbuffers.Offset,\n        inputsOffset: flatbuffers.Offset, outputsOffset: flatbuffers.Offset, attributesOffset: flatbuffers.Offset,\n        inputArgCountsOffset: flatbuffers.Offset, implicitInputsOffset: flatbuffers.Offset): flatbuffers.Offset {\n      Node.startNode(builder);\n      Node.addName(builder, nameOffset);\n      Node.addDocString(builder, docStringOffset);\n      Node.addDomain(builder, domainOffset);\n      Node.addSinceVersion(builder, sinceVersion);\n      Node.addIndex(builder, index);\n      Node.addOpType(builder, opTypeOffset);\n      Node.addType(builder, type);\n      Node.addExecutionProviderType(builder, executionProviderTypeOffset);\n      Node.addInputs(builder, inputsOffset);\n      Node.addOutputs(builder, outputsOffset);\n      Node.addAttributes(builder, attributesOffset);\n      Node.addInputArgCounts(builder, inputArgCountsOffset);\n      Node.addImplicitInputs(builder, implicitInputsOffset);\n      return Node.endNode(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class ValueInfo {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns ValueInfo\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): ValueInfo {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param ValueInfo= obj\n     * @returns ValueInfo\n     */\n    static getRootAsValueInfo(bb: flatbuffers.ByteBuffer, obj?: ValueInfo): ValueInfo {\n      return (obj || new ValueInfo()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param ValueInfo= obj\n     * @returns ValueInfo\n     */\n    static getSizePrefixedRootAsValueInfo(bb: flatbuffers.ByteBuffer, obj?: ValueInfo): ValueInfo {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new ValueInfo()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    name(): string|null;\n    name(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    name(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    docString(): string|null;\n    docString(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    docString(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param onnxruntime.experimental.fbs.TypeInfo= obj\n     * @returns onnxruntime.experimental.fbs.TypeInfo|null\n     */\n    type(obj?: onnxruntime.experimental.fbs.TypeInfo): onnxruntime.experimental.fbs.TypeInfo|null {\n      let offset = this.bb!.__offset(this.bb_pos, 8);\n      return offset ? (obj || new onnxruntime.experimental.fbs.TypeInfo())\n                          .__init(this.bb!.__indirect(this.bb_pos + offset), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startValueInfo(builder: flatbuffers.Builder) {\n      builder.startObject(3);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset nameOffset\n     */\n    static addName(builder: flatbuffers.Builder, nameOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(0, nameOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset docStringOffset\n     */\n    static addDocString(builder: flatbuffers.Builder, docStringOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(1, docStringOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset typeOffset\n     */\n    static addType(builder: flatbuffers.Builder, typeOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(2, typeOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endValueInfo(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createValueInfo(\n        builder: flatbuffers.Builder, nameOffset: flatbuffers.Offset, docStringOffset: flatbuffers.Offset,\n        typeOffset: flatbuffers.Offset): flatbuffers.Offset {\n      ValueInfo.startValueInfo(builder);\n      ValueInfo.addName(builder, nameOffset);\n      ValueInfo.addDocString(builder, docStringOffset);\n      ValueInfo.addType(builder, typeOffset);\n      return ValueInfo.endValueInfo(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class TypeInfo {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns TypeInfo\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): TypeInfo {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param TypeInfo= obj\n     * @returns TypeInfo\n     */\n    static getRootAsTypeInfo(bb: flatbuffers.ByteBuffer, obj?: TypeInfo): TypeInfo {\n      return (obj || new TypeInfo()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param TypeInfo= obj\n     * @returns TypeInfo\n     */\n    static getSizePrefixedRootAsTypeInfo(bb: flatbuffers.ByteBuffer, obj?: TypeInfo): TypeInfo {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new TypeInfo()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    denotation(): string|null;\n    denotation(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    denotation(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @returns onnxruntime.experimental.fbs.TypeInfoValue\n     */\n    valueType(): onnxruntime.experimental.fbs.TypeInfoValue {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? /**  */ (this.bb!.readUint8(this.bb_pos + offset)) :\n                      onnxruntime.experimental.fbs.TypeInfoValue.NONE;\n    }\n\n    /**\n     * @param flatbuffers.Table obj\n     * @returns ?flatbuffers.Table\n     */\n    value<T extends flatbuffers.Table>(obj: T): T|null {\n      let offset = this.bb!.__offset(this.bb_pos, 8);\n      return offset ? this.bb!.__union(obj, this.bb_pos + offset) : null;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startTypeInfo(builder: flatbuffers.Builder) {\n      builder.startObject(3);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset denotationOffset\n     */\n    static addDenotation(builder: flatbuffers.Builder, denotationOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(0, denotationOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param onnxruntime.experimental.fbs.TypeInfoValue valueType\n     */\n    static addValueType(builder: flatbuffers.Builder, valueType: onnxruntime.experimental.fbs.TypeInfoValue) {\n      builder.addFieldInt8(1, valueType, onnxruntime.experimental.fbs.TypeInfoValue.NONE);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset valueOffset\n     */\n    static addValue(builder: flatbuffers.Builder, valueOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(2, valueOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endTypeInfo(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createTypeInfo(\n        builder: flatbuffers.Builder, denotationOffset: flatbuffers.Offset,\n        valueType: onnxruntime.experimental.fbs.TypeInfoValue, valueOffset: flatbuffers.Offset): flatbuffers.Offset {\n      TypeInfo.startTypeInfo(builder);\n      TypeInfo.addDenotation(builder, denotationOffset);\n      TypeInfo.addValueType(builder, valueType);\n      TypeInfo.addValue(builder, valueOffset);\n      return TypeInfo.endTypeInfo(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class OperatorSetId {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns OperatorSetId\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): OperatorSetId {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param OperatorSetId= obj\n     * @returns OperatorSetId\n     */\n    static getRootAsOperatorSetId(bb: flatbuffers.ByteBuffer, obj?: OperatorSetId): OperatorSetId {\n      return (obj || new OperatorSetId()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param OperatorSetId= obj\n     * @returns OperatorSetId\n     */\n    static getSizePrefixedRootAsOperatorSetId(bb: flatbuffers.ByteBuffer, obj?: OperatorSetId): OperatorSetId {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new OperatorSetId()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    domain(): string|null;\n    domain(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    domain(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @returns flatbuffers.Long\n     */\n    version(): flatbuffers.Long {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? this.bb!.readInt64(this.bb_pos + offset) : this.bb!.createLong(0, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startOperatorSetId(builder: flatbuffers.Builder) {\n      builder.startObject(2);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset domainOffset\n     */\n    static addDomain(builder: flatbuffers.Builder, domainOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(0, domainOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Long version\n     */\n    static addVersion(builder: flatbuffers.Builder, version: flatbuffers.Long) {\n      builder.addFieldInt64(1, version, builder.createLong(0, 0));\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endOperatorSetId(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createOperatorSetId(\n        builder: flatbuffers.Builder, domainOffset: flatbuffers.Offset, version: flatbuffers.Long): flatbuffers.Offset {\n      OperatorSetId.startOperatorSetId(builder);\n      OperatorSetId.addDomain(builder, domainOffset);\n      OperatorSetId.addVersion(builder, version);\n      return OperatorSetId.endOperatorSetId(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class Tensor {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns Tensor\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): Tensor {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param Tensor= obj\n     * @returns Tensor\n     */\n    static getRootAsTensor(bb: flatbuffers.ByteBuffer, obj?: Tensor): Tensor {\n      return (obj || new Tensor()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param Tensor= obj\n     * @returns Tensor\n     */\n    static getSizePrefixedRootAsTensor(bb: flatbuffers.ByteBuffer, obj?: Tensor): Tensor {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new Tensor()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    name(): string|null;\n    name(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    name(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    docString(): string|null;\n    docString(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    docString(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param number index\n     * @returns flatbuffers.Long\n     */\n    dims(index: number): flatbuffers.Long|null {\n      let offset = this.bb!.__offset(this.bb_pos, 8);\n      return offset ? this.bb!.readInt64(this.bb!.__vector(this.bb_pos + offset) + index * 8) :\n                      this.bb!.createLong(0, 0);\n    }\n\n    /**\n     * @returns number\n     */\n    dimsLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 8);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @returns onnxruntime.experimental.fbs.TensorDataType\n     */\n    dataType(): onnxruntime.experimental.fbs.TensorDataType {\n      let offset = this.bb!.__offset(this.bb_pos, 10);\n      return offset ? /**  */ (this.bb!.readInt32(this.bb_pos + offset)) :\n                      onnxruntime.experimental.fbs.TensorDataType.UNDEFINED;\n    }\n\n    /**\n     * @param number index\n     * @returns number\n     */\n    rawData(index: number): number|null {\n      let offset = this.bb!.__offset(this.bb_pos, 12);\n      return offset ? this.bb!.readUint8(this.bb!.__vector(this.bb_pos + offset) + index) : 0;\n    }\n\n    /**\n     * @returns number\n     */\n    rawDataLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 12);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @returns Uint8Array\n     */\n    rawDataArray(): Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 12);\n      return offset ?\n          new Uint8Array(\n              this.bb!.bytes().buffer, this.bb!.bytes().byteOffset + this.bb!.__vector(this.bb_pos + offset),\n              this.bb!.__vector_len(this.bb_pos + offset)) :\n          null;\n    }\n\n    /**\n     * @param number index\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array\n     */\n    stringData(index: number): string;\n    stringData(index: number, optionalEncoding: flatbuffers.Encoding): string|Uint8Array;\n    stringData(index: number, optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 14);\n      return offset ? this.bb!.__string(this.bb!.__vector(this.bb_pos + offset) + index * 4, optionalEncoding) : null;\n    }\n\n    /**\n     * @returns number\n     */\n    stringDataLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 14);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startTensor(builder: flatbuffers.Builder) {\n      builder.startObject(6);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset nameOffset\n     */\n    static addName(builder: flatbuffers.Builder, nameOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(0, nameOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset docStringOffset\n     */\n    static addDocString(builder: flatbuffers.Builder, docStringOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(1, docStringOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset dimsOffset\n     */\n    static addDims(builder: flatbuffers.Builder, dimsOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(2, dimsOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Long> data\n     * @returns flatbuffers.Offset\n     */\n    static createDimsVector(builder: flatbuffers.Builder, data: flatbuffers.Long[]): flatbuffers.Offset {\n      builder.startVector(8, data.length, 8);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addInt64(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startDimsVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(8, numElems, 8);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param onnxruntime.experimental.fbs.TensorDataType dataType\n     */\n    static addDataType(builder: flatbuffers.Builder, dataType: onnxruntime.experimental.fbs.TensorDataType) {\n      builder.addFieldInt32(3, dataType, onnxruntime.experimental.fbs.TensorDataType.UNDEFINED);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset rawDataOffset\n     */\n    static addRawData(builder: flatbuffers.Builder, rawDataOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(4, rawDataOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<number> data\n     * @returns flatbuffers.Offset\n     */\n    static createRawDataVector(builder: flatbuffers.Builder, data: number[]|Uint8Array): flatbuffers.Offset {\n      builder.startVector(1, data.length, 1);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addInt8(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startRawDataVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(1, numElems, 1);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset stringDataOffset\n     */\n    static addStringData(builder: flatbuffers.Builder, stringDataOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(5, stringDataOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createStringDataVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startStringDataVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endTensor(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createTensor(\n        builder: flatbuffers.Builder, nameOffset: flatbuffers.Offset, docStringOffset: flatbuffers.Offset,\n        dimsOffset: flatbuffers.Offset, dataType: onnxruntime.experimental.fbs.TensorDataType,\n        rawDataOffset: flatbuffers.Offset, stringDataOffset: flatbuffers.Offset): flatbuffers.Offset {\n      Tensor.startTensor(builder);\n      Tensor.addName(builder, nameOffset);\n      Tensor.addDocString(builder, docStringOffset);\n      Tensor.addDims(builder, dimsOffset);\n      Tensor.addDataType(builder, dataType);\n      Tensor.addRawData(builder, rawDataOffset);\n      Tensor.addStringData(builder, stringDataOffset);\n      return Tensor.endTensor(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class SparseTensor {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns SparseTensor\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): SparseTensor {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param SparseTensor= obj\n     * @returns SparseTensor\n     */\n    static getRootAsSparseTensor(bb: flatbuffers.ByteBuffer, obj?: SparseTensor): SparseTensor {\n      return (obj || new SparseTensor()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param SparseTensor= obj\n     * @returns SparseTensor\n     */\n    static getSizePrefixedRootAsSparseTensor(bb: flatbuffers.ByteBuffer, obj?: SparseTensor): SparseTensor {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new SparseTensor()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param onnxruntime.experimental.fbs.Tensor= obj\n     * @returns onnxruntime.experimental.fbs.Tensor|null\n     */\n    values(obj?: onnxruntime.experimental.fbs.Tensor): onnxruntime.experimental.fbs.Tensor|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? (obj || new onnxruntime.experimental.fbs.Tensor())\n                          .__init(this.bb!.__indirect(this.bb_pos + offset), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @param onnxruntime.experimental.fbs.Tensor= obj\n     * @returns onnxruntime.experimental.fbs.Tensor|null\n     */\n    indices(obj?: onnxruntime.experimental.fbs.Tensor): onnxruntime.experimental.fbs.Tensor|null {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? (obj || new onnxruntime.experimental.fbs.Tensor())\n                          .__init(this.bb!.__indirect(this.bb_pos + offset), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @param number index\n     * @returns flatbuffers.Long\n     */\n    dims(index: number): flatbuffers.Long|null {\n      let offset = this.bb!.__offset(this.bb_pos, 8);\n      return offset ? this.bb!.readInt64(this.bb!.__vector(this.bb_pos + offset) + index * 8) :\n                      this.bb!.createLong(0, 0);\n    }\n\n    /**\n     * @returns number\n     */\n    dimsLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 8);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startSparseTensor(builder: flatbuffers.Builder) {\n      builder.startObject(3);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset valuesOffset\n     */\n    static addValues(builder: flatbuffers.Builder, valuesOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(0, valuesOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset indicesOffset\n     */\n    static addIndices(builder: flatbuffers.Builder, indicesOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(1, indicesOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset dimsOffset\n     */\n    static addDims(builder: flatbuffers.Builder, dimsOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(2, dimsOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Long> data\n     * @returns flatbuffers.Offset\n     */\n    static createDimsVector(builder: flatbuffers.Builder, data: flatbuffers.Long[]): flatbuffers.Offset {\n      builder.startVector(8, data.length, 8);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addInt64(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startDimsVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(8, numElems, 8);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endSparseTensor(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createSparseTensor(\n        builder: flatbuffers.Builder, valuesOffset: flatbuffers.Offset, indicesOffset: flatbuffers.Offset,\n        dimsOffset: flatbuffers.Offset): flatbuffers.Offset {\n      SparseTensor.startSparseTensor(builder);\n      SparseTensor.addValues(builder, valuesOffset);\n      SparseTensor.addIndices(builder, indicesOffset);\n      SparseTensor.addDims(builder, dimsOffset);\n      return SparseTensor.endSparseTensor(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class Attribute {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns Attribute\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): Attribute {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param Attribute= obj\n     * @returns Attribute\n     */\n    static getRootAsAttribute(bb: flatbuffers.ByteBuffer, obj?: Attribute): Attribute {\n      return (obj || new Attribute()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param Attribute= obj\n     * @returns Attribute\n     */\n    static getSizePrefixedRootAsAttribute(bb: flatbuffers.ByteBuffer, obj?: Attribute): Attribute {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new Attribute()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    name(): string|null;\n    name(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    name(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    docString(): string|null;\n    docString(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    docString(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @returns onnxruntime.experimental.fbs.AttributeType\n     */\n    type(): onnxruntime.experimental.fbs.AttributeType {\n      let offset = this.bb!.__offset(this.bb_pos, 8);\n      return offset ? /**  */ (this.bb!.readInt32(this.bb_pos + offset)) :\n                      onnxruntime.experimental.fbs.AttributeType.UNDEFINED;\n    }\n\n    /**\n     * @returns number\n     */\n    f(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 10);\n      return offset ? this.bb!.readFloat32(this.bb_pos + offset) : 0.0;\n    }\n\n    /**\n     * @returns flatbuffers.Long\n     */\n    i(): flatbuffers.Long {\n      let offset = this.bb!.__offset(this.bb_pos, 12);\n      return offset ? this.bb!.readInt64(this.bb_pos + offset) : this.bb!.createLong(0, 0);\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    s(): string|null;\n    s(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    s(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 14);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param onnxruntime.experimental.fbs.Tensor= obj\n     * @returns onnxruntime.experimental.fbs.Tensor|null\n     */\n    t(obj?: onnxruntime.experimental.fbs.Tensor): onnxruntime.experimental.fbs.Tensor|null {\n      let offset = this.bb!.__offset(this.bb_pos, 16);\n      return offset ? (obj || new onnxruntime.experimental.fbs.Tensor())\n                          .__init(this.bb!.__indirect(this.bb_pos + offset), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @param onnxruntime.experimental.fbs.Graph= obj\n     * @returns onnxruntime.experimental.fbs.Graph|null\n     */\n    g(obj?: onnxruntime.experimental.fbs.Graph): onnxruntime.experimental.fbs.Graph|null {\n      let offset = this.bb!.__offset(this.bb_pos, 18);\n      return offset ? (obj || new onnxruntime.experimental.fbs.Graph())\n                          .__init(this.bb!.__indirect(this.bb_pos + offset), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @param number index\n     * @returns number\n     */\n    floats(index: number): number|null {\n      let offset = this.bb!.__offset(this.bb_pos, 20);\n      return offset ? this.bb!.readFloat32(this.bb!.__vector(this.bb_pos + offset) + index * 4) : 0;\n    }\n\n    /**\n     * @returns number\n     */\n    floatsLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 20);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @returns Float32Array\n     */\n    floatsArray(): Float32Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 20);\n      return offset ?\n          new Float32Array(\n              this.bb!.bytes().buffer, this.bb!.bytes().byteOffset + this.bb!.__vector(this.bb_pos + offset),\n              this.bb!.__vector_len(this.bb_pos + offset)) :\n          null;\n    }\n\n    /**\n     * @param number index\n     * @returns flatbuffers.Long\n     */\n    ints(index: number): flatbuffers.Long|null {\n      let offset = this.bb!.__offset(this.bb_pos, 22);\n      return offset ? this.bb!.readInt64(this.bb!.__vector(this.bb_pos + offset) + index * 8) :\n                      this.bb!.createLong(0, 0);\n    }\n\n    /**\n     * @returns number\n     */\n    intsLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 22);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param number index\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array\n     */\n    strings(index: number): string;\n    strings(index: number, optionalEncoding: flatbuffers.Encoding): string|Uint8Array;\n    strings(index: number, optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 24);\n      return offset ? this.bb!.__string(this.bb!.__vector(this.bb_pos + offset) + index * 4, optionalEncoding) : null;\n    }\n\n    /**\n     * @returns number\n     */\n    stringsLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 24);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param number index\n     * @param onnxruntime.experimental.fbs.Tensor= obj\n     * @returns onnxruntime.experimental.fbs.Tensor\n     */\n    tensors(index: number, obj?: onnxruntime.experimental.fbs.Tensor): onnxruntime.experimental.fbs.Tensor|null {\n      let offset = this.bb!.__offset(this.bb_pos, 26);\n      return offset ? (obj || new onnxruntime.experimental.fbs.Tensor())\n                          .__init(this.bb!.__indirect(this.bb!.__vector(this.bb_pos + offset) + index * 4), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @returns number\n     */\n    tensorsLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 26);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param number index\n     * @param onnxruntime.experimental.fbs.Graph= obj\n     * @returns onnxruntime.experimental.fbs.Graph\n     */\n    graphs(index: number, obj?: onnxruntime.experimental.fbs.Graph): onnxruntime.experimental.fbs.Graph|null {\n      let offset = this.bb!.__offset(this.bb_pos, 28);\n      return offset ? (obj || new onnxruntime.experimental.fbs.Graph())\n                          .__init(this.bb!.__indirect(this.bb!.__vector(this.bb_pos + offset) + index * 4), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @returns number\n     */\n    graphsLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 28);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startAttribute(builder: flatbuffers.Builder) {\n      builder.startObject(13);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset nameOffset\n     */\n    static addName(builder: flatbuffers.Builder, nameOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(0, nameOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset docStringOffset\n     */\n    static addDocString(builder: flatbuffers.Builder, docStringOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(1, docStringOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param onnxruntime.experimental.fbs.AttributeType type\n     */\n    static addType(builder: flatbuffers.Builder, type: onnxruntime.experimental.fbs.AttributeType) {\n      builder.addFieldInt32(2, type, onnxruntime.experimental.fbs.AttributeType.UNDEFINED);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number f\n     */\n    static addF(builder: flatbuffers.Builder, f: number) {\n      builder.addFieldFloat32(3, f, 0.0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Long i\n     */\n    static addI(builder: flatbuffers.Builder, i: flatbuffers.Long) {\n      builder.addFieldInt64(4, i, builder.createLong(0, 0));\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset sOffset\n     */\n    static addS(builder: flatbuffers.Builder, sOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(5, sOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset tOffset\n     */\n    static addT(builder: flatbuffers.Builder, tOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(6, tOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset gOffset\n     */\n    static addG(builder: flatbuffers.Builder, gOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(7, gOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset floatsOffset\n     */\n    static addFloats(builder: flatbuffers.Builder, floatsOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(8, floatsOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<number> data\n     * @returns flatbuffers.Offset\n     */\n    static createFloatsVector(builder: flatbuffers.Builder, data: number[]|Uint8Array): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addFloat32(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startFloatsVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset intsOffset\n     */\n    static addInts(builder: flatbuffers.Builder, intsOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(9, intsOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Long> data\n     * @returns flatbuffers.Offset\n     */\n    static createIntsVector(builder: flatbuffers.Builder, data: flatbuffers.Long[]): flatbuffers.Offset {\n      builder.startVector(8, data.length, 8);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addInt64(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startIntsVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(8, numElems, 8);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset stringsOffset\n     */\n    static addStrings(builder: flatbuffers.Builder, stringsOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(10, stringsOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createStringsVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startStringsVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset tensorsOffset\n     */\n    static addTensors(builder: flatbuffers.Builder, tensorsOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(11, tensorsOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createTensorsVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startTensorsVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset graphsOffset\n     */\n    static addGraphs(builder: flatbuffers.Builder, graphsOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(12, graphsOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createGraphsVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startGraphsVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endAttribute(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createAttribute(\n        builder: flatbuffers.Builder, nameOffset: flatbuffers.Offset, docStringOffset: flatbuffers.Offset,\n        type: onnxruntime.experimental.fbs.AttributeType, f: number, i: flatbuffers.Long, sOffset: flatbuffers.Offset,\n        tOffset: flatbuffers.Offset, gOffset: flatbuffers.Offset, floatsOffset: flatbuffers.Offset,\n        intsOffset: flatbuffers.Offset, stringsOffset: flatbuffers.Offset, tensorsOffset: flatbuffers.Offset,\n        graphsOffset: flatbuffers.Offset): flatbuffers.Offset {\n      Attribute.startAttribute(builder);\n      Attribute.addName(builder, nameOffset);\n      Attribute.addDocString(builder, docStringOffset);\n      Attribute.addType(builder, type);\n      Attribute.addF(builder, f);\n      Attribute.addI(builder, i);\n      Attribute.addS(builder, sOffset);\n      Attribute.addT(builder, tOffset);\n      Attribute.addG(builder, gOffset);\n      Attribute.addFloats(builder, floatsOffset);\n      Attribute.addInts(builder, intsOffset);\n      Attribute.addStrings(builder, stringsOffset);\n      Attribute.addTensors(builder, tensorsOffset);\n      Attribute.addGraphs(builder, graphsOffset);\n      return Attribute.endAttribute(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class Graph {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns Graph\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): Graph {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param Graph= obj\n     * @returns Graph\n     */\n    static getRootAsGraph(bb: flatbuffers.ByteBuffer, obj?: Graph): Graph {\n      return (obj || new Graph()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param Graph= obj\n     * @returns Graph\n     */\n    static getSizePrefixedRootAsGraph(bb: flatbuffers.ByteBuffer, obj?: Graph): Graph {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new Graph()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param number index\n     * @param onnxruntime.experimental.fbs.Tensor= obj\n     * @returns onnxruntime.experimental.fbs.Tensor\n     */\n    initializers(index: number, obj?: onnxruntime.experimental.fbs.Tensor): onnxruntime.experimental.fbs.Tensor|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? (obj || new onnxruntime.experimental.fbs.Tensor())\n                          .__init(this.bb!.__indirect(this.bb!.__vector(this.bb_pos + offset) + index * 4), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @returns number\n     */\n    initializersLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param number index\n     * @param onnxruntime.experimental.fbs.ValueInfo= obj\n     * @returns onnxruntime.experimental.fbs.ValueInfo\n     */\n    nodeArgs(index: number, obj?: onnxruntime.experimental.fbs.ValueInfo): onnxruntime.experimental.fbs.ValueInfo|null {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? (obj || new onnxruntime.experimental.fbs.ValueInfo())\n                          .__init(this.bb!.__indirect(this.bb!.__vector(this.bb_pos + offset) + index * 4), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @returns number\n     */\n    nodeArgsLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param number index\n     * @param onnxruntime.experimental.fbs.Node= obj\n     * @returns onnxruntime.experimental.fbs.Node\n     */\n    nodes(index: number, obj?: onnxruntime.experimental.fbs.Node): onnxruntime.experimental.fbs.Node|null {\n      let offset = this.bb!.__offset(this.bb_pos, 8);\n      return offset ? (obj || new onnxruntime.experimental.fbs.Node())\n                          .__init(this.bb!.__indirect(this.bb!.__vector(this.bb_pos + offset) + index * 4), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @returns number\n     */\n    nodesLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 8);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @returns number\n     */\n    maxNodeIndex(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 10);\n      return offset ? this.bb!.readUint32(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param number index\n     * @param onnxruntime.experimental.fbs.NodeEdge= obj\n     * @returns onnxruntime.experimental.fbs.NodeEdge\n     */\n    nodeEdges(index: number, obj?: onnxruntime.experimental.fbs.NodeEdge): onnxruntime.experimental.fbs.NodeEdge|null {\n      let offset = this.bb!.__offset(this.bb_pos, 12);\n      return offset ? (obj || new onnxruntime.experimental.fbs.NodeEdge())\n                          .__init(this.bb!.__indirect(this.bb!.__vector(this.bb_pos + offset) + index * 4), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @returns number\n     */\n    nodeEdgesLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 12);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param number index\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array\n     */\n    inputs(index: number): string;\n    inputs(index: number, optionalEncoding: flatbuffers.Encoding): string|Uint8Array;\n    inputs(index: number, optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 14);\n      return offset ? this.bb!.__string(this.bb!.__vector(this.bb_pos + offset) + index * 4, optionalEncoding) : null;\n    }\n\n    /**\n     * @returns number\n     */\n    inputsLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 14);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param number index\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array\n     */\n    outputs(index: number): string;\n    outputs(index: number, optionalEncoding: flatbuffers.Encoding): string|Uint8Array;\n    outputs(index: number, optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 16);\n      return offset ? this.bb!.__string(this.bb!.__vector(this.bb_pos + offset) + index * 4, optionalEncoding) : null;\n    }\n\n    /**\n     * @returns number\n     */\n    outputsLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 16);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param number index\n     * @param onnxruntime.experimental.fbs.SparseTensor= obj\n     * @returns onnxruntime.experimental.fbs.SparseTensor\n     */\n    sparseInitializers(index: number, obj?: onnxruntime.experimental.fbs.SparseTensor):\n        onnxruntime.experimental.fbs.SparseTensor|null {\n      let offset = this.bb!.__offset(this.bb_pos, 18);\n      return offset ? (obj || new onnxruntime.experimental.fbs.SparseTensor())\n                          .__init(this.bb!.__indirect(this.bb!.__vector(this.bb_pos + offset) + index * 4), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @returns number\n     */\n    sparseInitializersLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 18);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startGraph(builder: flatbuffers.Builder) {\n      builder.startObject(8);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset initializersOffset\n     */\n    static addInitializers(builder: flatbuffers.Builder, initializersOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(0, initializersOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createInitializersVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startInitializersVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset nodeArgsOffset\n     */\n    static addNodeArgs(builder: flatbuffers.Builder, nodeArgsOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(1, nodeArgsOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createNodeArgsVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startNodeArgsVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset nodesOffset\n     */\n    static addNodes(builder: flatbuffers.Builder, nodesOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(2, nodesOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createNodesVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startNodesVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number maxNodeIndex\n     */\n    static addMaxNodeIndex(builder: flatbuffers.Builder, maxNodeIndex: number) {\n      builder.addFieldInt32(3, maxNodeIndex, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset nodeEdgesOffset\n     */\n    static addNodeEdges(builder: flatbuffers.Builder, nodeEdgesOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(4, nodeEdgesOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createNodeEdgesVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startNodeEdgesVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset inputsOffset\n     */\n    static addInputs(builder: flatbuffers.Builder, inputsOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(5, inputsOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createInputsVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startInputsVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset outputsOffset\n     */\n    static addOutputs(builder: flatbuffers.Builder, outputsOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(6, outputsOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createOutputsVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startOutputsVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset sparseInitializersOffset\n     */\n    static addSparseInitializers(builder: flatbuffers.Builder, sparseInitializersOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(7, sparseInitializersOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createSparseInitializersVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]):\n        flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startSparseInitializersVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endGraph(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createGraph(\n        builder: flatbuffers.Builder, initializersOffset: flatbuffers.Offset, nodeArgsOffset: flatbuffers.Offset,\n        nodesOffset: flatbuffers.Offset, maxNodeIndex: number, nodeEdgesOffset: flatbuffers.Offset,\n        inputsOffset: flatbuffers.Offset, outputsOffset: flatbuffers.Offset,\n        sparseInitializersOffset: flatbuffers.Offset): flatbuffers.Offset {\n      Graph.startGraph(builder);\n      Graph.addInitializers(builder, initializersOffset);\n      Graph.addNodeArgs(builder, nodeArgsOffset);\n      Graph.addNodes(builder, nodesOffset);\n      Graph.addMaxNodeIndex(builder, maxNodeIndex);\n      Graph.addNodeEdges(builder, nodeEdgesOffset);\n      Graph.addInputs(builder, inputsOffset);\n      Graph.addOutputs(builder, outputsOffset);\n      Graph.addSparseInitializers(builder, sparseInitializersOffset);\n      return Graph.endGraph(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class Model {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns Model\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): Model {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param Model= obj\n     * @returns Model\n     */\n    static getRootAsModel(bb: flatbuffers.ByteBuffer, obj?: Model): Model {\n      return (obj || new Model()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param Model= obj\n     * @returns Model\n     */\n    static getSizePrefixedRootAsModel(bb: flatbuffers.ByteBuffer, obj?: Model): Model {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new Model()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @returns flatbuffers.Long\n     */\n    irVersion(): flatbuffers.Long {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? this.bb!.readInt64(this.bb_pos + offset) : this.bb!.createLong(0, 0);\n    }\n\n    /**\n     * @param number index\n     * @param onnxruntime.experimental.fbs.OperatorSetId= obj\n     * @returns onnxruntime.experimental.fbs.OperatorSetId\n     */\n    opsetImport(index: number, obj?: onnxruntime.experimental.fbs.OperatorSetId):\n        onnxruntime.experimental.fbs.OperatorSetId|null {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? (obj || new onnxruntime.experimental.fbs.OperatorSetId())\n                          .__init(this.bb!.__indirect(this.bb!.__vector(this.bb_pos + offset) + index * 4), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @returns number\n     */\n    opsetImportLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    producerName(): string|null;\n    producerName(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    producerName(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 8);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    producerVersion(): string|null;\n    producerVersion(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    producerVersion(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 10);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    domain(): string|null;\n    domain(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    domain(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 12);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @returns flatbuffers.Long\n     */\n    modelVersion(): flatbuffers.Long {\n      let offset = this.bb!.__offset(this.bb_pos, 14);\n      return offset ? this.bb!.readInt64(this.bb_pos + offset) : this.bb!.createLong(0, 0);\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    docString(): string|null;\n    docString(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    docString(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 16);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param onnxruntime.experimental.fbs.Graph= obj\n     * @returns onnxruntime.experimental.fbs.Graph|null\n     */\n    graph(obj?: onnxruntime.experimental.fbs.Graph): onnxruntime.experimental.fbs.Graph|null {\n      let offset = this.bb!.__offset(this.bb_pos, 18);\n      return offset ? (obj || new onnxruntime.experimental.fbs.Graph())\n                          .__init(this.bb!.__indirect(this.bb_pos + offset), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    graphDocString(): string|null;\n    graphDocString(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    graphDocString(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 20);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startModel(builder: flatbuffers.Builder) {\n      builder.startObject(9);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Long irVersion\n     */\n    static addIrVersion(builder: flatbuffers.Builder, irVersion: flatbuffers.Long) {\n      builder.addFieldInt64(0, irVersion, builder.createLong(0, 0));\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset opsetImportOffset\n     */\n    static addOpsetImport(builder: flatbuffers.Builder, opsetImportOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(1, opsetImportOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createOpsetImportVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startOpsetImportVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset producerNameOffset\n     */\n    static addProducerName(builder: flatbuffers.Builder, producerNameOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(2, producerNameOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset producerVersionOffset\n     */\n    static addProducerVersion(builder: flatbuffers.Builder, producerVersionOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(3, producerVersionOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset domainOffset\n     */\n    static addDomain(builder: flatbuffers.Builder, domainOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(4, domainOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Long modelVersion\n     */\n    static addModelVersion(builder: flatbuffers.Builder, modelVersion: flatbuffers.Long) {\n      builder.addFieldInt64(5, modelVersion, builder.createLong(0, 0));\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset docStringOffset\n     */\n    static addDocString(builder: flatbuffers.Builder, docStringOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(6, docStringOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset graphOffset\n     */\n    static addGraph(builder: flatbuffers.Builder, graphOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(7, graphOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset graphDocStringOffset\n     */\n    static addGraphDocString(builder: flatbuffers.Builder, graphDocStringOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(8, graphDocStringOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endModel(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createModel(\n        builder: flatbuffers.Builder, irVersion: flatbuffers.Long, opsetImportOffset: flatbuffers.Offset,\n        producerNameOffset: flatbuffers.Offset, producerVersionOffset: flatbuffers.Offset,\n        domainOffset: flatbuffers.Offset, modelVersion: flatbuffers.Long, docStringOffset: flatbuffers.Offset,\n        graphOffset: flatbuffers.Offset, graphDocStringOffset: flatbuffers.Offset): flatbuffers.Offset {\n      Model.startModel(builder);\n      Model.addIrVersion(builder, irVersion);\n      Model.addOpsetImport(builder, opsetImportOffset);\n      Model.addProducerName(builder, producerNameOffset);\n      Model.addProducerVersion(builder, producerVersionOffset);\n      Model.addDomain(builder, domainOffset);\n      Model.addModelVersion(builder, modelVersion);\n      Model.addDocString(builder, docStringOffset);\n      Model.addGraph(builder, graphOffset);\n      Model.addGraphDocString(builder, graphDocStringOffset);\n      return Model.endModel(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class KernelCreateInfos {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns KernelCreateInfos\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): KernelCreateInfos {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param KernelCreateInfos= obj\n     * @returns KernelCreateInfos\n     */\n    static getRootAsKernelCreateInfos(bb: flatbuffers.ByteBuffer, obj?: KernelCreateInfos): KernelCreateInfos {\n      return (obj || new KernelCreateInfos()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param KernelCreateInfos= obj\n     * @returns KernelCreateInfos\n     */\n    static getSizePrefixedRootAsKernelCreateInfos(bb: flatbuffers.ByteBuffer, obj?: KernelCreateInfos):\n        KernelCreateInfos {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new KernelCreateInfos()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param number index\n     * @returns number\n     */\n    nodeIndices(index: number): number|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? this.bb!.readUint32(this.bb!.__vector(this.bb_pos + offset) + index * 4) : 0;\n    }\n\n    /**\n     * @returns number\n     */\n    nodeIndicesLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @returns Uint32Array\n     */\n    nodeIndicesArray(): Uint32Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ?\n          new Uint32Array(\n              this.bb!.bytes().buffer, this.bb!.bytes().byteOffset + this.bb!.__vector(this.bb_pos + offset),\n              this.bb!.__vector_len(this.bb_pos + offset)) :\n          null;\n    }\n\n    /**\n     * @param number index\n     * @returns flatbuffers.Long\n     */\n    kernelDefHashes(index: number): flatbuffers.Long|null {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? this.bb!.readUint64(this.bb!.__vector(this.bb_pos + offset) + index * 8) :\n                      this.bb!.createLong(0, 0);\n    }\n\n    /**\n     * @returns number\n     */\n    kernelDefHashesLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startKernelCreateInfos(builder: flatbuffers.Builder) {\n      builder.startObject(2);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset nodeIndicesOffset\n     */\n    static addNodeIndices(builder: flatbuffers.Builder, nodeIndicesOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(0, nodeIndicesOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<number> data\n     * @returns flatbuffers.Offset\n     */\n    static createNodeIndicesVector(builder: flatbuffers.Builder, data: number[]|Uint8Array): flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addInt32(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startNodeIndicesVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset kernelDefHashesOffset\n     */\n    static addKernelDefHashes(builder: flatbuffers.Builder, kernelDefHashesOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(1, kernelDefHashesOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Long> data\n     * @returns flatbuffers.Offset\n     */\n    static createKernelDefHashesVector(builder: flatbuffers.Builder, data: flatbuffers.Long[]): flatbuffers.Offset {\n      builder.startVector(8, data.length, 8);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addInt64(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startKernelDefHashesVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(8, numElems, 8);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endKernelCreateInfos(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createKernelCreateInfos(\n        builder: flatbuffers.Builder, nodeIndicesOffset: flatbuffers.Offset,\n        kernelDefHashesOffset: flatbuffers.Offset): flatbuffers.Offset {\n      KernelCreateInfos.startKernelCreateInfos(builder);\n      KernelCreateInfos.addNodeIndices(builder, nodeIndicesOffset);\n      KernelCreateInfos.addKernelDefHashes(builder, kernelDefHashesOffset);\n      return KernelCreateInfos.endKernelCreateInfos(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class SubGraphSessionState {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns SubGraphSessionState\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): SubGraphSessionState {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param SubGraphSessionState= obj\n     * @returns SubGraphSessionState\n     */\n    static getRootAsSubGraphSessionState(bb: flatbuffers.ByteBuffer, obj?: SubGraphSessionState): SubGraphSessionState {\n      return (obj || new SubGraphSessionState()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param SubGraphSessionState= obj\n     * @returns SubGraphSessionState\n     */\n    static getSizePrefixedRootAsSubGraphSessionState(bb: flatbuffers.ByteBuffer, obj?: SubGraphSessionState):\n        SubGraphSessionState {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new SubGraphSessionState()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    graphId(): string|null;\n    graphId(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    graphId(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param onnxruntime.experimental.fbs.SessionState= obj\n     * @returns onnxruntime.experimental.fbs.SessionState|null\n     */\n    sessionState(obj?: onnxruntime.experimental.fbs.SessionState): onnxruntime.experimental.fbs.SessionState|null {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? (obj || new onnxruntime.experimental.fbs.SessionState())\n                          .__init(this.bb!.__indirect(this.bb_pos + offset), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startSubGraphSessionState(builder: flatbuffers.Builder) {\n      builder.startObject(2);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset graphIdOffset\n     */\n    static addGraphId(builder: flatbuffers.Builder, graphIdOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(0, graphIdOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset sessionStateOffset\n     */\n    static addSessionState(builder: flatbuffers.Builder, sessionStateOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(1, sessionStateOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endSubGraphSessionState(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      builder.requiredField(offset, 4);  // graph_id\n      return offset;\n    }\n\n    static createSubGraphSessionState(\n        builder: flatbuffers.Builder, graphIdOffset: flatbuffers.Offset,\n        sessionStateOffset: flatbuffers.Offset): flatbuffers.Offset {\n      SubGraphSessionState.startSubGraphSessionState(builder);\n      SubGraphSessionState.addGraphId(builder, graphIdOffset);\n      SubGraphSessionState.addSessionState(builder, sessionStateOffset);\n      return SubGraphSessionState.endSubGraphSessionState(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class SessionState {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns SessionState\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): SessionState {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param SessionState= obj\n     * @returns SessionState\n     */\n    static getRootAsSessionState(bb: flatbuffers.ByteBuffer, obj?: SessionState): SessionState {\n      return (obj || new SessionState()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param SessionState= obj\n     * @returns SessionState\n     */\n    static getSizePrefixedRootAsSessionState(bb: flatbuffers.ByteBuffer, obj?: SessionState): SessionState {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new SessionState()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param onnxruntime.experimental.fbs.KernelCreateInfos= obj\n     * @returns onnxruntime.experimental.fbs.KernelCreateInfos|null\n     */\n    kernels(obj?: onnxruntime.experimental.fbs.KernelCreateInfos): onnxruntime.experimental.fbs.KernelCreateInfos|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? (obj || new onnxruntime.experimental.fbs.KernelCreateInfos())\n                          .__init(this.bb!.__indirect(this.bb_pos + offset), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @param number index\n     * @param onnxruntime.experimental.fbs.SubGraphSessionState= obj\n     * @returns onnxruntime.experimental.fbs.SubGraphSessionState\n     */\n    subGraphSessionStates(index: number, obj?: onnxruntime.experimental.fbs.SubGraphSessionState):\n        onnxruntime.experimental.fbs.SubGraphSessionState|null {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? (obj || new onnxruntime.experimental.fbs.SubGraphSessionState())\n                          .__init(this.bb!.__indirect(this.bb!.__vector(this.bb_pos + offset) + index * 4), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @returns number\n     */\n    subGraphSessionStatesLength(): number {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? this.bb!.__vector_len(this.bb_pos + offset) : 0;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startSessionState(builder: flatbuffers.Builder) {\n      builder.startObject(2);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset kernelsOffset\n     */\n    static addKernels(builder: flatbuffers.Builder, kernelsOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(0, kernelsOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset subGraphSessionStatesOffset\n     */\n    static addSubGraphSessionStates(builder: flatbuffers.Builder, subGraphSessionStatesOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(1, subGraphSessionStatesOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param Array.<flatbuffers.Offset> data\n     * @returns flatbuffers.Offset\n     */\n    static createSubGraphSessionStatesVector(builder: flatbuffers.Builder, data: flatbuffers.Offset[]):\n        flatbuffers.Offset {\n      builder.startVector(4, data.length, 4);\n      for (let i = data.length - 1; i >= 0; i--) {\n        builder.addOffset(data[i]);\n      }\n      return builder.endVector();\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param number numElems\n     */\n    static startSubGraphSessionStatesVector(builder: flatbuffers.Builder, numElems: number) {\n      builder.startVector(4, numElems, 4);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endSessionState(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    static createSessionState(\n        builder: flatbuffers.Builder, kernelsOffset: flatbuffers.Offset,\n        subGraphSessionStatesOffset: flatbuffers.Offset): flatbuffers.Offset {\n      SessionState.startSessionState(builder);\n      SessionState.addKernels(builder, kernelsOffset);\n      SessionState.addSubGraphSessionStates(builder, subGraphSessionStatesOffset);\n      return SessionState.endSessionState(builder);\n    }\n  }\n}\n/**\n * @constructor\n */\nexport namespace onnxruntime.experimental.fbs {\n  export class InferenceSession {\n    bb: flatbuffers.ByteBuffer|null = null;\n\n    bb_pos = 0;\n    /**\n     * @param number i\n     * @param flatbuffers.ByteBuffer bb\n     * @returns InferenceSession\n     */\n    __init(i: number, bb: flatbuffers.ByteBuffer): InferenceSession {\n      this.bb_pos = i;\n      this.bb = bb;\n      return this;\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param InferenceSession= obj\n     * @returns InferenceSession\n     */\n    static getRootAsInferenceSession(bb: flatbuffers.ByteBuffer, obj?: InferenceSession): InferenceSession {\n      return (obj || new InferenceSession()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @param InferenceSession= obj\n     * @returns InferenceSession\n     */\n    static getSizePrefixedRootAsInferenceSession(bb: flatbuffers.ByteBuffer, obj?: InferenceSession): InferenceSession {\n      bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);\n      return (obj || new InferenceSession()).__init(bb.readInt32(bb.position()) + bb.position(), bb);\n    }\n\n    /**\n     * @param flatbuffers.ByteBuffer bb\n     * @returns boolean\n     */\n    static bufferHasIdentifier(bb: flatbuffers.ByteBuffer): boolean {\n      return bb.__has_identifier('ORTM');\n    }\n\n    /**\n     * @param flatbuffers.Encoding= optionalEncoding\n     * @returns string|Uint8Array|null\n     */\n    ortVersion(): string|null;\n    ortVersion(optionalEncoding: flatbuffers.Encoding): string|Uint8Array|null;\n    ortVersion(optionalEncoding?: any): string|Uint8Array|null {\n      let offset = this.bb!.__offset(this.bb_pos, 4);\n      return offset ? this.bb!.__string(this.bb_pos + offset, optionalEncoding) : null;\n    }\n\n    /**\n     * @param onnxruntime.experimental.fbs.Model= obj\n     * @returns onnxruntime.experimental.fbs.Model|null\n     */\n    model(obj?: onnxruntime.experimental.fbs.Model): onnxruntime.experimental.fbs.Model|null {\n      let offset = this.bb!.__offset(this.bb_pos, 6);\n      return offset ? (obj || new onnxruntime.experimental.fbs.Model())\n                          .__init(this.bb!.__indirect(this.bb_pos + offset), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @param onnxruntime.experimental.fbs.SessionState= obj\n     * @returns onnxruntime.experimental.fbs.SessionState|null\n     */\n    sessionState(obj?: onnxruntime.experimental.fbs.SessionState): onnxruntime.experimental.fbs.SessionState|null {\n      let offset = this.bb!.__offset(this.bb_pos, 8);\n      return offset ? (obj || new onnxruntime.experimental.fbs.SessionState())\n                          .__init(this.bb!.__indirect(this.bb_pos + offset), this.bb!) :\n                      null;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     */\n    static startInferenceSession(builder: flatbuffers.Builder) {\n      builder.startObject(3);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset ortVersionOffset\n     */\n    static addOrtVersion(builder: flatbuffers.Builder, ortVersionOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(0, ortVersionOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset modelOffset\n     */\n    static addModel(builder: flatbuffers.Builder, modelOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(1, modelOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset sessionStateOffset\n     */\n    static addSessionState(builder: flatbuffers.Builder, sessionStateOffset: flatbuffers.Offset) {\n      builder.addFieldOffset(2, sessionStateOffset, 0);\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @returns flatbuffers.Offset\n     */\n    static endInferenceSession(builder: flatbuffers.Builder): flatbuffers.Offset {\n      let offset = builder.endObject();\n      return offset;\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset offset\n     */\n    static finishInferenceSessionBuffer(builder: flatbuffers.Builder, offset: flatbuffers.Offset) {\n      builder.finish(offset, 'ORTM');\n    }\n\n    /**\n     * @param flatbuffers.Builder builder\n     * @param flatbuffers.Offset offset\n     */\n    static finishSizePrefixedInferenceSessionBuffer(builder: flatbuffers.Builder, offset: flatbuffers.Offset) {\n      builder.finish(offset, 'ORTM', true);\n    }\n\n    static createInferenceSession(\n        builder: flatbuffers.Builder, ortVersionOffset: flatbuffers.Offset, modelOffset: flatbuffers.Offset,\n        sessionStateOffset: flatbuffers.Offset): flatbuffers.Offset {\n      InferenceSession.startInferenceSession(builder);\n      InferenceSession.addOrtVersion(builder, ortVersionOffset);\n      InferenceSession.addModel(builder, modelOffset);\n      InferenceSession.addSessionState(builder, sessionStateOffset);\n      return InferenceSession.endInferenceSession(builder);\n    }\n  }\n}\n","\"use strict\";\r\nmodule.exports = asPromise;\r\n\r\n/**\r\n * Callback as used by {@link util.asPromise}.\r\n * @typedef asPromiseCallback\r\n * @type {function}\r\n * @param {Error|null} error Error, if any\r\n * @param {...*} params Additional arguments\r\n * @returns {undefined}\r\n */\r\n\r\n/**\r\n * Returns a promise from a node-style callback function.\r\n * @memberof util\r\n * @param {asPromiseCallback} fn Function to call\r\n * @param {*} ctx Function context\r\n * @param {...*} params Function arguments\r\n * @returns {Promise<*>} Promisified function\r\n */\r\nfunction asPromise(fn, ctx/*, varargs */) {\r\n    var params  = new Array(arguments.length - 1),\r\n        offset  = 0,\r\n        index   = 2,\r\n        pending = true;\r\n    while (index < arguments.length)\r\n        params[offset++] = arguments[index++];\r\n    return new Promise(function executor(resolve, reject) {\r\n        params[offset] = function callback(err/*, varargs */) {\r\n            if (pending) {\r\n                pending = false;\r\n                if (err)\r\n                    reject(err);\r\n                else {\r\n                    var params = new Array(arguments.length - 1),\r\n                        offset = 0;\r\n                    while (offset < params.length)\r\n                        params[offset++] = arguments[offset];\r\n                    resolve.apply(null, params);\r\n                }\r\n            }\r\n        };\r\n        try {\r\n            fn.apply(ctx || null, params);\r\n        } catch (err) {\r\n            if (pending) {\r\n                pending = false;\r\n                reject(err);\r\n            }\r\n        }\r\n    });\r\n}\r\n","\"use strict\";\r\n\r\n/**\r\n * A minimal base64 implementation for number arrays.\r\n * @memberof util\r\n * @namespace\r\n */\r\nvar base64 = exports;\r\n\r\n/**\r\n * Calculates the byte length of a base64 encoded string.\r\n * @param {string} string Base64 encoded string\r\n * @returns {number} Byte length\r\n */\r\nbase64.length = function length(string) {\r\n    var p = string.length;\r\n    if (!p)\r\n        return 0;\r\n    var n = 0;\r\n    while (--p % 4 > 1 && string.charAt(p) === \"=\")\r\n        ++n;\r\n    return Math.ceil(string.length * 3) / 4 - n;\r\n};\r\n\r\n// Base64 encoding table\r\nvar b64 = new Array(64);\r\n\r\n// Base64 decoding table\r\nvar s64 = new Array(123);\r\n\r\n// 65..90, 97..122, 48..57, 43, 47\r\nfor (var i = 0; i < 64;)\r\n    s64[b64[i] = i < 26 ? i + 65 : i < 52 ? i + 71 : i < 62 ? i - 4 : i - 59 | 43] = i++;\r\n\r\n/**\r\n * Encodes a buffer to a base64 encoded string.\r\n * @param {Uint8Array} buffer Source buffer\r\n * @param {number} start Source start\r\n * @param {number} end Source end\r\n * @returns {string} Base64 encoded string\r\n */\r\nbase64.encode = function encode(buffer, start, end) {\r\n    var parts = null,\r\n        chunk = [];\r\n    var i = 0, // output index\r\n        j = 0, // goto index\r\n        t;     // temporary\r\n    while (start < end) {\r\n        var b = buffer[start++];\r\n        switch (j) {\r\n            case 0:\r\n                chunk[i++] = b64[b >> 2];\r\n                t = (b & 3) << 4;\r\n                j = 1;\r\n                break;\r\n            case 1:\r\n                chunk[i++] = b64[t | b >> 4];\r\n                t = (b & 15) << 2;\r\n                j = 2;\r\n                break;\r\n            case 2:\r\n                chunk[i++] = b64[t | b >> 6];\r\n                chunk[i++] = b64[b & 63];\r\n                j = 0;\r\n                break;\r\n        }\r\n        if (i > 8191) {\r\n            (parts || (parts = [])).push(String.fromCharCode.apply(String, chunk));\r\n            i = 0;\r\n        }\r\n    }\r\n    if (j) {\r\n        chunk[i++] = b64[t];\r\n        chunk[i++] = 61;\r\n        if (j === 1)\r\n            chunk[i++] = 61;\r\n    }\r\n    if (parts) {\r\n        if (i)\r\n            parts.push(String.fromCharCode.apply(String, chunk.slice(0, i)));\r\n        return parts.join(\"\");\r\n    }\r\n    return String.fromCharCode.apply(String, chunk.slice(0, i));\r\n};\r\n\r\nvar invalidEncoding = \"invalid encoding\";\r\n\r\n/**\r\n * Decodes a base64 encoded string to a buffer.\r\n * @param {string} string Source string\r\n * @param {Uint8Array} buffer Destination buffer\r\n * @param {number} offset Destination offset\r\n * @returns {number} Number of bytes written\r\n * @throws {Error} If encoding is invalid\r\n */\r\nbase64.decode = function decode(string, buffer, offset) {\r\n    var start = offset;\r\n    var j = 0, // goto index\r\n        t;     // temporary\r\n    for (var i = 0; i < string.length;) {\r\n        var c = string.charCodeAt(i++);\r\n        if (c === 61 && j > 1)\r\n            break;\r\n        if ((c = s64[c]) === undefined)\r\n            throw Error(invalidEncoding);\r\n        switch (j) {\r\n            case 0:\r\n                t = c;\r\n                j = 1;\r\n                break;\r\n            case 1:\r\n                buffer[offset++] = t << 2 | (c & 48) >> 4;\r\n                t = c;\r\n                j = 2;\r\n                break;\r\n            case 2:\r\n                buffer[offset++] = (t & 15) << 4 | (c & 60) >> 2;\r\n                t = c;\r\n                j = 3;\r\n                break;\r\n            case 3:\r\n                buffer[offset++] = (t & 3) << 6 | c;\r\n                j = 0;\r\n                break;\r\n        }\r\n    }\r\n    if (j === 1)\r\n        throw Error(invalidEncoding);\r\n    return offset - start;\r\n};\r\n\r\n/**\r\n * Tests if the specified string appears to be base64 encoded.\r\n * @param {string} string String to test\r\n * @returns {boolean} `true` if probably base64 encoded, otherwise false\r\n */\r\nbase64.test = function test(string) {\r\n    return /^(?:[A-Za-z0-9+/]{4})*(?:[A-Za-z0-9+/]{2}==|[A-Za-z0-9+/]{3}=)?$/.test(string);\r\n};\r\n","\"use strict\";\r\nmodule.exports = EventEmitter;\r\n\r\n/**\r\n * Constructs a new event emitter instance.\r\n * @classdesc A minimal event emitter.\r\n * @memberof util\r\n * @constructor\r\n */\r\nfunction EventEmitter() {\r\n\r\n    /**\r\n     * Registered listeners.\r\n     * @type {Object.<string,*>}\r\n     * @private\r\n     */\r\n    this._listeners = {};\r\n}\r\n\r\n/**\r\n * Registers an event listener.\r\n * @param {string} evt Event name\r\n * @param {function} fn Listener\r\n * @param {*} [ctx] Listener context\r\n * @returns {util.EventEmitter} `this`\r\n */\r\nEventEmitter.prototype.on = function on(evt, fn, ctx) {\r\n    (this._listeners[evt] || (this._listeners[evt] = [])).push({\r\n        fn  : fn,\r\n        ctx : ctx || this\r\n    });\r\n    return this;\r\n};\r\n\r\n/**\r\n * Removes an event listener or any matching listeners if arguments are omitted.\r\n * @param {string} [evt] Event name. Removes all listeners if omitted.\r\n * @param {function} [fn] Listener to remove. Removes all listeners of `evt` if omitted.\r\n * @returns {util.EventEmitter} `this`\r\n */\r\nEventEmitter.prototype.off = function off(evt, fn) {\r\n    if (evt === undefined)\r\n        this._listeners = {};\r\n    else {\r\n        if (fn === undefined)\r\n            this._listeners[evt] = [];\r\n        else {\r\n            var listeners = this._listeners[evt];\r\n            for (var i = 0; i < listeners.length;)\r\n                if (listeners[i].fn === fn)\r\n                    listeners.splice(i, 1);\r\n                else\r\n                    ++i;\r\n        }\r\n    }\r\n    return this;\r\n};\r\n\r\n/**\r\n * Emits an event by calling its listeners with the specified arguments.\r\n * @param {string} evt Event name\r\n * @param {...*} args Arguments\r\n * @returns {util.EventEmitter} `this`\r\n */\r\nEventEmitter.prototype.emit = function emit(evt) {\r\n    var listeners = this._listeners[evt];\r\n    if (listeners) {\r\n        var args = [],\r\n            i = 1;\r\n        for (; i < arguments.length;)\r\n            args.push(arguments[i++]);\r\n        for (i = 0; i < listeners.length;)\r\n            listeners[i].fn.apply(listeners[i++].ctx, args);\r\n    }\r\n    return this;\r\n};\r\n","\"use strict\";\r\n\r\nmodule.exports = factory(factory);\r\n\r\n/**\r\n * Reads / writes floats / doubles from / to buffers.\r\n * @name util.float\r\n * @namespace\r\n */\r\n\r\n/**\r\n * Writes a 32 bit float to a buffer using little endian byte order.\r\n * @name util.float.writeFloatLE\r\n * @function\r\n * @param {number} val Value to write\r\n * @param {Uint8Array} buf Target buffer\r\n * @param {number} pos Target buffer offset\r\n * @returns {undefined}\r\n */\r\n\r\n/**\r\n * Writes a 32 bit float to a buffer using big endian byte order.\r\n * @name util.float.writeFloatBE\r\n * @function\r\n * @param {number} val Value to write\r\n * @param {Uint8Array} buf Target buffer\r\n * @param {number} pos Target buffer offset\r\n * @returns {undefined}\r\n */\r\n\r\n/**\r\n * Reads a 32 bit float from a buffer using little endian byte order.\r\n * @name util.float.readFloatLE\r\n * @function\r\n * @param {Uint8Array} buf Source buffer\r\n * @param {number} pos Source buffer offset\r\n * @returns {number} Value read\r\n */\r\n\r\n/**\r\n * Reads a 32 bit float from a buffer using big endian byte order.\r\n * @name util.float.readFloatBE\r\n * @function\r\n * @param {Uint8Array} buf Source buffer\r\n * @param {number} pos Source buffer offset\r\n * @returns {number} Value read\r\n */\r\n\r\n/**\r\n * Writes a 64 bit double to a buffer using little endian byte order.\r\n * @name util.float.writeDoubleLE\r\n * @function\r\n * @param {number} val Value to write\r\n * @param {Uint8Array} buf Target buffer\r\n * @param {number} pos Target buffer offset\r\n * @returns {undefined}\r\n */\r\n\r\n/**\r\n * Writes a 64 bit double to a buffer using big endian byte order.\r\n * @name util.float.writeDoubleBE\r\n * @function\r\n * @param {number} val Value to write\r\n * @param {Uint8Array} buf Target buffer\r\n * @param {number} pos Target buffer offset\r\n * @returns {undefined}\r\n */\r\n\r\n/**\r\n * Reads a 64 bit double from a buffer using little endian byte order.\r\n * @name util.float.readDoubleLE\r\n * @function\r\n * @param {Uint8Array} buf Source buffer\r\n * @param {number} pos Source buffer offset\r\n * @returns {number} Value read\r\n */\r\n\r\n/**\r\n * Reads a 64 bit double from a buffer using big endian byte order.\r\n * @name util.float.readDoubleBE\r\n * @function\r\n * @param {Uint8Array} buf Source buffer\r\n * @param {number} pos Source buffer offset\r\n * @returns {number} Value read\r\n */\r\n\r\n// Factory function for the purpose of node-based testing in modified global environments\r\nfunction factory(exports) {\r\n\r\n    // float: typed array\r\n    if (typeof Float32Array !== \"undefined\") (function() {\r\n\r\n        var f32 = new Float32Array([ -0 ]),\r\n            f8b = new Uint8Array(f32.buffer),\r\n            le  = f8b[3] === 128;\r\n\r\n        function writeFloat_f32_cpy(val, buf, pos) {\r\n            f32[0] = val;\r\n            buf[pos    ] = f8b[0];\r\n            buf[pos + 1] = f8b[1];\r\n            buf[pos + 2] = f8b[2];\r\n            buf[pos + 3] = f8b[3];\r\n        }\r\n\r\n        function writeFloat_f32_rev(val, buf, pos) {\r\n            f32[0] = val;\r\n            buf[pos    ] = f8b[3];\r\n            buf[pos + 1] = f8b[2];\r\n            buf[pos + 2] = f8b[1];\r\n            buf[pos + 3] = f8b[0];\r\n        }\r\n\r\n        /* istanbul ignore next */\r\n        exports.writeFloatLE = le ? writeFloat_f32_cpy : writeFloat_f32_rev;\r\n        /* istanbul ignore next */\r\n        exports.writeFloatBE = le ? writeFloat_f32_rev : writeFloat_f32_cpy;\r\n\r\n        function readFloat_f32_cpy(buf, pos) {\r\n            f8b[0] = buf[pos    ];\r\n            f8b[1] = buf[pos + 1];\r\n            f8b[2] = buf[pos + 2];\r\n            f8b[3] = buf[pos + 3];\r\n            return f32[0];\r\n        }\r\n\r\n        function readFloat_f32_rev(buf, pos) {\r\n            f8b[3] = buf[pos    ];\r\n            f8b[2] = buf[pos + 1];\r\n            f8b[1] = buf[pos + 2];\r\n            f8b[0] = buf[pos + 3];\r\n            return f32[0];\r\n        }\r\n\r\n        /* istanbul ignore next */\r\n        exports.readFloatLE = le ? readFloat_f32_cpy : readFloat_f32_rev;\r\n        /* istanbul ignore next */\r\n        exports.readFloatBE = le ? readFloat_f32_rev : readFloat_f32_cpy;\r\n\r\n    // float: ieee754\r\n    })(); else (function() {\r\n\r\n        function writeFloat_ieee754(writeUint, val, buf, pos) {\r\n            var sign = val < 0 ? 1 : 0;\r\n            if (sign)\r\n                val = -val;\r\n            if (val === 0)\r\n                writeUint(1 / val > 0 ? /* positive */ 0 : /* negative 0 */ 2147483648, buf, pos);\r\n            else if (isNaN(val))\r\n                writeUint(2143289344, buf, pos);\r\n            else if (val > 3.4028234663852886e+38) // +-Infinity\r\n                writeUint((sign << 31 | 2139095040) >>> 0, buf, pos);\r\n            else if (val < 1.1754943508222875e-38) // denormal\r\n                writeUint((sign << 31 | Math.round(val / 1.401298464324817e-45)) >>> 0, buf, pos);\r\n            else {\r\n                var exponent = Math.floor(Math.log(val) / Math.LN2),\r\n                    mantissa = Math.round(val * Math.pow(2, -exponent) * 8388608) & 8388607;\r\n                writeUint((sign << 31 | exponent + 127 << 23 | mantissa) >>> 0, buf, pos);\r\n            }\r\n        }\r\n\r\n        exports.writeFloatLE = writeFloat_ieee754.bind(null, writeUintLE);\r\n        exports.writeFloatBE = writeFloat_ieee754.bind(null, writeUintBE);\r\n\r\n        function readFloat_ieee754(readUint, buf, pos) {\r\n            var uint = readUint(buf, pos),\r\n                sign = (uint >> 31) * 2 + 1,\r\n                exponent = uint >>> 23 & 255,\r\n                mantissa = uint & 8388607;\r\n            return exponent === 255\r\n                ? mantissa\r\n                ? NaN\r\n                : sign * Infinity\r\n                : exponent === 0 // denormal\r\n                ? sign * 1.401298464324817e-45 * mantissa\r\n                : sign * Math.pow(2, exponent - 150) * (mantissa + 8388608);\r\n        }\r\n\r\n        exports.readFloatLE = readFloat_ieee754.bind(null, readUintLE);\r\n        exports.readFloatBE = readFloat_ieee754.bind(null, readUintBE);\r\n\r\n    })();\r\n\r\n    // double: typed array\r\n    if (typeof Float64Array !== \"undefined\") (function() {\r\n\r\n        var f64 = new Float64Array([-0]),\r\n            f8b = new Uint8Array(f64.buffer),\r\n            le  = f8b[7] === 128;\r\n\r\n        function writeDouble_f64_cpy(val, buf, pos) {\r\n            f64[0] = val;\r\n            buf[pos    ] = f8b[0];\r\n            buf[pos + 1] = f8b[1];\r\n            buf[pos + 2] = f8b[2];\r\n            buf[pos + 3] = f8b[3];\r\n            buf[pos + 4] = f8b[4];\r\n            buf[pos + 5] = f8b[5];\r\n            buf[pos + 6] = f8b[6];\r\n            buf[pos + 7] = f8b[7];\r\n        }\r\n\r\n        function writeDouble_f64_rev(val, buf, pos) {\r\n            f64[0] = val;\r\n            buf[pos    ] = f8b[7];\r\n            buf[pos + 1] = f8b[6];\r\n            buf[pos + 2] = f8b[5];\r\n            buf[pos + 3] = f8b[4];\r\n            buf[pos + 4] = f8b[3];\r\n            buf[pos + 5] = f8b[2];\r\n            buf[pos + 6] = f8b[1];\r\n            buf[pos + 7] = f8b[0];\r\n        }\r\n\r\n        /* istanbul ignore next */\r\n        exports.writeDoubleLE = le ? writeDouble_f64_cpy : writeDouble_f64_rev;\r\n        /* istanbul ignore next */\r\n        exports.writeDoubleBE = le ? writeDouble_f64_rev : writeDouble_f64_cpy;\r\n\r\n        function readDouble_f64_cpy(buf, pos) {\r\n            f8b[0] = buf[pos    ];\r\n            f8b[1] = buf[pos + 1];\r\n            f8b[2] = buf[pos + 2];\r\n            f8b[3] = buf[pos + 3];\r\n            f8b[4] = buf[pos + 4];\r\n            f8b[5] = buf[pos + 5];\r\n            f8b[6] = buf[pos + 6];\r\n            f8b[7] = buf[pos + 7];\r\n            return f64[0];\r\n        }\r\n\r\n        function readDouble_f64_rev(buf, pos) {\r\n            f8b[7] = buf[pos    ];\r\n            f8b[6] = buf[pos + 1];\r\n            f8b[5] = buf[pos + 2];\r\n            f8b[4] = buf[pos + 3];\r\n            f8b[3] = buf[pos + 4];\r\n            f8b[2] = buf[pos + 5];\r\n            f8b[1] = buf[pos + 6];\r\n            f8b[0] = buf[pos + 7];\r\n            return f64[0];\r\n        }\r\n\r\n        /* istanbul ignore next */\r\n        exports.readDoubleLE = le ? readDouble_f64_cpy : readDouble_f64_rev;\r\n        /* istanbul ignore next */\r\n        exports.readDoubleBE = le ? readDouble_f64_rev : readDouble_f64_cpy;\r\n\r\n    // double: ieee754\r\n    })(); else (function() {\r\n\r\n        function writeDouble_ieee754(writeUint, off0, off1, val, buf, pos) {\r\n            var sign = val < 0 ? 1 : 0;\r\n            if (sign)\r\n                val = -val;\r\n            if (val === 0) {\r\n                writeUint(0, buf, pos + off0);\r\n                writeUint(1 / val > 0 ? /* positive */ 0 : /* negative 0 */ 2147483648, buf, pos + off1);\r\n            } else if (isNaN(val)) {\r\n                writeUint(0, buf, pos + off0);\r\n                writeUint(2146959360, buf, pos + off1);\r\n            } else if (val > 1.7976931348623157e+308) { // +-Infinity\r\n                writeUint(0, buf, pos + off0);\r\n                writeUint((sign << 31 | 2146435072) >>> 0, buf, pos + off1);\r\n            } else {\r\n                var mantissa;\r\n                if (val < 2.2250738585072014e-308) { // denormal\r\n                    mantissa = val / 5e-324;\r\n                    writeUint(mantissa >>> 0, buf, pos + off0);\r\n                    writeUint((sign << 31 | mantissa / 4294967296) >>> 0, buf, pos + off1);\r\n                } else {\r\n                    var exponent = Math.floor(Math.log(val) / Math.LN2);\r\n                    if (exponent === 1024)\r\n                        exponent = 1023;\r\n                    mantissa = val * Math.pow(2, -exponent);\r\n                    writeUint(mantissa * 4503599627370496 >>> 0, buf, pos + off0);\r\n                    writeUint((sign << 31 | exponent + 1023 << 20 | mantissa * 1048576 & 1048575) >>> 0, buf, pos + off1);\r\n                }\r\n            }\r\n        }\r\n\r\n        exports.writeDoubleLE = writeDouble_ieee754.bind(null, writeUintLE, 0, 4);\r\n        exports.writeDoubleBE = writeDouble_ieee754.bind(null, writeUintBE, 4, 0);\r\n\r\n        function readDouble_ieee754(readUint, off0, off1, buf, pos) {\r\n            var lo = readUint(buf, pos + off0),\r\n                hi = readUint(buf, pos + off1);\r\n            var sign = (hi >> 31) * 2 + 1,\r\n                exponent = hi >>> 20 & 2047,\r\n                mantissa = 4294967296 * (hi & 1048575) + lo;\r\n            return exponent === 2047\r\n                ? mantissa\r\n                ? NaN\r\n                : sign * Infinity\r\n                : exponent === 0 // denormal\r\n                ? sign * 5e-324 * mantissa\r\n                : sign * Math.pow(2, exponent - 1075) * (mantissa + 4503599627370496);\r\n        }\r\n\r\n        exports.readDoubleLE = readDouble_ieee754.bind(null, readUintLE, 0, 4);\r\n        exports.readDoubleBE = readDouble_ieee754.bind(null, readUintBE, 4, 0);\r\n\r\n    })();\r\n\r\n    return exports;\r\n}\r\n\r\n// uint helpers\r\n\r\nfunction writeUintLE(val, buf, pos) {\r\n    buf[pos    ] =  val        & 255;\r\n    buf[pos + 1] =  val >>> 8  & 255;\r\n    buf[pos + 2] =  val >>> 16 & 255;\r\n    buf[pos + 3] =  val >>> 24;\r\n}\r\n\r\nfunction writeUintBE(val, buf, pos) {\r\n    buf[pos    ] =  val >>> 24;\r\n    buf[pos + 1] =  val >>> 16 & 255;\r\n    buf[pos + 2] =  val >>> 8  & 255;\r\n    buf[pos + 3] =  val        & 255;\r\n}\r\n\r\nfunction readUintLE(buf, pos) {\r\n    return (buf[pos    ]\r\n          | buf[pos + 1] << 8\r\n          | buf[pos + 2] << 16\r\n          | buf[pos + 3] << 24) >>> 0;\r\n}\r\n\r\nfunction readUintBE(buf, pos) {\r\n    return (buf[pos    ] << 24\r\n          | buf[pos + 1] << 16\r\n          | buf[pos + 2] << 8\r\n          | buf[pos + 3]) >>> 0;\r\n}\r\n","\"use strict\";\r\nmodule.exports = inquire;\r\n\r\n/**\r\n * Requires a module only if available.\r\n * @memberof util\r\n * @param {string} moduleName Module to require\r\n * @returns {?Object} Required module if available and not empty, otherwise `null`\r\n */\r\nfunction inquire(moduleName) {\r\n    try {\r\n        var mod = eval(\"quire\".replace(/^/,\"re\"))(moduleName); // eslint-disable-line no-eval\r\n        if (mod && (mod.length || Object.keys(mod).length))\r\n            return mod;\r\n    } catch (e) {} // eslint-disable-line no-empty\r\n    return null;\r\n}\r\n","\"use strict\";\r\n\r\n/**\r\n * A minimal UTF8 implementation for number arrays.\r\n * @memberof util\r\n * @namespace\r\n */\r\nvar utf8 = exports;\r\n\r\n/**\r\n * Calculates the UTF8 byte length of a string.\r\n * @param {string} string String\r\n * @returns {number} Byte length\r\n */\r\nutf8.length = function utf8_length(string) {\r\n    var len = 0,\r\n        c = 0;\r\n    for (var i = 0; i < string.length; ++i) {\r\n        c = string.charCodeAt(i);\r\n        if (c < 128)\r\n            len += 1;\r\n        else if (c < 2048)\r\n            len += 2;\r\n        else if ((c & 0xFC00) === 0xD800 && (string.charCodeAt(i + 1) & 0xFC00) === 0xDC00) {\r\n            ++i;\r\n            len += 4;\r\n        } else\r\n            len += 3;\r\n    }\r\n    return len;\r\n};\r\n\r\n/**\r\n * Reads UTF8 bytes as a string.\r\n * @param {Uint8Array} buffer Source buffer\r\n * @param {number} start Source start\r\n * @param {number} end Source end\r\n * @returns {string} String read\r\n */\r\nutf8.read = function utf8_read(buffer, start, end) {\r\n    var len = end - start;\r\n    if (len < 1)\r\n        return \"\";\r\n    var parts = null,\r\n        chunk = [],\r\n        i = 0, // char offset\r\n        t;     // temporary\r\n    while (start < end) {\r\n        t = buffer[start++];\r\n        if (t < 128)\r\n            chunk[i++] = t;\r\n        else if (t > 191 && t < 224)\r\n            chunk[i++] = (t & 31) << 6 | buffer[start++] & 63;\r\n        else if (t > 239 && t < 365) {\r\n            t = ((t & 7) << 18 | (buffer[start++] & 63) << 12 | (buffer[start++] & 63) << 6 | buffer[start++] & 63) - 0x10000;\r\n            chunk[i++] = 0xD800 + (t >> 10);\r\n            chunk[i++] = 0xDC00 + (t & 1023);\r\n        } else\r\n            chunk[i++] = (t & 15) << 12 | (buffer[start++] & 63) << 6 | buffer[start++] & 63;\r\n        if (i > 8191) {\r\n            (parts || (parts = [])).push(String.fromCharCode.apply(String, chunk));\r\n            i = 0;\r\n        }\r\n    }\r\n    if (parts) {\r\n        if (i)\r\n            parts.push(String.fromCharCode.apply(String, chunk.slice(0, i)));\r\n        return parts.join(\"\");\r\n    }\r\n    return String.fromCharCode.apply(String, chunk.slice(0, i));\r\n};\r\n\r\n/**\r\n * Writes a string as UTF8 bytes.\r\n * @param {string} string Source string\r\n * @param {Uint8Array} buffer Destination buffer\r\n * @param {number} offset Destination offset\r\n * @returns {number} Bytes written\r\n */\r\nutf8.write = function utf8_write(string, buffer, offset) {\r\n    var start = offset,\r\n        c1, // character 1\r\n        c2; // character 2\r\n    for (var i = 0; i < string.length; ++i) {\r\n        c1 = string.charCodeAt(i);\r\n        if (c1 < 128) {\r\n            buffer[offset++] = c1;\r\n        } else if (c1 < 2048) {\r\n            buffer[offset++] = c1 >> 6       | 192;\r\n            buffer[offset++] = c1       & 63 | 128;\r\n        } else if ((c1 & 0xFC00) === 0xD800 && ((c2 = string.charCodeAt(i + 1)) & 0xFC00) === 0xDC00) {\r\n            c1 = 0x10000 + ((c1 & 0x03FF) << 10) + (c2 & 0x03FF);\r\n            ++i;\r\n            buffer[offset++] = c1 >> 18      | 240;\r\n            buffer[offset++] = c1 >> 12 & 63 | 128;\r\n            buffer[offset++] = c1 >> 6  & 63 | 128;\r\n            buffer[offset++] = c1       & 63 | 128;\r\n        } else {\r\n            buffer[offset++] = c1 >> 12      | 224;\r\n            buffer[offset++] = c1 >> 6  & 63 | 128;\r\n            buffer[offset++] = c1       & 63 | 128;\r\n        }\r\n    }\r\n    return offset - start;\r\n};\r\n","\"use strict\";\r\nmodule.exports = pool;\r\n\r\n/**\r\n * An allocator as used by {@link util.pool}.\r\n * @typedef PoolAllocator\r\n * @type {function}\r\n * @param {number} size Buffer size\r\n * @returns {Uint8Array} Buffer\r\n */\r\n\r\n/**\r\n * A slicer as used by {@link util.pool}.\r\n * @typedef PoolSlicer\r\n * @type {function}\r\n * @param {number} start Start offset\r\n * @param {number} end End offset\r\n * @returns {Uint8Array} Buffer slice\r\n * @this {Uint8Array}\r\n */\r\n\r\n/**\r\n * A general purpose buffer pool.\r\n * @memberof util\r\n * @function\r\n * @param {PoolAllocator} alloc Allocator\r\n * @param {PoolSlicer} slice Slicer\r\n * @param {number} [size=8192] Slab size\r\n * @returns {PoolAllocator} Pooled allocator\r\n */\r\nfunction pool(alloc, slice, size) {\r\n    var SIZE   = size || 8192;\r\n    var MAX    = SIZE >>> 1;\r\n    var slab   = null;\r\n    var offset = SIZE;\r\n    return function pool_alloc(size) {\r\n        if (size < 1 || size > MAX)\r\n            return alloc(size);\r\n        if (offset + size > SIZE) {\r\n            slab = alloc(SIZE);\r\n            offset = 0;\r\n        }\r\n        var buf = slice.call(slab, offset, offset += size);\r\n        if (offset & 7) // align to 32 bit\r\n            offset = (offset | 7) + 1;\r\n        return buf;\r\n    };\r\n}\r\n","\"use strict\";\nmodule.exports = LongBits;\n\nvar util = require(\"../util/minimal\");\n\n/**\n * Constructs new long bits.\n * @classdesc Helper class for working with the low and high bits of a 64 bit value.\n * @memberof util\n * @constructor\n * @param {number} lo Low 32 bits, unsigned\n * @param {number} hi High 32 bits, unsigned\n */\nfunction LongBits(lo, hi) {\n\n    // note that the casts below are theoretically unnecessary as of today, but older statically\n    // generated converter code might still call the ctor with signed 32bits. kept for compat.\n\n    /**\n     * Low bits.\n     * @type {number}\n     */\n    this.lo = lo >>> 0;\n\n    /**\n     * High bits.\n     * @type {number}\n     */\n    this.hi = hi >>> 0;\n}\n\n/**\n * Zero bits.\n * @memberof util.LongBits\n * @type {util.LongBits}\n */\nvar zero = LongBits.zero = new LongBits(0, 0);\n\nzero.toNumber = function() { return 0; };\nzero.zzEncode = zero.zzDecode = function() { return this; };\nzero.length = function() { return 1; };\n\n/**\n * Zero hash.\n * @memberof util.LongBits\n * @type {string}\n */\nvar zeroHash = LongBits.zeroHash = \"\\0\\0\\0\\0\\0\\0\\0\\0\";\n\n/**\n * Constructs new long bits from the specified number.\n * @param {number} value Value\n * @returns {util.LongBits} Instance\n */\nLongBits.fromNumber = function fromNumber(value) {\n    if (value === 0)\n        return zero;\n    var sign = value < 0;\n    if (sign)\n        value = -value;\n    var lo = value >>> 0,\n        hi = (value - lo) / 4294967296 >>> 0;\n    if (sign) {\n        hi = ~hi >>> 0;\n        lo = ~lo >>> 0;\n        if (++lo > 4294967295) {\n            lo = 0;\n            if (++hi > 4294967295)\n                hi = 0;\n        }\n    }\n    return new LongBits(lo, hi);\n};\n\n/**\n * Constructs new long bits from a number, long or string.\n * @param {Long|number|string} value Value\n * @returns {util.LongBits} Instance\n */\nLongBits.from = function from(value) {\n    if (typeof value === \"number\")\n        return LongBits.fromNumber(value);\n    if (util.isString(value)) {\n        /* istanbul ignore else */\n        if (util.Long)\n            value = util.Long.fromString(value);\n        else\n            return LongBits.fromNumber(parseInt(value, 10));\n    }\n    return value.low || value.high ? new LongBits(value.low >>> 0, value.high >>> 0) : zero;\n};\n\n/**\n * Converts this long bits to a possibly unsafe JavaScript number.\n * @param {boolean} [unsigned=false] Whether unsigned or not\n * @returns {number} Possibly unsafe number\n */\nLongBits.prototype.toNumber = function toNumber(unsigned) {\n    if (!unsigned && this.hi >>> 31) {\n        var lo = ~this.lo + 1 >>> 0,\n            hi = ~this.hi     >>> 0;\n        if (!lo)\n            hi = hi + 1 >>> 0;\n        return -(lo + hi * 4294967296);\n    }\n    return this.lo + this.hi * 4294967296;\n};\n\n/**\n * Converts this long bits to a long.\n * @param {boolean} [unsigned=false] Whether unsigned or not\n * @returns {Long} Long\n */\nLongBits.prototype.toLong = function toLong(unsigned) {\n    return util.Long\n        ? new util.Long(this.lo | 0, this.hi | 0, Boolean(unsigned))\n        /* istanbul ignore next */\n        : { low: this.lo | 0, high: this.hi | 0, unsigned: Boolean(unsigned) };\n};\n\nvar charCodeAt = String.prototype.charCodeAt;\n\n/**\n * Constructs new long bits from the specified 8 characters long hash.\n * @param {string} hash Hash\n * @returns {util.LongBits} Bits\n */\nLongBits.fromHash = function fromHash(hash) {\n    if (hash === zeroHash)\n        return zero;\n    return new LongBits(\n        ( charCodeAt.call(hash, 0)\n        | charCodeAt.call(hash, 1) << 8\n        | charCodeAt.call(hash, 2) << 16\n        | charCodeAt.call(hash, 3) << 24) >>> 0\n    ,\n        ( charCodeAt.call(hash, 4)\n        | charCodeAt.call(hash, 5) << 8\n        | charCodeAt.call(hash, 6) << 16\n        | charCodeAt.call(hash, 7) << 24) >>> 0\n    );\n};\n\n/**\n * Converts this long bits to a 8 characters long hash.\n * @returns {string} Hash\n */\nLongBits.prototype.toHash = function toHash() {\n    return String.fromCharCode(\n        this.lo        & 255,\n        this.lo >>> 8  & 255,\n        this.lo >>> 16 & 255,\n        this.lo >>> 24      ,\n        this.hi        & 255,\n        this.hi >>> 8  & 255,\n        this.hi >>> 16 & 255,\n        this.hi >>> 24\n    );\n};\n\n/**\n * Zig-zag encodes this long bits.\n * @returns {util.LongBits} `this`\n */\nLongBits.prototype.zzEncode = function zzEncode() {\n    var mask =   this.hi >> 31;\n    this.hi  = ((this.hi << 1 | this.lo >>> 31) ^ mask) >>> 0;\n    this.lo  = ( this.lo << 1                   ^ mask) >>> 0;\n    return this;\n};\n\n/**\n * Zig-zag decodes this long bits.\n * @returns {util.LongBits} `this`\n */\nLongBits.prototype.zzDecode = function zzDecode() {\n    var mask = -(this.lo & 1);\n    this.lo  = ((this.lo >>> 1 | this.hi << 31) ^ mask) >>> 0;\n    this.hi  = ( this.hi >>> 1                  ^ mask) >>> 0;\n    return this;\n};\n\n/**\n * Calculates the length of this longbits when encoded as a varint.\n * @returns {number} Length\n */\nLongBits.prototype.length = function length() {\n    var part0 =  this.lo,\n        part1 = (this.lo >>> 28 | this.hi << 4) >>> 0,\n        part2 =  this.hi >>> 24;\n    return part2 === 0\n         ? part1 === 0\n           ? part0 < 16384\n             ? part0 < 128 ? 1 : 2\n             : part0 < 2097152 ? 3 : 4\n           : part1 < 16384\n             ? part1 < 128 ? 5 : 6\n             : part1 < 2097152 ? 7 : 8\n         : part2 < 128 ? 9 : 10;\n};\n","\"use strict\";\nvar util = exports;\n\n// used to return a Promise where callback is omitted\nutil.asPromise = require(\"@protobufjs/aspromise\");\n\n// converts to / from base64 encoded strings\nutil.base64 = require(\"@protobufjs/base64\");\n\n// base class of rpc.Service\nutil.EventEmitter = require(\"@protobufjs/eventemitter\");\n\n// float handling accross browsers\nutil.float = require(\"@protobufjs/float\");\n\n// requires modules optionally and hides the call from bundlers\nutil.inquire = require(\"@protobufjs/inquire\");\n\n// converts to / from utf8 encoded strings\nutil.utf8 = require(\"@protobufjs/utf8\");\n\n// provides a node-like buffer pool in the browser\nutil.pool = require(\"@protobufjs/pool\");\n\n// utility to work with the low and high bits of a 64 bit value\nutil.LongBits = require(\"./longbits\");\n\n/**\n * Whether running within node or not.\n * @memberof util\n * @type {boolean}\n */\nutil.isNode = Boolean(typeof global !== \"undefined\"\n                   && global\n                   && global.process\n                   && global.process.versions\n                   && global.process.versions.node);\n\n/**\n * Global object reference.\n * @memberof util\n * @type {Object}\n */\nutil.global = util.isNode && global\n           || typeof window !== \"undefined\" && window\n           || typeof self   !== \"undefined\" && self\n           || this; // eslint-disable-line no-invalid-this\n\n/**\n * An immuable empty array.\n * @memberof util\n * @type {Array.<*>}\n * @const\n */\nutil.emptyArray = Object.freeze ? Object.freeze([]) : /* istanbul ignore next */ []; // used on prototypes\n\n/**\n * An immutable empty object.\n * @type {Object}\n * @const\n */\nutil.emptyObject = Object.freeze ? Object.freeze({}) : /* istanbul ignore next */ {}; // used on prototypes\n\n/**\n * Tests if the specified value is an integer.\n * @function\n * @param {*} value Value to test\n * @returns {boolean} `true` if the value is an integer\n */\nutil.isInteger = Number.isInteger || /* istanbul ignore next */ function isInteger(value) {\n    return typeof value === \"number\" && isFinite(value) && Math.floor(value) === value;\n};\n\n/**\n * Tests if the specified value is a string.\n * @param {*} value Value to test\n * @returns {boolean} `true` if the value is a string\n */\nutil.isString = function isString(value) {\n    return typeof value === \"string\" || value instanceof String;\n};\n\n/**\n * Tests if the specified value is a non-null object.\n * @param {*} value Value to test\n * @returns {boolean} `true` if the value is a non-null object\n */\nutil.isObject = function isObject(value) {\n    return value && typeof value === \"object\";\n};\n\n/**\n * Checks if a property on a message is considered to be present.\n * This is an alias of {@link util.isSet}.\n * @function\n * @param {Object} obj Plain object or message instance\n * @param {string} prop Property name\n * @returns {boolean} `true` if considered to be present, otherwise `false`\n */\nutil.isset =\n\n/**\n * Checks if a property on a message is considered to be present.\n * @param {Object} obj Plain object or message instance\n * @param {string} prop Property name\n * @returns {boolean} `true` if considered to be present, otherwise `false`\n */\nutil.isSet = function isSet(obj, prop) {\n    var value = obj[prop];\n    if (value != null && obj.hasOwnProperty(prop)) // eslint-disable-line eqeqeq, no-prototype-builtins\n        return typeof value !== \"object\" || (Array.isArray(value) ? value.length : Object.keys(value).length) > 0;\n    return false;\n};\n\n/**\n * Any compatible Buffer instance.\n * This is a minimal stand-alone definition of a Buffer instance. The actual type is that exported by node's typings.\n * @interface Buffer\n * @extends Uint8Array\n */\n\n/**\n * Node's Buffer class if available.\n * @type {Constructor<Buffer>}\n */\nutil.Buffer = (function() {\n    try {\n        var Buffer = util.inquire(\"buffer\").Buffer;\n        // refuse to use non-node buffers if not explicitly assigned (perf reasons):\n        return Buffer.prototype.utf8Write ? Buffer : /* istanbul ignore next */ null;\n    } catch (e) {\n        /* istanbul ignore next */\n        return null;\n    }\n})();\n\n// Internal alias of or polyfull for Buffer.from.\nutil._Buffer_from = null;\n\n// Internal alias of or polyfill for Buffer.allocUnsafe.\nutil._Buffer_allocUnsafe = null;\n\n/**\n * Creates a new buffer of whatever type supported by the environment.\n * @param {number|number[]} [sizeOrArray=0] Buffer size or number array\n * @returns {Uint8Array|Buffer} Buffer\n */\nutil.newBuffer = function newBuffer(sizeOrArray) {\n    /* istanbul ignore next */\n    return typeof sizeOrArray === \"number\"\n        ? util.Buffer\n            ? util._Buffer_allocUnsafe(sizeOrArray)\n            : new util.Array(sizeOrArray)\n        : util.Buffer\n            ? util._Buffer_from(sizeOrArray)\n            : typeof Uint8Array === \"undefined\"\n                ? sizeOrArray\n                : new Uint8Array(sizeOrArray);\n};\n\n/**\n * Array implementation used in the browser. `Uint8Array` if supported, otherwise `Array`.\n * @type {Constructor<Uint8Array>}\n */\nutil.Array = typeof Uint8Array !== \"undefined\" ? Uint8Array /* istanbul ignore next */ : Array;\n\n/**\n * Any compatible Long instance.\n * This is a minimal stand-alone definition of a Long instance. The actual type is that exported by long.js.\n * @interface Long\n * @property {number} low Low bits\n * @property {number} high High bits\n * @property {boolean} unsigned Whether unsigned or not\n */\n\n/**\n * Long.js's Long class if available.\n * @type {Constructor<Long>}\n */\nutil.Long = /* istanbul ignore next */ util.global.dcodeIO && /* istanbul ignore next */ util.global.dcodeIO.Long\n         || /* istanbul ignore next */ util.global.Long\n         || util.inquire(\"long\");\n\n/**\n * Regular expression used to verify 2 bit (`bool`) map keys.\n * @type {RegExp}\n * @const\n */\nutil.key2Re = /^true|false|0|1$/;\n\n/**\n * Regular expression used to verify 32 bit (`int32` etc.) map keys.\n * @type {RegExp}\n * @const\n */\nutil.key32Re = /^-?(?:0|[1-9][0-9]*)$/;\n\n/**\n * Regular expression used to verify 64 bit (`int64` etc.) map keys.\n * @type {RegExp}\n * @const\n */\nutil.key64Re = /^(?:[\\\\x00-\\\\xff]{8}|-?(?:0|[1-9][0-9]*))$/;\n\n/**\n * Converts a number or long to an 8 characters long hash string.\n * @param {Long|number} value Value to convert\n * @returns {string} Hash\n */\nutil.longToHash = function longToHash(value) {\n    return value\n        ? util.LongBits.from(value).toHash()\n        : util.LongBits.zeroHash;\n};\n\n/**\n * Converts an 8 characters long hash string to a long or number.\n * @param {string} hash Hash\n * @param {boolean} [unsigned=false] Whether unsigned or not\n * @returns {Long|number} Original value\n */\nutil.longFromHash = function longFromHash(hash, unsigned) {\n    var bits = util.LongBits.fromHash(hash);\n    if (util.Long)\n        return util.Long.fromBits(bits.lo, bits.hi, unsigned);\n    return bits.toNumber(Boolean(unsigned));\n};\n\n/**\n * Merges the properties of the source object into the destination object.\n * @memberof util\n * @param {Object.<string,*>} dst Destination object\n * @param {Object.<string,*>} src Source object\n * @param {boolean} [ifNotSet=false] Merges only if the key is not already set\n * @returns {Object.<string,*>} Destination object\n */\nfunction merge(dst, src, ifNotSet) { // used by converters\n    for (var keys = Object.keys(src), i = 0; i < keys.length; ++i)\n        if (dst[keys[i]] === undefined || !ifNotSet)\n            dst[keys[i]] = src[keys[i]];\n    return dst;\n}\n\nutil.merge = merge;\n\n/**\n * Converts the first character of a string to lower case.\n * @param {string} str String to convert\n * @returns {string} Converted string\n */\nutil.lcFirst = function lcFirst(str) {\n    return str.charAt(0).toLowerCase() + str.substring(1);\n};\n\n/**\n * Creates a custom error constructor.\n * @memberof util\n * @param {string} name Error name\n * @returns {Constructor<Error>} Custom error constructor\n */\nfunction newError(name) {\n\n    function CustomError(message, properties) {\n\n        if (!(this instanceof CustomError))\n            return new CustomError(message, properties);\n\n        // Error.call(this, message);\n        // ^ just returns a new error instance because the ctor can be called as a function\n\n        Object.defineProperty(this, \"message\", { get: function() { return message; } });\n\n        /* istanbul ignore next */\n        if (Error.captureStackTrace) // node\n            Error.captureStackTrace(this, CustomError);\n        else\n            Object.defineProperty(this, \"stack\", { value: new Error().stack || \"\" });\n\n        if (properties)\n            merge(this, properties);\n    }\n\n    CustomError.prototype = Object.create(Error.prototype, {\n        constructor: {\n            value: CustomError,\n            writable: true,\n            enumerable: false,\n            configurable: true,\n        },\n        name: {\n            get: function get() { return name; },\n            set: undefined,\n            enumerable: false,\n            // configurable: false would accurately preserve the behavior of\n            // the original, but I'm guessing that was not intentional.\n            // For an actual error subclass, this property would\n            // be configurable.\n            configurable: true,\n        },\n        toString: {\n            value: function value() { return this.name + \": \" + this.message; },\n            writable: true,\n            enumerable: false,\n            configurable: true,\n        },\n    });\n\n    return CustomError;\n}\n\nutil.newError = newError;\n\n/**\n * Constructs a new protocol error.\n * @classdesc Error subclass indicating a protocol specifc error.\n * @memberof util\n * @extends Error\n * @template T extends Message<T>\n * @constructor\n * @param {string} message Error message\n * @param {Object.<string,*>} [properties] Additional properties\n * @example\n * try {\n *     MyMessage.decode(someBuffer); // throws if required fields are missing\n * } catch (e) {\n *     if (e instanceof ProtocolError && e.instance)\n *         console.log(\"decoded so far: \" + JSON.stringify(e.instance));\n * }\n */\nutil.ProtocolError = newError(\"ProtocolError\");\n\n/**\n * So far decoded message instance.\n * @name util.ProtocolError#instance\n * @type {Message<T>}\n */\n\n/**\n * A OneOf getter as returned by {@link util.oneOfGetter}.\n * @typedef OneOfGetter\n * @type {function}\n * @returns {string|undefined} Set field name, if any\n */\n\n/**\n * Builds a getter for a oneof's present field name.\n * @param {string[]} fieldNames Field names\n * @returns {OneOfGetter} Unbound getter\n */\nutil.oneOfGetter = function getOneOf(fieldNames) {\n    var fieldMap = {};\n    for (var i = 0; i < fieldNames.length; ++i)\n        fieldMap[fieldNames[i]] = 1;\n\n    /**\n     * @returns {string|undefined} Set field name, if any\n     * @this Object\n     * @ignore\n     */\n    return function() { // eslint-disable-line consistent-return\n        for (var keys = Object.keys(this), i = keys.length - 1; i > -1; --i)\n            if (fieldMap[keys[i]] === 1 && this[keys[i]] !== undefined && this[keys[i]] !== null)\n                return keys[i];\n    };\n};\n\n/**\n * A OneOf setter as returned by {@link util.oneOfSetter}.\n * @typedef OneOfSetter\n * @type {function}\n * @param {string|undefined} value Field name\n * @returns {undefined}\n */\n\n/**\n * Builds a setter for a oneof's present field name.\n * @param {string[]} fieldNames Field names\n * @returns {OneOfSetter} Unbound setter\n */\nutil.oneOfSetter = function setOneOf(fieldNames) {\n\n    /**\n     * @param {string} name Field name\n     * @returns {undefined}\n     * @this Object\n     * @ignore\n     */\n    return function(name) {\n        for (var i = 0; i < fieldNames.length; ++i)\n            if (fieldNames[i] !== name)\n                delete this[fieldNames[i]];\n    };\n};\n\n/**\n * Default conversion options used for {@link Message#toJSON} implementations.\n *\n * These options are close to proto3's JSON mapping with the exception that internal types like Any are handled just like messages. More precisely:\n *\n * - Longs become strings\n * - Enums become string keys\n * - Bytes become base64 encoded strings\n * - (Sub-)Messages become plain objects\n * - Maps become plain objects with all string keys\n * - Repeated fields become arrays\n * - NaN and Infinity for float and double fields become strings\n *\n * @type {IConversionOptions}\n * @see https://developers.google.com/protocol-buffers/docs/proto3?hl=en#json\n */\nutil.toJSONOptions = {\n    longs: String,\n    enums: String,\n    bytes: String,\n    json: true\n};\n\n// Sets up buffer utility according to the environment (called in index-minimal)\nutil._configure = function() {\n    var Buffer = util.Buffer;\n    /* istanbul ignore if */\n    if (!Buffer) {\n        util._Buffer_from = util._Buffer_allocUnsafe = null;\n        return;\n    }\n    // because node 4.x buffers are incompatible & immutable\n    // see: https://github.com/dcodeIO/protobuf.js/pull/665\n    util._Buffer_from = Buffer.from !== Uint8Array.from && Buffer.from ||\n        /* istanbul ignore next */\n        function Buffer_from(value, encoding) {\n            return new Buffer(value, encoding);\n        };\n    util._Buffer_allocUnsafe = Buffer.allocUnsafe ||\n        /* istanbul ignore next */\n        function Buffer_allocUnsafe(size) {\n            return new Buffer(size);\n        };\n};\n","\"use strict\";\nmodule.exports = Writer;\n\nvar util      = require(\"./util/minimal\");\n\nvar BufferWriter; // cyclic\n\nvar LongBits  = util.LongBits,\n    base64    = util.base64,\n    utf8      = util.utf8;\n\n/**\n * Constructs a new writer operation instance.\n * @classdesc Scheduled writer operation.\n * @constructor\n * @param {function(*, Uint8Array, number)} fn Function to call\n * @param {number} len Value byte length\n * @param {*} val Value to write\n * @ignore\n */\nfunction Op(fn, len, val) {\n\n    /**\n     * Function to call.\n     * @type {function(Uint8Array, number, *)}\n     */\n    this.fn = fn;\n\n    /**\n     * Value byte length.\n     * @type {number}\n     */\n    this.len = len;\n\n    /**\n     * Next operation.\n     * @type {Writer.Op|undefined}\n     */\n    this.next = undefined;\n\n    /**\n     * Value to write.\n     * @type {*}\n     */\n    this.val = val; // type varies\n}\n\n/* istanbul ignore next */\nfunction noop() {} // eslint-disable-line no-empty-function\n\n/**\n * Constructs a new writer state instance.\n * @classdesc Copied writer state.\n * @memberof Writer\n * @constructor\n * @param {Writer} writer Writer to copy state from\n * @ignore\n */\nfunction State(writer) {\n\n    /**\n     * Current head.\n     * @type {Writer.Op}\n     */\n    this.head = writer.head;\n\n    /**\n     * Current tail.\n     * @type {Writer.Op}\n     */\n    this.tail = writer.tail;\n\n    /**\n     * Current buffer length.\n     * @type {number}\n     */\n    this.len = writer.len;\n\n    /**\n     * Next state.\n     * @type {State|null}\n     */\n    this.next = writer.states;\n}\n\n/**\n * Constructs a new writer instance.\n * @classdesc Wire format writer using `Uint8Array` if available, otherwise `Array`.\n * @constructor\n */\nfunction Writer() {\n\n    /**\n     * Current length.\n     * @type {number}\n     */\n    this.len = 0;\n\n    /**\n     * Operations head.\n     * @type {Object}\n     */\n    this.head = new Op(noop, 0, 0);\n\n    /**\n     * Operations tail\n     * @type {Object}\n     */\n    this.tail = this.head;\n\n    /**\n     * Linked forked states.\n     * @type {Object|null}\n     */\n    this.states = null;\n\n    // When a value is written, the writer calculates its byte length and puts it into a linked\n    // list of operations to perform when finish() is called. This both allows us to allocate\n    // buffers of the exact required size and reduces the amount of work we have to do compared\n    // to first calculating over objects and then encoding over objects. In our case, the encoding\n    // part is just a linked list walk calling operations with already prepared values.\n}\n\nvar create = function create() {\n    return util.Buffer\n        ? function create_buffer_setup() {\n            return (Writer.create = function create_buffer() {\n                return new BufferWriter();\n            })();\n        }\n        /* istanbul ignore next */\n        : function create_array() {\n            return new Writer();\n        };\n};\n\n/**\n * Creates a new writer.\n * @function\n * @returns {BufferWriter|Writer} A {@link BufferWriter} when Buffers are supported, otherwise a {@link Writer}\n */\nWriter.create = create();\n\n/**\n * Allocates a buffer of the specified size.\n * @param {number} size Buffer size\n * @returns {Uint8Array} Buffer\n */\nWriter.alloc = function alloc(size) {\n    return new util.Array(size);\n};\n\n// Use Uint8Array buffer pool in the browser, just like node does with buffers\n/* istanbul ignore else */\nif (util.Array !== Array)\n    Writer.alloc = util.pool(Writer.alloc, util.Array.prototype.subarray);\n\n/**\n * Pushes a new operation to the queue.\n * @param {function(Uint8Array, number, *)} fn Function to call\n * @param {number} len Value byte length\n * @param {number} val Value to write\n * @returns {Writer} `this`\n * @private\n */\nWriter.prototype._push = function push(fn, len, val) {\n    this.tail = this.tail.next = new Op(fn, len, val);\n    this.len += len;\n    return this;\n};\n\nfunction writeByte(val, buf, pos) {\n    buf[pos] = val & 255;\n}\n\nfunction writeVarint32(val, buf, pos) {\n    while (val > 127) {\n        buf[pos++] = val & 127 | 128;\n        val >>>= 7;\n    }\n    buf[pos] = val;\n}\n\n/**\n * Constructs a new varint writer operation instance.\n * @classdesc Scheduled varint writer operation.\n * @extends Op\n * @constructor\n * @param {number} len Value byte length\n * @param {number} val Value to write\n * @ignore\n */\nfunction VarintOp(len, val) {\n    this.len = len;\n    this.next = undefined;\n    this.val = val;\n}\n\nVarintOp.prototype = Object.create(Op.prototype);\nVarintOp.prototype.fn = writeVarint32;\n\n/**\n * Writes an unsigned 32 bit value as a varint.\n * @param {number} value Value to write\n * @returns {Writer} `this`\n */\nWriter.prototype.uint32 = function write_uint32(value) {\n    // here, the call to this.push has been inlined and a varint specific Op subclass is used.\n    // uint32 is by far the most frequently used operation and benefits significantly from this.\n    this.len += (this.tail = this.tail.next = new VarintOp(\n        (value = value >>> 0)\n                < 128       ? 1\n        : value < 16384     ? 2\n        : value < 2097152   ? 3\n        : value < 268435456 ? 4\n        :                     5,\n    value)).len;\n    return this;\n};\n\n/**\n * Writes a signed 32 bit value as a varint.\n * @function\n * @param {number} value Value to write\n * @returns {Writer} `this`\n */\nWriter.prototype.int32 = function write_int32(value) {\n    return value < 0\n        ? this._push(writeVarint64, 10, LongBits.fromNumber(value)) // 10 bytes per spec\n        : this.uint32(value);\n};\n\n/**\n * Writes a 32 bit value as a varint, zig-zag encoded.\n * @param {number} value Value to write\n * @returns {Writer} `this`\n */\nWriter.prototype.sint32 = function write_sint32(value) {\n    return this.uint32((value << 1 ^ value >> 31) >>> 0);\n};\n\nfunction writeVarint64(val, buf, pos) {\n    while (val.hi) {\n        buf[pos++] = val.lo & 127 | 128;\n        val.lo = (val.lo >>> 7 | val.hi << 25) >>> 0;\n        val.hi >>>= 7;\n    }\n    while (val.lo > 127) {\n        buf[pos++] = val.lo & 127 | 128;\n        val.lo = val.lo >>> 7;\n    }\n    buf[pos++] = val.lo;\n}\n\n/**\n * Writes an unsigned 64 bit value as a varint.\n * @param {Long|number|string} value Value to write\n * @returns {Writer} `this`\n * @throws {TypeError} If `value` is a string and no long library is present.\n */\nWriter.prototype.uint64 = function write_uint64(value) {\n    var bits = LongBits.from(value);\n    return this._push(writeVarint64, bits.length(), bits);\n};\n\n/**\n * Writes a signed 64 bit value as a varint.\n * @function\n * @param {Long|number|string} value Value to write\n * @returns {Writer} `this`\n * @throws {TypeError} If `value` is a string and no long library is present.\n */\nWriter.prototype.int64 = Writer.prototype.uint64;\n\n/**\n * Writes a signed 64 bit value as a varint, zig-zag encoded.\n * @param {Long|number|string} value Value to write\n * @returns {Writer} `this`\n * @throws {TypeError} If `value` is a string and no long library is present.\n */\nWriter.prototype.sint64 = function write_sint64(value) {\n    var bits = LongBits.from(value).zzEncode();\n    return this._push(writeVarint64, bits.length(), bits);\n};\n\n/**\n * Writes a boolish value as a varint.\n * @param {boolean} value Value to write\n * @returns {Writer} `this`\n */\nWriter.prototype.bool = function write_bool(value) {\n    return this._push(writeByte, 1, value ? 1 : 0);\n};\n\nfunction writeFixed32(val, buf, pos) {\n    buf[pos    ] =  val         & 255;\n    buf[pos + 1] =  val >>> 8   & 255;\n    buf[pos + 2] =  val >>> 16  & 255;\n    buf[pos + 3] =  val >>> 24;\n}\n\n/**\n * Writes an unsigned 32 bit value as fixed 32 bits.\n * @param {number} value Value to write\n * @returns {Writer} `this`\n */\nWriter.prototype.fixed32 = function write_fixed32(value) {\n    return this._push(writeFixed32, 4, value >>> 0);\n};\n\n/**\n * Writes a signed 32 bit value as fixed 32 bits.\n * @function\n * @param {number} value Value to write\n * @returns {Writer} `this`\n */\nWriter.prototype.sfixed32 = Writer.prototype.fixed32;\n\n/**\n * Writes an unsigned 64 bit value as fixed 64 bits.\n * @param {Long|number|string} value Value to write\n * @returns {Writer} `this`\n * @throws {TypeError} If `value` is a string and no long library is present.\n */\nWriter.prototype.fixed64 = function write_fixed64(value) {\n    var bits = LongBits.from(value);\n    return this._push(writeFixed32, 4, bits.lo)._push(writeFixed32, 4, bits.hi);\n};\n\n/**\n * Writes a signed 64 bit value as fixed 64 bits.\n * @function\n * @param {Long|number|string} value Value to write\n * @returns {Writer} `this`\n * @throws {TypeError} If `value` is a string and no long library is present.\n */\nWriter.prototype.sfixed64 = Writer.prototype.fixed64;\n\n/**\n * Writes a float (32 bit).\n * @function\n * @param {number} value Value to write\n * @returns {Writer} `this`\n */\nWriter.prototype.float = function write_float(value) {\n    return this._push(util.float.writeFloatLE, 4, value);\n};\n\n/**\n * Writes a double (64 bit float).\n * @function\n * @param {number} value Value to write\n * @returns {Writer} `this`\n */\nWriter.prototype.double = function write_double(value) {\n    return this._push(util.float.writeDoubleLE, 8, value);\n};\n\nvar writeBytes = util.Array.prototype.set\n    ? function writeBytes_set(val, buf, pos) {\n        buf.set(val, pos); // also works for plain array values\n    }\n    /* istanbul ignore next */\n    : function writeBytes_for(val, buf, pos) {\n        for (var i = 0; i < val.length; ++i)\n            buf[pos + i] = val[i];\n    };\n\n/**\n * Writes a sequence of bytes.\n * @param {Uint8Array|string} value Buffer or base64 encoded string to write\n * @returns {Writer} `this`\n */\nWriter.prototype.bytes = function write_bytes(value) {\n    var len = value.length >>> 0;\n    if (!len)\n        return this._push(writeByte, 1, 0);\n    if (util.isString(value)) {\n        var buf = Writer.alloc(len = base64.length(value));\n        base64.decode(value, buf, 0);\n        value = buf;\n    }\n    return this.uint32(len)._push(writeBytes, len, value);\n};\n\n/**\n * Writes a string.\n * @param {string} value Value to write\n * @returns {Writer} `this`\n */\nWriter.prototype.string = function write_string(value) {\n    var len = utf8.length(value);\n    return len\n        ? this.uint32(len)._push(utf8.write, len, value)\n        : this._push(writeByte, 1, 0);\n};\n\n/**\n * Forks this writer's state by pushing it to a stack.\n * Calling {@link Writer#reset|reset} or {@link Writer#ldelim|ldelim} resets the writer to the previous state.\n * @returns {Writer} `this`\n */\nWriter.prototype.fork = function fork() {\n    this.states = new State(this);\n    this.head = this.tail = new Op(noop, 0, 0);\n    this.len = 0;\n    return this;\n};\n\n/**\n * Resets this instance to the last state.\n * @returns {Writer} `this`\n */\nWriter.prototype.reset = function reset() {\n    if (this.states) {\n        this.head   = this.states.head;\n        this.tail   = this.states.tail;\n        this.len    = this.states.len;\n        this.states = this.states.next;\n    } else {\n        this.head = this.tail = new Op(noop, 0, 0);\n        this.len  = 0;\n    }\n    return this;\n};\n\n/**\n * Resets to the last state and appends the fork state's current write length as a varint followed by its operations.\n * @returns {Writer} `this`\n */\nWriter.prototype.ldelim = function ldelim() {\n    var head = this.head,\n        tail = this.tail,\n        len  = this.len;\n    this.reset().uint32(len);\n    if (len) {\n        this.tail.next = head.next; // skip noop\n        this.tail = tail;\n        this.len += len;\n    }\n    return this;\n};\n\n/**\n * Finishes the write operation.\n * @returns {Uint8Array} Finished buffer\n */\nWriter.prototype.finish = function finish() {\n    var head = this.head.next, // skip noop\n        buf  = this.constructor.alloc(this.len),\n        pos  = 0;\n    while (head) {\n        head.fn(head.val, buf, pos);\n        pos += head.len;\n        head = head.next;\n    }\n    // this.head = this.tail = null;\n    return buf;\n};\n\nWriter._configure = function(BufferWriter_) {\n    BufferWriter = BufferWriter_;\n    Writer.create = create();\n    BufferWriter._configure();\n};\n","\"use strict\";\nmodule.exports = BufferWriter;\n\n// extends Writer\nvar Writer = require(\"./writer\");\n(BufferWriter.prototype = Object.create(Writer.prototype)).constructor = BufferWriter;\n\nvar util = require(\"./util/minimal\");\n\n/**\n * Constructs a new buffer writer instance.\n * @classdesc Wire format writer using node buffers.\n * @extends Writer\n * @constructor\n */\nfunction BufferWriter() {\n    Writer.call(this);\n}\n\nBufferWriter._configure = function () {\n    /**\n     * Allocates a buffer of the specified size.\n     * @function\n     * @param {number} size Buffer size\n     * @returns {Buffer} Buffer\n     */\n    BufferWriter.alloc = util._Buffer_allocUnsafe;\n\n    BufferWriter.writeBytesBuffer = util.Buffer && util.Buffer.prototype instanceof Uint8Array && util.Buffer.prototype.set.name === \"set\"\n        ? function writeBytesBuffer_set(val, buf, pos) {\n          buf.set(val, pos); // faster than copy (requires node >= 4 where Buffers extend Uint8Array and set is properly inherited)\n          // also works for plain array values\n        }\n        /* istanbul ignore next */\n        : function writeBytesBuffer_copy(val, buf, pos) {\n          if (val.copy) // Buffer values\n            val.copy(buf, pos, 0, val.length);\n          else for (var i = 0; i < val.length;) // plain array values\n            buf[pos++] = val[i++];\n        };\n};\n\n\n/**\n * @override\n */\nBufferWriter.prototype.bytes = function write_bytes_buffer(value) {\n    if (util.isString(value))\n        value = util._Buffer_from(value, \"base64\");\n    var len = value.length >>> 0;\n    this.uint32(len);\n    if (len)\n        this._push(BufferWriter.writeBytesBuffer, len, value);\n    return this;\n};\n\nfunction writeStringBuffer(val, buf, pos) {\n    if (val.length < 40) // plain js is faster for short strings (probably due to redundant assertions)\n        util.utf8.write(val, buf, pos);\n    else if (buf.utf8Write)\n        buf.utf8Write(val, pos);\n    else\n        buf.write(val, pos);\n}\n\n/**\n * @override\n */\nBufferWriter.prototype.string = function write_string_buffer(value) {\n    var len = util.Buffer.byteLength(value);\n    this.uint32(len);\n    if (len)\n        this._push(writeStringBuffer, len, value);\n    return this;\n};\n\n\n/**\n * Finishes the write operation.\n * @name BufferWriter#finish\n * @function\n * @returns {Buffer} Finished buffer\n */\n\nBufferWriter._configure();\n","\"use strict\";\nmodule.exports = Reader;\n\nvar util      = require(\"./util/minimal\");\n\nvar BufferReader; // cyclic\n\nvar LongBits  = util.LongBits,\n    utf8      = util.utf8;\n\n/* istanbul ignore next */\nfunction indexOutOfRange(reader, writeLength) {\n    return RangeError(\"index out of range: \" + reader.pos + \" + \" + (writeLength || 1) + \" > \" + reader.len);\n}\n\n/**\n * Constructs a new reader instance using the specified buffer.\n * @classdesc Wire format reader using `Uint8Array` if available, otherwise `Array`.\n * @constructor\n * @param {Uint8Array} buffer Buffer to read from\n */\nfunction Reader(buffer) {\n\n    /**\n     * Read buffer.\n     * @type {Uint8Array}\n     */\n    this.buf = buffer;\n\n    /**\n     * Read buffer position.\n     * @type {number}\n     */\n    this.pos = 0;\n\n    /**\n     * Read buffer length.\n     * @type {number}\n     */\n    this.len = buffer.length;\n}\n\nvar create_array = typeof Uint8Array !== \"undefined\"\n    ? function create_typed_array(buffer) {\n        if (buffer instanceof Uint8Array || Array.isArray(buffer))\n            return new Reader(buffer);\n        throw Error(\"illegal buffer\");\n    }\n    /* istanbul ignore next */\n    : function create_array(buffer) {\n        if (Array.isArray(buffer))\n            return new Reader(buffer);\n        throw Error(\"illegal buffer\");\n    };\n\nvar create = function create() {\n    return util.Buffer\n        ? function create_buffer_setup(buffer) {\n            return (Reader.create = function create_buffer(buffer) {\n                return util.Buffer.isBuffer(buffer)\n                    ? new BufferReader(buffer)\n                    /* istanbul ignore next */\n                    : create_array(buffer);\n            })(buffer);\n        }\n        /* istanbul ignore next */\n        : create_array;\n};\n\n/**\n * Creates a new reader using the specified buffer.\n * @function\n * @param {Uint8Array|Buffer} buffer Buffer to read from\n * @returns {Reader|BufferReader} A {@link BufferReader} if `buffer` is a Buffer, otherwise a {@link Reader}\n * @throws {Error} If `buffer` is not a valid buffer\n */\nReader.create = create();\n\nReader.prototype._slice = util.Array.prototype.subarray || /* istanbul ignore next */ util.Array.prototype.slice;\n\n/**\n * Reads a varint as an unsigned 32 bit value.\n * @function\n * @returns {number} Value read\n */\nReader.prototype.uint32 = (function read_uint32_setup() {\n    var value = 4294967295; // optimizer type-hint, tends to deopt otherwise (?!)\n    return function read_uint32() {\n        value = (         this.buf[this.pos] & 127       ) >>> 0; if (this.buf[this.pos++] < 128) return value;\n        value = (value | (this.buf[this.pos] & 127) <<  7) >>> 0; if (this.buf[this.pos++] < 128) return value;\n        value = (value | (this.buf[this.pos] & 127) << 14) >>> 0; if (this.buf[this.pos++] < 128) return value;\n        value = (value | (this.buf[this.pos] & 127) << 21) >>> 0; if (this.buf[this.pos++] < 128) return value;\n        value = (value | (this.buf[this.pos] &  15) << 28) >>> 0; if (this.buf[this.pos++] < 128) return value;\n\n        /* istanbul ignore if */\n        if ((this.pos += 5) > this.len) {\n            this.pos = this.len;\n            throw indexOutOfRange(this, 10);\n        }\n        return value;\n    };\n})();\n\n/**\n * Reads a varint as a signed 32 bit value.\n * @returns {number} Value read\n */\nReader.prototype.int32 = function read_int32() {\n    return this.uint32() | 0;\n};\n\n/**\n * Reads a zig-zag encoded varint as a signed 32 bit value.\n * @returns {number} Value read\n */\nReader.prototype.sint32 = function read_sint32() {\n    var value = this.uint32();\n    return value >>> 1 ^ -(value & 1) | 0;\n};\n\n/* eslint-disable no-invalid-this */\n\nfunction readLongVarint() {\n    // tends to deopt with local vars for octet etc.\n    var bits = new LongBits(0, 0);\n    var i = 0;\n    if (this.len - this.pos > 4) { // fast route (lo)\n        for (; i < 4; ++i) {\n            // 1st..4th\n            bits.lo = (bits.lo | (this.buf[this.pos] & 127) << i * 7) >>> 0;\n            if (this.buf[this.pos++] < 128)\n                return bits;\n        }\n        // 5th\n        bits.lo = (bits.lo | (this.buf[this.pos] & 127) << 28) >>> 0;\n        bits.hi = (bits.hi | (this.buf[this.pos] & 127) >>  4) >>> 0;\n        if (this.buf[this.pos++] < 128)\n            return bits;\n        i = 0;\n    } else {\n        for (; i < 3; ++i) {\n            /* istanbul ignore if */\n            if (this.pos >= this.len)\n                throw indexOutOfRange(this);\n            // 1st..3th\n            bits.lo = (bits.lo | (this.buf[this.pos] & 127) << i * 7) >>> 0;\n            if (this.buf[this.pos++] < 128)\n                return bits;\n        }\n        // 4th\n        bits.lo = (bits.lo | (this.buf[this.pos++] & 127) << i * 7) >>> 0;\n        return bits;\n    }\n    if (this.len - this.pos > 4) { // fast route (hi)\n        for (; i < 5; ++i) {\n            // 6th..10th\n            bits.hi = (bits.hi | (this.buf[this.pos] & 127) << i * 7 + 3) >>> 0;\n            if (this.buf[this.pos++] < 128)\n                return bits;\n        }\n    } else {\n        for (; i < 5; ++i) {\n            /* istanbul ignore if */\n            if (this.pos >= this.len)\n                throw indexOutOfRange(this);\n            // 6th..10th\n            bits.hi = (bits.hi | (this.buf[this.pos] & 127) << i * 7 + 3) >>> 0;\n            if (this.buf[this.pos++] < 128)\n                return bits;\n        }\n    }\n    /* istanbul ignore next */\n    throw Error(\"invalid varint encoding\");\n}\n\n/* eslint-enable no-invalid-this */\n\n/**\n * Reads a varint as a signed 64 bit value.\n * @name Reader#int64\n * @function\n * @returns {Long} Value read\n */\n\n/**\n * Reads a varint as an unsigned 64 bit value.\n * @name Reader#uint64\n * @function\n * @returns {Long} Value read\n */\n\n/**\n * Reads a zig-zag encoded varint as a signed 64 bit value.\n * @name Reader#sint64\n * @function\n * @returns {Long} Value read\n */\n\n/**\n * Reads a varint as a boolean.\n * @returns {boolean} Value read\n */\nReader.prototype.bool = function read_bool() {\n    return this.uint32() !== 0;\n};\n\nfunction readFixed32_end(buf, end) { // note that this uses `end`, not `pos`\n    return (buf[end - 4]\n          | buf[end - 3] << 8\n          | buf[end - 2] << 16\n          | buf[end - 1] << 24) >>> 0;\n}\n\n/**\n * Reads fixed 32 bits as an unsigned 32 bit integer.\n * @returns {number} Value read\n */\nReader.prototype.fixed32 = function read_fixed32() {\n\n    /* istanbul ignore if */\n    if (this.pos + 4 > this.len)\n        throw indexOutOfRange(this, 4);\n\n    return readFixed32_end(this.buf, this.pos += 4);\n};\n\n/**\n * Reads fixed 32 bits as a signed 32 bit integer.\n * @returns {number} Value read\n */\nReader.prototype.sfixed32 = function read_sfixed32() {\n\n    /* istanbul ignore if */\n    if (this.pos + 4 > this.len)\n        throw indexOutOfRange(this, 4);\n\n    return readFixed32_end(this.buf, this.pos += 4) | 0;\n};\n\n/* eslint-disable no-invalid-this */\n\nfunction readFixed64(/* this: Reader */) {\n\n    /* istanbul ignore if */\n    if (this.pos + 8 > this.len)\n        throw indexOutOfRange(this, 8);\n\n    return new LongBits(readFixed32_end(this.buf, this.pos += 4), readFixed32_end(this.buf, this.pos += 4));\n}\n\n/* eslint-enable no-invalid-this */\n\n/**\n * Reads fixed 64 bits.\n * @name Reader#fixed64\n * @function\n * @returns {Long} Value read\n */\n\n/**\n * Reads zig-zag encoded fixed 64 bits.\n * @name Reader#sfixed64\n * @function\n * @returns {Long} Value read\n */\n\n/**\n * Reads a float (32 bit) as a number.\n * @function\n * @returns {number} Value read\n */\nReader.prototype.float = function read_float() {\n\n    /* istanbul ignore if */\n    if (this.pos + 4 > this.len)\n        throw indexOutOfRange(this, 4);\n\n    var value = util.float.readFloatLE(this.buf, this.pos);\n    this.pos += 4;\n    return value;\n};\n\n/**\n * Reads a double (64 bit float) as a number.\n * @function\n * @returns {number} Value read\n */\nReader.prototype.double = function read_double() {\n\n    /* istanbul ignore if */\n    if (this.pos + 8 > this.len)\n        throw indexOutOfRange(this, 4);\n\n    var value = util.float.readDoubleLE(this.buf, this.pos);\n    this.pos += 8;\n    return value;\n};\n\n/**\n * Reads a sequence of bytes preceeded by its length as a varint.\n * @returns {Uint8Array} Value read\n */\nReader.prototype.bytes = function read_bytes() {\n    var length = this.uint32(),\n        start  = this.pos,\n        end    = this.pos + length;\n\n    /* istanbul ignore if */\n    if (end > this.len)\n        throw indexOutOfRange(this, length);\n\n    this.pos += length;\n    if (Array.isArray(this.buf)) // plain array\n        return this.buf.slice(start, end);\n\n    if (start === end) { // fix for IE 10/Win8 and others' subarray returning array of size 1\n        var nativeBuffer = util.Buffer;\n        return nativeBuffer\n            ? nativeBuffer.alloc(0)\n            : new this.buf.constructor(0);\n    }\n    return this._slice.call(this.buf, start, end);\n};\n\n/**\n * Reads a string preceeded by its byte length as a varint.\n * @returns {string} Value read\n */\nReader.prototype.string = function read_string() {\n    var bytes = this.bytes();\n    return utf8.read(bytes, 0, bytes.length);\n};\n\n/**\n * Skips the specified number of bytes if specified, otherwise skips a varint.\n * @param {number} [length] Length if known, otherwise a varint is assumed\n * @returns {Reader} `this`\n */\nReader.prototype.skip = function skip(length) {\n    if (typeof length === \"number\") {\n        /* istanbul ignore if */\n        if (this.pos + length > this.len)\n            throw indexOutOfRange(this, length);\n        this.pos += length;\n    } else {\n        do {\n            /* istanbul ignore if */\n            if (this.pos >= this.len)\n                throw indexOutOfRange(this);\n        } while (this.buf[this.pos++] & 128);\n    }\n    return this;\n};\n\n/**\n * Skips the next element of the specified wire type.\n * @param {number} wireType Wire type received\n * @returns {Reader} `this`\n */\nReader.prototype.skipType = function(wireType) {\n    switch (wireType) {\n        case 0:\n            this.skip();\n            break;\n        case 1:\n            this.skip(8);\n            break;\n        case 2:\n            this.skip(this.uint32());\n            break;\n        case 3:\n            while ((wireType = this.uint32() & 7) !== 4) {\n                this.skipType(wireType);\n            }\n            break;\n        case 5:\n            this.skip(4);\n            break;\n\n        /* istanbul ignore next */\n        default:\n            throw Error(\"invalid wire type \" + wireType + \" at offset \" + this.pos);\n    }\n    return this;\n};\n\nReader._configure = function(BufferReader_) {\n    BufferReader = BufferReader_;\n    Reader.create = create();\n    BufferReader._configure();\n\n    var fn = util.Long ? \"toLong\" : /* istanbul ignore next */ \"toNumber\";\n    util.merge(Reader.prototype, {\n\n        int64: function read_int64() {\n            return readLongVarint.call(this)[fn](false);\n        },\n\n        uint64: function read_uint64() {\n            return readLongVarint.call(this)[fn](true);\n        },\n\n        sint64: function read_sint64() {\n            return readLongVarint.call(this).zzDecode()[fn](false);\n        },\n\n        fixed64: function read_fixed64() {\n            return readFixed64.call(this)[fn](true);\n        },\n\n        sfixed64: function read_sfixed64() {\n            return readFixed64.call(this)[fn](false);\n        }\n\n    });\n};\n","\"use strict\";\nmodule.exports = BufferReader;\n\n// extends Reader\nvar Reader = require(\"./reader\");\n(BufferReader.prototype = Object.create(Reader.prototype)).constructor = BufferReader;\n\nvar util = require(\"./util/minimal\");\n\n/**\n * Constructs a new buffer reader instance.\n * @classdesc Wire format reader using node buffers.\n * @extends Reader\n * @constructor\n * @param {Buffer} buffer Buffer to read from\n */\nfunction BufferReader(buffer) {\n    Reader.call(this, buffer);\n\n    /**\n     * Read buffer.\n     * @name BufferReader#buf\n     * @type {Buffer}\n     */\n}\n\nBufferReader._configure = function () {\n    /* istanbul ignore else */\n    if (util.Buffer)\n        BufferReader.prototype._slice = util.Buffer.prototype.slice;\n};\n\n\n/**\n * @override\n */\nBufferReader.prototype.string = function read_string_buffer() {\n    var len = this.uint32(); // modifies pos\n    return this.buf.utf8Slice\n        ? this.buf.utf8Slice(this.pos, this.pos = Math.min(this.pos + len, this.len))\n        : this.buf.toString(\"utf-8\", this.pos, this.pos = Math.min(this.pos + len, this.len));\n};\n\n/**\n * Reads a sequence of bytes preceeded by its length as a varint.\n * @name BufferReader#bytes\n * @function\n * @returns {Buffer} Value read\n */\n\nBufferReader._configure();\n","\"use strict\";\nmodule.exports = Service;\n\nvar util = require(\"../util/minimal\");\n\n// Extends EventEmitter\n(Service.prototype = Object.create(util.EventEmitter.prototype)).constructor = Service;\n\n/**\n * A service method callback as used by {@link rpc.ServiceMethod|ServiceMethod}.\n *\n * Differs from {@link RPCImplCallback} in that it is an actual callback of a service method which may not return `response = null`.\n * @typedef rpc.ServiceMethodCallback\n * @template TRes extends Message<TRes>\n * @type {function}\n * @param {Error|null} error Error, if any\n * @param {TRes} [response] Response message\n * @returns {undefined}\n */\n\n/**\n * A service method part of a {@link rpc.Service} as created by {@link Service.create}.\n * @typedef rpc.ServiceMethod\n * @template TReq extends Message<TReq>\n * @template TRes extends Message<TRes>\n * @type {function}\n * @param {TReq|Properties<TReq>} request Request message or plain object\n * @param {rpc.ServiceMethodCallback<TRes>} [callback] Node-style callback called with the error, if any, and the response message\n * @returns {Promise<Message<TRes>>} Promise if `callback` has been omitted, otherwise `undefined`\n */\n\n/**\n * Constructs a new RPC service instance.\n * @classdesc An RPC service as returned by {@link Service#create}.\n * @exports rpc.Service\n * @extends util.EventEmitter\n * @constructor\n * @param {RPCImpl} rpcImpl RPC implementation\n * @param {boolean} [requestDelimited=false] Whether requests are length-delimited\n * @param {boolean} [responseDelimited=false] Whether responses are length-delimited\n */\nfunction Service(rpcImpl, requestDelimited, responseDelimited) {\n\n    if (typeof rpcImpl !== \"function\")\n        throw TypeError(\"rpcImpl must be a function\");\n\n    util.EventEmitter.call(this);\n\n    /**\n     * RPC implementation. Becomes `null` once the service is ended.\n     * @type {RPCImpl|null}\n     */\n    this.rpcImpl = rpcImpl;\n\n    /**\n     * Whether requests are length-delimited.\n     * @type {boolean}\n     */\n    this.requestDelimited = Boolean(requestDelimited);\n\n    /**\n     * Whether responses are length-delimited.\n     * @type {boolean}\n     */\n    this.responseDelimited = Boolean(responseDelimited);\n}\n\n/**\n * Calls a service method through {@link rpc.Service#rpcImpl|rpcImpl}.\n * @param {Method|rpc.ServiceMethod<TReq,TRes>} method Reflected or static method\n * @param {Constructor<TReq>} requestCtor Request constructor\n * @param {Constructor<TRes>} responseCtor Response constructor\n * @param {TReq|Properties<TReq>} request Request message or plain object\n * @param {rpc.ServiceMethodCallback<TRes>} callback Service callback\n * @returns {undefined}\n * @template TReq extends Message<TReq>\n * @template TRes extends Message<TRes>\n */\nService.prototype.rpcCall = function rpcCall(method, requestCtor, responseCtor, request, callback) {\n\n    if (!request)\n        throw TypeError(\"request must be specified\");\n\n    var self = this;\n    if (!callback)\n        return util.asPromise(rpcCall, self, method, requestCtor, responseCtor, request);\n\n    if (!self.rpcImpl) {\n        setTimeout(function() { callback(Error(\"already ended\")); }, 0);\n        return undefined;\n    }\n\n    try {\n        return self.rpcImpl(\n            method,\n            requestCtor[self.requestDelimited ? \"encodeDelimited\" : \"encode\"](request).finish(),\n            function rpcCallback(err, response) {\n\n                if (err) {\n                    self.emit(\"error\", err, method);\n                    return callback(err);\n                }\n\n                if (response === null) {\n                    self.end(/* endedByRPC */ true);\n                    return undefined;\n                }\n\n                if (!(response instanceof responseCtor)) {\n                    try {\n                        response = responseCtor[self.responseDelimited ? \"decodeDelimited\" : \"decode\"](response);\n                    } catch (err) {\n                        self.emit(\"error\", err, method);\n                        return callback(err);\n                    }\n                }\n\n                self.emit(\"data\", response, method);\n                return callback(null, response);\n            }\n        );\n    } catch (err) {\n        self.emit(\"error\", err, method);\n        setTimeout(function() { callback(err); }, 0);\n        return undefined;\n    }\n};\n\n/**\n * Ends this service and emits the `end` event.\n * @param {boolean} [endedByRPC=false] Whether the service has been ended by the RPC implementation.\n * @returns {rpc.Service} `this`\n */\nService.prototype.end = function end(endedByRPC) {\n    if (this.rpcImpl) {\n        if (!endedByRPC) // signal end to rpcImpl\n            this.rpcImpl(null, null, null);\n        this.rpcImpl = null;\n        this.emit(\"end\").off();\n    }\n    return this;\n};\n","\"use strict\";\n\n/**\n * Streaming RPC helpers.\n * @namespace\n */\nvar rpc = exports;\n\n/**\n * RPC implementation passed to {@link Service#create} performing a service request on network level, i.e. by utilizing http requests or websockets.\n * @typedef RPCImpl\n * @type {function}\n * @param {Method|rpc.ServiceMethod<Message<{}>,Message<{}>>} method Reflected or static method being called\n * @param {Uint8Array} requestData Request data\n * @param {RPCImplCallback} callback Callback function\n * @returns {undefined}\n * @example\n * function rpcImpl(method, requestData, callback) {\n *     if (protobuf.util.lcFirst(method.name) !== \"myMethod\") // compatible with static code\n *         throw Error(\"no such method\");\n *     asynchronouslyObtainAResponse(requestData, function(err, responseData) {\n *         callback(err, responseData);\n *     });\n * }\n */\n\n/**\n * Node-style callback as used by {@link RPCImpl}.\n * @typedef RPCImplCallback\n * @type {function}\n * @param {Error|null} error Error, if any, otherwise `null`\n * @param {Uint8Array|null} [response] Response data or `null` to signal end of stream, if there hasn't been an error\n * @returns {undefined}\n */\n\nrpc.Service = require(\"./rpc/service\");\n","\"use strict\";\nmodule.exports = {};\n\n/**\n * Named roots.\n * This is where pbjs stores generated structures (the option `-r, --root` specifies a name).\n * Can also be used manually to make roots available across modules.\n * @name roots\n * @type {Object.<string,Root>}\n * @example\n * // pbjs -r myroot -o compiled.js ...\n *\n * // in another module:\n * require(\"./compiled.js\");\n *\n * // in any subsequent module:\n * var root = protobuf.roots[\"myroot\"];\n */\n","\"use strict\";\nvar protobuf = exports;\n\n/**\n * Build type, one of `\"full\"`, `\"light\"` or `\"minimal\"`.\n * @name build\n * @type {string}\n * @const\n */\nprotobuf.build = \"minimal\";\n\n// Serialization\nprotobuf.Writer       = require(\"./writer\");\nprotobuf.BufferWriter = require(\"./writer_buffer\");\nprotobuf.Reader       = require(\"./reader\");\nprotobuf.BufferReader = require(\"./reader_buffer\");\n\n// Utility\nprotobuf.util         = require(\"./util/minimal\");\nprotobuf.rpc          = require(\"./rpc\");\nprotobuf.roots        = require(\"./roots\");\nprotobuf.configure    = configure;\n\n/* istanbul ignore next */\n/**\n * Reconfigures the library according to the environment.\n * @returns {undefined}\n */\nfunction configure() {\n    protobuf.util._configure();\n    protobuf.Writer._configure(protobuf.BufferWriter);\n    protobuf.Reader._configure(protobuf.BufferReader);\n}\n\n// Set up buffer utility according to the environment\nconfigure();\n","// minimal library entry point.\n\n\"use strict\";\nmodule.exports = require(\"./src/index-minimal\");\n","/*eslint-disable block-scoped-var, id-length, no-control-regex, no-magic-numbers, no-prototype-builtins, no-redeclare, no-shadow, no-var, sort-vars*/\n\"use strict\";\n\nvar $protobuf = require(\"protobufjs/minimal\");\n\n// Common aliases\nvar $Reader = $protobuf.Reader, $Writer = $protobuf.Writer, $util = $protobuf.util;\n\n// Exported root namespace\nvar $root = $protobuf.roots[\"default\"] || ($protobuf.roots[\"default\"] = {});\n\n$root.onnx = (function() {\n\n    /**\n     * Namespace onnx.\n     * @exports onnx\n     * @namespace\n     */\n    var onnx = {};\n\n    /**\n     * Version enum.\n     * @name onnx.Version\n     * @enum {number}\n     * @property {number} _START_VERSION=0 _START_VERSION value\n     * @property {number} IR_VERSION_2017_10_10=1 IR_VERSION_2017_10_10 value\n     * @property {number} IR_VERSION_2017_10_30=2 IR_VERSION_2017_10_30 value\n     * @property {number} IR_VERSION_2017_11_3=3 IR_VERSION_2017_11_3 value\n     * @property {number} IR_VERSION_2019_1_22=4 IR_VERSION_2019_1_22 value\n     * @property {number} IR_VERSION_2019_3_18=5 IR_VERSION_2019_3_18 value\n     * @property {number} IR_VERSION_2019_9_19=6 IR_VERSION_2019_9_19 value\n     * @property {number} IR_VERSION_2020_5_8=7 IR_VERSION_2020_5_8 value\n     * @property {number} IR_VERSION_2021_7_30=8 IR_VERSION_2021_7_30 value\n     * @property {number} IR_VERSION=9 IR_VERSION value\n     */\n    onnx.Version = (function() {\n        var valuesById = {}, values = Object.create(valuesById);\n        values[valuesById[0] = \"_START_VERSION\"] = 0;\n        values[valuesById[1] = \"IR_VERSION_2017_10_10\"] = 1;\n        values[valuesById[2] = \"IR_VERSION_2017_10_30\"] = 2;\n        values[valuesById[3] = \"IR_VERSION_2017_11_3\"] = 3;\n        values[valuesById[4] = \"IR_VERSION_2019_1_22\"] = 4;\n        values[valuesById[5] = \"IR_VERSION_2019_3_18\"] = 5;\n        values[valuesById[6] = \"IR_VERSION_2019_9_19\"] = 6;\n        values[valuesById[7] = \"IR_VERSION_2020_5_8\"] = 7;\n        values[valuesById[8] = \"IR_VERSION_2021_7_30\"] = 8;\n        values[valuesById[9] = \"IR_VERSION\"] = 9;\n        return values;\n    })();\n\n    onnx.AttributeProto = (function() {\n\n        /**\n         * Properties of an AttributeProto.\n         * @memberof onnx\n         * @interface IAttributeProto\n         * @property {string|null} [name] AttributeProto name\n         * @property {string|null} [refAttrName] AttributeProto refAttrName\n         * @property {string|null} [docString] AttributeProto docString\n         * @property {onnx.AttributeProto.AttributeType|null} [type] AttributeProto type\n         * @property {number|null} [f] AttributeProto f\n         * @property {number|Long|null} [i] AttributeProto i\n         * @property {Uint8Array|null} [s] AttributeProto s\n         * @property {onnx.ITensorProto|null} [t] AttributeProto t\n         * @property {onnx.IGraphProto|null} [g] AttributeProto g\n         * @property {onnx.ISparseTensorProto|null} [sparseTensor] AttributeProto sparseTensor\n         * @property {onnx.ITypeProto|null} [tp] AttributeProto tp\n         * @property {Array.<number>|null} [floats] AttributeProto floats\n         * @property {Array.<number|Long>|null} [ints] AttributeProto ints\n         * @property {Array.<Uint8Array>|null} [strings] AttributeProto strings\n         * @property {Array.<onnx.ITensorProto>|null} [tensors] AttributeProto tensors\n         * @property {Array.<onnx.IGraphProto>|null} [graphs] AttributeProto graphs\n         * @property {Array.<onnx.ISparseTensorProto>|null} [sparseTensors] AttributeProto sparseTensors\n         * @property {Array.<onnx.ITypeProto>|null} [typeProtos] AttributeProto typeProtos\n         */\n\n        /**\n         * Constructs a new AttributeProto.\n         * @memberof onnx\n         * @classdesc Represents an AttributeProto.\n         * @implements IAttributeProto\n         * @constructor\n         * @param {onnx.IAttributeProto=} [properties] Properties to set\n         */\n        function AttributeProto(properties) {\n            this.floats = [];\n            this.ints = [];\n            this.strings = [];\n            this.tensors = [];\n            this.graphs = [];\n            this.sparseTensors = [];\n            this.typeProtos = [];\n            if (properties)\n                for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                    if (properties[keys[i]] != null)\n                        this[keys[i]] = properties[keys[i]];\n        }\n\n        /**\n         * AttributeProto name.\n         * @member {string} name\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.name = \"\";\n\n        /**\n         * AttributeProto refAttrName.\n         * @member {string} refAttrName\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.refAttrName = \"\";\n\n        /**\n         * AttributeProto docString.\n         * @member {string} docString\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.docString = \"\";\n\n        /**\n         * AttributeProto type.\n         * @member {onnx.AttributeProto.AttributeType} type\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.type = 0;\n\n        /**\n         * AttributeProto f.\n         * @member {number} f\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.f = 0;\n\n        /**\n         * AttributeProto i.\n         * @member {number|Long} i\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.i = $util.Long ? $util.Long.fromBits(0,0,false) : 0;\n\n        /**\n         * AttributeProto s.\n         * @member {Uint8Array} s\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.s = $util.newBuffer([]);\n\n        /**\n         * AttributeProto t.\n         * @member {onnx.ITensorProto|null|undefined} t\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.t = null;\n\n        /**\n         * AttributeProto g.\n         * @member {onnx.IGraphProto|null|undefined} g\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.g = null;\n\n        /**\n         * AttributeProto sparseTensor.\n         * @member {onnx.ISparseTensorProto|null|undefined} sparseTensor\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.sparseTensor = null;\n\n        /**\n         * AttributeProto tp.\n         * @member {onnx.ITypeProto|null|undefined} tp\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.tp = null;\n\n        /**\n         * AttributeProto floats.\n         * @member {Array.<number>} floats\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.floats = $util.emptyArray;\n\n        /**\n         * AttributeProto ints.\n         * @member {Array.<number|Long>} ints\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.ints = $util.emptyArray;\n\n        /**\n         * AttributeProto strings.\n         * @member {Array.<Uint8Array>} strings\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.strings = $util.emptyArray;\n\n        /**\n         * AttributeProto tensors.\n         * @member {Array.<onnx.ITensorProto>} tensors\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.tensors = $util.emptyArray;\n\n        /**\n         * AttributeProto graphs.\n         * @member {Array.<onnx.IGraphProto>} graphs\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.graphs = $util.emptyArray;\n\n        /**\n         * AttributeProto sparseTensors.\n         * @member {Array.<onnx.ISparseTensorProto>} sparseTensors\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.sparseTensors = $util.emptyArray;\n\n        /**\n         * AttributeProto typeProtos.\n         * @member {Array.<onnx.ITypeProto>} typeProtos\n         * @memberof onnx.AttributeProto\n         * @instance\n         */\n        AttributeProto.prototype.typeProtos = $util.emptyArray;\n\n        /**\n         * Creates a new AttributeProto instance using the specified properties.\n         * @function create\n         * @memberof onnx.AttributeProto\n         * @static\n         * @param {onnx.IAttributeProto=} [properties] Properties to set\n         * @returns {onnx.AttributeProto} AttributeProto instance\n         */\n        AttributeProto.create = function create(properties) {\n            return new AttributeProto(properties);\n        };\n\n        /**\n         * Encodes the specified AttributeProto message. Does not implicitly {@link onnx.AttributeProto.verify|verify} messages.\n         * @function encode\n         * @memberof onnx.AttributeProto\n         * @static\n         * @param {onnx.IAttributeProto} message AttributeProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        AttributeProto.encode = function encode(message, writer) {\n            if (!writer)\n                writer = $Writer.create();\n            if (message.name != null && Object.hasOwnProperty.call(message, \"name\"))\n                writer.uint32(/* id 1, wireType 2 =*/10).string(message.name);\n            if (message.f != null && Object.hasOwnProperty.call(message, \"f\"))\n                writer.uint32(/* id 2, wireType 5 =*/21).float(message.f);\n            if (message.i != null && Object.hasOwnProperty.call(message, \"i\"))\n                writer.uint32(/* id 3, wireType 0 =*/24).int64(message.i);\n            if (message.s != null && Object.hasOwnProperty.call(message, \"s\"))\n                writer.uint32(/* id 4, wireType 2 =*/34).bytes(message.s);\n            if (message.t != null && Object.hasOwnProperty.call(message, \"t\"))\n                $root.onnx.TensorProto.encode(message.t, writer.uint32(/* id 5, wireType 2 =*/42).fork()).ldelim();\n            if (message.g != null && Object.hasOwnProperty.call(message, \"g\"))\n                $root.onnx.GraphProto.encode(message.g, writer.uint32(/* id 6, wireType 2 =*/50).fork()).ldelim();\n            if (message.floats != null && message.floats.length) {\n                writer.uint32(/* id 7, wireType 2 =*/58).fork();\n                for (var i = 0; i < message.floats.length; ++i)\n                    writer.float(message.floats[i]);\n                writer.ldelim();\n            }\n            if (message.ints != null && message.ints.length) {\n                writer.uint32(/* id 8, wireType 2 =*/66).fork();\n                for (var i = 0; i < message.ints.length; ++i)\n                    writer.int64(message.ints[i]);\n                writer.ldelim();\n            }\n            if (message.strings != null && message.strings.length)\n                for (var i = 0; i < message.strings.length; ++i)\n                    writer.uint32(/* id 9, wireType 2 =*/74).bytes(message.strings[i]);\n            if (message.tensors != null && message.tensors.length)\n                for (var i = 0; i < message.tensors.length; ++i)\n                    $root.onnx.TensorProto.encode(message.tensors[i], writer.uint32(/* id 10, wireType 2 =*/82).fork()).ldelim();\n            if (message.graphs != null && message.graphs.length)\n                for (var i = 0; i < message.graphs.length; ++i)\n                    $root.onnx.GraphProto.encode(message.graphs[i], writer.uint32(/* id 11, wireType 2 =*/90).fork()).ldelim();\n            if (message.docString != null && Object.hasOwnProperty.call(message, \"docString\"))\n                writer.uint32(/* id 13, wireType 2 =*/106).string(message.docString);\n            if (message.tp != null && Object.hasOwnProperty.call(message, \"tp\"))\n                $root.onnx.TypeProto.encode(message.tp, writer.uint32(/* id 14, wireType 2 =*/114).fork()).ldelim();\n            if (message.typeProtos != null && message.typeProtos.length)\n                for (var i = 0; i < message.typeProtos.length; ++i)\n                    $root.onnx.TypeProto.encode(message.typeProtos[i], writer.uint32(/* id 15, wireType 2 =*/122).fork()).ldelim();\n            if (message.type != null && Object.hasOwnProperty.call(message, \"type\"))\n                writer.uint32(/* id 20, wireType 0 =*/160).int32(message.type);\n            if (message.refAttrName != null && Object.hasOwnProperty.call(message, \"refAttrName\"))\n                writer.uint32(/* id 21, wireType 2 =*/170).string(message.refAttrName);\n            if (message.sparseTensor != null && Object.hasOwnProperty.call(message, \"sparseTensor\"))\n                $root.onnx.SparseTensorProto.encode(message.sparseTensor, writer.uint32(/* id 22, wireType 2 =*/178).fork()).ldelim();\n            if (message.sparseTensors != null && message.sparseTensors.length)\n                for (var i = 0; i < message.sparseTensors.length; ++i)\n                    $root.onnx.SparseTensorProto.encode(message.sparseTensors[i], writer.uint32(/* id 23, wireType 2 =*/186).fork()).ldelim();\n            return writer;\n        };\n\n        /**\n         * Encodes the specified AttributeProto message, length delimited. Does not implicitly {@link onnx.AttributeProto.verify|verify} messages.\n         * @function encodeDelimited\n         * @memberof onnx.AttributeProto\n         * @static\n         * @param {onnx.IAttributeProto} message AttributeProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        AttributeProto.encodeDelimited = function encodeDelimited(message, writer) {\n            return this.encode(message, writer).ldelim();\n        };\n\n        /**\n         * Decodes an AttributeProto message from the specified reader or buffer.\n         * @function decode\n         * @memberof onnx.AttributeProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @param {number} [length] Message length if known beforehand\n         * @returns {onnx.AttributeProto} AttributeProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        AttributeProto.decode = function decode(reader, length) {\n            if (!(reader instanceof $Reader))\n                reader = $Reader.create(reader);\n            var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.AttributeProto();\n            while (reader.pos < end) {\n                var tag = reader.uint32();\n                switch (tag >>> 3) {\n                case 1: {\n                        message.name = reader.string();\n                        break;\n                    }\n                case 21: {\n                        message.refAttrName = reader.string();\n                        break;\n                    }\n                case 13: {\n                        message.docString = reader.string();\n                        break;\n                    }\n                case 20: {\n                        message.type = reader.int32();\n                        break;\n                    }\n                case 2: {\n                        message.f = reader.float();\n                        break;\n                    }\n                case 3: {\n                        message.i = reader.int64();\n                        break;\n                    }\n                case 4: {\n                        message.s = reader.bytes();\n                        break;\n                    }\n                case 5: {\n                        message.t = $root.onnx.TensorProto.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 6: {\n                        message.g = $root.onnx.GraphProto.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 22: {\n                        message.sparseTensor = $root.onnx.SparseTensorProto.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 14: {\n                        message.tp = $root.onnx.TypeProto.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 7: {\n                        if (!(message.floats && message.floats.length))\n                            message.floats = [];\n                        if ((tag & 7) === 2) {\n                            var end2 = reader.uint32() + reader.pos;\n                            while (reader.pos < end2)\n                                message.floats.push(reader.float());\n                        } else\n                            message.floats.push(reader.float());\n                        break;\n                    }\n                case 8: {\n                        if (!(message.ints && message.ints.length))\n                            message.ints = [];\n                        if ((tag & 7) === 2) {\n                            var end2 = reader.uint32() + reader.pos;\n                            while (reader.pos < end2)\n                                message.ints.push(reader.int64());\n                        } else\n                            message.ints.push(reader.int64());\n                        break;\n                    }\n                case 9: {\n                        if (!(message.strings && message.strings.length))\n                            message.strings = [];\n                        message.strings.push(reader.bytes());\n                        break;\n                    }\n                case 10: {\n                        if (!(message.tensors && message.tensors.length))\n                            message.tensors = [];\n                        message.tensors.push($root.onnx.TensorProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 11: {\n                        if (!(message.graphs && message.graphs.length))\n                            message.graphs = [];\n                        message.graphs.push($root.onnx.GraphProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 23: {\n                        if (!(message.sparseTensors && message.sparseTensors.length))\n                            message.sparseTensors = [];\n                        message.sparseTensors.push($root.onnx.SparseTensorProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 15: {\n                        if (!(message.typeProtos && message.typeProtos.length))\n                            message.typeProtos = [];\n                        message.typeProtos.push($root.onnx.TypeProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                default:\n                    reader.skipType(tag & 7);\n                    break;\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Decodes an AttributeProto message from the specified reader or buffer, length delimited.\n         * @function decodeDelimited\n         * @memberof onnx.AttributeProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @returns {onnx.AttributeProto} AttributeProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        AttributeProto.decodeDelimited = function decodeDelimited(reader) {\n            if (!(reader instanceof $Reader))\n                reader = new $Reader(reader);\n            return this.decode(reader, reader.uint32());\n        };\n\n        /**\n         * Verifies an AttributeProto message.\n         * @function verify\n         * @memberof onnx.AttributeProto\n         * @static\n         * @param {Object.<string,*>} message Plain object to verify\n         * @returns {string|null} `null` if valid, otherwise the reason why it is not\n         */\n        AttributeProto.verify = function verify(message) {\n            if (typeof message !== \"object\" || message === null)\n                return \"object expected\";\n            if (message.name != null && message.hasOwnProperty(\"name\"))\n                if (!$util.isString(message.name))\n                    return \"name: string expected\";\n            if (message.refAttrName != null && message.hasOwnProperty(\"refAttrName\"))\n                if (!$util.isString(message.refAttrName))\n                    return \"refAttrName: string expected\";\n            if (message.docString != null && message.hasOwnProperty(\"docString\"))\n                if (!$util.isString(message.docString))\n                    return \"docString: string expected\";\n            if (message.type != null && message.hasOwnProperty(\"type\"))\n                switch (message.type) {\n                default:\n                    return \"type: enum value expected\";\n                case 0:\n                case 1:\n                case 2:\n                case 3:\n                case 4:\n                case 5:\n                case 11:\n                case 13:\n                case 6:\n                case 7:\n                case 8:\n                case 9:\n                case 10:\n                case 12:\n                case 14:\n                    break;\n                }\n            if (message.f != null && message.hasOwnProperty(\"f\"))\n                if (typeof message.f !== \"number\")\n                    return \"f: number expected\";\n            if (message.i != null && message.hasOwnProperty(\"i\"))\n                if (!$util.isInteger(message.i) && !(message.i && $util.isInteger(message.i.low) && $util.isInteger(message.i.high)))\n                    return \"i: integer|Long expected\";\n            if (message.s != null && message.hasOwnProperty(\"s\"))\n                if (!(message.s && typeof message.s.length === \"number\" || $util.isString(message.s)))\n                    return \"s: buffer expected\";\n            if (message.t != null && message.hasOwnProperty(\"t\")) {\n                var error = $root.onnx.TensorProto.verify(message.t);\n                if (error)\n                    return \"t.\" + error;\n            }\n            if (message.g != null && message.hasOwnProperty(\"g\")) {\n                var error = $root.onnx.GraphProto.verify(message.g);\n                if (error)\n                    return \"g.\" + error;\n            }\n            if (message.sparseTensor != null && message.hasOwnProperty(\"sparseTensor\")) {\n                var error = $root.onnx.SparseTensorProto.verify(message.sparseTensor);\n                if (error)\n                    return \"sparseTensor.\" + error;\n            }\n            if (message.tp != null && message.hasOwnProperty(\"tp\")) {\n                var error = $root.onnx.TypeProto.verify(message.tp);\n                if (error)\n                    return \"tp.\" + error;\n            }\n            if (message.floats != null && message.hasOwnProperty(\"floats\")) {\n                if (!Array.isArray(message.floats))\n                    return \"floats: array expected\";\n                for (var i = 0; i < message.floats.length; ++i)\n                    if (typeof message.floats[i] !== \"number\")\n                        return \"floats: number[] expected\";\n            }\n            if (message.ints != null && message.hasOwnProperty(\"ints\")) {\n                if (!Array.isArray(message.ints))\n                    return \"ints: array expected\";\n                for (var i = 0; i < message.ints.length; ++i)\n                    if (!$util.isInteger(message.ints[i]) && !(message.ints[i] && $util.isInteger(message.ints[i].low) && $util.isInteger(message.ints[i].high)))\n                        return \"ints: integer|Long[] expected\";\n            }\n            if (message.strings != null && message.hasOwnProperty(\"strings\")) {\n                if (!Array.isArray(message.strings))\n                    return \"strings: array expected\";\n                for (var i = 0; i < message.strings.length; ++i)\n                    if (!(message.strings[i] && typeof message.strings[i].length === \"number\" || $util.isString(message.strings[i])))\n                        return \"strings: buffer[] expected\";\n            }\n            if (message.tensors != null && message.hasOwnProperty(\"tensors\")) {\n                if (!Array.isArray(message.tensors))\n                    return \"tensors: array expected\";\n                for (var i = 0; i < message.tensors.length; ++i) {\n                    var error = $root.onnx.TensorProto.verify(message.tensors[i]);\n                    if (error)\n                        return \"tensors.\" + error;\n                }\n            }\n            if (message.graphs != null && message.hasOwnProperty(\"graphs\")) {\n                if (!Array.isArray(message.graphs))\n                    return \"graphs: array expected\";\n                for (var i = 0; i < message.graphs.length; ++i) {\n                    var error = $root.onnx.GraphProto.verify(message.graphs[i]);\n                    if (error)\n                        return \"graphs.\" + error;\n                }\n            }\n            if (message.sparseTensors != null && message.hasOwnProperty(\"sparseTensors\")) {\n                if (!Array.isArray(message.sparseTensors))\n                    return \"sparseTensors: array expected\";\n                for (var i = 0; i < message.sparseTensors.length; ++i) {\n                    var error = $root.onnx.SparseTensorProto.verify(message.sparseTensors[i]);\n                    if (error)\n                        return \"sparseTensors.\" + error;\n                }\n            }\n            if (message.typeProtos != null && message.hasOwnProperty(\"typeProtos\")) {\n                if (!Array.isArray(message.typeProtos))\n                    return \"typeProtos: array expected\";\n                for (var i = 0; i < message.typeProtos.length; ++i) {\n                    var error = $root.onnx.TypeProto.verify(message.typeProtos[i]);\n                    if (error)\n                        return \"typeProtos.\" + error;\n                }\n            }\n            return null;\n        };\n\n        /**\n         * Creates an AttributeProto message from a plain object. Also converts values to their respective internal types.\n         * @function fromObject\n         * @memberof onnx.AttributeProto\n         * @static\n         * @param {Object.<string,*>} object Plain object\n         * @returns {onnx.AttributeProto} AttributeProto\n         */\n        AttributeProto.fromObject = function fromObject(object) {\n            if (object instanceof $root.onnx.AttributeProto)\n                return object;\n            var message = new $root.onnx.AttributeProto();\n            if (object.name != null)\n                message.name = String(object.name);\n            if (object.refAttrName != null)\n                message.refAttrName = String(object.refAttrName);\n            if (object.docString != null)\n                message.docString = String(object.docString);\n            switch (object.type) {\n            default:\n                if (typeof object.type === \"number\") {\n                    message.type = object.type;\n                    break;\n                }\n                break;\n            case \"UNDEFINED\":\n            case 0:\n                message.type = 0;\n                break;\n            case \"FLOAT\":\n            case 1:\n                message.type = 1;\n                break;\n            case \"INT\":\n            case 2:\n                message.type = 2;\n                break;\n            case \"STRING\":\n            case 3:\n                message.type = 3;\n                break;\n            case \"TENSOR\":\n            case 4:\n                message.type = 4;\n                break;\n            case \"GRAPH\":\n            case 5:\n                message.type = 5;\n                break;\n            case \"SPARSE_TENSOR\":\n            case 11:\n                message.type = 11;\n                break;\n            case \"TYPE_PROTO\":\n            case 13:\n                message.type = 13;\n                break;\n            case \"FLOATS\":\n            case 6:\n                message.type = 6;\n                break;\n            case \"INTS\":\n            case 7:\n                message.type = 7;\n                break;\n            case \"STRINGS\":\n            case 8:\n                message.type = 8;\n                break;\n            case \"TENSORS\":\n            case 9:\n                message.type = 9;\n                break;\n            case \"GRAPHS\":\n            case 10:\n                message.type = 10;\n                break;\n            case \"SPARSE_TENSORS\":\n            case 12:\n                message.type = 12;\n                break;\n            case \"TYPE_PROTOS\":\n            case 14:\n                message.type = 14;\n                break;\n            }\n            if (object.f != null)\n                message.f = Number(object.f);\n            if (object.i != null)\n                if ($util.Long)\n                    (message.i = $util.Long.fromValue(object.i)).unsigned = false;\n                else if (typeof object.i === \"string\")\n                    message.i = parseInt(object.i, 10);\n                else if (typeof object.i === \"number\")\n                    message.i = object.i;\n                else if (typeof object.i === \"object\")\n                    message.i = new $util.LongBits(object.i.low >>> 0, object.i.high >>> 0).toNumber();\n            if (object.s != null)\n                if (typeof object.s === \"string\")\n                    $util.base64.decode(object.s, message.s = $util.newBuffer($util.base64.length(object.s)), 0);\n                else if (object.s.length >= 0)\n                    message.s = object.s;\n            if (object.t != null) {\n                if (typeof object.t !== \"object\")\n                    throw TypeError(\".onnx.AttributeProto.t: object expected\");\n                message.t = $root.onnx.TensorProto.fromObject(object.t);\n            }\n            if (object.g != null) {\n                if (typeof object.g !== \"object\")\n                    throw TypeError(\".onnx.AttributeProto.g: object expected\");\n                message.g = $root.onnx.GraphProto.fromObject(object.g);\n            }\n            if (object.sparseTensor != null) {\n                if (typeof object.sparseTensor !== \"object\")\n                    throw TypeError(\".onnx.AttributeProto.sparseTensor: object expected\");\n                message.sparseTensor = $root.onnx.SparseTensorProto.fromObject(object.sparseTensor);\n            }\n            if (object.tp != null) {\n                if (typeof object.tp !== \"object\")\n                    throw TypeError(\".onnx.AttributeProto.tp: object expected\");\n                message.tp = $root.onnx.TypeProto.fromObject(object.tp);\n            }\n            if (object.floats) {\n                if (!Array.isArray(object.floats))\n                    throw TypeError(\".onnx.AttributeProto.floats: array expected\");\n                message.floats = [];\n                for (var i = 0; i < object.floats.length; ++i)\n                    message.floats[i] = Number(object.floats[i]);\n            }\n            if (object.ints) {\n                if (!Array.isArray(object.ints))\n                    throw TypeError(\".onnx.AttributeProto.ints: array expected\");\n                message.ints = [];\n                for (var i = 0; i < object.ints.length; ++i)\n                    if ($util.Long)\n                        (message.ints[i] = $util.Long.fromValue(object.ints[i])).unsigned = false;\n                    else if (typeof object.ints[i] === \"string\")\n                        message.ints[i] = parseInt(object.ints[i], 10);\n                    else if (typeof object.ints[i] === \"number\")\n                        message.ints[i] = object.ints[i];\n                    else if (typeof object.ints[i] === \"object\")\n                        message.ints[i] = new $util.LongBits(object.ints[i].low >>> 0, object.ints[i].high >>> 0).toNumber();\n            }\n            if (object.strings) {\n                if (!Array.isArray(object.strings))\n                    throw TypeError(\".onnx.AttributeProto.strings: array expected\");\n                message.strings = [];\n                for (var i = 0; i < object.strings.length; ++i)\n                    if (typeof object.strings[i] === \"string\")\n                        $util.base64.decode(object.strings[i], message.strings[i] = $util.newBuffer($util.base64.length(object.strings[i])), 0);\n                    else if (object.strings[i].length >= 0)\n                        message.strings[i] = object.strings[i];\n            }\n            if (object.tensors) {\n                if (!Array.isArray(object.tensors))\n                    throw TypeError(\".onnx.AttributeProto.tensors: array expected\");\n                message.tensors = [];\n                for (var i = 0; i < object.tensors.length; ++i) {\n                    if (typeof object.tensors[i] !== \"object\")\n                        throw TypeError(\".onnx.AttributeProto.tensors: object expected\");\n                    message.tensors[i] = $root.onnx.TensorProto.fromObject(object.tensors[i]);\n                }\n            }\n            if (object.graphs) {\n                if (!Array.isArray(object.graphs))\n                    throw TypeError(\".onnx.AttributeProto.graphs: array expected\");\n                message.graphs = [];\n                for (var i = 0; i < object.graphs.length; ++i) {\n                    if (typeof object.graphs[i] !== \"object\")\n                        throw TypeError(\".onnx.AttributeProto.graphs: object expected\");\n                    message.graphs[i] = $root.onnx.GraphProto.fromObject(object.graphs[i]);\n                }\n            }\n            if (object.sparseTensors) {\n                if (!Array.isArray(object.sparseTensors))\n                    throw TypeError(\".onnx.AttributeProto.sparseTensors: array expected\");\n                message.sparseTensors = [];\n                for (var i = 0; i < object.sparseTensors.length; ++i) {\n                    if (typeof object.sparseTensors[i] !== \"object\")\n                        throw TypeError(\".onnx.AttributeProto.sparseTensors: object expected\");\n                    message.sparseTensors[i] = $root.onnx.SparseTensorProto.fromObject(object.sparseTensors[i]);\n                }\n            }\n            if (object.typeProtos) {\n                if (!Array.isArray(object.typeProtos))\n                    throw TypeError(\".onnx.AttributeProto.typeProtos: array expected\");\n                message.typeProtos = [];\n                for (var i = 0; i < object.typeProtos.length; ++i) {\n                    if (typeof object.typeProtos[i] !== \"object\")\n                        throw TypeError(\".onnx.AttributeProto.typeProtos: object expected\");\n                    message.typeProtos[i] = $root.onnx.TypeProto.fromObject(object.typeProtos[i]);\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Creates a plain object from an AttributeProto message. Also converts values to other types if specified.\n         * @function toObject\n         * @memberof onnx.AttributeProto\n         * @static\n         * @param {onnx.AttributeProto} message AttributeProto\n         * @param {$protobuf.IConversionOptions} [options] Conversion options\n         * @returns {Object.<string,*>} Plain object\n         */\n        AttributeProto.toObject = function toObject(message, options) {\n            if (!options)\n                options = {};\n            var object = {};\n            if (options.arrays || options.defaults) {\n                object.floats = [];\n                object.ints = [];\n                object.strings = [];\n                object.tensors = [];\n                object.graphs = [];\n                object.typeProtos = [];\n                object.sparseTensors = [];\n            }\n            if (options.defaults) {\n                object.name = \"\";\n                object.f = 0;\n                if ($util.Long) {\n                    var long = new $util.Long(0, 0, false);\n                    object.i = options.longs === String ? long.toString() : options.longs === Number ? long.toNumber() : long;\n                } else\n                    object.i = options.longs === String ? \"0\" : 0;\n                if (options.bytes === String)\n                    object.s = \"\";\n                else {\n                    object.s = [];\n                    if (options.bytes !== Array)\n                        object.s = $util.newBuffer(object.s);\n                }\n                object.t = null;\n                object.g = null;\n                object.docString = \"\";\n                object.tp = null;\n                object.type = options.enums === String ? \"UNDEFINED\" : 0;\n                object.refAttrName = \"\";\n                object.sparseTensor = null;\n            }\n            if (message.name != null && message.hasOwnProperty(\"name\"))\n                object.name = message.name;\n            if (message.f != null && message.hasOwnProperty(\"f\"))\n                object.f = options.json && !isFinite(message.f) ? String(message.f) : message.f;\n            if (message.i != null && message.hasOwnProperty(\"i\"))\n                if (typeof message.i === \"number\")\n                    object.i = options.longs === String ? String(message.i) : message.i;\n                else\n                    object.i = options.longs === String ? $util.Long.prototype.toString.call(message.i) : options.longs === Number ? new $util.LongBits(message.i.low >>> 0, message.i.high >>> 0).toNumber() : message.i;\n            if (message.s != null && message.hasOwnProperty(\"s\"))\n                object.s = options.bytes === String ? $util.base64.encode(message.s, 0, message.s.length) : options.bytes === Array ? Array.prototype.slice.call(message.s) : message.s;\n            if (message.t != null && message.hasOwnProperty(\"t\"))\n                object.t = $root.onnx.TensorProto.toObject(message.t, options);\n            if (message.g != null && message.hasOwnProperty(\"g\"))\n                object.g = $root.onnx.GraphProto.toObject(message.g, options);\n            if (message.floats && message.floats.length) {\n                object.floats = [];\n                for (var j = 0; j < message.floats.length; ++j)\n                    object.floats[j] = options.json && !isFinite(message.floats[j]) ? String(message.floats[j]) : message.floats[j];\n            }\n            if (message.ints && message.ints.length) {\n                object.ints = [];\n                for (var j = 0; j < message.ints.length; ++j)\n                    if (typeof message.ints[j] === \"number\")\n                        object.ints[j] = options.longs === String ? String(message.ints[j]) : message.ints[j];\n                    else\n                        object.ints[j] = options.longs === String ? $util.Long.prototype.toString.call(message.ints[j]) : options.longs === Number ? new $util.LongBits(message.ints[j].low >>> 0, message.ints[j].high >>> 0).toNumber() : message.ints[j];\n            }\n            if (message.strings && message.strings.length) {\n                object.strings = [];\n                for (var j = 0; j < message.strings.length; ++j)\n                    object.strings[j] = options.bytes === String ? $util.base64.encode(message.strings[j], 0, message.strings[j].length) : options.bytes === Array ? Array.prototype.slice.call(message.strings[j]) : message.strings[j];\n            }\n            if (message.tensors && message.tensors.length) {\n                object.tensors = [];\n                for (var j = 0; j < message.tensors.length; ++j)\n                    object.tensors[j] = $root.onnx.TensorProto.toObject(message.tensors[j], options);\n            }\n            if (message.graphs && message.graphs.length) {\n                object.graphs = [];\n                for (var j = 0; j < message.graphs.length; ++j)\n                    object.graphs[j] = $root.onnx.GraphProto.toObject(message.graphs[j], options);\n            }\n            if (message.docString != null && message.hasOwnProperty(\"docString\"))\n                object.docString = message.docString;\n            if (message.tp != null && message.hasOwnProperty(\"tp\"))\n                object.tp = $root.onnx.TypeProto.toObject(message.tp, options);\n            if (message.typeProtos && message.typeProtos.length) {\n                object.typeProtos = [];\n                for (var j = 0; j < message.typeProtos.length; ++j)\n                    object.typeProtos[j] = $root.onnx.TypeProto.toObject(message.typeProtos[j], options);\n            }\n            if (message.type != null && message.hasOwnProperty(\"type\"))\n                object.type = options.enums === String ? $root.onnx.AttributeProto.AttributeType[message.type] === undefined ? message.type : $root.onnx.AttributeProto.AttributeType[message.type] : message.type;\n            if (message.refAttrName != null && message.hasOwnProperty(\"refAttrName\"))\n                object.refAttrName = message.refAttrName;\n            if (message.sparseTensor != null && message.hasOwnProperty(\"sparseTensor\"))\n                object.sparseTensor = $root.onnx.SparseTensorProto.toObject(message.sparseTensor, options);\n            if (message.sparseTensors && message.sparseTensors.length) {\n                object.sparseTensors = [];\n                for (var j = 0; j < message.sparseTensors.length; ++j)\n                    object.sparseTensors[j] = $root.onnx.SparseTensorProto.toObject(message.sparseTensors[j], options);\n            }\n            return object;\n        };\n\n        /**\n         * Converts this AttributeProto to JSON.\n         * @function toJSON\n         * @memberof onnx.AttributeProto\n         * @instance\n         * @returns {Object.<string,*>} JSON object\n         */\n        AttributeProto.prototype.toJSON = function toJSON() {\n            return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n        };\n\n        /**\n         * Gets the default type url for AttributeProto\n         * @function getTypeUrl\n         * @memberof onnx.AttributeProto\n         * @static\n         * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n         * @returns {string} The default type url\n         */\n        AttributeProto.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n            if (typeUrlPrefix === undefined) {\n                typeUrlPrefix = \"type.googleapis.com\";\n            }\n            return typeUrlPrefix + \"/onnx.AttributeProto\";\n        };\n\n        /**\n         * AttributeType enum.\n         * @name onnx.AttributeProto.AttributeType\n         * @enum {number}\n         * @property {number} UNDEFINED=0 UNDEFINED value\n         * @property {number} FLOAT=1 FLOAT value\n         * @property {number} INT=2 INT value\n         * @property {number} STRING=3 STRING value\n         * @property {number} TENSOR=4 TENSOR value\n         * @property {number} GRAPH=5 GRAPH value\n         * @property {number} SPARSE_TENSOR=11 SPARSE_TENSOR value\n         * @property {number} TYPE_PROTO=13 TYPE_PROTO value\n         * @property {number} FLOATS=6 FLOATS value\n         * @property {number} INTS=7 INTS value\n         * @property {number} STRINGS=8 STRINGS value\n         * @property {number} TENSORS=9 TENSORS value\n         * @property {number} GRAPHS=10 GRAPHS value\n         * @property {number} SPARSE_TENSORS=12 SPARSE_TENSORS value\n         * @property {number} TYPE_PROTOS=14 TYPE_PROTOS value\n         */\n        AttributeProto.AttributeType = (function() {\n            var valuesById = {}, values = Object.create(valuesById);\n            values[valuesById[0] = \"UNDEFINED\"] = 0;\n            values[valuesById[1] = \"FLOAT\"] = 1;\n            values[valuesById[2] = \"INT\"] = 2;\n            values[valuesById[3] = \"STRING\"] = 3;\n            values[valuesById[4] = \"TENSOR\"] = 4;\n            values[valuesById[5] = \"GRAPH\"] = 5;\n            values[valuesById[11] = \"SPARSE_TENSOR\"] = 11;\n            values[valuesById[13] = \"TYPE_PROTO\"] = 13;\n            values[valuesById[6] = \"FLOATS\"] = 6;\n            values[valuesById[7] = \"INTS\"] = 7;\n            values[valuesById[8] = \"STRINGS\"] = 8;\n            values[valuesById[9] = \"TENSORS\"] = 9;\n            values[valuesById[10] = \"GRAPHS\"] = 10;\n            values[valuesById[12] = \"SPARSE_TENSORS\"] = 12;\n            values[valuesById[14] = \"TYPE_PROTOS\"] = 14;\n            return values;\n        })();\n\n        return AttributeProto;\n    })();\n\n    onnx.ValueInfoProto = (function() {\n\n        /**\n         * Properties of a ValueInfoProto.\n         * @memberof onnx\n         * @interface IValueInfoProto\n         * @property {string|null} [name] ValueInfoProto name\n         * @property {onnx.ITypeProto|null} [type] ValueInfoProto type\n         * @property {string|null} [docString] ValueInfoProto docString\n         */\n\n        /**\n         * Constructs a new ValueInfoProto.\n         * @memberof onnx\n         * @classdesc Represents a ValueInfoProto.\n         * @implements IValueInfoProto\n         * @constructor\n         * @param {onnx.IValueInfoProto=} [properties] Properties to set\n         */\n        function ValueInfoProto(properties) {\n            if (properties)\n                for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                    if (properties[keys[i]] != null)\n                        this[keys[i]] = properties[keys[i]];\n        }\n\n        /**\n         * ValueInfoProto name.\n         * @member {string} name\n         * @memberof onnx.ValueInfoProto\n         * @instance\n         */\n        ValueInfoProto.prototype.name = \"\";\n\n        /**\n         * ValueInfoProto type.\n         * @member {onnx.ITypeProto|null|undefined} type\n         * @memberof onnx.ValueInfoProto\n         * @instance\n         */\n        ValueInfoProto.prototype.type = null;\n\n        /**\n         * ValueInfoProto docString.\n         * @member {string} docString\n         * @memberof onnx.ValueInfoProto\n         * @instance\n         */\n        ValueInfoProto.prototype.docString = \"\";\n\n        /**\n         * Creates a new ValueInfoProto instance using the specified properties.\n         * @function create\n         * @memberof onnx.ValueInfoProto\n         * @static\n         * @param {onnx.IValueInfoProto=} [properties] Properties to set\n         * @returns {onnx.ValueInfoProto} ValueInfoProto instance\n         */\n        ValueInfoProto.create = function create(properties) {\n            return new ValueInfoProto(properties);\n        };\n\n        /**\n         * Encodes the specified ValueInfoProto message. Does not implicitly {@link onnx.ValueInfoProto.verify|verify} messages.\n         * @function encode\n         * @memberof onnx.ValueInfoProto\n         * @static\n         * @param {onnx.IValueInfoProto} message ValueInfoProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        ValueInfoProto.encode = function encode(message, writer) {\n            if (!writer)\n                writer = $Writer.create();\n            if (message.name != null && Object.hasOwnProperty.call(message, \"name\"))\n                writer.uint32(/* id 1, wireType 2 =*/10).string(message.name);\n            if (message.type != null && Object.hasOwnProperty.call(message, \"type\"))\n                $root.onnx.TypeProto.encode(message.type, writer.uint32(/* id 2, wireType 2 =*/18).fork()).ldelim();\n            if (message.docString != null && Object.hasOwnProperty.call(message, \"docString\"))\n                writer.uint32(/* id 3, wireType 2 =*/26).string(message.docString);\n            return writer;\n        };\n\n        /**\n         * Encodes the specified ValueInfoProto message, length delimited. Does not implicitly {@link onnx.ValueInfoProto.verify|verify} messages.\n         * @function encodeDelimited\n         * @memberof onnx.ValueInfoProto\n         * @static\n         * @param {onnx.IValueInfoProto} message ValueInfoProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        ValueInfoProto.encodeDelimited = function encodeDelimited(message, writer) {\n            return this.encode(message, writer).ldelim();\n        };\n\n        /**\n         * Decodes a ValueInfoProto message from the specified reader or buffer.\n         * @function decode\n         * @memberof onnx.ValueInfoProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @param {number} [length] Message length if known beforehand\n         * @returns {onnx.ValueInfoProto} ValueInfoProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        ValueInfoProto.decode = function decode(reader, length) {\n            if (!(reader instanceof $Reader))\n                reader = $Reader.create(reader);\n            var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.ValueInfoProto();\n            while (reader.pos < end) {\n                var tag = reader.uint32();\n                switch (tag >>> 3) {\n                case 1: {\n                        message.name = reader.string();\n                        break;\n                    }\n                case 2: {\n                        message.type = $root.onnx.TypeProto.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 3: {\n                        message.docString = reader.string();\n                        break;\n                    }\n                default:\n                    reader.skipType(tag & 7);\n                    break;\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Decodes a ValueInfoProto message from the specified reader or buffer, length delimited.\n         * @function decodeDelimited\n         * @memberof onnx.ValueInfoProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @returns {onnx.ValueInfoProto} ValueInfoProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        ValueInfoProto.decodeDelimited = function decodeDelimited(reader) {\n            if (!(reader instanceof $Reader))\n                reader = new $Reader(reader);\n            return this.decode(reader, reader.uint32());\n        };\n\n        /**\n         * Verifies a ValueInfoProto message.\n         * @function verify\n         * @memberof onnx.ValueInfoProto\n         * @static\n         * @param {Object.<string,*>} message Plain object to verify\n         * @returns {string|null} `null` if valid, otherwise the reason why it is not\n         */\n        ValueInfoProto.verify = function verify(message) {\n            if (typeof message !== \"object\" || message === null)\n                return \"object expected\";\n            if (message.name != null && message.hasOwnProperty(\"name\"))\n                if (!$util.isString(message.name))\n                    return \"name: string expected\";\n            if (message.type != null && message.hasOwnProperty(\"type\")) {\n                var error = $root.onnx.TypeProto.verify(message.type);\n                if (error)\n                    return \"type.\" + error;\n            }\n            if (message.docString != null && message.hasOwnProperty(\"docString\"))\n                if (!$util.isString(message.docString))\n                    return \"docString: string expected\";\n            return null;\n        };\n\n        /**\n         * Creates a ValueInfoProto message from a plain object. Also converts values to their respective internal types.\n         * @function fromObject\n         * @memberof onnx.ValueInfoProto\n         * @static\n         * @param {Object.<string,*>} object Plain object\n         * @returns {onnx.ValueInfoProto} ValueInfoProto\n         */\n        ValueInfoProto.fromObject = function fromObject(object) {\n            if (object instanceof $root.onnx.ValueInfoProto)\n                return object;\n            var message = new $root.onnx.ValueInfoProto();\n            if (object.name != null)\n                message.name = String(object.name);\n            if (object.type != null) {\n                if (typeof object.type !== \"object\")\n                    throw TypeError(\".onnx.ValueInfoProto.type: object expected\");\n                message.type = $root.onnx.TypeProto.fromObject(object.type);\n            }\n            if (object.docString != null)\n                message.docString = String(object.docString);\n            return message;\n        };\n\n        /**\n         * Creates a plain object from a ValueInfoProto message. Also converts values to other types if specified.\n         * @function toObject\n         * @memberof onnx.ValueInfoProto\n         * @static\n         * @param {onnx.ValueInfoProto} message ValueInfoProto\n         * @param {$protobuf.IConversionOptions} [options] Conversion options\n         * @returns {Object.<string,*>} Plain object\n         */\n        ValueInfoProto.toObject = function toObject(message, options) {\n            if (!options)\n                options = {};\n            var object = {};\n            if (options.defaults) {\n                object.name = \"\";\n                object.type = null;\n                object.docString = \"\";\n            }\n            if (message.name != null && message.hasOwnProperty(\"name\"))\n                object.name = message.name;\n            if (message.type != null && message.hasOwnProperty(\"type\"))\n                object.type = $root.onnx.TypeProto.toObject(message.type, options);\n            if (message.docString != null && message.hasOwnProperty(\"docString\"))\n                object.docString = message.docString;\n            return object;\n        };\n\n        /**\n         * Converts this ValueInfoProto to JSON.\n         * @function toJSON\n         * @memberof onnx.ValueInfoProto\n         * @instance\n         * @returns {Object.<string,*>} JSON object\n         */\n        ValueInfoProto.prototype.toJSON = function toJSON() {\n            return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n        };\n\n        /**\n         * Gets the default type url for ValueInfoProto\n         * @function getTypeUrl\n         * @memberof onnx.ValueInfoProto\n         * @static\n         * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n         * @returns {string} The default type url\n         */\n        ValueInfoProto.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n            if (typeUrlPrefix === undefined) {\n                typeUrlPrefix = \"type.googleapis.com\";\n            }\n            return typeUrlPrefix + \"/onnx.ValueInfoProto\";\n        };\n\n        return ValueInfoProto;\n    })();\n\n    onnx.NodeProto = (function() {\n\n        /**\n         * Properties of a NodeProto.\n         * @memberof onnx\n         * @interface INodeProto\n         * @property {Array.<string>|null} [input] NodeProto input\n         * @property {Array.<string>|null} [output] NodeProto output\n         * @property {string|null} [name] NodeProto name\n         * @property {string|null} [opType] NodeProto opType\n         * @property {string|null} [domain] NodeProto domain\n         * @property {Array.<onnx.IAttributeProto>|null} [attribute] NodeProto attribute\n         * @property {string|null} [docString] NodeProto docString\n         */\n\n        /**\n         * Constructs a new NodeProto.\n         * @memberof onnx\n         * @classdesc Represents a NodeProto.\n         * @implements INodeProto\n         * @constructor\n         * @param {onnx.INodeProto=} [properties] Properties to set\n         */\n        function NodeProto(properties) {\n            this.input = [];\n            this.output = [];\n            this.attribute = [];\n            if (properties)\n                for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                    if (properties[keys[i]] != null)\n                        this[keys[i]] = properties[keys[i]];\n        }\n\n        /**\n         * NodeProto input.\n         * @member {Array.<string>} input\n         * @memberof onnx.NodeProto\n         * @instance\n         */\n        NodeProto.prototype.input = $util.emptyArray;\n\n        /**\n         * NodeProto output.\n         * @member {Array.<string>} output\n         * @memberof onnx.NodeProto\n         * @instance\n         */\n        NodeProto.prototype.output = $util.emptyArray;\n\n        /**\n         * NodeProto name.\n         * @member {string} name\n         * @memberof onnx.NodeProto\n         * @instance\n         */\n        NodeProto.prototype.name = \"\";\n\n        /**\n         * NodeProto opType.\n         * @member {string} opType\n         * @memberof onnx.NodeProto\n         * @instance\n         */\n        NodeProto.prototype.opType = \"\";\n\n        /**\n         * NodeProto domain.\n         * @member {string} domain\n         * @memberof onnx.NodeProto\n         * @instance\n         */\n        NodeProto.prototype.domain = \"\";\n\n        /**\n         * NodeProto attribute.\n         * @member {Array.<onnx.IAttributeProto>} attribute\n         * @memberof onnx.NodeProto\n         * @instance\n         */\n        NodeProto.prototype.attribute = $util.emptyArray;\n\n        /**\n         * NodeProto docString.\n         * @member {string} docString\n         * @memberof onnx.NodeProto\n         * @instance\n         */\n        NodeProto.prototype.docString = \"\";\n\n        /**\n         * Creates a new NodeProto instance using the specified properties.\n         * @function create\n         * @memberof onnx.NodeProto\n         * @static\n         * @param {onnx.INodeProto=} [properties] Properties to set\n         * @returns {onnx.NodeProto} NodeProto instance\n         */\n        NodeProto.create = function create(properties) {\n            return new NodeProto(properties);\n        };\n\n        /**\n         * Encodes the specified NodeProto message. Does not implicitly {@link onnx.NodeProto.verify|verify} messages.\n         * @function encode\n         * @memberof onnx.NodeProto\n         * @static\n         * @param {onnx.INodeProto} message NodeProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        NodeProto.encode = function encode(message, writer) {\n            if (!writer)\n                writer = $Writer.create();\n            if (message.input != null && message.input.length)\n                for (var i = 0; i < message.input.length; ++i)\n                    writer.uint32(/* id 1, wireType 2 =*/10).string(message.input[i]);\n            if (message.output != null && message.output.length)\n                for (var i = 0; i < message.output.length; ++i)\n                    writer.uint32(/* id 2, wireType 2 =*/18).string(message.output[i]);\n            if (message.name != null && Object.hasOwnProperty.call(message, \"name\"))\n                writer.uint32(/* id 3, wireType 2 =*/26).string(message.name);\n            if (message.opType != null && Object.hasOwnProperty.call(message, \"opType\"))\n                writer.uint32(/* id 4, wireType 2 =*/34).string(message.opType);\n            if (message.attribute != null && message.attribute.length)\n                for (var i = 0; i < message.attribute.length; ++i)\n                    $root.onnx.AttributeProto.encode(message.attribute[i], writer.uint32(/* id 5, wireType 2 =*/42).fork()).ldelim();\n            if (message.docString != null && Object.hasOwnProperty.call(message, \"docString\"))\n                writer.uint32(/* id 6, wireType 2 =*/50).string(message.docString);\n            if (message.domain != null && Object.hasOwnProperty.call(message, \"domain\"))\n                writer.uint32(/* id 7, wireType 2 =*/58).string(message.domain);\n            return writer;\n        };\n\n        /**\n         * Encodes the specified NodeProto message, length delimited. Does not implicitly {@link onnx.NodeProto.verify|verify} messages.\n         * @function encodeDelimited\n         * @memberof onnx.NodeProto\n         * @static\n         * @param {onnx.INodeProto} message NodeProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        NodeProto.encodeDelimited = function encodeDelimited(message, writer) {\n            return this.encode(message, writer).ldelim();\n        };\n\n        /**\n         * Decodes a NodeProto message from the specified reader or buffer.\n         * @function decode\n         * @memberof onnx.NodeProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @param {number} [length] Message length if known beforehand\n         * @returns {onnx.NodeProto} NodeProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        NodeProto.decode = function decode(reader, length) {\n            if (!(reader instanceof $Reader))\n                reader = $Reader.create(reader);\n            var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.NodeProto();\n            while (reader.pos < end) {\n                var tag = reader.uint32();\n                switch (tag >>> 3) {\n                case 1: {\n                        if (!(message.input && message.input.length))\n                            message.input = [];\n                        message.input.push(reader.string());\n                        break;\n                    }\n                case 2: {\n                        if (!(message.output && message.output.length))\n                            message.output = [];\n                        message.output.push(reader.string());\n                        break;\n                    }\n                case 3: {\n                        message.name = reader.string();\n                        break;\n                    }\n                case 4: {\n                        message.opType = reader.string();\n                        break;\n                    }\n                case 7: {\n                        message.domain = reader.string();\n                        break;\n                    }\n                case 5: {\n                        if (!(message.attribute && message.attribute.length))\n                            message.attribute = [];\n                        message.attribute.push($root.onnx.AttributeProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 6: {\n                        message.docString = reader.string();\n                        break;\n                    }\n                default:\n                    reader.skipType(tag & 7);\n                    break;\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Decodes a NodeProto message from the specified reader or buffer, length delimited.\n         * @function decodeDelimited\n         * @memberof onnx.NodeProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @returns {onnx.NodeProto} NodeProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        NodeProto.decodeDelimited = function decodeDelimited(reader) {\n            if (!(reader instanceof $Reader))\n                reader = new $Reader(reader);\n            return this.decode(reader, reader.uint32());\n        };\n\n        /**\n         * Verifies a NodeProto message.\n         * @function verify\n         * @memberof onnx.NodeProto\n         * @static\n         * @param {Object.<string,*>} message Plain object to verify\n         * @returns {string|null} `null` if valid, otherwise the reason why it is not\n         */\n        NodeProto.verify = function verify(message) {\n            if (typeof message !== \"object\" || message === null)\n                return \"object expected\";\n            if (message.input != null && message.hasOwnProperty(\"input\")) {\n                if (!Array.isArray(message.input))\n                    return \"input: array expected\";\n                for (var i = 0; i < message.input.length; ++i)\n                    if (!$util.isString(message.input[i]))\n                        return \"input: string[] expected\";\n            }\n            if (message.output != null && message.hasOwnProperty(\"output\")) {\n                if (!Array.isArray(message.output))\n                    return \"output: array expected\";\n                for (var i = 0; i < message.output.length; ++i)\n                    if (!$util.isString(message.output[i]))\n                        return \"output: string[] expected\";\n            }\n            if (message.name != null && message.hasOwnProperty(\"name\"))\n                if (!$util.isString(message.name))\n                    return \"name: string expected\";\n            if (message.opType != null && message.hasOwnProperty(\"opType\"))\n                if (!$util.isString(message.opType))\n                    return \"opType: string expected\";\n            if (message.domain != null && message.hasOwnProperty(\"domain\"))\n                if (!$util.isString(message.domain))\n                    return \"domain: string expected\";\n            if (message.attribute != null && message.hasOwnProperty(\"attribute\")) {\n                if (!Array.isArray(message.attribute))\n                    return \"attribute: array expected\";\n                for (var i = 0; i < message.attribute.length; ++i) {\n                    var error = $root.onnx.AttributeProto.verify(message.attribute[i]);\n                    if (error)\n                        return \"attribute.\" + error;\n                }\n            }\n            if (message.docString != null && message.hasOwnProperty(\"docString\"))\n                if (!$util.isString(message.docString))\n                    return \"docString: string expected\";\n            return null;\n        };\n\n        /**\n         * Creates a NodeProto message from a plain object. Also converts values to their respective internal types.\n         * @function fromObject\n         * @memberof onnx.NodeProto\n         * @static\n         * @param {Object.<string,*>} object Plain object\n         * @returns {onnx.NodeProto} NodeProto\n         */\n        NodeProto.fromObject = function fromObject(object) {\n            if (object instanceof $root.onnx.NodeProto)\n                return object;\n            var message = new $root.onnx.NodeProto();\n            if (object.input) {\n                if (!Array.isArray(object.input))\n                    throw TypeError(\".onnx.NodeProto.input: array expected\");\n                message.input = [];\n                for (var i = 0; i < object.input.length; ++i)\n                    message.input[i] = String(object.input[i]);\n            }\n            if (object.output) {\n                if (!Array.isArray(object.output))\n                    throw TypeError(\".onnx.NodeProto.output: array expected\");\n                message.output = [];\n                for (var i = 0; i < object.output.length; ++i)\n                    message.output[i] = String(object.output[i]);\n            }\n            if (object.name != null)\n                message.name = String(object.name);\n            if (object.opType != null)\n                message.opType = String(object.opType);\n            if (object.domain != null)\n                message.domain = String(object.domain);\n            if (object.attribute) {\n                if (!Array.isArray(object.attribute))\n                    throw TypeError(\".onnx.NodeProto.attribute: array expected\");\n                message.attribute = [];\n                for (var i = 0; i < object.attribute.length; ++i) {\n                    if (typeof object.attribute[i] !== \"object\")\n                        throw TypeError(\".onnx.NodeProto.attribute: object expected\");\n                    message.attribute[i] = $root.onnx.AttributeProto.fromObject(object.attribute[i]);\n                }\n            }\n            if (object.docString != null)\n                message.docString = String(object.docString);\n            return message;\n        };\n\n        /**\n         * Creates a plain object from a NodeProto message. Also converts values to other types if specified.\n         * @function toObject\n         * @memberof onnx.NodeProto\n         * @static\n         * @param {onnx.NodeProto} message NodeProto\n         * @param {$protobuf.IConversionOptions} [options] Conversion options\n         * @returns {Object.<string,*>} Plain object\n         */\n        NodeProto.toObject = function toObject(message, options) {\n            if (!options)\n                options = {};\n            var object = {};\n            if (options.arrays || options.defaults) {\n                object.input = [];\n                object.output = [];\n                object.attribute = [];\n            }\n            if (options.defaults) {\n                object.name = \"\";\n                object.opType = \"\";\n                object.docString = \"\";\n                object.domain = \"\";\n            }\n            if (message.input && message.input.length) {\n                object.input = [];\n                for (var j = 0; j < message.input.length; ++j)\n                    object.input[j] = message.input[j];\n            }\n            if (message.output && message.output.length) {\n                object.output = [];\n                for (var j = 0; j < message.output.length; ++j)\n                    object.output[j] = message.output[j];\n            }\n            if (message.name != null && message.hasOwnProperty(\"name\"))\n                object.name = message.name;\n            if (message.opType != null && message.hasOwnProperty(\"opType\"))\n                object.opType = message.opType;\n            if (message.attribute && message.attribute.length) {\n                object.attribute = [];\n                for (var j = 0; j < message.attribute.length; ++j)\n                    object.attribute[j] = $root.onnx.AttributeProto.toObject(message.attribute[j], options);\n            }\n            if (message.docString != null && message.hasOwnProperty(\"docString\"))\n                object.docString = message.docString;\n            if (message.domain != null && message.hasOwnProperty(\"domain\"))\n                object.domain = message.domain;\n            return object;\n        };\n\n        /**\n         * Converts this NodeProto to JSON.\n         * @function toJSON\n         * @memberof onnx.NodeProto\n         * @instance\n         * @returns {Object.<string,*>} JSON object\n         */\n        NodeProto.prototype.toJSON = function toJSON() {\n            return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n        };\n\n        /**\n         * Gets the default type url for NodeProto\n         * @function getTypeUrl\n         * @memberof onnx.NodeProto\n         * @static\n         * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n         * @returns {string} The default type url\n         */\n        NodeProto.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n            if (typeUrlPrefix === undefined) {\n                typeUrlPrefix = \"type.googleapis.com\";\n            }\n            return typeUrlPrefix + \"/onnx.NodeProto\";\n        };\n\n        return NodeProto;\n    })();\n\n    onnx.TrainingInfoProto = (function() {\n\n        /**\n         * Properties of a TrainingInfoProto.\n         * @memberof onnx\n         * @interface ITrainingInfoProto\n         * @property {onnx.IGraphProto|null} [initialization] TrainingInfoProto initialization\n         * @property {onnx.IGraphProto|null} [algorithm] TrainingInfoProto algorithm\n         * @property {Array.<onnx.IStringStringEntryProto>|null} [initializationBinding] TrainingInfoProto initializationBinding\n         * @property {Array.<onnx.IStringStringEntryProto>|null} [updateBinding] TrainingInfoProto updateBinding\n         */\n\n        /**\n         * Constructs a new TrainingInfoProto.\n         * @memberof onnx\n         * @classdesc Represents a TrainingInfoProto.\n         * @implements ITrainingInfoProto\n         * @constructor\n         * @param {onnx.ITrainingInfoProto=} [properties] Properties to set\n         */\n        function TrainingInfoProto(properties) {\n            this.initializationBinding = [];\n            this.updateBinding = [];\n            if (properties)\n                for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                    if (properties[keys[i]] != null)\n                        this[keys[i]] = properties[keys[i]];\n        }\n\n        /**\n         * TrainingInfoProto initialization.\n         * @member {onnx.IGraphProto|null|undefined} initialization\n         * @memberof onnx.TrainingInfoProto\n         * @instance\n         */\n        TrainingInfoProto.prototype.initialization = null;\n\n        /**\n         * TrainingInfoProto algorithm.\n         * @member {onnx.IGraphProto|null|undefined} algorithm\n         * @memberof onnx.TrainingInfoProto\n         * @instance\n         */\n        TrainingInfoProto.prototype.algorithm = null;\n\n        /**\n         * TrainingInfoProto initializationBinding.\n         * @member {Array.<onnx.IStringStringEntryProto>} initializationBinding\n         * @memberof onnx.TrainingInfoProto\n         * @instance\n         */\n        TrainingInfoProto.prototype.initializationBinding = $util.emptyArray;\n\n        /**\n         * TrainingInfoProto updateBinding.\n         * @member {Array.<onnx.IStringStringEntryProto>} updateBinding\n         * @memberof onnx.TrainingInfoProto\n         * @instance\n         */\n        TrainingInfoProto.prototype.updateBinding = $util.emptyArray;\n\n        /**\n         * Creates a new TrainingInfoProto instance using the specified properties.\n         * @function create\n         * @memberof onnx.TrainingInfoProto\n         * @static\n         * @param {onnx.ITrainingInfoProto=} [properties] Properties to set\n         * @returns {onnx.TrainingInfoProto} TrainingInfoProto instance\n         */\n        TrainingInfoProto.create = function create(properties) {\n            return new TrainingInfoProto(properties);\n        };\n\n        /**\n         * Encodes the specified TrainingInfoProto message. Does not implicitly {@link onnx.TrainingInfoProto.verify|verify} messages.\n         * @function encode\n         * @memberof onnx.TrainingInfoProto\n         * @static\n         * @param {onnx.ITrainingInfoProto} message TrainingInfoProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        TrainingInfoProto.encode = function encode(message, writer) {\n            if (!writer)\n                writer = $Writer.create();\n            if (message.initialization != null && Object.hasOwnProperty.call(message, \"initialization\"))\n                $root.onnx.GraphProto.encode(message.initialization, writer.uint32(/* id 1, wireType 2 =*/10).fork()).ldelim();\n            if (message.algorithm != null && Object.hasOwnProperty.call(message, \"algorithm\"))\n                $root.onnx.GraphProto.encode(message.algorithm, writer.uint32(/* id 2, wireType 2 =*/18).fork()).ldelim();\n            if (message.initializationBinding != null && message.initializationBinding.length)\n                for (var i = 0; i < message.initializationBinding.length; ++i)\n                    $root.onnx.StringStringEntryProto.encode(message.initializationBinding[i], writer.uint32(/* id 3, wireType 2 =*/26).fork()).ldelim();\n            if (message.updateBinding != null && message.updateBinding.length)\n                for (var i = 0; i < message.updateBinding.length; ++i)\n                    $root.onnx.StringStringEntryProto.encode(message.updateBinding[i], writer.uint32(/* id 4, wireType 2 =*/34).fork()).ldelim();\n            return writer;\n        };\n\n        /**\n         * Encodes the specified TrainingInfoProto message, length delimited. Does not implicitly {@link onnx.TrainingInfoProto.verify|verify} messages.\n         * @function encodeDelimited\n         * @memberof onnx.TrainingInfoProto\n         * @static\n         * @param {onnx.ITrainingInfoProto} message TrainingInfoProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        TrainingInfoProto.encodeDelimited = function encodeDelimited(message, writer) {\n            return this.encode(message, writer).ldelim();\n        };\n\n        /**\n         * Decodes a TrainingInfoProto message from the specified reader or buffer.\n         * @function decode\n         * @memberof onnx.TrainingInfoProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @param {number} [length] Message length if known beforehand\n         * @returns {onnx.TrainingInfoProto} TrainingInfoProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        TrainingInfoProto.decode = function decode(reader, length) {\n            if (!(reader instanceof $Reader))\n                reader = $Reader.create(reader);\n            var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.TrainingInfoProto();\n            while (reader.pos < end) {\n                var tag = reader.uint32();\n                switch (tag >>> 3) {\n                case 1: {\n                        message.initialization = $root.onnx.GraphProto.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 2: {\n                        message.algorithm = $root.onnx.GraphProto.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 3: {\n                        if (!(message.initializationBinding && message.initializationBinding.length))\n                            message.initializationBinding = [];\n                        message.initializationBinding.push($root.onnx.StringStringEntryProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 4: {\n                        if (!(message.updateBinding && message.updateBinding.length))\n                            message.updateBinding = [];\n                        message.updateBinding.push($root.onnx.StringStringEntryProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                default:\n                    reader.skipType(tag & 7);\n                    break;\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Decodes a TrainingInfoProto message from the specified reader or buffer, length delimited.\n         * @function decodeDelimited\n         * @memberof onnx.TrainingInfoProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @returns {onnx.TrainingInfoProto} TrainingInfoProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        TrainingInfoProto.decodeDelimited = function decodeDelimited(reader) {\n            if (!(reader instanceof $Reader))\n                reader = new $Reader(reader);\n            return this.decode(reader, reader.uint32());\n        };\n\n        /**\n         * Verifies a TrainingInfoProto message.\n         * @function verify\n         * @memberof onnx.TrainingInfoProto\n         * @static\n         * @param {Object.<string,*>} message Plain object to verify\n         * @returns {string|null} `null` if valid, otherwise the reason why it is not\n         */\n        TrainingInfoProto.verify = function verify(message) {\n            if (typeof message !== \"object\" || message === null)\n                return \"object expected\";\n            if (message.initialization != null && message.hasOwnProperty(\"initialization\")) {\n                var error = $root.onnx.GraphProto.verify(message.initialization);\n                if (error)\n                    return \"initialization.\" + error;\n            }\n            if (message.algorithm != null && message.hasOwnProperty(\"algorithm\")) {\n                var error = $root.onnx.GraphProto.verify(message.algorithm);\n                if (error)\n                    return \"algorithm.\" + error;\n            }\n            if (message.initializationBinding != null && message.hasOwnProperty(\"initializationBinding\")) {\n                if (!Array.isArray(message.initializationBinding))\n                    return \"initializationBinding: array expected\";\n                for (var i = 0; i < message.initializationBinding.length; ++i) {\n                    var error = $root.onnx.StringStringEntryProto.verify(message.initializationBinding[i]);\n                    if (error)\n                        return \"initializationBinding.\" + error;\n                }\n            }\n            if (message.updateBinding != null && message.hasOwnProperty(\"updateBinding\")) {\n                if (!Array.isArray(message.updateBinding))\n                    return \"updateBinding: array expected\";\n                for (var i = 0; i < message.updateBinding.length; ++i) {\n                    var error = $root.onnx.StringStringEntryProto.verify(message.updateBinding[i]);\n                    if (error)\n                        return \"updateBinding.\" + error;\n                }\n            }\n            return null;\n        };\n\n        /**\n         * Creates a TrainingInfoProto message from a plain object. Also converts values to their respective internal types.\n         * @function fromObject\n         * @memberof onnx.TrainingInfoProto\n         * @static\n         * @param {Object.<string,*>} object Plain object\n         * @returns {onnx.TrainingInfoProto} TrainingInfoProto\n         */\n        TrainingInfoProto.fromObject = function fromObject(object) {\n            if (object instanceof $root.onnx.TrainingInfoProto)\n                return object;\n            var message = new $root.onnx.TrainingInfoProto();\n            if (object.initialization != null) {\n                if (typeof object.initialization !== \"object\")\n                    throw TypeError(\".onnx.TrainingInfoProto.initialization: object expected\");\n                message.initialization = $root.onnx.GraphProto.fromObject(object.initialization);\n            }\n            if (object.algorithm != null) {\n                if (typeof object.algorithm !== \"object\")\n                    throw TypeError(\".onnx.TrainingInfoProto.algorithm: object expected\");\n                message.algorithm = $root.onnx.GraphProto.fromObject(object.algorithm);\n            }\n            if (object.initializationBinding) {\n                if (!Array.isArray(object.initializationBinding))\n                    throw TypeError(\".onnx.TrainingInfoProto.initializationBinding: array expected\");\n                message.initializationBinding = [];\n                for (var i = 0; i < object.initializationBinding.length; ++i) {\n                    if (typeof object.initializationBinding[i] !== \"object\")\n                        throw TypeError(\".onnx.TrainingInfoProto.initializationBinding: object expected\");\n                    message.initializationBinding[i] = $root.onnx.StringStringEntryProto.fromObject(object.initializationBinding[i]);\n                }\n            }\n            if (object.updateBinding) {\n                if (!Array.isArray(object.updateBinding))\n                    throw TypeError(\".onnx.TrainingInfoProto.updateBinding: array expected\");\n                message.updateBinding = [];\n                for (var i = 0; i < object.updateBinding.length; ++i) {\n                    if (typeof object.updateBinding[i] !== \"object\")\n                        throw TypeError(\".onnx.TrainingInfoProto.updateBinding: object expected\");\n                    message.updateBinding[i] = $root.onnx.StringStringEntryProto.fromObject(object.updateBinding[i]);\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Creates a plain object from a TrainingInfoProto message. Also converts values to other types if specified.\n         * @function toObject\n         * @memberof onnx.TrainingInfoProto\n         * @static\n         * @param {onnx.TrainingInfoProto} message TrainingInfoProto\n         * @param {$protobuf.IConversionOptions} [options] Conversion options\n         * @returns {Object.<string,*>} Plain object\n         */\n        TrainingInfoProto.toObject = function toObject(message, options) {\n            if (!options)\n                options = {};\n            var object = {};\n            if (options.arrays || options.defaults) {\n                object.initializationBinding = [];\n                object.updateBinding = [];\n            }\n            if (options.defaults) {\n                object.initialization = null;\n                object.algorithm = null;\n            }\n            if (message.initialization != null && message.hasOwnProperty(\"initialization\"))\n                object.initialization = $root.onnx.GraphProto.toObject(message.initialization, options);\n            if (message.algorithm != null && message.hasOwnProperty(\"algorithm\"))\n                object.algorithm = $root.onnx.GraphProto.toObject(message.algorithm, options);\n            if (message.initializationBinding && message.initializationBinding.length) {\n                object.initializationBinding = [];\n                for (var j = 0; j < message.initializationBinding.length; ++j)\n                    object.initializationBinding[j] = $root.onnx.StringStringEntryProto.toObject(message.initializationBinding[j], options);\n            }\n            if (message.updateBinding && message.updateBinding.length) {\n                object.updateBinding = [];\n                for (var j = 0; j < message.updateBinding.length; ++j)\n                    object.updateBinding[j] = $root.onnx.StringStringEntryProto.toObject(message.updateBinding[j], options);\n            }\n            return object;\n        };\n\n        /**\n         * Converts this TrainingInfoProto to JSON.\n         * @function toJSON\n         * @memberof onnx.TrainingInfoProto\n         * @instance\n         * @returns {Object.<string,*>} JSON object\n         */\n        TrainingInfoProto.prototype.toJSON = function toJSON() {\n            return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n        };\n\n        /**\n         * Gets the default type url for TrainingInfoProto\n         * @function getTypeUrl\n         * @memberof onnx.TrainingInfoProto\n         * @static\n         * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n         * @returns {string} The default type url\n         */\n        TrainingInfoProto.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n            if (typeUrlPrefix === undefined) {\n                typeUrlPrefix = \"type.googleapis.com\";\n            }\n            return typeUrlPrefix + \"/onnx.TrainingInfoProto\";\n        };\n\n        return TrainingInfoProto;\n    })();\n\n    onnx.ModelProto = (function() {\n\n        /**\n         * Properties of a ModelProto.\n         * @memberof onnx\n         * @interface IModelProto\n         * @property {number|Long|null} [irVersion] ModelProto irVersion\n         * @property {Array.<onnx.IOperatorSetIdProto>|null} [opsetImport] ModelProto opsetImport\n         * @property {string|null} [producerName] ModelProto producerName\n         * @property {string|null} [producerVersion] ModelProto producerVersion\n         * @property {string|null} [domain] ModelProto domain\n         * @property {number|Long|null} [modelVersion] ModelProto modelVersion\n         * @property {string|null} [docString] ModelProto docString\n         * @property {onnx.IGraphProto|null} [graph] ModelProto graph\n         * @property {Array.<onnx.IStringStringEntryProto>|null} [metadataProps] ModelProto metadataProps\n         * @property {Array.<onnx.ITrainingInfoProto>|null} [trainingInfo] ModelProto trainingInfo\n         * @property {Array.<onnx.IFunctionProto>|null} [functions] ModelProto functions\n         */\n\n        /**\n         * Constructs a new ModelProto.\n         * @memberof onnx\n         * @classdesc Represents a ModelProto.\n         * @implements IModelProto\n         * @constructor\n         * @param {onnx.IModelProto=} [properties] Properties to set\n         */\n        function ModelProto(properties) {\n            this.opsetImport = [];\n            this.metadataProps = [];\n            this.trainingInfo = [];\n            this.functions = [];\n            if (properties)\n                for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                    if (properties[keys[i]] != null)\n                        this[keys[i]] = properties[keys[i]];\n        }\n\n        /**\n         * ModelProto irVersion.\n         * @member {number|Long} irVersion\n         * @memberof onnx.ModelProto\n         * @instance\n         */\n        ModelProto.prototype.irVersion = $util.Long ? $util.Long.fromBits(0,0,false) : 0;\n\n        /**\n         * ModelProto opsetImport.\n         * @member {Array.<onnx.IOperatorSetIdProto>} opsetImport\n         * @memberof onnx.ModelProto\n         * @instance\n         */\n        ModelProto.prototype.opsetImport = $util.emptyArray;\n\n        /**\n         * ModelProto producerName.\n         * @member {string} producerName\n         * @memberof onnx.ModelProto\n         * @instance\n         */\n        ModelProto.prototype.producerName = \"\";\n\n        /**\n         * ModelProto producerVersion.\n         * @member {string} producerVersion\n         * @memberof onnx.ModelProto\n         * @instance\n         */\n        ModelProto.prototype.producerVersion = \"\";\n\n        /**\n         * ModelProto domain.\n         * @member {string} domain\n         * @memberof onnx.ModelProto\n         * @instance\n         */\n        ModelProto.prototype.domain = \"\";\n\n        /**\n         * ModelProto modelVersion.\n         * @member {number|Long} modelVersion\n         * @memberof onnx.ModelProto\n         * @instance\n         */\n        ModelProto.prototype.modelVersion = $util.Long ? $util.Long.fromBits(0,0,false) : 0;\n\n        /**\n         * ModelProto docString.\n         * @member {string} docString\n         * @memberof onnx.ModelProto\n         * @instance\n         */\n        ModelProto.prototype.docString = \"\";\n\n        /**\n         * ModelProto graph.\n         * @member {onnx.IGraphProto|null|undefined} graph\n         * @memberof onnx.ModelProto\n         * @instance\n         */\n        ModelProto.prototype.graph = null;\n\n        /**\n         * ModelProto metadataProps.\n         * @member {Array.<onnx.IStringStringEntryProto>} metadataProps\n         * @memberof onnx.ModelProto\n         * @instance\n         */\n        ModelProto.prototype.metadataProps = $util.emptyArray;\n\n        /**\n         * ModelProto trainingInfo.\n         * @member {Array.<onnx.ITrainingInfoProto>} trainingInfo\n         * @memberof onnx.ModelProto\n         * @instance\n         */\n        ModelProto.prototype.trainingInfo = $util.emptyArray;\n\n        /**\n         * ModelProto functions.\n         * @member {Array.<onnx.IFunctionProto>} functions\n         * @memberof onnx.ModelProto\n         * @instance\n         */\n        ModelProto.prototype.functions = $util.emptyArray;\n\n        /**\n         * Creates a new ModelProto instance using the specified properties.\n         * @function create\n         * @memberof onnx.ModelProto\n         * @static\n         * @param {onnx.IModelProto=} [properties] Properties to set\n         * @returns {onnx.ModelProto} ModelProto instance\n         */\n        ModelProto.create = function create(properties) {\n            return new ModelProto(properties);\n        };\n\n        /**\n         * Encodes the specified ModelProto message. Does not implicitly {@link onnx.ModelProto.verify|verify} messages.\n         * @function encode\n         * @memberof onnx.ModelProto\n         * @static\n         * @param {onnx.IModelProto} message ModelProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        ModelProto.encode = function encode(message, writer) {\n            if (!writer)\n                writer = $Writer.create();\n            if (message.irVersion != null && Object.hasOwnProperty.call(message, \"irVersion\"))\n                writer.uint32(/* id 1, wireType 0 =*/8).int64(message.irVersion);\n            if (message.producerName != null && Object.hasOwnProperty.call(message, \"producerName\"))\n                writer.uint32(/* id 2, wireType 2 =*/18).string(message.producerName);\n            if (message.producerVersion != null && Object.hasOwnProperty.call(message, \"producerVersion\"))\n                writer.uint32(/* id 3, wireType 2 =*/26).string(message.producerVersion);\n            if (message.domain != null && Object.hasOwnProperty.call(message, \"domain\"))\n                writer.uint32(/* id 4, wireType 2 =*/34).string(message.domain);\n            if (message.modelVersion != null && Object.hasOwnProperty.call(message, \"modelVersion\"))\n                writer.uint32(/* id 5, wireType 0 =*/40).int64(message.modelVersion);\n            if (message.docString != null && Object.hasOwnProperty.call(message, \"docString\"))\n                writer.uint32(/* id 6, wireType 2 =*/50).string(message.docString);\n            if (message.graph != null && Object.hasOwnProperty.call(message, \"graph\"))\n                $root.onnx.GraphProto.encode(message.graph, writer.uint32(/* id 7, wireType 2 =*/58).fork()).ldelim();\n            if (message.opsetImport != null && message.opsetImport.length)\n                for (var i = 0; i < message.opsetImport.length; ++i)\n                    $root.onnx.OperatorSetIdProto.encode(message.opsetImport[i], writer.uint32(/* id 8, wireType 2 =*/66).fork()).ldelim();\n            if (message.metadataProps != null && message.metadataProps.length)\n                for (var i = 0; i < message.metadataProps.length; ++i)\n                    $root.onnx.StringStringEntryProto.encode(message.metadataProps[i], writer.uint32(/* id 14, wireType 2 =*/114).fork()).ldelim();\n            if (message.trainingInfo != null && message.trainingInfo.length)\n                for (var i = 0; i < message.trainingInfo.length; ++i)\n                    $root.onnx.TrainingInfoProto.encode(message.trainingInfo[i], writer.uint32(/* id 20, wireType 2 =*/162).fork()).ldelim();\n            if (message.functions != null && message.functions.length)\n                for (var i = 0; i < message.functions.length; ++i)\n                    $root.onnx.FunctionProto.encode(message.functions[i], writer.uint32(/* id 25, wireType 2 =*/202).fork()).ldelim();\n            return writer;\n        };\n\n        /**\n         * Encodes the specified ModelProto message, length delimited. Does not implicitly {@link onnx.ModelProto.verify|verify} messages.\n         * @function encodeDelimited\n         * @memberof onnx.ModelProto\n         * @static\n         * @param {onnx.IModelProto} message ModelProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        ModelProto.encodeDelimited = function encodeDelimited(message, writer) {\n            return this.encode(message, writer).ldelim();\n        };\n\n        /**\n         * Decodes a ModelProto message from the specified reader or buffer.\n         * @function decode\n         * @memberof onnx.ModelProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @param {number} [length] Message length if known beforehand\n         * @returns {onnx.ModelProto} ModelProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        ModelProto.decode = function decode(reader, length) {\n            if (!(reader instanceof $Reader))\n                reader = $Reader.create(reader);\n            var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.ModelProto();\n            while (reader.pos < end) {\n                var tag = reader.uint32();\n                switch (tag >>> 3) {\n                case 1: {\n                        message.irVersion = reader.int64();\n                        break;\n                    }\n                case 8: {\n                        if (!(message.opsetImport && message.opsetImport.length))\n                            message.opsetImport = [];\n                        message.opsetImport.push($root.onnx.OperatorSetIdProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 2: {\n                        message.producerName = reader.string();\n                        break;\n                    }\n                case 3: {\n                        message.producerVersion = reader.string();\n                        break;\n                    }\n                case 4: {\n                        message.domain = reader.string();\n                        break;\n                    }\n                case 5: {\n                        message.modelVersion = reader.int64();\n                        break;\n                    }\n                case 6: {\n                        message.docString = reader.string();\n                        break;\n                    }\n                case 7: {\n                        message.graph = $root.onnx.GraphProto.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 14: {\n                        if (!(message.metadataProps && message.metadataProps.length))\n                            message.metadataProps = [];\n                        message.metadataProps.push($root.onnx.StringStringEntryProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 20: {\n                        if (!(message.trainingInfo && message.trainingInfo.length))\n                            message.trainingInfo = [];\n                        message.trainingInfo.push($root.onnx.TrainingInfoProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 25: {\n                        if (!(message.functions && message.functions.length))\n                            message.functions = [];\n                        message.functions.push($root.onnx.FunctionProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                default:\n                    reader.skipType(tag & 7);\n                    break;\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Decodes a ModelProto message from the specified reader or buffer, length delimited.\n         * @function decodeDelimited\n         * @memberof onnx.ModelProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @returns {onnx.ModelProto} ModelProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        ModelProto.decodeDelimited = function decodeDelimited(reader) {\n            if (!(reader instanceof $Reader))\n                reader = new $Reader(reader);\n            return this.decode(reader, reader.uint32());\n        };\n\n        /**\n         * Verifies a ModelProto message.\n         * @function verify\n         * @memberof onnx.ModelProto\n         * @static\n         * @param {Object.<string,*>} message Plain object to verify\n         * @returns {string|null} `null` if valid, otherwise the reason why it is not\n         */\n        ModelProto.verify = function verify(message) {\n            if (typeof message !== \"object\" || message === null)\n                return \"object expected\";\n            if (message.irVersion != null && message.hasOwnProperty(\"irVersion\"))\n                if (!$util.isInteger(message.irVersion) && !(message.irVersion && $util.isInteger(message.irVersion.low) && $util.isInteger(message.irVersion.high)))\n                    return \"irVersion: integer|Long expected\";\n            if (message.opsetImport != null && message.hasOwnProperty(\"opsetImport\")) {\n                if (!Array.isArray(message.opsetImport))\n                    return \"opsetImport: array expected\";\n                for (var i = 0; i < message.opsetImport.length; ++i) {\n                    var error = $root.onnx.OperatorSetIdProto.verify(message.opsetImport[i]);\n                    if (error)\n                        return \"opsetImport.\" + error;\n                }\n            }\n            if (message.producerName != null && message.hasOwnProperty(\"producerName\"))\n                if (!$util.isString(message.producerName))\n                    return \"producerName: string expected\";\n            if (message.producerVersion != null && message.hasOwnProperty(\"producerVersion\"))\n                if (!$util.isString(message.producerVersion))\n                    return \"producerVersion: string expected\";\n            if (message.domain != null && message.hasOwnProperty(\"domain\"))\n                if (!$util.isString(message.domain))\n                    return \"domain: string expected\";\n            if (message.modelVersion != null && message.hasOwnProperty(\"modelVersion\"))\n                if (!$util.isInteger(message.modelVersion) && !(message.modelVersion && $util.isInteger(message.modelVersion.low) && $util.isInteger(message.modelVersion.high)))\n                    return \"modelVersion: integer|Long expected\";\n            if (message.docString != null && message.hasOwnProperty(\"docString\"))\n                if (!$util.isString(message.docString))\n                    return \"docString: string expected\";\n            if (message.graph != null && message.hasOwnProperty(\"graph\")) {\n                var error = $root.onnx.GraphProto.verify(message.graph);\n                if (error)\n                    return \"graph.\" + error;\n            }\n            if (message.metadataProps != null && message.hasOwnProperty(\"metadataProps\")) {\n                if (!Array.isArray(message.metadataProps))\n                    return \"metadataProps: array expected\";\n                for (var i = 0; i < message.metadataProps.length; ++i) {\n                    var error = $root.onnx.StringStringEntryProto.verify(message.metadataProps[i]);\n                    if (error)\n                        return \"metadataProps.\" + error;\n                }\n            }\n            if (message.trainingInfo != null && message.hasOwnProperty(\"trainingInfo\")) {\n                if (!Array.isArray(message.trainingInfo))\n                    return \"trainingInfo: array expected\";\n                for (var i = 0; i < message.trainingInfo.length; ++i) {\n                    var error = $root.onnx.TrainingInfoProto.verify(message.trainingInfo[i]);\n                    if (error)\n                        return \"trainingInfo.\" + error;\n                }\n            }\n            if (message.functions != null && message.hasOwnProperty(\"functions\")) {\n                if (!Array.isArray(message.functions))\n                    return \"functions: array expected\";\n                for (var i = 0; i < message.functions.length; ++i) {\n                    var error = $root.onnx.FunctionProto.verify(message.functions[i]);\n                    if (error)\n                        return \"functions.\" + error;\n                }\n            }\n            return null;\n        };\n\n        /**\n         * Creates a ModelProto message from a plain object. Also converts values to their respective internal types.\n         * @function fromObject\n         * @memberof onnx.ModelProto\n         * @static\n         * @param {Object.<string,*>} object Plain object\n         * @returns {onnx.ModelProto} ModelProto\n         */\n        ModelProto.fromObject = function fromObject(object) {\n            if (object instanceof $root.onnx.ModelProto)\n                return object;\n            var message = new $root.onnx.ModelProto();\n            if (object.irVersion != null)\n                if ($util.Long)\n                    (message.irVersion = $util.Long.fromValue(object.irVersion)).unsigned = false;\n                else if (typeof object.irVersion === \"string\")\n                    message.irVersion = parseInt(object.irVersion, 10);\n                else if (typeof object.irVersion === \"number\")\n                    message.irVersion = object.irVersion;\n                else if (typeof object.irVersion === \"object\")\n                    message.irVersion = new $util.LongBits(object.irVersion.low >>> 0, object.irVersion.high >>> 0).toNumber();\n            if (object.opsetImport) {\n                if (!Array.isArray(object.opsetImport))\n                    throw TypeError(\".onnx.ModelProto.opsetImport: array expected\");\n                message.opsetImport = [];\n                for (var i = 0; i < object.opsetImport.length; ++i) {\n                    if (typeof object.opsetImport[i] !== \"object\")\n                        throw TypeError(\".onnx.ModelProto.opsetImport: object expected\");\n                    message.opsetImport[i] = $root.onnx.OperatorSetIdProto.fromObject(object.opsetImport[i]);\n                }\n            }\n            if (object.producerName != null)\n                message.producerName = String(object.producerName);\n            if (object.producerVersion != null)\n                message.producerVersion = String(object.producerVersion);\n            if (object.domain != null)\n                message.domain = String(object.domain);\n            if (object.modelVersion != null)\n                if ($util.Long)\n                    (message.modelVersion = $util.Long.fromValue(object.modelVersion)).unsigned = false;\n                else if (typeof object.modelVersion === \"string\")\n                    message.modelVersion = parseInt(object.modelVersion, 10);\n                else if (typeof object.modelVersion === \"number\")\n                    message.modelVersion = object.modelVersion;\n                else if (typeof object.modelVersion === \"object\")\n                    message.modelVersion = new $util.LongBits(object.modelVersion.low >>> 0, object.modelVersion.high >>> 0).toNumber();\n            if (object.docString != null)\n                message.docString = String(object.docString);\n            if (object.graph != null) {\n                if (typeof object.graph !== \"object\")\n                    throw TypeError(\".onnx.ModelProto.graph: object expected\");\n                message.graph = $root.onnx.GraphProto.fromObject(object.graph);\n            }\n            if (object.metadataProps) {\n                if (!Array.isArray(object.metadataProps))\n                    throw TypeError(\".onnx.ModelProto.metadataProps: array expected\");\n                message.metadataProps = [];\n                for (var i = 0; i < object.metadataProps.length; ++i) {\n                    if (typeof object.metadataProps[i] !== \"object\")\n                        throw TypeError(\".onnx.ModelProto.metadataProps: object expected\");\n                    message.metadataProps[i] = $root.onnx.StringStringEntryProto.fromObject(object.metadataProps[i]);\n                }\n            }\n            if (object.trainingInfo) {\n                if (!Array.isArray(object.trainingInfo))\n                    throw TypeError(\".onnx.ModelProto.trainingInfo: array expected\");\n                message.trainingInfo = [];\n                for (var i = 0; i < object.trainingInfo.length; ++i) {\n                    if (typeof object.trainingInfo[i] !== \"object\")\n                        throw TypeError(\".onnx.ModelProto.trainingInfo: object expected\");\n                    message.trainingInfo[i] = $root.onnx.TrainingInfoProto.fromObject(object.trainingInfo[i]);\n                }\n            }\n            if (object.functions) {\n                if (!Array.isArray(object.functions))\n                    throw TypeError(\".onnx.ModelProto.functions: array expected\");\n                message.functions = [];\n                for (var i = 0; i < object.functions.length; ++i) {\n                    if (typeof object.functions[i] !== \"object\")\n                        throw TypeError(\".onnx.ModelProto.functions: object expected\");\n                    message.functions[i] = $root.onnx.FunctionProto.fromObject(object.functions[i]);\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Creates a plain object from a ModelProto message. Also converts values to other types if specified.\n         * @function toObject\n         * @memberof onnx.ModelProto\n         * @static\n         * @param {onnx.ModelProto} message ModelProto\n         * @param {$protobuf.IConversionOptions} [options] Conversion options\n         * @returns {Object.<string,*>} Plain object\n         */\n        ModelProto.toObject = function toObject(message, options) {\n            if (!options)\n                options = {};\n            var object = {};\n            if (options.arrays || options.defaults) {\n                object.opsetImport = [];\n                object.metadataProps = [];\n                object.trainingInfo = [];\n                object.functions = [];\n            }\n            if (options.defaults) {\n                if ($util.Long) {\n                    var long = new $util.Long(0, 0, false);\n                    object.irVersion = options.longs === String ? long.toString() : options.longs === Number ? long.toNumber() : long;\n                } else\n                    object.irVersion = options.longs === String ? \"0\" : 0;\n                object.producerName = \"\";\n                object.producerVersion = \"\";\n                object.domain = \"\";\n                if ($util.Long) {\n                    var long = new $util.Long(0, 0, false);\n                    object.modelVersion = options.longs === String ? long.toString() : options.longs === Number ? long.toNumber() : long;\n                } else\n                    object.modelVersion = options.longs === String ? \"0\" : 0;\n                object.docString = \"\";\n                object.graph = null;\n            }\n            if (message.irVersion != null && message.hasOwnProperty(\"irVersion\"))\n                if (typeof message.irVersion === \"number\")\n                    object.irVersion = options.longs === String ? String(message.irVersion) : message.irVersion;\n                else\n                    object.irVersion = options.longs === String ? $util.Long.prototype.toString.call(message.irVersion) : options.longs === Number ? new $util.LongBits(message.irVersion.low >>> 0, message.irVersion.high >>> 0).toNumber() : message.irVersion;\n            if (message.producerName != null && message.hasOwnProperty(\"producerName\"))\n                object.producerName = message.producerName;\n            if (message.producerVersion != null && message.hasOwnProperty(\"producerVersion\"))\n                object.producerVersion = message.producerVersion;\n            if (message.domain != null && message.hasOwnProperty(\"domain\"))\n                object.domain = message.domain;\n            if (message.modelVersion != null && message.hasOwnProperty(\"modelVersion\"))\n                if (typeof message.modelVersion === \"number\")\n                    object.modelVersion = options.longs === String ? String(message.modelVersion) : message.modelVersion;\n                else\n                    object.modelVersion = options.longs === String ? $util.Long.prototype.toString.call(message.modelVersion) : options.longs === Number ? new $util.LongBits(message.modelVersion.low >>> 0, message.modelVersion.high >>> 0).toNumber() : message.modelVersion;\n            if (message.docString != null && message.hasOwnProperty(\"docString\"))\n                object.docString = message.docString;\n            if (message.graph != null && message.hasOwnProperty(\"graph\"))\n                object.graph = $root.onnx.GraphProto.toObject(message.graph, options);\n            if (message.opsetImport && message.opsetImport.length) {\n                object.opsetImport = [];\n                for (var j = 0; j < message.opsetImport.length; ++j)\n                    object.opsetImport[j] = $root.onnx.OperatorSetIdProto.toObject(message.opsetImport[j], options);\n            }\n            if (message.metadataProps && message.metadataProps.length) {\n                object.metadataProps = [];\n                for (var j = 0; j < message.metadataProps.length; ++j)\n                    object.metadataProps[j] = $root.onnx.StringStringEntryProto.toObject(message.metadataProps[j], options);\n            }\n            if (message.trainingInfo && message.trainingInfo.length) {\n                object.trainingInfo = [];\n                for (var j = 0; j < message.trainingInfo.length; ++j)\n                    object.trainingInfo[j] = $root.onnx.TrainingInfoProto.toObject(message.trainingInfo[j], options);\n            }\n            if (message.functions && message.functions.length) {\n                object.functions = [];\n                for (var j = 0; j < message.functions.length; ++j)\n                    object.functions[j] = $root.onnx.FunctionProto.toObject(message.functions[j], options);\n            }\n            return object;\n        };\n\n        /**\n         * Converts this ModelProto to JSON.\n         * @function toJSON\n         * @memberof onnx.ModelProto\n         * @instance\n         * @returns {Object.<string,*>} JSON object\n         */\n        ModelProto.prototype.toJSON = function toJSON() {\n            return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n        };\n\n        /**\n         * Gets the default type url for ModelProto\n         * @function getTypeUrl\n         * @memberof onnx.ModelProto\n         * @static\n         * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n         * @returns {string} The default type url\n         */\n        ModelProto.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n            if (typeUrlPrefix === undefined) {\n                typeUrlPrefix = \"type.googleapis.com\";\n            }\n            return typeUrlPrefix + \"/onnx.ModelProto\";\n        };\n\n        return ModelProto;\n    })();\n\n    onnx.StringStringEntryProto = (function() {\n\n        /**\n         * Properties of a StringStringEntryProto.\n         * @memberof onnx\n         * @interface IStringStringEntryProto\n         * @property {string|null} [key] StringStringEntryProto key\n         * @property {string|null} [value] StringStringEntryProto value\n         */\n\n        /**\n         * Constructs a new StringStringEntryProto.\n         * @memberof onnx\n         * @classdesc Represents a StringStringEntryProto.\n         * @implements IStringStringEntryProto\n         * @constructor\n         * @param {onnx.IStringStringEntryProto=} [properties] Properties to set\n         */\n        function StringStringEntryProto(properties) {\n            if (properties)\n                for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                    if (properties[keys[i]] != null)\n                        this[keys[i]] = properties[keys[i]];\n        }\n\n        /**\n         * StringStringEntryProto key.\n         * @member {string} key\n         * @memberof onnx.StringStringEntryProto\n         * @instance\n         */\n        StringStringEntryProto.prototype.key = \"\";\n\n        /**\n         * StringStringEntryProto value.\n         * @member {string} value\n         * @memberof onnx.StringStringEntryProto\n         * @instance\n         */\n        StringStringEntryProto.prototype.value = \"\";\n\n        /**\n         * Creates a new StringStringEntryProto instance using the specified properties.\n         * @function create\n         * @memberof onnx.StringStringEntryProto\n         * @static\n         * @param {onnx.IStringStringEntryProto=} [properties] Properties to set\n         * @returns {onnx.StringStringEntryProto} StringStringEntryProto instance\n         */\n        StringStringEntryProto.create = function create(properties) {\n            return new StringStringEntryProto(properties);\n        };\n\n        /**\n         * Encodes the specified StringStringEntryProto message. Does not implicitly {@link onnx.StringStringEntryProto.verify|verify} messages.\n         * @function encode\n         * @memberof onnx.StringStringEntryProto\n         * @static\n         * @param {onnx.IStringStringEntryProto} message StringStringEntryProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        StringStringEntryProto.encode = function encode(message, writer) {\n            if (!writer)\n                writer = $Writer.create();\n            if (message.key != null && Object.hasOwnProperty.call(message, \"key\"))\n                writer.uint32(/* id 1, wireType 2 =*/10).string(message.key);\n            if (message.value != null && Object.hasOwnProperty.call(message, \"value\"))\n                writer.uint32(/* id 2, wireType 2 =*/18).string(message.value);\n            return writer;\n        };\n\n        /**\n         * Encodes the specified StringStringEntryProto message, length delimited. Does not implicitly {@link onnx.StringStringEntryProto.verify|verify} messages.\n         * @function encodeDelimited\n         * @memberof onnx.StringStringEntryProto\n         * @static\n         * @param {onnx.IStringStringEntryProto} message StringStringEntryProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        StringStringEntryProto.encodeDelimited = function encodeDelimited(message, writer) {\n            return this.encode(message, writer).ldelim();\n        };\n\n        /**\n         * Decodes a StringStringEntryProto message from the specified reader or buffer.\n         * @function decode\n         * @memberof onnx.StringStringEntryProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @param {number} [length] Message length if known beforehand\n         * @returns {onnx.StringStringEntryProto} StringStringEntryProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        StringStringEntryProto.decode = function decode(reader, length) {\n            if (!(reader instanceof $Reader))\n                reader = $Reader.create(reader);\n            var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.StringStringEntryProto();\n            while (reader.pos < end) {\n                var tag = reader.uint32();\n                switch (tag >>> 3) {\n                case 1: {\n                        message.key = reader.string();\n                        break;\n                    }\n                case 2: {\n                        message.value = reader.string();\n                        break;\n                    }\n                default:\n                    reader.skipType(tag & 7);\n                    break;\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Decodes a StringStringEntryProto message from the specified reader or buffer, length delimited.\n         * @function decodeDelimited\n         * @memberof onnx.StringStringEntryProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @returns {onnx.StringStringEntryProto} StringStringEntryProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        StringStringEntryProto.decodeDelimited = function decodeDelimited(reader) {\n            if (!(reader instanceof $Reader))\n                reader = new $Reader(reader);\n            return this.decode(reader, reader.uint32());\n        };\n\n        /**\n         * Verifies a StringStringEntryProto message.\n         * @function verify\n         * @memberof onnx.StringStringEntryProto\n         * @static\n         * @param {Object.<string,*>} message Plain object to verify\n         * @returns {string|null} `null` if valid, otherwise the reason why it is not\n         */\n        StringStringEntryProto.verify = function verify(message) {\n            if (typeof message !== \"object\" || message === null)\n                return \"object expected\";\n            if (message.key != null && message.hasOwnProperty(\"key\"))\n                if (!$util.isString(message.key))\n                    return \"key: string expected\";\n            if (message.value != null && message.hasOwnProperty(\"value\"))\n                if (!$util.isString(message.value))\n                    return \"value: string expected\";\n            return null;\n        };\n\n        /**\n         * Creates a StringStringEntryProto message from a plain object. Also converts values to their respective internal types.\n         * @function fromObject\n         * @memberof onnx.StringStringEntryProto\n         * @static\n         * @param {Object.<string,*>} object Plain object\n         * @returns {onnx.StringStringEntryProto} StringStringEntryProto\n         */\n        StringStringEntryProto.fromObject = function fromObject(object) {\n            if (object instanceof $root.onnx.StringStringEntryProto)\n                return object;\n            var message = new $root.onnx.StringStringEntryProto();\n            if (object.key != null)\n                message.key = String(object.key);\n            if (object.value != null)\n                message.value = String(object.value);\n            return message;\n        };\n\n        /**\n         * Creates a plain object from a StringStringEntryProto message. Also converts values to other types if specified.\n         * @function toObject\n         * @memberof onnx.StringStringEntryProto\n         * @static\n         * @param {onnx.StringStringEntryProto} message StringStringEntryProto\n         * @param {$protobuf.IConversionOptions} [options] Conversion options\n         * @returns {Object.<string,*>} Plain object\n         */\n        StringStringEntryProto.toObject = function toObject(message, options) {\n            if (!options)\n                options = {};\n            var object = {};\n            if (options.defaults) {\n                object.key = \"\";\n                object.value = \"\";\n            }\n            if (message.key != null && message.hasOwnProperty(\"key\"))\n                object.key = message.key;\n            if (message.value != null && message.hasOwnProperty(\"value\"))\n                object.value = message.value;\n            return object;\n        };\n\n        /**\n         * Converts this StringStringEntryProto to JSON.\n         * @function toJSON\n         * @memberof onnx.StringStringEntryProto\n         * @instance\n         * @returns {Object.<string,*>} JSON object\n         */\n        StringStringEntryProto.prototype.toJSON = function toJSON() {\n            return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n        };\n\n        /**\n         * Gets the default type url for StringStringEntryProto\n         * @function getTypeUrl\n         * @memberof onnx.StringStringEntryProto\n         * @static\n         * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n         * @returns {string} The default type url\n         */\n        StringStringEntryProto.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n            if (typeUrlPrefix === undefined) {\n                typeUrlPrefix = \"type.googleapis.com\";\n            }\n            return typeUrlPrefix + \"/onnx.StringStringEntryProto\";\n        };\n\n        return StringStringEntryProto;\n    })();\n\n    onnx.TensorAnnotation = (function() {\n\n        /**\n         * Properties of a TensorAnnotation.\n         * @memberof onnx\n         * @interface ITensorAnnotation\n         * @property {string|null} [tensorName] TensorAnnotation tensorName\n         * @property {Array.<onnx.IStringStringEntryProto>|null} [quantParameterTensorNames] TensorAnnotation quantParameterTensorNames\n         */\n\n        /**\n         * Constructs a new TensorAnnotation.\n         * @memberof onnx\n         * @classdesc Represents a TensorAnnotation.\n         * @implements ITensorAnnotation\n         * @constructor\n         * @param {onnx.ITensorAnnotation=} [properties] Properties to set\n         */\n        function TensorAnnotation(properties) {\n            this.quantParameterTensorNames = [];\n            if (properties)\n                for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                    if (properties[keys[i]] != null)\n                        this[keys[i]] = properties[keys[i]];\n        }\n\n        /**\n         * TensorAnnotation tensorName.\n         * @member {string} tensorName\n         * @memberof onnx.TensorAnnotation\n         * @instance\n         */\n        TensorAnnotation.prototype.tensorName = \"\";\n\n        /**\n         * TensorAnnotation quantParameterTensorNames.\n         * @member {Array.<onnx.IStringStringEntryProto>} quantParameterTensorNames\n         * @memberof onnx.TensorAnnotation\n         * @instance\n         */\n        TensorAnnotation.prototype.quantParameterTensorNames = $util.emptyArray;\n\n        /**\n         * Creates a new TensorAnnotation instance using the specified properties.\n         * @function create\n         * @memberof onnx.TensorAnnotation\n         * @static\n         * @param {onnx.ITensorAnnotation=} [properties] Properties to set\n         * @returns {onnx.TensorAnnotation} TensorAnnotation instance\n         */\n        TensorAnnotation.create = function create(properties) {\n            return new TensorAnnotation(properties);\n        };\n\n        /**\n         * Encodes the specified TensorAnnotation message. Does not implicitly {@link onnx.TensorAnnotation.verify|verify} messages.\n         * @function encode\n         * @memberof onnx.TensorAnnotation\n         * @static\n         * @param {onnx.ITensorAnnotation} message TensorAnnotation message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        TensorAnnotation.encode = function encode(message, writer) {\n            if (!writer)\n                writer = $Writer.create();\n            if (message.tensorName != null && Object.hasOwnProperty.call(message, \"tensorName\"))\n                writer.uint32(/* id 1, wireType 2 =*/10).string(message.tensorName);\n            if (message.quantParameterTensorNames != null && message.quantParameterTensorNames.length)\n                for (var i = 0; i < message.quantParameterTensorNames.length; ++i)\n                    $root.onnx.StringStringEntryProto.encode(message.quantParameterTensorNames[i], writer.uint32(/* id 2, wireType 2 =*/18).fork()).ldelim();\n            return writer;\n        };\n\n        /**\n         * Encodes the specified TensorAnnotation message, length delimited. Does not implicitly {@link onnx.TensorAnnotation.verify|verify} messages.\n         * @function encodeDelimited\n         * @memberof onnx.TensorAnnotation\n         * @static\n         * @param {onnx.ITensorAnnotation} message TensorAnnotation message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        TensorAnnotation.encodeDelimited = function encodeDelimited(message, writer) {\n            return this.encode(message, writer).ldelim();\n        };\n\n        /**\n         * Decodes a TensorAnnotation message from the specified reader or buffer.\n         * @function decode\n         * @memberof onnx.TensorAnnotation\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @param {number} [length] Message length if known beforehand\n         * @returns {onnx.TensorAnnotation} TensorAnnotation\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        TensorAnnotation.decode = function decode(reader, length) {\n            if (!(reader instanceof $Reader))\n                reader = $Reader.create(reader);\n            var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.TensorAnnotation();\n            while (reader.pos < end) {\n                var tag = reader.uint32();\n                switch (tag >>> 3) {\n                case 1: {\n                        message.tensorName = reader.string();\n                        break;\n                    }\n                case 2: {\n                        if (!(message.quantParameterTensorNames && message.quantParameterTensorNames.length))\n                            message.quantParameterTensorNames = [];\n                        message.quantParameterTensorNames.push($root.onnx.StringStringEntryProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                default:\n                    reader.skipType(tag & 7);\n                    break;\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Decodes a TensorAnnotation message from the specified reader or buffer, length delimited.\n         * @function decodeDelimited\n         * @memberof onnx.TensorAnnotation\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @returns {onnx.TensorAnnotation} TensorAnnotation\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        TensorAnnotation.decodeDelimited = function decodeDelimited(reader) {\n            if (!(reader instanceof $Reader))\n                reader = new $Reader(reader);\n            return this.decode(reader, reader.uint32());\n        };\n\n        /**\n         * Verifies a TensorAnnotation message.\n         * @function verify\n         * @memberof onnx.TensorAnnotation\n         * @static\n         * @param {Object.<string,*>} message Plain object to verify\n         * @returns {string|null} `null` if valid, otherwise the reason why it is not\n         */\n        TensorAnnotation.verify = function verify(message) {\n            if (typeof message !== \"object\" || message === null)\n                return \"object expected\";\n            if (message.tensorName != null && message.hasOwnProperty(\"tensorName\"))\n                if (!$util.isString(message.tensorName))\n                    return \"tensorName: string expected\";\n            if (message.quantParameterTensorNames != null && message.hasOwnProperty(\"quantParameterTensorNames\")) {\n                if (!Array.isArray(message.quantParameterTensorNames))\n                    return \"quantParameterTensorNames: array expected\";\n                for (var i = 0; i < message.quantParameterTensorNames.length; ++i) {\n                    var error = $root.onnx.StringStringEntryProto.verify(message.quantParameterTensorNames[i]);\n                    if (error)\n                        return \"quantParameterTensorNames.\" + error;\n                }\n            }\n            return null;\n        };\n\n        /**\n         * Creates a TensorAnnotation message from a plain object. Also converts values to their respective internal types.\n         * @function fromObject\n         * @memberof onnx.TensorAnnotation\n         * @static\n         * @param {Object.<string,*>} object Plain object\n         * @returns {onnx.TensorAnnotation} TensorAnnotation\n         */\n        TensorAnnotation.fromObject = function fromObject(object) {\n            if (object instanceof $root.onnx.TensorAnnotation)\n                return object;\n            var message = new $root.onnx.TensorAnnotation();\n            if (object.tensorName != null)\n                message.tensorName = String(object.tensorName);\n            if (object.quantParameterTensorNames) {\n                if (!Array.isArray(object.quantParameterTensorNames))\n                    throw TypeError(\".onnx.TensorAnnotation.quantParameterTensorNames: array expected\");\n                message.quantParameterTensorNames = [];\n                for (var i = 0; i < object.quantParameterTensorNames.length; ++i) {\n                    if (typeof object.quantParameterTensorNames[i] !== \"object\")\n                        throw TypeError(\".onnx.TensorAnnotation.quantParameterTensorNames: object expected\");\n                    message.quantParameterTensorNames[i] = $root.onnx.StringStringEntryProto.fromObject(object.quantParameterTensorNames[i]);\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Creates a plain object from a TensorAnnotation message. Also converts values to other types if specified.\n         * @function toObject\n         * @memberof onnx.TensorAnnotation\n         * @static\n         * @param {onnx.TensorAnnotation} message TensorAnnotation\n         * @param {$protobuf.IConversionOptions} [options] Conversion options\n         * @returns {Object.<string,*>} Plain object\n         */\n        TensorAnnotation.toObject = function toObject(message, options) {\n            if (!options)\n                options = {};\n            var object = {};\n            if (options.arrays || options.defaults)\n                object.quantParameterTensorNames = [];\n            if (options.defaults)\n                object.tensorName = \"\";\n            if (message.tensorName != null && message.hasOwnProperty(\"tensorName\"))\n                object.tensorName = message.tensorName;\n            if (message.quantParameterTensorNames && message.quantParameterTensorNames.length) {\n                object.quantParameterTensorNames = [];\n                for (var j = 0; j < message.quantParameterTensorNames.length; ++j)\n                    object.quantParameterTensorNames[j] = $root.onnx.StringStringEntryProto.toObject(message.quantParameterTensorNames[j], options);\n            }\n            return object;\n        };\n\n        /**\n         * Converts this TensorAnnotation to JSON.\n         * @function toJSON\n         * @memberof onnx.TensorAnnotation\n         * @instance\n         * @returns {Object.<string,*>} JSON object\n         */\n        TensorAnnotation.prototype.toJSON = function toJSON() {\n            return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n        };\n\n        /**\n         * Gets the default type url for TensorAnnotation\n         * @function getTypeUrl\n         * @memberof onnx.TensorAnnotation\n         * @static\n         * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n         * @returns {string} The default type url\n         */\n        TensorAnnotation.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n            if (typeUrlPrefix === undefined) {\n                typeUrlPrefix = \"type.googleapis.com\";\n            }\n            return typeUrlPrefix + \"/onnx.TensorAnnotation\";\n        };\n\n        return TensorAnnotation;\n    })();\n\n    onnx.GraphProto = (function() {\n\n        /**\n         * Properties of a GraphProto.\n         * @memberof onnx\n         * @interface IGraphProto\n         * @property {Array.<onnx.INodeProto>|null} [node] GraphProto node\n         * @property {string|null} [name] GraphProto name\n         * @property {Array.<onnx.ITensorProto>|null} [initializer] GraphProto initializer\n         * @property {Array.<onnx.ISparseTensorProto>|null} [sparseInitializer] GraphProto sparseInitializer\n         * @property {string|null} [docString] GraphProto docString\n         * @property {Array.<onnx.IValueInfoProto>|null} [input] GraphProto input\n         * @property {Array.<onnx.IValueInfoProto>|null} [output] GraphProto output\n         * @property {Array.<onnx.IValueInfoProto>|null} [valueInfo] GraphProto valueInfo\n         * @property {Array.<onnx.ITensorAnnotation>|null} [quantizationAnnotation] GraphProto quantizationAnnotation\n         */\n\n        /**\n         * Constructs a new GraphProto.\n         * @memberof onnx\n         * @classdesc Represents a GraphProto.\n         * @implements IGraphProto\n         * @constructor\n         * @param {onnx.IGraphProto=} [properties] Properties to set\n         */\n        function GraphProto(properties) {\n            this.node = [];\n            this.initializer = [];\n            this.sparseInitializer = [];\n            this.input = [];\n            this.output = [];\n            this.valueInfo = [];\n            this.quantizationAnnotation = [];\n            if (properties)\n                for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                    if (properties[keys[i]] != null)\n                        this[keys[i]] = properties[keys[i]];\n        }\n\n        /**\n         * GraphProto node.\n         * @member {Array.<onnx.INodeProto>} node\n         * @memberof onnx.GraphProto\n         * @instance\n         */\n        GraphProto.prototype.node = $util.emptyArray;\n\n        /**\n         * GraphProto name.\n         * @member {string} name\n         * @memberof onnx.GraphProto\n         * @instance\n         */\n        GraphProto.prototype.name = \"\";\n\n        /**\n         * GraphProto initializer.\n         * @member {Array.<onnx.ITensorProto>} initializer\n         * @memberof onnx.GraphProto\n         * @instance\n         */\n        GraphProto.prototype.initializer = $util.emptyArray;\n\n        /**\n         * GraphProto sparseInitializer.\n         * @member {Array.<onnx.ISparseTensorProto>} sparseInitializer\n         * @memberof onnx.GraphProto\n         * @instance\n         */\n        GraphProto.prototype.sparseInitializer = $util.emptyArray;\n\n        /**\n         * GraphProto docString.\n         * @member {string} docString\n         * @memberof onnx.GraphProto\n         * @instance\n         */\n        GraphProto.prototype.docString = \"\";\n\n        /**\n         * GraphProto input.\n         * @member {Array.<onnx.IValueInfoProto>} input\n         * @memberof onnx.GraphProto\n         * @instance\n         */\n        GraphProto.prototype.input = $util.emptyArray;\n\n        /**\n         * GraphProto output.\n         * @member {Array.<onnx.IValueInfoProto>} output\n         * @memberof onnx.GraphProto\n         * @instance\n         */\n        GraphProto.prototype.output = $util.emptyArray;\n\n        /**\n         * GraphProto valueInfo.\n         * @member {Array.<onnx.IValueInfoProto>} valueInfo\n         * @memberof onnx.GraphProto\n         * @instance\n         */\n        GraphProto.prototype.valueInfo = $util.emptyArray;\n\n        /**\n         * GraphProto quantizationAnnotation.\n         * @member {Array.<onnx.ITensorAnnotation>} quantizationAnnotation\n         * @memberof onnx.GraphProto\n         * @instance\n         */\n        GraphProto.prototype.quantizationAnnotation = $util.emptyArray;\n\n        /**\n         * Creates a new GraphProto instance using the specified properties.\n         * @function create\n         * @memberof onnx.GraphProto\n         * @static\n         * @param {onnx.IGraphProto=} [properties] Properties to set\n         * @returns {onnx.GraphProto} GraphProto instance\n         */\n        GraphProto.create = function create(properties) {\n            return new GraphProto(properties);\n        };\n\n        /**\n         * Encodes the specified GraphProto message. Does not implicitly {@link onnx.GraphProto.verify|verify} messages.\n         * @function encode\n         * @memberof onnx.GraphProto\n         * @static\n         * @param {onnx.IGraphProto} message GraphProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        GraphProto.encode = function encode(message, writer) {\n            if (!writer)\n                writer = $Writer.create();\n            if (message.node != null && message.node.length)\n                for (var i = 0; i < message.node.length; ++i)\n                    $root.onnx.NodeProto.encode(message.node[i], writer.uint32(/* id 1, wireType 2 =*/10).fork()).ldelim();\n            if (message.name != null && Object.hasOwnProperty.call(message, \"name\"))\n                writer.uint32(/* id 2, wireType 2 =*/18).string(message.name);\n            if (message.initializer != null && message.initializer.length)\n                for (var i = 0; i < message.initializer.length; ++i)\n                    $root.onnx.TensorProto.encode(message.initializer[i], writer.uint32(/* id 5, wireType 2 =*/42).fork()).ldelim();\n            if (message.docString != null && Object.hasOwnProperty.call(message, \"docString\"))\n                writer.uint32(/* id 10, wireType 2 =*/82).string(message.docString);\n            if (message.input != null && message.input.length)\n                for (var i = 0; i < message.input.length; ++i)\n                    $root.onnx.ValueInfoProto.encode(message.input[i], writer.uint32(/* id 11, wireType 2 =*/90).fork()).ldelim();\n            if (message.output != null && message.output.length)\n                for (var i = 0; i < message.output.length; ++i)\n                    $root.onnx.ValueInfoProto.encode(message.output[i], writer.uint32(/* id 12, wireType 2 =*/98).fork()).ldelim();\n            if (message.valueInfo != null && message.valueInfo.length)\n                for (var i = 0; i < message.valueInfo.length; ++i)\n                    $root.onnx.ValueInfoProto.encode(message.valueInfo[i], writer.uint32(/* id 13, wireType 2 =*/106).fork()).ldelim();\n            if (message.quantizationAnnotation != null && message.quantizationAnnotation.length)\n                for (var i = 0; i < message.quantizationAnnotation.length; ++i)\n                    $root.onnx.TensorAnnotation.encode(message.quantizationAnnotation[i], writer.uint32(/* id 14, wireType 2 =*/114).fork()).ldelim();\n            if (message.sparseInitializer != null && message.sparseInitializer.length)\n                for (var i = 0; i < message.sparseInitializer.length; ++i)\n                    $root.onnx.SparseTensorProto.encode(message.sparseInitializer[i], writer.uint32(/* id 15, wireType 2 =*/122).fork()).ldelim();\n            return writer;\n        };\n\n        /**\n         * Encodes the specified GraphProto message, length delimited. Does not implicitly {@link onnx.GraphProto.verify|verify} messages.\n         * @function encodeDelimited\n         * @memberof onnx.GraphProto\n         * @static\n         * @param {onnx.IGraphProto} message GraphProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        GraphProto.encodeDelimited = function encodeDelimited(message, writer) {\n            return this.encode(message, writer).ldelim();\n        };\n\n        /**\n         * Decodes a GraphProto message from the specified reader or buffer.\n         * @function decode\n         * @memberof onnx.GraphProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @param {number} [length] Message length if known beforehand\n         * @returns {onnx.GraphProto} GraphProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        GraphProto.decode = function decode(reader, length) {\n            if (!(reader instanceof $Reader))\n                reader = $Reader.create(reader);\n            var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.GraphProto();\n            while (reader.pos < end) {\n                var tag = reader.uint32();\n                switch (tag >>> 3) {\n                case 1: {\n                        if (!(message.node && message.node.length))\n                            message.node = [];\n                        message.node.push($root.onnx.NodeProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 2: {\n                        message.name = reader.string();\n                        break;\n                    }\n                case 5: {\n                        if (!(message.initializer && message.initializer.length))\n                            message.initializer = [];\n                        message.initializer.push($root.onnx.TensorProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 15: {\n                        if (!(message.sparseInitializer && message.sparseInitializer.length))\n                            message.sparseInitializer = [];\n                        message.sparseInitializer.push($root.onnx.SparseTensorProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 10: {\n                        message.docString = reader.string();\n                        break;\n                    }\n                case 11: {\n                        if (!(message.input && message.input.length))\n                            message.input = [];\n                        message.input.push($root.onnx.ValueInfoProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 12: {\n                        if (!(message.output && message.output.length))\n                            message.output = [];\n                        message.output.push($root.onnx.ValueInfoProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 13: {\n                        if (!(message.valueInfo && message.valueInfo.length))\n                            message.valueInfo = [];\n                        message.valueInfo.push($root.onnx.ValueInfoProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 14: {\n                        if (!(message.quantizationAnnotation && message.quantizationAnnotation.length))\n                            message.quantizationAnnotation = [];\n                        message.quantizationAnnotation.push($root.onnx.TensorAnnotation.decode(reader, reader.uint32()));\n                        break;\n                    }\n                default:\n                    reader.skipType(tag & 7);\n                    break;\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Decodes a GraphProto message from the specified reader or buffer, length delimited.\n         * @function decodeDelimited\n         * @memberof onnx.GraphProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @returns {onnx.GraphProto} GraphProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        GraphProto.decodeDelimited = function decodeDelimited(reader) {\n            if (!(reader instanceof $Reader))\n                reader = new $Reader(reader);\n            return this.decode(reader, reader.uint32());\n        };\n\n        /**\n         * Verifies a GraphProto message.\n         * @function verify\n         * @memberof onnx.GraphProto\n         * @static\n         * @param {Object.<string,*>} message Plain object to verify\n         * @returns {string|null} `null` if valid, otherwise the reason why it is not\n         */\n        GraphProto.verify = function verify(message) {\n            if (typeof message !== \"object\" || message === null)\n                return \"object expected\";\n            if (message.node != null && message.hasOwnProperty(\"node\")) {\n                if (!Array.isArray(message.node))\n                    return \"node: array expected\";\n                for (var i = 0; i < message.node.length; ++i) {\n                    var error = $root.onnx.NodeProto.verify(message.node[i]);\n                    if (error)\n                        return \"node.\" + error;\n                }\n            }\n            if (message.name != null && message.hasOwnProperty(\"name\"))\n                if (!$util.isString(message.name))\n                    return \"name: string expected\";\n            if (message.initializer != null && message.hasOwnProperty(\"initializer\")) {\n                if (!Array.isArray(message.initializer))\n                    return \"initializer: array expected\";\n                for (var i = 0; i < message.initializer.length; ++i) {\n                    var error = $root.onnx.TensorProto.verify(message.initializer[i]);\n                    if (error)\n                        return \"initializer.\" + error;\n                }\n            }\n            if (message.sparseInitializer != null && message.hasOwnProperty(\"sparseInitializer\")) {\n                if (!Array.isArray(message.sparseInitializer))\n                    return \"sparseInitializer: array expected\";\n                for (var i = 0; i < message.sparseInitializer.length; ++i) {\n                    var error = $root.onnx.SparseTensorProto.verify(message.sparseInitializer[i]);\n                    if (error)\n                        return \"sparseInitializer.\" + error;\n                }\n            }\n            if (message.docString != null && message.hasOwnProperty(\"docString\"))\n                if (!$util.isString(message.docString))\n                    return \"docString: string expected\";\n            if (message.input != null && message.hasOwnProperty(\"input\")) {\n                if (!Array.isArray(message.input))\n                    return \"input: array expected\";\n                for (var i = 0; i < message.input.length; ++i) {\n                    var error = $root.onnx.ValueInfoProto.verify(message.input[i]);\n                    if (error)\n                        return \"input.\" + error;\n                }\n            }\n            if (message.output != null && message.hasOwnProperty(\"output\")) {\n                if (!Array.isArray(message.output))\n                    return \"output: array expected\";\n                for (var i = 0; i < message.output.length; ++i) {\n                    var error = $root.onnx.ValueInfoProto.verify(message.output[i]);\n                    if (error)\n                        return \"output.\" + error;\n                }\n            }\n            if (message.valueInfo != null && message.hasOwnProperty(\"valueInfo\")) {\n                if (!Array.isArray(message.valueInfo))\n                    return \"valueInfo: array expected\";\n                for (var i = 0; i < message.valueInfo.length; ++i) {\n                    var error = $root.onnx.ValueInfoProto.verify(message.valueInfo[i]);\n                    if (error)\n                        return \"valueInfo.\" + error;\n                }\n            }\n            if (message.quantizationAnnotation != null && message.hasOwnProperty(\"quantizationAnnotation\")) {\n                if (!Array.isArray(message.quantizationAnnotation))\n                    return \"quantizationAnnotation: array expected\";\n                for (var i = 0; i < message.quantizationAnnotation.length; ++i) {\n                    var error = $root.onnx.TensorAnnotation.verify(message.quantizationAnnotation[i]);\n                    if (error)\n                        return \"quantizationAnnotation.\" + error;\n                }\n            }\n            return null;\n        };\n\n        /**\n         * Creates a GraphProto message from a plain object. Also converts values to their respective internal types.\n         * @function fromObject\n         * @memberof onnx.GraphProto\n         * @static\n         * @param {Object.<string,*>} object Plain object\n         * @returns {onnx.GraphProto} GraphProto\n         */\n        GraphProto.fromObject = function fromObject(object) {\n            if (object instanceof $root.onnx.GraphProto)\n                return object;\n            var message = new $root.onnx.GraphProto();\n            if (object.node) {\n                if (!Array.isArray(object.node))\n                    throw TypeError(\".onnx.GraphProto.node: array expected\");\n                message.node = [];\n                for (var i = 0; i < object.node.length; ++i) {\n                    if (typeof object.node[i] !== \"object\")\n                        throw TypeError(\".onnx.GraphProto.node: object expected\");\n                    message.node[i] = $root.onnx.NodeProto.fromObject(object.node[i]);\n                }\n            }\n            if (object.name != null)\n                message.name = String(object.name);\n            if (object.initializer) {\n                if (!Array.isArray(object.initializer))\n                    throw TypeError(\".onnx.GraphProto.initializer: array expected\");\n                message.initializer = [];\n                for (var i = 0; i < object.initializer.length; ++i) {\n                    if (typeof object.initializer[i] !== \"object\")\n                        throw TypeError(\".onnx.GraphProto.initializer: object expected\");\n                    message.initializer[i] = $root.onnx.TensorProto.fromObject(object.initializer[i]);\n                }\n            }\n            if (object.sparseInitializer) {\n                if (!Array.isArray(object.sparseInitializer))\n                    throw TypeError(\".onnx.GraphProto.sparseInitializer: array expected\");\n                message.sparseInitializer = [];\n                for (var i = 0; i < object.sparseInitializer.length; ++i) {\n                    if (typeof object.sparseInitializer[i] !== \"object\")\n                        throw TypeError(\".onnx.GraphProto.sparseInitializer: object expected\");\n                    message.sparseInitializer[i] = $root.onnx.SparseTensorProto.fromObject(object.sparseInitializer[i]);\n                }\n            }\n            if (object.docString != null)\n                message.docString = String(object.docString);\n            if (object.input) {\n                if (!Array.isArray(object.input))\n                    throw TypeError(\".onnx.GraphProto.input: array expected\");\n                message.input = [];\n                for (var i = 0; i < object.input.length; ++i) {\n                    if (typeof object.input[i] !== \"object\")\n                        throw TypeError(\".onnx.GraphProto.input: object expected\");\n                    message.input[i] = $root.onnx.ValueInfoProto.fromObject(object.input[i]);\n                }\n            }\n            if (object.output) {\n                if (!Array.isArray(object.output))\n                    throw TypeError(\".onnx.GraphProto.output: array expected\");\n                message.output = [];\n                for (var i = 0; i < object.output.length; ++i) {\n                    if (typeof object.output[i] !== \"object\")\n                        throw TypeError(\".onnx.GraphProto.output: object expected\");\n                    message.output[i] = $root.onnx.ValueInfoProto.fromObject(object.output[i]);\n                }\n            }\n            if (object.valueInfo) {\n                if (!Array.isArray(object.valueInfo))\n                    throw TypeError(\".onnx.GraphProto.valueInfo: array expected\");\n                message.valueInfo = [];\n                for (var i = 0; i < object.valueInfo.length; ++i) {\n                    if (typeof object.valueInfo[i] !== \"object\")\n                        throw TypeError(\".onnx.GraphProto.valueInfo: object expected\");\n                    message.valueInfo[i] = $root.onnx.ValueInfoProto.fromObject(object.valueInfo[i]);\n                }\n            }\n            if (object.quantizationAnnotation) {\n                if (!Array.isArray(object.quantizationAnnotation))\n                    throw TypeError(\".onnx.GraphProto.quantizationAnnotation: array expected\");\n                message.quantizationAnnotation = [];\n                for (var i = 0; i < object.quantizationAnnotation.length; ++i) {\n                    if (typeof object.quantizationAnnotation[i] !== \"object\")\n                        throw TypeError(\".onnx.GraphProto.quantizationAnnotation: object expected\");\n                    message.quantizationAnnotation[i] = $root.onnx.TensorAnnotation.fromObject(object.quantizationAnnotation[i]);\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Creates a plain object from a GraphProto message. Also converts values to other types if specified.\n         * @function toObject\n         * @memberof onnx.GraphProto\n         * @static\n         * @param {onnx.GraphProto} message GraphProto\n         * @param {$protobuf.IConversionOptions} [options] Conversion options\n         * @returns {Object.<string,*>} Plain object\n         */\n        GraphProto.toObject = function toObject(message, options) {\n            if (!options)\n                options = {};\n            var object = {};\n            if (options.arrays || options.defaults) {\n                object.node = [];\n                object.initializer = [];\n                object.input = [];\n                object.output = [];\n                object.valueInfo = [];\n                object.quantizationAnnotation = [];\n                object.sparseInitializer = [];\n            }\n            if (options.defaults) {\n                object.name = \"\";\n                object.docString = \"\";\n            }\n            if (message.node && message.node.length) {\n                object.node = [];\n                for (var j = 0; j < message.node.length; ++j)\n                    object.node[j] = $root.onnx.NodeProto.toObject(message.node[j], options);\n            }\n            if (message.name != null && message.hasOwnProperty(\"name\"))\n                object.name = message.name;\n            if (message.initializer && message.initializer.length) {\n                object.initializer = [];\n                for (var j = 0; j < message.initializer.length; ++j)\n                    object.initializer[j] = $root.onnx.TensorProto.toObject(message.initializer[j], options);\n            }\n            if (message.docString != null && message.hasOwnProperty(\"docString\"))\n                object.docString = message.docString;\n            if (message.input && message.input.length) {\n                object.input = [];\n                for (var j = 0; j < message.input.length; ++j)\n                    object.input[j] = $root.onnx.ValueInfoProto.toObject(message.input[j], options);\n            }\n            if (message.output && message.output.length) {\n                object.output = [];\n                for (var j = 0; j < message.output.length; ++j)\n                    object.output[j] = $root.onnx.ValueInfoProto.toObject(message.output[j], options);\n            }\n            if (message.valueInfo && message.valueInfo.length) {\n                object.valueInfo = [];\n                for (var j = 0; j < message.valueInfo.length; ++j)\n                    object.valueInfo[j] = $root.onnx.ValueInfoProto.toObject(message.valueInfo[j], options);\n            }\n            if (message.quantizationAnnotation && message.quantizationAnnotation.length) {\n                object.quantizationAnnotation = [];\n                for (var j = 0; j < message.quantizationAnnotation.length; ++j)\n                    object.quantizationAnnotation[j] = $root.onnx.TensorAnnotation.toObject(message.quantizationAnnotation[j], options);\n            }\n            if (message.sparseInitializer && message.sparseInitializer.length) {\n                object.sparseInitializer = [];\n                for (var j = 0; j < message.sparseInitializer.length; ++j)\n                    object.sparseInitializer[j] = $root.onnx.SparseTensorProto.toObject(message.sparseInitializer[j], options);\n            }\n            return object;\n        };\n\n        /**\n         * Converts this GraphProto to JSON.\n         * @function toJSON\n         * @memberof onnx.GraphProto\n         * @instance\n         * @returns {Object.<string,*>} JSON object\n         */\n        GraphProto.prototype.toJSON = function toJSON() {\n            return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n        };\n\n        /**\n         * Gets the default type url for GraphProto\n         * @function getTypeUrl\n         * @memberof onnx.GraphProto\n         * @static\n         * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n         * @returns {string} The default type url\n         */\n        GraphProto.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n            if (typeUrlPrefix === undefined) {\n                typeUrlPrefix = \"type.googleapis.com\";\n            }\n            return typeUrlPrefix + \"/onnx.GraphProto\";\n        };\n\n        return GraphProto;\n    })();\n\n    onnx.TensorProto = (function() {\n\n        /**\n         * Properties of a TensorProto.\n         * @memberof onnx\n         * @interface ITensorProto\n         * @property {Array.<number|Long>|null} [dims] TensorProto dims\n         * @property {number|null} [dataType] TensorProto dataType\n         * @property {onnx.TensorProto.ISegment|null} [segment] TensorProto segment\n         * @property {Array.<number>|null} [floatData] TensorProto floatData\n         * @property {Array.<number>|null} [int32Data] TensorProto int32Data\n         * @property {Array.<Uint8Array>|null} [stringData] TensorProto stringData\n         * @property {Array.<number|Long>|null} [int64Data] TensorProto int64Data\n         * @property {string|null} [name] TensorProto name\n         * @property {string|null} [docString] TensorProto docString\n         * @property {Uint8Array|null} [rawData] TensorProto rawData\n         * @property {Array.<onnx.IStringStringEntryProto>|null} [externalData] TensorProto externalData\n         * @property {onnx.TensorProto.DataLocation|null} [dataLocation] TensorProto dataLocation\n         * @property {Array.<number>|null} [doubleData] TensorProto doubleData\n         * @property {Array.<number|Long>|null} [uint64Data] TensorProto uint64Data\n         */\n\n        /**\n         * Constructs a new TensorProto.\n         * @memberof onnx\n         * @classdesc Represents a TensorProto.\n         * @implements ITensorProto\n         * @constructor\n         * @param {onnx.ITensorProto=} [properties] Properties to set\n         */\n        function TensorProto(properties) {\n            this.dims = [];\n            this.floatData = [];\n            this.int32Data = [];\n            this.stringData = [];\n            this.int64Data = [];\n            this.externalData = [];\n            this.doubleData = [];\n            this.uint64Data = [];\n            if (properties)\n                for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                    if (properties[keys[i]] != null)\n                        this[keys[i]] = properties[keys[i]];\n        }\n\n        /**\n         * TensorProto dims.\n         * @member {Array.<number|Long>} dims\n         * @memberof onnx.TensorProto\n         * @instance\n         */\n        TensorProto.prototype.dims = $util.emptyArray;\n\n        /**\n         * TensorProto dataType.\n         * @member {number} dataType\n         * @memberof onnx.TensorProto\n         * @instance\n         */\n        TensorProto.prototype.dataType = 0;\n\n        /**\n         * TensorProto segment.\n         * @member {onnx.TensorProto.ISegment|null|undefined} segment\n         * @memberof onnx.TensorProto\n         * @instance\n         */\n        TensorProto.prototype.segment = null;\n\n        /**\n         * TensorProto floatData.\n         * @member {Array.<number>} floatData\n         * @memberof onnx.TensorProto\n         * @instance\n         */\n        TensorProto.prototype.floatData = $util.emptyArray;\n\n        /**\n         * TensorProto int32Data.\n         * @member {Array.<number>} int32Data\n         * @memberof onnx.TensorProto\n         * @instance\n         */\n        TensorProto.prototype.int32Data = $util.emptyArray;\n\n        /**\n         * TensorProto stringData.\n         * @member {Array.<Uint8Array>} stringData\n         * @memberof onnx.TensorProto\n         * @instance\n         */\n        TensorProto.prototype.stringData = $util.emptyArray;\n\n        /**\n         * TensorProto int64Data.\n         * @member {Array.<number|Long>} int64Data\n         * @memberof onnx.TensorProto\n         * @instance\n         */\n        TensorProto.prototype.int64Data = $util.emptyArray;\n\n        /**\n         * TensorProto name.\n         * @member {string} name\n         * @memberof onnx.TensorProto\n         * @instance\n         */\n        TensorProto.prototype.name = \"\";\n\n        /**\n         * TensorProto docString.\n         * @member {string} docString\n         * @memberof onnx.TensorProto\n         * @instance\n         */\n        TensorProto.prototype.docString = \"\";\n\n        /**\n         * TensorProto rawData.\n         * @member {Uint8Array} rawData\n         * @memberof onnx.TensorProto\n         * @instance\n         */\n        TensorProto.prototype.rawData = $util.newBuffer([]);\n\n        /**\n         * TensorProto externalData.\n         * @member {Array.<onnx.IStringStringEntryProto>} externalData\n         * @memberof onnx.TensorProto\n         * @instance\n         */\n        TensorProto.prototype.externalData = $util.emptyArray;\n\n        /**\n         * TensorProto dataLocation.\n         * @member {onnx.TensorProto.DataLocation} dataLocation\n         * @memberof onnx.TensorProto\n         * @instance\n         */\n        TensorProto.prototype.dataLocation = 0;\n\n        /**\n         * TensorProto doubleData.\n         * @member {Array.<number>} doubleData\n         * @memberof onnx.TensorProto\n         * @instance\n         */\n        TensorProto.prototype.doubleData = $util.emptyArray;\n\n        /**\n         * TensorProto uint64Data.\n         * @member {Array.<number|Long>} uint64Data\n         * @memberof onnx.TensorProto\n         * @instance\n         */\n        TensorProto.prototype.uint64Data = $util.emptyArray;\n\n        /**\n         * Creates a new TensorProto instance using the specified properties.\n         * @function create\n         * @memberof onnx.TensorProto\n         * @static\n         * @param {onnx.ITensorProto=} [properties] Properties to set\n         * @returns {onnx.TensorProto} TensorProto instance\n         */\n        TensorProto.create = function create(properties) {\n            return new TensorProto(properties);\n        };\n\n        /**\n         * Encodes the specified TensorProto message. Does not implicitly {@link onnx.TensorProto.verify|verify} messages.\n         * @function encode\n         * @memberof onnx.TensorProto\n         * @static\n         * @param {onnx.ITensorProto} message TensorProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        TensorProto.encode = function encode(message, writer) {\n            if (!writer)\n                writer = $Writer.create();\n            if (message.dims != null && message.dims.length) {\n                writer.uint32(/* id 1, wireType 2 =*/10).fork();\n                for (var i = 0; i < message.dims.length; ++i)\n                    writer.int64(message.dims[i]);\n                writer.ldelim();\n            }\n            if (message.dataType != null && Object.hasOwnProperty.call(message, \"dataType\"))\n                writer.uint32(/* id 2, wireType 0 =*/16).int32(message.dataType);\n            if (message.segment != null && Object.hasOwnProperty.call(message, \"segment\"))\n                $root.onnx.TensorProto.Segment.encode(message.segment, writer.uint32(/* id 3, wireType 2 =*/26).fork()).ldelim();\n            if (message.floatData != null && message.floatData.length) {\n                writer.uint32(/* id 4, wireType 2 =*/34).fork();\n                for (var i = 0; i < message.floatData.length; ++i)\n                    writer.float(message.floatData[i]);\n                writer.ldelim();\n            }\n            if (message.int32Data != null && message.int32Data.length) {\n                writer.uint32(/* id 5, wireType 2 =*/42).fork();\n                for (var i = 0; i < message.int32Data.length; ++i)\n                    writer.int32(message.int32Data[i]);\n                writer.ldelim();\n            }\n            if (message.stringData != null && message.stringData.length)\n                for (var i = 0; i < message.stringData.length; ++i)\n                    writer.uint32(/* id 6, wireType 2 =*/50).bytes(message.stringData[i]);\n            if (message.int64Data != null && message.int64Data.length) {\n                writer.uint32(/* id 7, wireType 2 =*/58).fork();\n                for (var i = 0; i < message.int64Data.length; ++i)\n                    writer.int64(message.int64Data[i]);\n                writer.ldelim();\n            }\n            if (message.name != null && Object.hasOwnProperty.call(message, \"name\"))\n                writer.uint32(/* id 8, wireType 2 =*/66).string(message.name);\n            if (message.rawData != null && Object.hasOwnProperty.call(message, \"rawData\"))\n                writer.uint32(/* id 9, wireType 2 =*/74).bytes(message.rawData);\n            if (message.doubleData != null && message.doubleData.length) {\n                writer.uint32(/* id 10, wireType 2 =*/82).fork();\n                for (var i = 0; i < message.doubleData.length; ++i)\n                    writer.double(message.doubleData[i]);\n                writer.ldelim();\n            }\n            if (message.uint64Data != null && message.uint64Data.length) {\n                writer.uint32(/* id 11, wireType 2 =*/90).fork();\n                for (var i = 0; i < message.uint64Data.length; ++i)\n                    writer.uint64(message.uint64Data[i]);\n                writer.ldelim();\n            }\n            if (message.docString != null && Object.hasOwnProperty.call(message, \"docString\"))\n                writer.uint32(/* id 12, wireType 2 =*/98).string(message.docString);\n            if (message.externalData != null && message.externalData.length)\n                for (var i = 0; i < message.externalData.length; ++i)\n                    $root.onnx.StringStringEntryProto.encode(message.externalData[i], writer.uint32(/* id 13, wireType 2 =*/106).fork()).ldelim();\n            if (message.dataLocation != null && Object.hasOwnProperty.call(message, \"dataLocation\"))\n                writer.uint32(/* id 14, wireType 0 =*/112).int32(message.dataLocation);\n            return writer;\n        };\n\n        /**\n         * Encodes the specified TensorProto message, length delimited. Does not implicitly {@link onnx.TensorProto.verify|verify} messages.\n         * @function encodeDelimited\n         * @memberof onnx.TensorProto\n         * @static\n         * @param {onnx.ITensorProto} message TensorProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        TensorProto.encodeDelimited = function encodeDelimited(message, writer) {\n            return this.encode(message, writer).ldelim();\n        };\n\n        /**\n         * Decodes a TensorProto message from the specified reader or buffer.\n         * @function decode\n         * @memberof onnx.TensorProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @param {number} [length] Message length if known beforehand\n         * @returns {onnx.TensorProto} TensorProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        TensorProto.decode = function decode(reader, length) {\n            if (!(reader instanceof $Reader))\n                reader = $Reader.create(reader);\n            var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.TensorProto();\n            while (reader.pos < end) {\n                var tag = reader.uint32();\n                switch (tag >>> 3) {\n                case 1: {\n                        if (!(message.dims && message.dims.length))\n                            message.dims = [];\n                        if ((tag & 7) === 2) {\n                            var end2 = reader.uint32() + reader.pos;\n                            while (reader.pos < end2)\n                                message.dims.push(reader.int64());\n                        } else\n                            message.dims.push(reader.int64());\n                        break;\n                    }\n                case 2: {\n                        message.dataType = reader.int32();\n                        break;\n                    }\n                case 3: {\n                        message.segment = $root.onnx.TensorProto.Segment.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 4: {\n                        if (!(message.floatData && message.floatData.length))\n                            message.floatData = [];\n                        if ((tag & 7) === 2) {\n                            var end2 = reader.uint32() + reader.pos;\n                            while (reader.pos < end2)\n                                message.floatData.push(reader.float());\n                        } else\n                            message.floatData.push(reader.float());\n                        break;\n                    }\n                case 5: {\n                        if (!(message.int32Data && message.int32Data.length))\n                            message.int32Data = [];\n                        if ((tag & 7) === 2) {\n                            var end2 = reader.uint32() + reader.pos;\n                            while (reader.pos < end2)\n                                message.int32Data.push(reader.int32());\n                        } else\n                            message.int32Data.push(reader.int32());\n                        break;\n                    }\n                case 6: {\n                        if (!(message.stringData && message.stringData.length))\n                            message.stringData = [];\n                        message.stringData.push(reader.bytes());\n                        break;\n                    }\n                case 7: {\n                        if (!(message.int64Data && message.int64Data.length))\n                            message.int64Data = [];\n                        if ((tag & 7) === 2) {\n                            var end2 = reader.uint32() + reader.pos;\n                            while (reader.pos < end2)\n                                message.int64Data.push(reader.int64());\n                        } else\n                            message.int64Data.push(reader.int64());\n                        break;\n                    }\n                case 8: {\n                        message.name = reader.string();\n                        break;\n                    }\n                case 12: {\n                        message.docString = reader.string();\n                        break;\n                    }\n                case 9: {\n                        message.rawData = reader.bytes();\n                        break;\n                    }\n                case 13: {\n                        if (!(message.externalData && message.externalData.length))\n                            message.externalData = [];\n                        message.externalData.push($root.onnx.StringStringEntryProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 14: {\n                        message.dataLocation = reader.int32();\n                        break;\n                    }\n                case 10: {\n                        if (!(message.doubleData && message.doubleData.length))\n                            message.doubleData = [];\n                        if ((tag & 7) === 2) {\n                            var end2 = reader.uint32() + reader.pos;\n                            while (reader.pos < end2)\n                                message.doubleData.push(reader.double());\n                        } else\n                            message.doubleData.push(reader.double());\n                        break;\n                    }\n                case 11: {\n                        if (!(message.uint64Data && message.uint64Data.length))\n                            message.uint64Data = [];\n                        if ((tag & 7) === 2) {\n                            var end2 = reader.uint32() + reader.pos;\n                            while (reader.pos < end2)\n                                message.uint64Data.push(reader.uint64());\n                        } else\n                            message.uint64Data.push(reader.uint64());\n                        break;\n                    }\n                default:\n                    reader.skipType(tag & 7);\n                    break;\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Decodes a TensorProto message from the specified reader or buffer, length delimited.\n         * @function decodeDelimited\n         * @memberof onnx.TensorProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @returns {onnx.TensorProto} TensorProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        TensorProto.decodeDelimited = function decodeDelimited(reader) {\n            if (!(reader instanceof $Reader))\n                reader = new $Reader(reader);\n            return this.decode(reader, reader.uint32());\n        };\n\n        /**\n         * Verifies a TensorProto message.\n         * @function verify\n         * @memberof onnx.TensorProto\n         * @static\n         * @param {Object.<string,*>} message Plain object to verify\n         * @returns {string|null} `null` if valid, otherwise the reason why it is not\n         */\n        TensorProto.verify = function verify(message) {\n            if (typeof message !== \"object\" || message === null)\n                return \"object expected\";\n            if (message.dims != null && message.hasOwnProperty(\"dims\")) {\n                if (!Array.isArray(message.dims))\n                    return \"dims: array expected\";\n                for (var i = 0; i < message.dims.length; ++i)\n                    if (!$util.isInteger(message.dims[i]) && !(message.dims[i] && $util.isInteger(message.dims[i].low) && $util.isInteger(message.dims[i].high)))\n                        return \"dims: integer|Long[] expected\";\n            }\n            if (message.dataType != null && message.hasOwnProperty(\"dataType\"))\n                if (!$util.isInteger(message.dataType))\n                    return \"dataType: integer expected\";\n            if (message.segment != null && message.hasOwnProperty(\"segment\")) {\n                var error = $root.onnx.TensorProto.Segment.verify(message.segment);\n                if (error)\n                    return \"segment.\" + error;\n            }\n            if (message.floatData != null && message.hasOwnProperty(\"floatData\")) {\n                if (!Array.isArray(message.floatData))\n                    return \"floatData: array expected\";\n                for (var i = 0; i < message.floatData.length; ++i)\n                    if (typeof message.floatData[i] !== \"number\")\n                        return \"floatData: number[] expected\";\n            }\n            if (message.int32Data != null && message.hasOwnProperty(\"int32Data\")) {\n                if (!Array.isArray(message.int32Data))\n                    return \"int32Data: array expected\";\n                for (var i = 0; i < message.int32Data.length; ++i)\n                    if (!$util.isInteger(message.int32Data[i]))\n                        return \"int32Data: integer[] expected\";\n            }\n            if (message.stringData != null && message.hasOwnProperty(\"stringData\")) {\n                if (!Array.isArray(message.stringData))\n                    return \"stringData: array expected\";\n                for (var i = 0; i < message.stringData.length; ++i)\n                    if (!(message.stringData[i] && typeof message.stringData[i].length === \"number\" || $util.isString(message.stringData[i])))\n                        return \"stringData: buffer[] expected\";\n            }\n            if (message.int64Data != null && message.hasOwnProperty(\"int64Data\")) {\n                if (!Array.isArray(message.int64Data))\n                    return \"int64Data: array expected\";\n                for (var i = 0; i < message.int64Data.length; ++i)\n                    if (!$util.isInteger(message.int64Data[i]) && !(message.int64Data[i] && $util.isInteger(message.int64Data[i].low) && $util.isInteger(message.int64Data[i].high)))\n                        return \"int64Data: integer|Long[] expected\";\n            }\n            if (message.name != null && message.hasOwnProperty(\"name\"))\n                if (!$util.isString(message.name))\n                    return \"name: string expected\";\n            if (message.docString != null && message.hasOwnProperty(\"docString\"))\n                if (!$util.isString(message.docString))\n                    return \"docString: string expected\";\n            if (message.rawData != null && message.hasOwnProperty(\"rawData\"))\n                if (!(message.rawData && typeof message.rawData.length === \"number\" || $util.isString(message.rawData)))\n                    return \"rawData: buffer expected\";\n            if (message.externalData != null && message.hasOwnProperty(\"externalData\")) {\n                if (!Array.isArray(message.externalData))\n                    return \"externalData: array expected\";\n                for (var i = 0; i < message.externalData.length; ++i) {\n                    var error = $root.onnx.StringStringEntryProto.verify(message.externalData[i]);\n                    if (error)\n                        return \"externalData.\" + error;\n                }\n            }\n            if (message.dataLocation != null && message.hasOwnProperty(\"dataLocation\"))\n                switch (message.dataLocation) {\n                default:\n                    return \"dataLocation: enum value expected\";\n                case 0:\n                case 1:\n                    break;\n                }\n            if (message.doubleData != null && message.hasOwnProperty(\"doubleData\")) {\n                if (!Array.isArray(message.doubleData))\n                    return \"doubleData: array expected\";\n                for (var i = 0; i < message.doubleData.length; ++i)\n                    if (typeof message.doubleData[i] !== \"number\")\n                        return \"doubleData: number[] expected\";\n            }\n            if (message.uint64Data != null && message.hasOwnProperty(\"uint64Data\")) {\n                if (!Array.isArray(message.uint64Data))\n                    return \"uint64Data: array expected\";\n                for (var i = 0; i < message.uint64Data.length; ++i)\n                    if (!$util.isInteger(message.uint64Data[i]) && !(message.uint64Data[i] && $util.isInteger(message.uint64Data[i].low) && $util.isInteger(message.uint64Data[i].high)))\n                        return \"uint64Data: integer|Long[] expected\";\n            }\n            return null;\n        };\n\n        /**\n         * Creates a TensorProto message from a plain object. Also converts values to their respective internal types.\n         * @function fromObject\n         * @memberof onnx.TensorProto\n         * @static\n         * @param {Object.<string,*>} object Plain object\n         * @returns {onnx.TensorProto} TensorProto\n         */\n        TensorProto.fromObject = function fromObject(object) {\n            if (object instanceof $root.onnx.TensorProto)\n                return object;\n            var message = new $root.onnx.TensorProto();\n            if (object.dims) {\n                if (!Array.isArray(object.dims))\n                    throw TypeError(\".onnx.TensorProto.dims: array expected\");\n                message.dims = [];\n                for (var i = 0; i < object.dims.length; ++i)\n                    if ($util.Long)\n                        (message.dims[i] = $util.Long.fromValue(object.dims[i])).unsigned = false;\n                    else if (typeof object.dims[i] === \"string\")\n                        message.dims[i] = parseInt(object.dims[i], 10);\n                    else if (typeof object.dims[i] === \"number\")\n                        message.dims[i] = object.dims[i];\n                    else if (typeof object.dims[i] === \"object\")\n                        message.dims[i] = new $util.LongBits(object.dims[i].low >>> 0, object.dims[i].high >>> 0).toNumber();\n            }\n            if (object.dataType != null)\n                message.dataType = object.dataType | 0;\n            if (object.segment != null) {\n                if (typeof object.segment !== \"object\")\n                    throw TypeError(\".onnx.TensorProto.segment: object expected\");\n                message.segment = $root.onnx.TensorProto.Segment.fromObject(object.segment);\n            }\n            if (object.floatData) {\n                if (!Array.isArray(object.floatData))\n                    throw TypeError(\".onnx.TensorProto.floatData: array expected\");\n                message.floatData = [];\n                for (var i = 0; i < object.floatData.length; ++i)\n                    message.floatData[i] = Number(object.floatData[i]);\n            }\n            if (object.int32Data) {\n                if (!Array.isArray(object.int32Data))\n                    throw TypeError(\".onnx.TensorProto.int32Data: array expected\");\n                message.int32Data = [];\n                for (var i = 0; i < object.int32Data.length; ++i)\n                    message.int32Data[i] = object.int32Data[i] | 0;\n            }\n            if (object.stringData) {\n                if (!Array.isArray(object.stringData))\n                    throw TypeError(\".onnx.TensorProto.stringData: array expected\");\n                message.stringData = [];\n                for (var i = 0; i < object.stringData.length; ++i)\n                    if (typeof object.stringData[i] === \"string\")\n                        $util.base64.decode(object.stringData[i], message.stringData[i] = $util.newBuffer($util.base64.length(object.stringData[i])), 0);\n                    else if (object.stringData[i].length >= 0)\n                        message.stringData[i] = object.stringData[i];\n            }\n            if (object.int64Data) {\n                if (!Array.isArray(object.int64Data))\n                    throw TypeError(\".onnx.TensorProto.int64Data: array expected\");\n                message.int64Data = [];\n                for (var i = 0; i < object.int64Data.length; ++i)\n                    if ($util.Long)\n                        (message.int64Data[i] = $util.Long.fromValue(object.int64Data[i])).unsigned = false;\n                    else if (typeof object.int64Data[i] === \"string\")\n                        message.int64Data[i] = parseInt(object.int64Data[i], 10);\n                    else if (typeof object.int64Data[i] === \"number\")\n                        message.int64Data[i] = object.int64Data[i];\n                    else if (typeof object.int64Data[i] === \"object\")\n                        message.int64Data[i] = new $util.LongBits(object.int64Data[i].low >>> 0, object.int64Data[i].high >>> 0).toNumber();\n            }\n            if (object.name != null)\n                message.name = String(object.name);\n            if (object.docString != null)\n                message.docString = String(object.docString);\n            if (object.rawData != null)\n                if (typeof object.rawData === \"string\")\n                    $util.base64.decode(object.rawData, message.rawData = $util.newBuffer($util.base64.length(object.rawData)), 0);\n                else if (object.rawData.length >= 0)\n                    message.rawData = object.rawData;\n            if (object.externalData) {\n                if (!Array.isArray(object.externalData))\n                    throw TypeError(\".onnx.TensorProto.externalData: array expected\");\n                message.externalData = [];\n                for (var i = 0; i < object.externalData.length; ++i) {\n                    if (typeof object.externalData[i] !== \"object\")\n                        throw TypeError(\".onnx.TensorProto.externalData: object expected\");\n                    message.externalData[i] = $root.onnx.StringStringEntryProto.fromObject(object.externalData[i]);\n                }\n            }\n            switch (object.dataLocation) {\n            default:\n                if (typeof object.dataLocation === \"number\") {\n                    message.dataLocation = object.dataLocation;\n                    break;\n                }\n                break;\n            case \"DEFAULT\":\n            case 0:\n                message.dataLocation = 0;\n                break;\n            case \"EXTERNAL\":\n            case 1:\n                message.dataLocation = 1;\n                break;\n            }\n            if (object.doubleData) {\n                if (!Array.isArray(object.doubleData))\n                    throw TypeError(\".onnx.TensorProto.doubleData: array expected\");\n                message.doubleData = [];\n                for (var i = 0; i < object.doubleData.length; ++i)\n                    message.doubleData[i] = Number(object.doubleData[i]);\n            }\n            if (object.uint64Data) {\n                if (!Array.isArray(object.uint64Data))\n                    throw TypeError(\".onnx.TensorProto.uint64Data: array expected\");\n                message.uint64Data = [];\n                for (var i = 0; i < object.uint64Data.length; ++i)\n                    if ($util.Long)\n                        (message.uint64Data[i] = $util.Long.fromValue(object.uint64Data[i])).unsigned = true;\n                    else if (typeof object.uint64Data[i] === \"string\")\n                        message.uint64Data[i] = parseInt(object.uint64Data[i], 10);\n                    else if (typeof object.uint64Data[i] === \"number\")\n                        message.uint64Data[i] = object.uint64Data[i];\n                    else if (typeof object.uint64Data[i] === \"object\")\n                        message.uint64Data[i] = new $util.LongBits(object.uint64Data[i].low >>> 0, object.uint64Data[i].high >>> 0).toNumber(true);\n            }\n            return message;\n        };\n\n        /**\n         * Creates a plain object from a TensorProto message. Also converts values to other types if specified.\n         * @function toObject\n         * @memberof onnx.TensorProto\n         * @static\n         * @param {onnx.TensorProto} message TensorProto\n         * @param {$protobuf.IConversionOptions} [options] Conversion options\n         * @returns {Object.<string,*>} Plain object\n         */\n        TensorProto.toObject = function toObject(message, options) {\n            if (!options)\n                options = {};\n            var object = {};\n            if (options.arrays || options.defaults) {\n                object.dims = [];\n                object.floatData = [];\n                object.int32Data = [];\n                object.stringData = [];\n                object.int64Data = [];\n                object.doubleData = [];\n                object.uint64Data = [];\n                object.externalData = [];\n            }\n            if (options.defaults) {\n                object.dataType = 0;\n                object.segment = null;\n                object.name = \"\";\n                if (options.bytes === String)\n                    object.rawData = \"\";\n                else {\n                    object.rawData = [];\n                    if (options.bytes !== Array)\n                        object.rawData = $util.newBuffer(object.rawData);\n                }\n                object.docString = \"\";\n                object.dataLocation = options.enums === String ? \"DEFAULT\" : 0;\n            }\n            if (message.dims && message.dims.length) {\n                object.dims = [];\n                for (var j = 0; j < message.dims.length; ++j)\n                    if (typeof message.dims[j] === \"number\")\n                        object.dims[j] = options.longs === String ? String(message.dims[j]) : message.dims[j];\n                    else\n                        object.dims[j] = options.longs === String ? $util.Long.prototype.toString.call(message.dims[j]) : options.longs === Number ? new $util.LongBits(message.dims[j].low >>> 0, message.dims[j].high >>> 0).toNumber() : message.dims[j];\n            }\n            if (message.dataType != null && message.hasOwnProperty(\"dataType\"))\n                object.dataType = message.dataType;\n            if (message.segment != null && message.hasOwnProperty(\"segment\"))\n                object.segment = $root.onnx.TensorProto.Segment.toObject(message.segment, options);\n            if (message.floatData && message.floatData.length) {\n                object.floatData = [];\n                for (var j = 0; j < message.floatData.length; ++j)\n                    object.floatData[j] = options.json && !isFinite(message.floatData[j]) ? String(message.floatData[j]) : message.floatData[j];\n            }\n            if (message.int32Data && message.int32Data.length) {\n                object.int32Data = [];\n                for (var j = 0; j < message.int32Data.length; ++j)\n                    object.int32Data[j] = message.int32Data[j];\n            }\n            if (message.stringData && message.stringData.length) {\n                object.stringData = [];\n                for (var j = 0; j < message.stringData.length; ++j)\n                    object.stringData[j] = options.bytes === String ? $util.base64.encode(message.stringData[j], 0, message.stringData[j].length) : options.bytes === Array ? Array.prototype.slice.call(message.stringData[j]) : message.stringData[j];\n            }\n            if (message.int64Data && message.int64Data.length) {\n                object.int64Data = [];\n                for (var j = 0; j < message.int64Data.length; ++j)\n                    if (typeof message.int64Data[j] === \"number\")\n                        object.int64Data[j] = options.longs === String ? String(message.int64Data[j]) : message.int64Data[j];\n                    else\n                        object.int64Data[j] = options.longs === String ? $util.Long.prototype.toString.call(message.int64Data[j]) : options.longs === Number ? new $util.LongBits(message.int64Data[j].low >>> 0, message.int64Data[j].high >>> 0).toNumber() : message.int64Data[j];\n            }\n            if (message.name != null && message.hasOwnProperty(\"name\"))\n                object.name = message.name;\n            if (message.rawData != null && message.hasOwnProperty(\"rawData\"))\n                object.rawData = options.bytes === String ? $util.base64.encode(message.rawData, 0, message.rawData.length) : options.bytes === Array ? Array.prototype.slice.call(message.rawData) : message.rawData;\n            if (message.doubleData && message.doubleData.length) {\n                object.doubleData = [];\n                for (var j = 0; j < message.doubleData.length; ++j)\n                    object.doubleData[j] = options.json && !isFinite(message.doubleData[j]) ? String(message.doubleData[j]) : message.doubleData[j];\n            }\n            if (message.uint64Data && message.uint64Data.length) {\n                object.uint64Data = [];\n                for (var j = 0; j < message.uint64Data.length; ++j)\n                    if (typeof message.uint64Data[j] === \"number\")\n                        object.uint64Data[j] = options.longs === String ? String(message.uint64Data[j]) : message.uint64Data[j];\n                    else\n                        object.uint64Data[j] = options.longs === String ? $util.Long.prototype.toString.call(message.uint64Data[j]) : options.longs === Number ? new $util.LongBits(message.uint64Data[j].low >>> 0, message.uint64Data[j].high >>> 0).toNumber(true) : message.uint64Data[j];\n            }\n            if (message.docString != null && message.hasOwnProperty(\"docString\"))\n                object.docString = message.docString;\n            if (message.externalData && message.externalData.length) {\n                object.externalData = [];\n                for (var j = 0; j < message.externalData.length; ++j)\n                    object.externalData[j] = $root.onnx.StringStringEntryProto.toObject(message.externalData[j], options);\n            }\n            if (message.dataLocation != null && message.hasOwnProperty(\"dataLocation\"))\n                object.dataLocation = options.enums === String ? $root.onnx.TensorProto.DataLocation[message.dataLocation] === undefined ? message.dataLocation : $root.onnx.TensorProto.DataLocation[message.dataLocation] : message.dataLocation;\n            return object;\n        };\n\n        /**\n         * Converts this TensorProto to JSON.\n         * @function toJSON\n         * @memberof onnx.TensorProto\n         * @instance\n         * @returns {Object.<string,*>} JSON object\n         */\n        TensorProto.prototype.toJSON = function toJSON() {\n            return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n        };\n\n        /**\n         * Gets the default type url for TensorProto\n         * @function getTypeUrl\n         * @memberof onnx.TensorProto\n         * @static\n         * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n         * @returns {string} The default type url\n         */\n        TensorProto.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n            if (typeUrlPrefix === undefined) {\n                typeUrlPrefix = \"type.googleapis.com\";\n            }\n            return typeUrlPrefix + \"/onnx.TensorProto\";\n        };\n\n        /**\n         * DataType enum.\n         * @name onnx.TensorProto.DataType\n         * @enum {number}\n         * @property {number} UNDEFINED=0 UNDEFINED value\n         * @property {number} FLOAT=1 FLOAT value\n         * @property {number} UINT8=2 UINT8 value\n         * @property {number} INT8=3 INT8 value\n         * @property {number} UINT16=4 UINT16 value\n         * @property {number} INT16=5 INT16 value\n         * @property {number} INT32=6 INT32 value\n         * @property {number} INT64=7 INT64 value\n         * @property {number} STRING=8 STRING value\n         * @property {number} BOOL=9 BOOL value\n         * @property {number} FLOAT16=10 FLOAT16 value\n         * @property {number} DOUBLE=11 DOUBLE value\n         * @property {number} UINT32=12 UINT32 value\n         * @property {number} UINT64=13 UINT64 value\n         * @property {number} COMPLEX64=14 COMPLEX64 value\n         * @property {number} COMPLEX128=15 COMPLEX128 value\n         * @property {number} BFLOAT16=16 BFLOAT16 value\n         * @property {number} FLOAT8E4M3FN=17 FLOAT8E4M3FN value\n         * @property {number} FLOAT8E4M3FNUZ=18 FLOAT8E4M3FNUZ value\n         * @property {number} FLOAT8E5M2=19 FLOAT8E5M2 value\n         * @property {number} FLOAT8E5M2FNUZ=20 FLOAT8E5M2FNUZ value\n         */\n        TensorProto.DataType = (function() {\n            var valuesById = {}, values = Object.create(valuesById);\n            values[valuesById[0] = \"UNDEFINED\"] = 0;\n            values[valuesById[1] = \"FLOAT\"] = 1;\n            values[valuesById[2] = \"UINT8\"] = 2;\n            values[valuesById[3] = \"INT8\"] = 3;\n            values[valuesById[4] = \"UINT16\"] = 4;\n            values[valuesById[5] = \"INT16\"] = 5;\n            values[valuesById[6] = \"INT32\"] = 6;\n            values[valuesById[7] = \"INT64\"] = 7;\n            values[valuesById[8] = \"STRING\"] = 8;\n            values[valuesById[9] = \"BOOL\"] = 9;\n            values[valuesById[10] = \"FLOAT16\"] = 10;\n            values[valuesById[11] = \"DOUBLE\"] = 11;\n            values[valuesById[12] = \"UINT32\"] = 12;\n            values[valuesById[13] = \"UINT64\"] = 13;\n            values[valuesById[14] = \"COMPLEX64\"] = 14;\n            values[valuesById[15] = \"COMPLEX128\"] = 15;\n            values[valuesById[16] = \"BFLOAT16\"] = 16;\n            values[valuesById[17] = \"FLOAT8E4M3FN\"] = 17;\n            values[valuesById[18] = \"FLOAT8E4M3FNUZ\"] = 18;\n            values[valuesById[19] = \"FLOAT8E5M2\"] = 19;\n            values[valuesById[20] = \"FLOAT8E5M2FNUZ\"] = 20;\n            return values;\n        })();\n\n        TensorProto.Segment = (function() {\n\n            /**\n             * Properties of a Segment.\n             * @memberof onnx.TensorProto\n             * @interface ISegment\n             * @property {number|Long|null} [begin] Segment begin\n             * @property {number|Long|null} [end] Segment end\n             */\n\n            /**\n             * Constructs a new Segment.\n             * @memberof onnx.TensorProto\n             * @classdesc Represents a Segment.\n             * @implements ISegment\n             * @constructor\n             * @param {onnx.TensorProto.ISegment=} [properties] Properties to set\n             */\n            function Segment(properties) {\n                if (properties)\n                    for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                        if (properties[keys[i]] != null)\n                            this[keys[i]] = properties[keys[i]];\n            }\n\n            /**\n             * Segment begin.\n             * @member {number|Long} begin\n             * @memberof onnx.TensorProto.Segment\n             * @instance\n             */\n            Segment.prototype.begin = $util.Long ? $util.Long.fromBits(0,0,false) : 0;\n\n            /**\n             * Segment end.\n             * @member {number|Long} end\n             * @memberof onnx.TensorProto.Segment\n             * @instance\n             */\n            Segment.prototype.end = $util.Long ? $util.Long.fromBits(0,0,false) : 0;\n\n            /**\n             * Creates a new Segment instance using the specified properties.\n             * @function create\n             * @memberof onnx.TensorProto.Segment\n             * @static\n             * @param {onnx.TensorProto.ISegment=} [properties] Properties to set\n             * @returns {onnx.TensorProto.Segment} Segment instance\n             */\n            Segment.create = function create(properties) {\n                return new Segment(properties);\n            };\n\n            /**\n             * Encodes the specified Segment message. Does not implicitly {@link onnx.TensorProto.Segment.verify|verify} messages.\n             * @function encode\n             * @memberof onnx.TensorProto.Segment\n             * @static\n             * @param {onnx.TensorProto.ISegment} message Segment message or plain object to encode\n             * @param {$protobuf.Writer} [writer] Writer to encode to\n             * @returns {$protobuf.Writer} Writer\n             */\n            Segment.encode = function encode(message, writer) {\n                if (!writer)\n                    writer = $Writer.create();\n                if (message.begin != null && Object.hasOwnProperty.call(message, \"begin\"))\n                    writer.uint32(/* id 1, wireType 0 =*/8).int64(message.begin);\n                if (message.end != null && Object.hasOwnProperty.call(message, \"end\"))\n                    writer.uint32(/* id 2, wireType 0 =*/16).int64(message.end);\n                return writer;\n            };\n\n            /**\n             * Encodes the specified Segment message, length delimited. Does not implicitly {@link onnx.TensorProto.Segment.verify|verify} messages.\n             * @function encodeDelimited\n             * @memberof onnx.TensorProto.Segment\n             * @static\n             * @param {onnx.TensorProto.ISegment} message Segment message or plain object to encode\n             * @param {$protobuf.Writer} [writer] Writer to encode to\n             * @returns {$protobuf.Writer} Writer\n             */\n            Segment.encodeDelimited = function encodeDelimited(message, writer) {\n                return this.encode(message, writer).ldelim();\n            };\n\n            /**\n             * Decodes a Segment message from the specified reader or buffer.\n             * @function decode\n             * @memberof onnx.TensorProto.Segment\n             * @static\n             * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n             * @param {number} [length] Message length if known beforehand\n             * @returns {onnx.TensorProto.Segment} Segment\n             * @throws {Error} If the payload is not a reader or valid buffer\n             * @throws {$protobuf.util.ProtocolError} If required fields are missing\n             */\n            Segment.decode = function decode(reader, length) {\n                if (!(reader instanceof $Reader))\n                    reader = $Reader.create(reader);\n                var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.TensorProto.Segment();\n                while (reader.pos < end) {\n                    var tag = reader.uint32();\n                    switch (tag >>> 3) {\n                    case 1: {\n                            message.begin = reader.int64();\n                            break;\n                        }\n                    case 2: {\n                            message.end = reader.int64();\n                            break;\n                        }\n                    default:\n                        reader.skipType(tag & 7);\n                        break;\n                    }\n                }\n                return message;\n            };\n\n            /**\n             * Decodes a Segment message from the specified reader or buffer, length delimited.\n             * @function decodeDelimited\n             * @memberof onnx.TensorProto.Segment\n             * @static\n             * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n             * @returns {onnx.TensorProto.Segment} Segment\n             * @throws {Error} If the payload is not a reader or valid buffer\n             * @throws {$protobuf.util.ProtocolError} If required fields are missing\n             */\n            Segment.decodeDelimited = function decodeDelimited(reader) {\n                if (!(reader instanceof $Reader))\n                    reader = new $Reader(reader);\n                return this.decode(reader, reader.uint32());\n            };\n\n            /**\n             * Verifies a Segment message.\n             * @function verify\n             * @memberof onnx.TensorProto.Segment\n             * @static\n             * @param {Object.<string,*>} message Plain object to verify\n             * @returns {string|null} `null` if valid, otherwise the reason why it is not\n             */\n            Segment.verify = function verify(message) {\n                if (typeof message !== \"object\" || message === null)\n                    return \"object expected\";\n                if (message.begin != null && message.hasOwnProperty(\"begin\"))\n                    if (!$util.isInteger(message.begin) && !(message.begin && $util.isInteger(message.begin.low) && $util.isInteger(message.begin.high)))\n                        return \"begin: integer|Long expected\";\n                if (message.end != null && message.hasOwnProperty(\"end\"))\n                    if (!$util.isInteger(message.end) && !(message.end && $util.isInteger(message.end.low) && $util.isInteger(message.end.high)))\n                        return \"end: integer|Long expected\";\n                return null;\n            };\n\n            /**\n             * Creates a Segment message from a plain object. Also converts values to their respective internal types.\n             * @function fromObject\n             * @memberof onnx.TensorProto.Segment\n             * @static\n             * @param {Object.<string,*>} object Plain object\n             * @returns {onnx.TensorProto.Segment} Segment\n             */\n            Segment.fromObject = function fromObject(object) {\n                if (object instanceof $root.onnx.TensorProto.Segment)\n                    return object;\n                var message = new $root.onnx.TensorProto.Segment();\n                if (object.begin != null)\n                    if ($util.Long)\n                        (message.begin = $util.Long.fromValue(object.begin)).unsigned = false;\n                    else if (typeof object.begin === \"string\")\n                        message.begin = parseInt(object.begin, 10);\n                    else if (typeof object.begin === \"number\")\n                        message.begin = object.begin;\n                    else if (typeof object.begin === \"object\")\n                        message.begin = new $util.LongBits(object.begin.low >>> 0, object.begin.high >>> 0).toNumber();\n                if (object.end != null)\n                    if ($util.Long)\n                        (message.end = $util.Long.fromValue(object.end)).unsigned = false;\n                    else if (typeof object.end === \"string\")\n                        message.end = parseInt(object.end, 10);\n                    else if (typeof object.end === \"number\")\n                        message.end = object.end;\n                    else if (typeof object.end === \"object\")\n                        message.end = new $util.LongBits(object.end.low >>> 0, object.end.high >>> 0).toNumber();\n                return message;\n            };\n\n            /**\n             * Creates a plain object from a Segment message. Also converts values to other types if specified.\n             * @function toObject\n             * @memberof onnx.TensorProto.Segment\n             * @static\n             * @param {onnx.TensorProto.Segment} message Segment\n             * @param {$protobuf.IConversionOptions} [options] Conversion options\n             * @returns {Object.<string,*>} Plain object\n             */\n            Segment.toObject = function toObject(message, options) {\n                if (!options)\n                    options = {};\n                var object = {};\n                if (options.defaults) {\n                    if ($util.Long) {\n                        var long = new $util.Long(0, 0, false);\n                        object.begin = options.longs === String ? long.toString() : options.longs === Number ? long.toNumber() : long;\n                    } else\n                        object.begin = options.longs === String ? \"0\" : 0;\n                    if ($util.Long) {\n                        var long = new $util.Long(0, 0, false);\n                        object.end = options.longs === String ? long.toString() : options.longs === Number ? long.toNumber() : long;\n                    } else\n                        object.end = options.longs === String ? \"0\" : 0;\n                }\n                if (message.begin != null && message.hasOwnProperty(\"begin\"))\n                    if (typeof message.begin === \"number\")\n                        object.begin = options.longs === String ? String(message.begin) : message.begin;\n                    else\n                        object.begin = options.longs === String ? $util.Long.prototype.toString.call(message.begin) : options.longs === Number ? new $util.LongBits(message.begin.low >>> 0, message.begin.high >>> 0).toNumber() : message.begin;\n                if (message.end != null && message.hasOwnProperty(\"end\"))\n                    if (typeof message.end === \"number\")\n                        object.end = options.longs === String ? String(message.end) : message.end;\n                    else\n                        object.end = options.longs === String ? $util.Long.prototype.toString.call(message.end) : options.longs === Number ? new $util.LongBits(message.end.low >>> 0, message.end.high >>> 0).toNumber() : message.end;\n                return object;\n            };\n\n            /**\n             * Converts this Segment to JSON.\n             * @function toJSON\n             * @memberof onnx.TensorProto.Segment\n             * @instance\n             * @returns {Object.<string,*>} JSON object\n             */\n            Segment.prototype.toJSON = function toJSON() {\n                return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n            };\n\n            /**\n             * Gets the default type url for Segment\n             * @function getTypeUrl\n             * @memberof onnx.TensorProto.Segment\n             * @static\n             * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n             * @returns {string} The default type url\n             */\n            Segment.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n                if (typeUrlPrefix === undefined) {\n                    typeUrlPrefix = \"type.googleapis.com\";\n                }\n                return typeUrlPrefix + \"/onnx.TensorProto.Segment\";\n            };\n\n            return Segment;\n        })();\n\n        /**\n         * DataLocation enum.\n         * @name onnx.TensorProto.DataLocation\n         * @enum {number}\n         * @property {number} DEFAULT=0 DEFAULT value\n         * @property {number} EXTERNAL=1 EXTERNAL value\n         */\n        TensorProto.DataLocation = (function() {\n            var valuesById = {}, values = Object.create(valuesById);\n            values[valuesById[0] = \"DEFAULT\"] = 0;\n            values[valuesById[1] = \"EXTERNAL\"] = 1;\n            return values;\n        })();\n\n        return TensorProto;\n    })();\n\n    onnx.SparseTensorProto = (function() {\n\n        /**\n         * Properties of a SparseTensorProto.\n         * @memberof onnx\n         * @interface ISparseTensorProto\n         * @property {onnx.ITensorProto|null} [values] SparseTensorProto values\n         * @property {onnx.ITensorProto|null} [indices] SparseTensorProto indices\n         * @property {Array.<number|Long>|null} [dims] SparseTensorProto dims\n         */\n\n        /**\n         * Constructs a new SparseTensorProto.\n         * @memberof onnx\n         * @classdesc Represents a SparseTensorProto.\n         * @implements ISparseTensorProto\n         * @constructor\n         * @param {onnx.ISparseTensorProto=} [properties] Properties to set\n         */\n        function SparseTensorProto(properties) {\n            this.dims = [];\n            if (properties)\n                for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                    if (properties[keys[i]] != null)\n                        this[keys[i]] = properties[keys[i]];\n        }\n\n        /**\n         * SparseTensorProto values.\n         * @member {onnx.ITensorProto|null|undefined} values\n         * @memberof onnx.SparseTensorProto\n         * @instance\n         */\n        SparseTensorProto.prototype.values = null;\n\n        /**\n         * SparseTensorProto indices.\n         * @member {onnx.ITensorProto|null|undefined} indices\n         * @memberof onnx.SparseTensorProto\n         * @instance\n         */\n        SparseTensorProto.prototype.indices = null;\n\n        /**\n         * SparseTensorProto dims.\n         * @member {Array.<number|Long>} dims\n         * @memberof onnx.SparseTensorProto\n         * @instance\n         */\n        SparseTensorProto.prototype.dims = $util.emptyArray;\n\n        /**\n         * Creates a new SparseTensorProto instance using the specified properties.\n         * @function create\n         * @memberof onnx.SparseTensorProto\n         * @static\n         * @param {onnx.ISparseTensorProto=} [properties] Properties to set\n         * @returns {onnx.SparseTensorProto} SparseTensorProto instance\n         */\n        SparseTensorProto.create = function create(properties) {\n            return new SparseTensorProto(properties);\n        };\n\n        /**\n         * Encodes the specified SparseTensorProto message. Does not implicitly {@link onnx.SparseTensorProto.verify|verify} messages.\n         * @function encode\n         * @memberof onnx.SparseTensorProto\n         * @static\n         * @param {onnx.ISparseTensorProto} message SparseTensorProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        SparseTensorProto.encode = function encode(message, writer) {\n            if (!writer)\n                writer = $Writer.create();\n            if (message.values != null && Object.hasOwnProperty.call(message, \"values\"))\n                $root.onnx.TensorProto.encode(message.values, writer.uint32(/* id 1, wireType 2 =*/10).fork()).ldelim();\n            if (message.indices != null && Object.hasOwnProperty.call(message, \"indices\"))\n                $root.onnx.TensorProto.encode(message.indices, writer.uint32(/* id 2, wireType 2 =*/18).fork()).ldelim();\n            if (message.dims != null && message.dims.length) {\n                writer.uint32(/* id 3, wireType 2 =*/26).fork();\n                for (var i = 0; i < message.dims.length; ++i)\n                    writer.int64(message.dims[i]);\n                writer.ldelim();\n            }\n            return writer;\n        };\n\n        /**\n         * Encodes the specified SparseTensorProto message, length delimited. Does not implicitly {@link onnx.SparseTensorProto.verify|verify} messages.\n         * @function encodeDelimited\n         * @memberof onnx.SparseTensorProto\n         * @static\n         * @param {onnx.ISparseTensorProto} message SparseTensorProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        SparseTensorProto.encodeDelimited = function encodeDelimited(message, writer) {\n            return this.encode(message, writer).ldelim();\n        };\n\n        /**\n         * Decodes a SparseTensorProto message from the specified reader or buffer.\n         * @function decode\n         * @memberof onnx.SparseTensorProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @param {number} [length] Message length if known beforehand\n         * @returns {onnx.SparseTensorProto} SparseTensorProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        SparseTensorProto.decode = function decode(reader, length) {\n            if (!(reader instanceof $Reader))\n                reader = $Reader.create(reader);\n            var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.SparseTensorProto();\n            while (reader.pos < end) {\n                var tag = reader.uint32();\n                switch (tag >>> 3) {\n                case 1: {\n                        message.values = $root.onnx.TensorProto.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 2: {\n                        message.indices = $root.onnx.TensorProto.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 3: {\n                        if (!(message.dims && message.dims.length))\n                            message.dims = [];\n                        if ((tag & 7) === 2) {\n                            var end2 = reader.uint32() + reader.pos;\n                            while (reader.pos < end2)\n                                message.dims.push(reader.int64());\n                        } else\n                            message.dims.push(reader.int64());\n                        break;\n                    }\n                default:\n                    reader.skipType(tag & 7);\n                    break;\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Decodes a SparseTensorProto message from the specified reader or buffer, length delimited.\n         * @function decodeDelimited\n         * @memberof onnx.SparseTensorProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @returns {onnx.SparseTensorProto} SparseTensorProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        SparseTensorProto.decodeDelimited = function decodeDelimited(reader) {\n            if (!(reader instanceof $Reader))\n                reader = new $Reader(reader);\n            return this.decode(reader, reader.uint32());\n        };\n\n        /**\n         * Verifies a SparseTensorProto message.\n         * @function verify\n         * @memberof onnx.SparseTensorProto\n         * @static\n         * @param {Object.<string,*>} message Plain object to verify\n         * @returns {string|null} `null` if valid, otherwise the reason why it is not\n         */\n        SparseTensorProto.verify = function verify(message) {\n            if (typeof message !== \"object\" || message === null)\n                return \"object expected\";\n            if (message.values != null && message.hasOwnProperty(\"values\")) {\n                var error = $root.onnx.TensorProto.verify(message.values);\n                if (error)\n                    return \"values.\" + error;\n            }\n            if (message.indices != null && message.hasOwnProperty(\"indices\")) {\n                var error = $root.onnx.TensorProto.verify(message.indices);\n                if (error)\n                    return \"indices.\" + error;\n            }\n            if (message.dims != null && message.hasOwnProperty(\"dims\")) {\n                if (!Array.isArray(message.dims))\n                    return \"dims: array expected\";\n                for (var i = 0; i < message.dims.length; ++i)\n                    if (!$util.isInteger(message.dims[i]) && !(message.dims[i] && $util.isInteger(message.dims[i].low) && $util.isInteger(message.dims[i].high)))\n                        return \"dims: integer|Long[] expected\";\n            }\n            return null;\n        };\n\n        /**\n         * Creates a SparseTensorProto message from a plain object. Also converts values to their respective internal types.\n         * @function fromObject\n         * @memberof onnx.SparseTensorProto\n         * @static\n         * @param {Object.<string,*>} object Plain object\n         * @returns {onnx.SparseTensorProto} SparseTensorProto\n         */\n        SparseTensorProto.fromObject = function fromObject(object) {\n            if (object instanceof $root.onnx.SparseTensorProto)\n                return object;\n            var message = new $root.onnx.SparseTensorProto();\n            if (object.values != null) {\n                if (typeof object.values !== \"object\")\n                    throw TypeError(\".onnx.SparseTensorProto.values: object expected\");\n                message.values = $root.onnx.TensorProto.fromObject(object.values);\n            }\n            if (object.indices != null) {\n                if (typeof object.indices !== \"object\")\n                    throw TypeError(\".onnx.SparseTensorProto.indices: object expected\");\n                message.indices = $root.onnx.TensorProto.fromObject(object.indices);\n            }\n            if (object.dims) {\n                if (!Array.isArray(object.dims))\n                    throw TypeError(\".onnx.SparseTensorProto.dims: array expected\");\n                message.dims = [];\n                for (var i = 0; i < object.dims.length; ++i)\n                    if ($util.Long)\n                        (message.dims[i] = $util.Long.fromValue(object.dims[i])).unsigned = false;\n                    else if (typeof object.dims[i] === \"string\")\n                        message.dims[i] = parseInt(object.dims[i], 10);\n                    else if (typeof object.dims[i] === \"number\")\n                        message.dims[i] = object.dims[i];\n                    else if (typeof object.dims[i] === \"object\")\n                        message.dims[i] = new $util.LongBits(object.dims[i].low >>> 0, object.dims[i].high >>> 0).toNumber();\n            }\n            return message;\n        };\n\n        /**\n         * Creates a plain object from a SparseTensorProto message. Also converts values to other types if specified.\n         * @function toObject\n         * @memberof onnx.SparseTensorProto\n         * @static\n         * @param {onnx.SparseTensorProto} message SparseTensorProto\n         * @param {$protobuf.IConversionOptions} [options] Conversion options\n         * @returns {Object.<string,*>} Plain object\n         */\n        SparseTensorProto.toObject = function toObject(message, options) {\n            if (!options)\n                options = {};\n            var object = {};\n            if (options.arrays || options.defaults)\n                object.dims = [];\n            if (options.defaults) {\n                object.values = null;\n                object.indices = null;\n            }\n            if (message.values != null && message.hasOwnProperty(\"values\"))\n                object.values = $root.onnx.TensorProto.toObject(message.values, options);\n            if (message.indices != null && message.hasOwnProperty(\"indices\"))\n                object.indices = $root.onnx.TensorProto.toObject(message.indices, options);\n            if (message.dims && message.dims.length) {\n                object.dims = [];\n                for (var j = 0; j < message.dims.length; ++j)\n                    if (typeof message.dims[j] === \"number\")\n                        object.dims[j] = options.longs === String ? String(message.dims[j]) : message.dims[j];\n                    else\n                        object.dims[j] = options.longs === String ? $util.Long.prototype.toString.call(message.dims[j]) : options.longs === Number ? new $util.LongBits(message.dims[j].low >>> 0, message.dims[j].high >>> 0).toNumber() : message.dims[j];\n            }\n            return object;\n        };\n\n        /**\n         * Converts this SparseTensorProto to JSON.\n         * @function toJSON\n         * @memberof onnx.SparseTensorProto\n         * @instance\n         * @returns {Object.<string,*>} JSON object\n         */\n        SparseTensorProto.prototype.toJSON = function toJSON() {\n            return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n        };\n\n        /**\n         * Gets the default type url for SparseTensorProto\n         * @function getTypeUrl\n         * @memberof onnx.SparseTensorProto\n         * @static\n         * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n         * @returns {string} The default type url\n         */\n        SparseTensorProto.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n            if (typeUrlPrefix === undefined) {\n                typeUrlPrefix = \"type.googleapis.com\";\n            }\n            return typeUrlPrefix + \"/onnx.SparseTensorProto\";\n        };\n\n        return SparseTensorProto;\n    })();\n\n    onnx.TensorShapeProto = (function() {\n\n        /**\n         * Properties of a TensorShapeProto.\n         * @memberof onnx\n         * @interface ITensorShapeProto\n         * @property {Array.<onnx.TensorShapeProto.IDimension>|null} [dim] TensorShapeProto dim\n         */\n\n        /**\n         * Constructs a new TensorShapeProto.\n         * @memberof onnx\n         * @classdesc Represents a TensorShapeProto.\n         * @implements ITensorShapeProto\n         * @constructor\n         * @param {onnx.ITensorShapeProto=} [properties] Properties to set\n         */\n        function TensorShapeProto(properties) {\n            this.dim = [];\n            if (properties)\n                for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                    if (properties[keys[i]] != null)\n                        this[keys[i]] = properties[keys[i]];\n        }\n\n        /**\n         * TensorShapeProto dim.\n         * @member {Array.<onnx.TensorShapeProto.IDimension>} dim\n         * @memberof onnx.TensorShapeProto\n         * @instance\n         */\n        TensorShapeProto.prototype.dim = $util.emptyArray;\n\n        /**\n         * Creates a new TensorShapeProto instance using the specified properties.\n         * @function create\n         * @memberof onnx.TensorShapeProto\n         * @static\n         * @param {onnx.ITensorShapeProto=} [properties] Properties to set\n         * @returns {onnx.TensorShapeProto} TensorShapeProto instance\n         */\n        TensorShapeProto.create = function create(properties) {\n            return new TensorShapeProto(properties);\n        };\n\n        /**\n         * Encodes the specified TensorShapeProto message. Does not implicitly {@link onnx.TensorShapeProto.verify|verify} messages.\n         * @function encode\n         * @memberof onnx.TensorShapeProto\n         * @static\n         * @param {onnx.ITensorShapeProto} message TensorShapeProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        TensorShapeProto.encode = function encode(message, writer) {\n            if (!writer)\n                writer = $Writer.create();\n            if (message.dim != null && message.dim.length)\n                for (var i = 0; i < message.dim.length; ++i)\n                    $root.onnx.TensorShapeProto.Dimension.encode(message.dim[i], writer.uint32(/* id 1, wireType 2 =*/10).fork()).ldelim();\n            return writer;\n        };\n\n        /**\n         * Encodes the specified TensorShapeProto message, length delimited. Does not implicitly {@link onnx.TensorShapeProto.verify|verify} messages.\n         * @function encodeDelimited\n         * @memberof onnx.TensorShapeProto\n         * @static\n         * @param {onnx.ITensorShapeProto} message TensorShapeProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        TensorShapeProto.encodeDelimited = function encodeDelimited(message, writer) {\n            return this.encode(message, writer).ldelim();\n        };\n\n        /**\n         * Decodes a TensorShapeProto message from the specified reader or buffer.\n         * @function decode\n         * @memberof onnx.TensorShapeProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @param {number} [length] Message length if known beforehand\n         * @returns {onnx.TensorShapeProto} TensorShapeProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        TensorShapeProto.decode = function decode(reader, length) {\n            if (!(reader instanceof $Reader))\n                reader = $Reader.create(reader);\n            var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.TensorShapeProto();\n            while (reader.pos < end) {\n                var tag = reader.uint32();\n                switch (tag >>> 3) {\n                case 1: {\n                        if (!(message.dim && message.dim.length))\n                            message.dim = [];\n                        message.dim.push($root.onnx.TensorShapeProto.Dimension.decode(reader, reader.uint32()));\n                        break;\n                    }\n                default:\n                    reader.skipType(tag & 7);\n                    break;\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Decodes a TensorShapeProto message from the specified reader or buffer, length delimited.\n         * @function decodeDelimited\n         * @memberof onnx.TensorShapeProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @returns {onnx.TensorShapeProto} TensorShapeProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        TensorShapeProto.decodeDelimited = function decodeDelimited(reader) {\n            if (!(reader instanceof $Reader))\n                reader = new $Reader(reader);\n            return this.decode(reader, reader.uint32());\n        };\n\n        /**\n         * Verifies a TensorShapeProto message.\n         * @function verify\n         * @memberof onnx.TensorShapeProto\n         * @static\n         * @param {Object.<string,*>} message Plain object to verify\n         * @returns {string|null} `null` if valid, otherwise the reason why it is not\n         */\n        TensorShapeProto.verify = function verify(message) {\n            if (typeof message !== \"object\" || message === null)\n                return \"object expected\";\n            if (message.dim != null && message.hasOwnProperty(\"dim\")) {\n                if (!Array.isArray(message.dim))\n                    return \"dim: array expected\";\n                for (var i = 0; i < message.dim.length; ++i) {\n                    var error = $root.onnx.TensorShapeProto.Dimension.verify(message.dim[i]);\n                    if (error)\n                        return \"dim.\" + error;\n                }\n            }\n            return null;\n        };\n\n        /**\n         * Creates a TensorShapeProto message from a plain object. Also converts values to their respective internal types.\n         * @function fromObject\n         * @memberof onnx.TensorShapeProto\n         * @static\n         * @param {Object.<string,*>} object Plain object\n         * @returns {onnx.TensorShapeProto} TensorShapeProto\n         */\n        TensorShapeProto.fromObject = function fromObject(object) {\n            if (object instanceof $root.onnx.TensorShapeProto)\n                return object;\n            var message = new $root.onnx.TensorShapeProto();\n            if (object.dim) {\n                if (!Array.isArray(object.dim))\n                    throw TypeError(\".onnx.TensorShapeProto.dim: array expected\");\n                message.dim = [];\n                for (var i = 0; i < object.dim.length; ++i) {\n                    if (typeof object.dim[i] !== \"object\")\n                        throw TypeError(\".onnx.TensorShapeProto.dim: object expected\");\n                    message.dim[i] = $root.onnx.TensorShapeProto.Dimension.fromObject(object.dim[i]);\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Creates a plain object from a TensorShapeProto message. Also converts values to other types if specified.\n         * @function toObject\n         * @memberof onnx.TensorShapeProto\n         * @static\n         * @param {onnx.TensorShapeProto} message TensorShapeProto\n         * @param {$protobuf.IConversionOptions} [options] Conversion options\n         * @returns {Object.<string,*>} Plain object\n         */\n        TensorShapeProto.toObject = function toObject(message, options) {\n            if (!options)\n                options = {};\n            var object = {};\n            if (options.arrays || options.defaults)\n                object.dim = [];\n            if (message.dim && message.dim.length) {\n                object.dim = [];\n                for (var j = 0; j < message.dim.length; ++j)\n                    object.dim[j] = $root.onnx.TensorShapeProto.Dimension.toObject(message.dim[j], options);\n            }\n            return object;\n        };\n\n        /**\n         * Converts this TensorShapeProto to JSON.\n         * @function toJSON\n         * @memberof onnx.TensorShapeProto\n         * @instance\n         * @returns {Object.<string,*>} JSON object\n         */\n        TensorShapeProto.prototype.toJSON = function toJSON() {\n            return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n        };\n\n        /**\n         * Gets the default type url for TensorShapeProto\n         * @function getTypeUrl\n         * @memberof onnx.TensorShapeProto\n         * @static\n         * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n         * @returns {string} The default type url\n         */\n        TensorShapeProto.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n            if (typeUrlPrefix === undefined) {\n                typeUrlPrefix = \"type.googleapis.com\";\n            }\n            return typeUrlPrefix + \"/onnx.TensorShapeProto\";\n        };\n\n        TensorShapeProto.Dimension = (function() {\n\n            /**\n             * Properties of a Dimension.\n             * @memberof onnx.TensorShapeProto\n             * @interface IDimension\n             * @property {number|Long|null} [dimValue] Dimension dimValue\n             * @property {string|null} [dimParam] Dimension dimParam\n             * @property {string|null} [denotation] Dimension denotation\n             */\n\n            /**\n             * Constructs a new Dimension.\n             * @memberof onnx.TensorShapeProto\n             * @classdesc Represents a Dimension.\n             * @implements IDimension\n             * @constructor\n             * @param {onnx.TensorShapeProto.IDimension=} [properties] Properties to set\n             */\n            function Dimension(properties) {\n                if (properties)\n                    for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                        if (properties[keys[i]] != null)\n                            this[keys[i]] = properties[keys[i]];\n            }\n\n            /**\n             * Dimension dimValue.\n             * @member {number|Long|null|undefined} dimValue\n             * @memberof onnx.TensorShapeProto.Dimension\n             * @instance\n             */\n            Dimension.prototype.dimValue = null;\n\n            /**\n             * Dimension dimParam.\n             * @member {string|null|undefined} dimParam\n             * @memberof onnx.TensorShapeProto.Dimension\n             * @instance\n             */\n            Dimension.prototype.dimParam = null;\n\n            /**\n             * Dimension denotation.\n             * @member {string} denotation\n             * @memberof onnx.TensorShapeProto.Dimension\n             * @instance\n             */\n            Dimension.prototype.denotation = \"\";\n\n            // OneOf field names bound to virtual getters and setters\n            var $oneOfFields;\n\n            /**\n             * Dimension value.\n             * @member {\"dimValue\"|\"dimParam\"|undefined} value\n             * @memberof onnx.TensorShapeProto.Dimension\n             * @instance\n             */\n            Object.defineProperty(Dimension.prototype, \"value\", {\n                get: $util.oneOfGetter($oneOfFields = [\"dimValue\", \"dimParam\"]),\n                set: $util.oneOfSetter($oneOfFields)\n            });\n\n            /**\n             * Creates a new Dimension instance using the specified properties.\n             * @function create\n             * @memberof onnx.TensorShapeProto.Dimension\n             * @static\n             * @param {onnx.TensorShapeProto.IDimension=} [properties] Properties to set\n             * @returns {onnx.TensorShapeProto.Dimension} Dimension instance\n             */\n            Dimension.create = function create(properties) {\n                return new Dimension(properties);\n            };\n\n            /**\n             * Encodes the specified Dimension message. Does not implicitly {@link onnx.TensorShapeProto.Dimension.verify|verify} messages.\n             * @function encode\n             * @memberof onnx.TensorShapeProto.Dimension\n             * @static\n             * @param {onnx.TensorShapeProto.IDimension} message Dimension message or plain object to encode\n             * @param {$protobuf.Writer} [writer] Writer to encode to\n             * @returns {$protobuf.Writer} Writer\n             */\n            Dimension.encode = function encode(message, writer) {\n                if (!writer)\n                    writer = $Writer.create();\n                if (message.dimValue != null && Object.hasOwnProperty.call(message, \"dimValue\"))\n                    writer.uint32(/* id 1, wireType 0 =*/8).int64(message.dimValue);\n                if (message.dimParam != null && Object.hasOwnProperty.call(message, \"dimParam\"))\n                    writer.uint32(/* id 2, wireType 2 =*/18).string(message.dimParam);\n                if (message.denotation != null && Object.hasOwnProperty.call(message, \"denotation\"))\n                    writer.uint32(/* id 3, wireType 2 =*/26).string(message.denotation);\n                return writer;\n            };\n\n            /**\n             * Encodes the specified Dimension message, length delimited. Does not implicitly {@link onnx.TensorShapeProto.Dimension.verify|verify} messages.\n             * @function encodeDelimited\n             * @memberof onnx.TensorShapeProto.Dimension\n             * @static\n             * @param {onnx.TensorShapeProto.IDimension} message Dimension message or plain object to encode\n             * @param {$protobuf.Writer} [writer] Writer to encode to\n             * @returns {$protobuf.Writer} Writer\n             */\n            Dimension.encodeDelimited = function encodeDelimited(message, writer) {\n                return this.encode(message, writer).ldelim();\n            };\n\n            /**\n             * Decodes a Dimension message from the specified reader or buffer.\n             * @function decode\n             * @memberof onnx.TensorShapeProto.Dimension\n             * @static\n             * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n             * @param {number} [length] Message length if known beforehand\n             * @returns {onnx.TensorShapeProto.Dimension} Dimension\n             * @throws {Error} If the payload is not a reader or valid buffer\n             * @throws {$protobuf.util.ProtocolError} If required fields are missing\n             */\n            Dimension.decode = function decode(reader, length) {\n                if (!(reader instanceof $Reader))\n                    reader = $Reader.create(reader);\n                var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.TensorShapeProto.Dimension();\n                while (reader.pos < end) {\n                    var tag = reader.uint32();\n                    switch (tag >>> 3) {\n                    case 1: {\n                            message.dimValue = reader.int64();\n                            break;\n                        }\n                    case 2: {\n                            message.dimParam = reader.string();\n                            break;\n                        }\n                    case 3: {\n                            message.denotation = reader.string();\n                            break;\n                        }\n                    default:\n                        reader.skipType(tag & 7);\n                        break;\n                    }\n                }\n                return message;\n            };\n\n            /**\n             * Decodes a Dimension message from the specified reader or buffer, length delimited.\n             * @function decodeDelimited\n             * @memberof onnx.TensorShapeProto.Dimension\n             * @static\n             * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n             * @returns {onnx.TensorShapeProto.Dimension} Dimension\n             * @throws {Error} If the payload is not a reader or valid buffer\n             * @throws {$protobuf.util.ProtocolError} If required fields are missing\n             */\n            Dimension.decodeDelimited = function decodeDelimited(reader) {\n                if (!(reader instanceof $Reader))\n                    reader = new $Reader(reader);\n                return this.decode(reader, reader.uint32());\n            };\n\n            /**\n             * Verifies a Dimension message.\n             * @function verify\n             * @memberof onnx.TensorShapeProto.Dimension\n             * @static\n             * @param {Object.<string,*>} message Plain object to verify\n             * @returns {string|null} `null` if valid, otherwise the reason why it is not\n             */\n            Dimension.verify = function verify(message) {\n                if (typeof message !== \"object\" || message === null)\n                    return \"object expected\";\n                var properties = {};\n                if (message.dimValue != null && message.hasOwnProperty(\"dimValue\")) {\n                    properties.value = 1;\n                    if (!$util.isInteger(message.dimValue) && !(message.dimValue && $util.isInteger(message.dimValue.low) && $util.isInteger(message.dimValue.high)))\n                        return \"dimValue: integer|Long expected\";\n                }\n                if (message.dimParam != null && message.hasOwnProperty(\"dimParam\")) {\n                    if (properties.value === 1)\n                        return \"value: multiple values\";\n                    properties.value = 1;\n                    if (!$util.isString(message.dimParam))\n                        return \"dimParam: string expected\";\n                }\n                if (message.denotation != null && message.hasOwnProperty(\"denotation\"))\n                    if (!$util.isString(message.denotation))\n                        return \"denotation: string expected\";\n                return null;\n            };\n\n            /**\n             * Creates a Dimension message from a plain object. Also converts values to their respective internal types.\n             * @function fromObject\n             * @memberof onnx.TensorShapeProto.Dimension\n             * @static\n             * @param {Object.<string,*>} object Plain object\n             * @returns {onnx.TensorShapeProto.Dimension} Dimension\n             */\n            Dimension.fromObject = function fromObject(object) {\n                if (object instanceof $root.onnx.TensorShapeProto.Dimension)\n                    return object;\n                var message = new $root.onnx.TensorShapeProto.Dimension();\n                if (object.dimValue != null)\n                    if ($util.Long)\n                        (message.dimValue = $util.Long.fromValue(object.dimValue)).unsigned = false;\n                    else if (typeof object.dimValue === \"string\")\n                        message.dimValue = parseInt(object.dimValue, 10);\n                    else if (typeof object.dimValue === \"number\")\n                        message.dimValue = object.dimValue;\n                    else if (typeof object.dimValue === \"object\")\n                        message.dimValue = new $util.LongBits(object.dimValue.low >>> 0, object.dimValue.high >>> 0).toNumber();\n                if (object.dimParam != null)\n                    message.dimParam = String(object.dimParam);\n                if (object.denotation != null)\n                    message.denotation = String(object.denotation);\n                return message;\n            };\n\n            /**\n             * Creates a plain object from a Dimension message. Also converts values to other types if specified.\n             * @function toObject\n             * @memberof onnx.TensorShapeProto.Dimension\n             * @static\n             * @param {onnx.TensorShapeProto.Dimension} message Dimension\n             * @param {$protobuf.IConversionOptions} [options] Conversion options\n             * @returns {Object.<string,*>} Plain object\n             */\n            Dimension.toObject = function toObject(message, options) {\n                if (!options)\n                    options = {};\n                var object = {};\n                if (options.defaults)\n                    object.denotation = \"\";\n                if (message.dimValue != null && message.hasOwnProperty(\"dimValue\")) {\n                    if (typeof message.dimValue === \"number\")\n                        object.dimValue = options.longs === String ? String(message.dimValue) : message.dimValue;\n                    else\n                        object.dimValue = options.longs === String ? $util.Long.prototype.toString.call(message.dimValue) : options.longs === Number ? new $util.LongBits(message.dimValue.low >>> 0, message.dimValue.high >>> 0).toNumber() : message.dimValue;\n                    if (options.oneofs)\n                        object.value = \"dimValue\";\n                }\n                if (message.dimParam != null && message.hasOwnProperty(\"dimParam\")) {\n                    object.dimParam = message.dimParam;\n                    if (options.oneofs)\n                        object.value = \"dimParam\";\n                }\n                if (message.denotation != null && message.hasOwnProperty(\"denotation\"))\n                    object.denotation = message.denotation;\n                return object;\n            };\n\n            /**\n             * Converts this Dimension to JSON.\n             * @function toJSON\n             * @memberof onnx.TensorShapeProto.Dimension\n             * @instance\n             * @returns {Object.<string,*>} JSON object\n             */\n            Dimension.prototype.toJSON = function toJSON() {\n                return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n            };\n\n            /**\n             * Gets the default type url for Dimension\n             * @function getTypeUrl\n             * @memberof onnx.TensorShapeProto.Dimension\n             * @static\n             * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n             * @returns {string} The default type url\n             */\n            Dimension.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n                if (typeUrlPrefix === undefined) {\n                    typeUrlPrefix = \"type.googleapis.com\";\n                }\n                return typeUrlPrefix + \"/onnx.TensorShapeProto.Dimension\";\n            };\n\n            return Dimension;\n        })();\n\n        return TensorShapeProto;\n    })();\n\n    onnx.TypeProto = (function() {\n\n        /**\n         * Properties of a TypeProto.\n         * @memberof onnx\n         * @interface ITypeProto\n         * @property {onnx.TypeProto.ITensor|null} [tensorType] TypeProto tensorType\n         * @property {onnx.TypeProto.ISequence|null} [sequenceType] TypeProto sequenceType\n         * @property {onnx.TypeProto.IMap|null} [mapType] TypeProto mapType\n         * @property {onnx.TypeProto.IOptional|null} [optionalType] TypeProto optionalType\n         * @property {onnx.TypeProto.ISparseTensor|null} [sparseTensorType] TypeProto sparseTensorType\n         * @property {string|null} [denotation] TypeProto denotation\n         */\n\n        /**\n         * Constructs a new TypeProto.\n         * @memberof onnx\n         * @classdesc Represents a TypeProto.\n         * @implements ITypeProto\n         * @constructor\n         * @param {onnx.ITypeProto=} [properties] Properties to set\n         */\n        function TypeProto(properties) {\n            if (properties)\n                for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                    if (properties[keys[i]] != null)\n                        this[keys[i]] = properties[keys[i]];\n        }\n\n        /**\n         * TypeProto tensorType.\n         * @member {onnx.TypeProto.ITensor|null|undefined} tensorType\n         * @memberof onnx.TypeProto\n         * @instance\n         */\n        TypeProto.prototype.tensorType = null;\n\n        /**\n         * TypeProto sequenceType.\n         * @member {onnx.TypeProto.ISequence|null|undefined} sequenceType\n         * @memberof onnx.TypeProto\n         * @instance\n         */\n        TypeProto.prototype.sequenceType = null;\n\n        /**\n         * TypeProto mapType.\n         * @member {onnx.TypeProto.IMap|null|undefined} mapType\n         * @memberof onnx.TypeProto\n         * @instance\n         */\n        TypeProto.prototype.mapType = null;\n\n        /**\n         * TypeProto optionalType.\n         * @member {onnx.TypeProto.IOptional|null|undefined} optionalType\n         * @memberof onnx.TypeProto\n         * @instance\n         */\n        TypeProto.prototype.optionalType = null;\n\n        /**\n         * TypeProto sparseTensorType.\n         * @member {onnx.TypeProto.ISparseTensor|null|undefined} sparseTensorType\n         * @memberof onnx.TypeProto\n         * @instance\n         */\n        TypeProto.prototype.sparseTensorType = null;\n\n        /**\n         * TypeProto denotation.\n         * @member {string} denotation\n         * @memberof onnx.TypeProto\n         * @instance\n         */\n        TypeProto.prototype.denotation = \"\";\n\n        // OneOf field names bound to virtual getters and setters\n        var $oneOfFields;\n\n        /**\n         * TypeProto value.\n         * @member {\"tensorType\"|\"sequenceType\"|\"mapType\"|\"optionalType\"|\"sparseTensorType\"|undefined} value\n         * @memberof onnx.TypeProto\n         * @instance\n         */\n        Object.defineProperty(TypeProto.prototype, \"value\", {\n            get: $util.oneOfGetter($oneOfFields = [\"tensorType\", \"sequenceType\", \"mapType\", \"optionalType\", \"sparseTensorType\"]),\n            set: $util.oneOfSetter($oneOfFields)\n        });\n\n        /**\n         * Creates a new TypeProto instance using the specified properties.\n         * @function create\n         * @memberof onnx.TypeProto\n         * @static\n         * @param {onnx.ITypeProto=} [properties] Properties to set\n         * @returns {onnx.TypeProto} TypeProto instance\n         */\n        TypeProto.create = function create(properties) {\n            return new TypeProto(properties);\n        };\n\n        /**\n         * Encodes the specified TypeProto message. Does not implicitly {@link onnx.TypeProto.verify|verify} messages.\n         * @function encode\n         * @memberof onnx.TypeProto\n         * @static\n         * @param {onnx.ITypeProto} message TypeProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        TypeProto.encode = function encode(message, writer) {\n            if (!writer)\n                writer = $Writer.create();\n            if (message.tensorType != null && Object.hasOwnProperty.call(message, \"tensorType\"))\n                $root.onnx.TypeProto.Tensor.encode(message.tensorType, writer.uint32(/* id 1, wireType 2 =*/10).fork()).ldelim();\n            if (message.sequenceType != null && Object.hasOwnProperty.call(message, \"sequenceType\"))\n                $root.onnx.TypeProto.Sequence.encode(message.sequenceType, writer.uint32(/* id 4, wireType 2 =*/34).fork()).ldelim();\n            if (message.mapType != null && Object.hasOwnProperty.call(message, \"mapType\"))\n                $root.onnx.TypeProto.Map.encode(message.mapType, writer.uint32(/* id 5, wireType 2 =*/42).fork()).ldelim();\n            if (message.denotation != null && Object.hasOwnProperty.call(message, \"denotation\"))\n                writer.uint32(/* id 6, wireType 2 =*/50).string(message.denotation);\n            if (message.sparseTensorType != null && Object.hasOwnProperty.call(message, \"sparseTensorType\"))\n                $root.onnx.TypeProto.SparseTensor.encode(message.sparseTensorType, writer.uint32(/* id 8, wireType 2 =*/66).fork()).ldelim();\n            if (message.optionalType != null && Object.hasOwnProperty.call(message, \"optionalType\"))\n                $root.onnx.TypeProto.Optional.encode(message.optionalType, writer.uint32(/* id 9, wireType 2 =*/74).fork()).ldelim();\n            return writer;\n        };\n\n        /**\n         * Encodes the specified TypeProto message, length delimited. Does not implicitly {@link onnx.TypeProto.verify|verify} messages.\n         * @function encodeDelimited\n         * @memberof onnx.TypeProto\n         * @static\n         * @param {onnx.ITypeProto} message TypeProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        TypeProto.encodeDelimited = function encodeDelimited(message, writer) {\n            return this.encode(message, writer).ldelim();\n        };\n\n        /**\n         * Decodes a TypeProto message from the specified reader or buffer.\n         * @function decode\n         * @memberof onnx.TypeProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @param {number} [length] Message length if known beforehand\n         * @returns {onnx.TypeProto} TypeProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        TypeProto.decode = function decode(reader, length) {\n            if (!(reader instanceof $Reader))\n                reader = $Reader.create(reader);\n            var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.TypeProto();\n            while (reader.pos < end) {\n                var tag = reader.uint32();\n                switch (tag >>> 3) {\n                case 1: {\n                        message.tensorType = $root.onnx.TypeProto.Tensor.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 4: {\n                        message.sequenceType = $root.onnx.TypeProto.Sequence.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 5: {\n                        message.mapType = $root.onnx.TypeProto.Map.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 9: {\n                        message.optionalType = $root.onnx.TypeProto.Optional.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 8: {\n                        message.sparseTensorType = $root.onnx.TypeProto.SparseTensor.decode(reader, reader.uint32());\n                        break;\n                    }\n                case 6: {\n                        message.denotation = reader.string();\n                        break;\n                    }\n                default:\n                    reader.skipType(tag & 7);\n                    break;\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Decodes a TypeProto message from the specified reader or buffer, length delimited.\n         * @function decodeDelimited\n         * @memberof onnx.TypeProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @returns {onnx.TypeProto} TypeProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        TypeProto.decodeDelimited = function decodeDelimited(reader) {\n            if (!(reader instanceof $Reader))\n                reader = new $Reader(reader);\n            return this.decode(reader, reader.uint32());\n        };\n\n        /**\n         * Verifies a TypeProto message.\n         * @function verify\n         * @memberof onnx.TypeProto\n         * @static\n         * @param {Object.<string,*>} message Plain object to verify\n         * @returns {string|null} `null` if valid, otherwise the reason why it is not\n         */\n        TypeProto.verify = function verify(message) {\n            if (typeof message !== \"object\" || message === null)\n                return \"object expected\";\n            var properties = {};\n            if (message.tensorType != null && message.hasOwnProperty(\"tensorType\")) {\n                properties.value = 1;\n                {\n                    var error = $root.onnx.TypeProto.Tensor.verify(message.tensorType);\n                    if (error)\n                        return \"tensorType.\" + error;\n                }\n            }\n            if (message.sequenceType != null && message.hasOwnProperty(\"sequenceType\")) {\n                if (properties.value === 1)\n                    return \"value: multiple values\";\n                properties.value = 1;\n                {\n                    var error = $root.onnx.TypeProto.Sequence.verify(message.sequenceType);\n                    if (error)\n                        return \"sequenceType.\" + error;\n                }\n            }\n            if (message.mapType != null && message.hasOwnProperty(\"mapType\")) {\n                if (properties.value === 1)\n                    return \"value: multiple values\";\n                properties.value = 1;\n                {\n                    var error = $root.onnx.TypeProto.Map.verify(message.mapType);\n                    if (error)\n                        return \"mapType.\" + error;\n                }\n            }\n            if (message.optionalType != null && message.hasOwnProperty(\"optionalType\")) {\n                if (properties.value === 1)\n                    return \"value: multiple values\";\n                properties.value = 1;\n                {\n                    var error = $root.onnx.TypeProto.Optional.verify(message.optionalType);\n                    if (error)\n                        return \"optionalType.\" + error;\n                }\n            }\n            if (message.sparseTensorType != null && message.hasOwnProperty(\"sparseTensorType\")) {\n                if (properties.value === 1)\n                    return \"value: multiple values\";\n                properties.value = 1;\n                {\n                    var error = $root.onnx.TypeProto.SparseTensor.verify(message.sparseTensorType);\n                    if (error)\n                        return \"sparseTensorType.\" + error;\n                }\n            }\n            if (message.denotation != null && message.hasOwnProperty(\"denotation\"))\n                if (!$util.isString(message.denotation))\n                    return \"denotation: string expected\";\n            return null;\n        };\n\n        /**\n         * Creates a TypeProto message from a plain object. Also converts values to their respective internal types.\n         * @function fromObject\n         * @memberof onnx.TypeProto\n         * @static\n         * @param {Object.<string,*>} object Plain object\n         * @returns {onnx.TypeProto} TypeProto\n         */\n        TypeProto.fromObject = function fromObject(object) {\n            if (object instanceof $root.onnx.TypeProto)\n                return object;\n            var message = new $root.onnx.TypeProto();\n            if (object.tensorType != null) {\n                if (typeof object.tensorType !== \"object\")\n                    throw TypeError(\".onnx.TypeProto.tensorType: object expected\");\n                message.tensorType = $root.onnx.TypeProto.Tensor.fromObject(object.tensorType);\n            }\n            if (object.sequenceType != null) {\n                if (typeof object.sequenceType !== \"object\")\n                    throw TypeError(\".onnx.TypeProto.sequenceType: object expected\");\n                message.sequenceType = $root.onnx.TypeProto.Sequence.fromObject(object.sequenceType);\n            }\n            if (object.mapType != null) {\n                if (typeof object.mapType !== \"object\")\n                    throw TypeError(\".onnx.TypeProto.mapType: object expected\");\n                message.mapType = $root.onnx.TypeProto.Map.fromObject(object.mapType);\n            }\n            if (object.optionalType != null) {\n                if (typeof object.optionalType !== \"object\")\n                    throw TypeError(\".onnx.TypeProto.optionalType: object expected\");\n                message.optionalType = $root.onnx.TypeProto.Optional.fromObject(object.optionalType);\n            }\n            if (object.sparseTensorType != null) {\n                if (typeof object.sparseTensorType !== \"object\")\n                    throw TypeError(\".onnx.TypeProto.sparseTensorType: object expected\");\n                message.sparseTensorType = $root.onnx.TypeProto.SparseTensor.fromObject(object.sparseTensorType);\n            }\n            if (object.denotation != null)\n                message.denotation = String(object.denotation);\n            return message;\n        };\n\n        /**\n         * Creates a plain object from a TypeProto message. Also converts values to other types if specified.\n         * @function toObject\n         * @memberof onnx.TypeProto\n         * @static\n         * @param {onnx.TypeProto} message TypeProto\n         * @param {$protobuf.IConversionOptions} [options] Conversion options\n         * @returns {Object.<string,*>} Plain object\n         */\n        TypeProto.toObject = function toObject(message, options) {\n            if (!options)\n                options = {};\n            var object = {};\n            if (options.defaults)\n                object.denotation = \"\";\n            if (message.tensorType != null && message.hasOwnProperty(\"tensorType\")) {\n                object.tensorType = $root.onnx.TypeProto.Tensor.toObject(message.tensorType, options);\n                if (options.oneofs)\n                    object.value = \"tensorType\";\n            }\n            if (message.sequenceType != null && message.hasOwnProperty(\"sequenceType\")) {\n                object.sequenceType = $root.onnx.TypeProto.Sequence.toObject(message.sequenceType, options);\n                if (options.oneofs)\n                    object.value = \"sequenceType\";\n            }\n            if (message.mapType != null && message.hasOwnProperty(\"mapType\")) {\n                object.mapType = $root.onnx.TypeProto.Map.toObject(message.mapType, options);\n                if (options.oneofs)\n                    object.value = \"mapType\";\n            }\n            if (message.denotation != null && message.hasOwnProperty(\"denotation\"))\n                object.denotation = message.denotation;\n            if (message.sparseTensorType != null && message.hasOwnProperty(\"sparseTensorType\")) {\n                object.sparseTensorType = $root.onnx.TypeProto.SparseTensor.toObject(message.sparseTensorType, options);\n                if (options.oneofs)\n                    object.value = \"sparseTensorType\";\n            }\n            if (message.optionalType != null && message.hasOwnProperty(\"optionalType\")) {\n                object.optionalType = $root.onnx.TypeProto.Optional.toObject(message.optionalType, options);\n                if (options.oneofs)\n                    object.value = \"optionalType\";\n            }\n            return object;\n        };\n\n        /**\n         * Converts this TypeProto to JSON.\n         * @function toJSON\n         * @memberof onnx.TypeProto\n         * @instance\n         * @returns {Object.<string,*>} JSON object\n         */\n        TypeProto.prototype.toJSON = function toJSON() {\n            return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n        };\n\n        /**\n         * Gets the default type url for TypeProto\n         * @function getTypeUrl\n         * @memberof onnx.TypeProto\n         * @static\n         * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n         * @returns {string} The default type url\n         */\n        TypeProto.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n            if (typeUrlPrefix === undefined) {\n                typeUrlPrefix = \"type.googleapis.com\";\n            }\n            return typeUrlPrefix + \"/onnx.TypeProto\";\n        };\n\n        TypeProto.Tensor = (function() {\n\n            /**\n             * Properties of a Tensor.\n             * @memberof onnx.TypeProto\n             * @interface ITensor\n             * @property {number|null} [elemType] Tensor elemType\n             * @property {onnx.ITensorShapeProto|null} [shape] Tensor shape\n             */\n\n            /**\n             * Constructs a new Tensor.\n             * @memberof onnx.TypeProto\n             * @classdesc Represents a Tensor.\n             * @implements ITensor\n             * @constructor\n             * @param {onnx.TypeProto.ITensor=} [properties] Properties to set\n             */\n            function Tensor(properties) {\n                if (properties)\n                    for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                        if (properties[keys[i]] != null)\n                            this[keys[i]] = properties[keys[i]];\n            }\n\n            /**\n             * Tensor elemType.\n             * @member {number} elemType\n             * @memberof onnx.TypeProto.Tensor\n             * @instance\n             */\n            Tensor.prototype.elemType = 0;\n\n            /**\n             * Tensor shape.\n             * @member {onnx.ITensorShapeProto|null|undefined} shape\n             * @memberof onnx.TypeProto.Tensor\n             * @instance\n             */\n            Tensor.prototype.shape = null;\n\n            /**\n             * Creates a new Tensor instance using the specified properties.\n             * @function create\n             * @memberof onnx.TypeProto.Tensor\n             * @static\n             * @param {onnx.TypeProto.ITensor=} [properties] Properties to set\n             * @returns {onnx.TypeProto.Tensor} Tensor instance\n             */\n            Tensor.create = function create(properties) {\n                return new Tensor(properties);\n            };\n\n            /**\n             * Encodes the specified Tensor message. Does not implicitly {@link onnx.TypeProto.Tensor.verify|verify} messages.\n             * @function encode\n             * @memberof onnx.TypeProto.Tensor\n             * @static\n             * @param {onnx.TypeProto.ITensor} message Tensor message or plain object to encode\n             * @param {$protobuf.Writer} [writer] Writer to encode to\n             * @returns {$protobuf.Writer} Writer\n             */\n            Tensor.encode = function encode(message, writer) {\n                if (!writer)\n                    writer = $Writer.create();\n                if (message.elemType != null && Object.hasOwnProperty.call(message, \"elemType\"))\n                    writer.uint32(/* id 1, wireType 0 =*/8).int32(message.elemType);\n                if (message.shape != null && Object.hasOwnProperty.call(message, \"shape\"))\n                    $root.onnx.TensorShapeProto.encode(message.shape, writer.uint32(/* id 2, wireType 2 =*/18).fork()).ldelim();\n                return writer;\n            };\n\n            /**\n             * Encodes the specified Tensor message, length delimited. Does not implicitly {@link onnx.TypeProto.Tensor.verify|verify} messages.\n             * @function encodeDelimited\n             * @memberof onnx.TypeProto.Tensor\n             * @static\n             * @param {onnx.TypeProto.ITensor} message Tensor message or plain object to encode\n             * @param {$protobuf.Writer} [writer] Writer to encode to\n             * @returns {$protobuf.Writer} Writer\n             */\n            Tensor.encodeDelimited = function encodeDelimited(message, writer) {\n                return this.encode(message, writer).ldelim();\n            };\n\n            /**\n             * Decodes a Tensor message from the specified reader or buffer.\n             * @function decode\n             * @memberof onnx.TypeProto.Tensor\n             * @static\n             * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n             * @param {number} [length] Message length if known beforehand\n             * @returns {onnx.TypeProto.Tensor} Tensor\n             * @throws {Error} If the payload is not a reader or valid buffer\n             * @throws {$protobuf.util.ProtocolError} If required fields are missing\n             */\n            Tensor.decode = function decode(reader, length) {\n                if (!(reader instanceof $Reader))\n                    reader = $Reader.create(reader);\n                var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.TypeProto.Tensor();\n                while (reader.pos < end) {\n                    var tag = reader.uint32();\n                    switch (tag >>> 3) {\n                    case 1: {\n                            message.elemType = reader.int32();\n                            break;\n                        }\n                    case 2: {\n                            message.shape = $root.onnx.TensorShapeProto.decode(reader, reader.uint32());\n                            break;\n                        }\n                    default:\n                        reader.skipType(tag & 7);\n                        break;\n                    }\n                }\n                return message;\n            };\n\n            /**\n             * Decodes a Tensor message from the specified reader or buffer, length delimited.\n             * @function decodeDelimited\n             * @memberof onnx.TypeProto.Tensor\n             * @static\n             * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n             * @returns {onnx.TypeProto.Tensor} Tensor\n             * @throws {Error} If the payload is not a reader or valid buffer\n             * @throws {$protobuf.util.ProtocolError} If required fields are missing\n             */\n            Tensor.decodeDelimited = function decodeDelimited(reader) {\n                if (!(reader instanceof $Reader))\n                    reader = new $Reader(reader);\n                return this.decode(reader, reader.uint32());\n            };\n\n            /**\n             * Verifies a Tensor message.\n             * @function verify\n             * @memberof onnx.TypeProto.Tensor\n             * @static\n             * @param {Object.<string,*>} message Plain object to verify\n             * @returns {string|null} `null` if valid, otherwise the reason why it is not\n             */\n            Tensor.verify = function verify(message) {\n                if (typeof message !== \"object\" || message === null)\n                    return \"object expected\";\n                if (message.elemType != null && message.hasOwnProperty(\"elemType\"))\n                    if (!$util.isInteger(message.elemType))\n                        return \"elemType: integer expected\";\n                if (message.shape != null && message.hasOwnProperty(\"shape\")) {\n                    var error = $root.onnx.TensorShapeProto.verify(message.shape);\n                    if (error)\n                        return \"shape.\" + error;\n                }\n                return null;\n            };\n\n            /**\n             * Creates a Tensor message from a plain object. Also converts values to their respective internal types.\n             * @function fromObject\n             * @memberof onnx.TypeProto.Tensor\n             * @static\n             * @param {Object.<string,*>} object Plain object\n             * @returns {onnx.TypeProto.Tensor} Tensor\n             */\n            Tensor.fromObject = function fromObject(object) {\n                if (object instanceof $root.onnx.TypeProto.Tensor)\n                    return object;\n                var message = new $root.onnx.TypeProto.Tensor();\n                if (object.elemType != null)\n                    message.elemType = object.elemType | 0;\n                if (object.shape != null) {\n                    if (typeof object.shape !== \"object\")\n                        throw TypeError(\".onnx.TypeProto.Tensor.shape: object expected\");\n                    message.shape = $root.onnx.TensorShapeProto.fromObject(object.shape);\n                }\n                return message;\n            };\n\n            /**\n             * Creates a plain object from a Tensor message. Also converts values to other types if specified.\n             * @function toObject\n             * @memberof onnx.TypeProto.Tensor\n             * @static\n             * @param {onnx.TypeProto.Tensor} message Tensor\n             * @param {$protobuf.IConversionOptions} [options] Conversion options\n             * @returns {Object.<string,*>} Plain object\n             */\n            Tensor.toObject = function toObject(message, options) {\n                if (!options)\n                    options = {};\n                var object = {};\n                if (options.defaults) {\n                    object.elemType = 0;\n                    object.shape = null;\n                }\n                if (message.elemType != null && message.hasOwnProperty(\"elemType\"))\n                    object.elemType = message.elemType;\n                if (message.shape != null && message.hasOwnProperty(\"shape\"))\n                    object.shape = $root.onnx.TensorShapeProto.toObject(message.shape, options);\n                return object;\n            };\n\n            /**\n             * Converts this Tensor to JSON.\n             * @function toJSON\n             * @memberof onnx.TypeProto.Tensor\n             * @instance\n             * @returns {Object.<string,*>} JSON object\n             */\n            Tensor.prototype.toJSON = function toJSON() {\n                return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n            };\n\n            /**\n             * Gets the default type url for Tensor\n             * @function getTypeUrl\n             * @memberof onnx.TypeProto.Tensor\n             * @static\n             * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n             * @returns {string} The default type url\n             */\n            Tensor.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n                if (typeUrlPrefix === undefined) {\n                    typeUrlPrefix = \"type.googleapis.com\";\n                }\n                return typeUrlPrefix + \"/onnx.TypeProto.Tensor\";\n            };\n\n            return Tensor;\n        })();\n\n        TypeProto.Sequence = (function() {\n\n            /**\n             * Properties of a Sequence.\n             * @memberof onnx.TypeProto\n             * @interface ISequence\n             * @property {onnx.ITypeProto|null} [elemType] Sequence elemType\n             */\n\n            /**\n             * Constructs a new Sequence.\n             * @memberof onnx.TypeProto\n             * @classdesc Represents a Sequence.\n             * @implements ISequence\n             * @constructor\n             * @param {onnx.TypeProto.ISequence=} [properties] Properties to set\n             */\n            function Sequence(properties) {\n                if (properties)\n                    for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                        if (properties[keys[i]] != null)\n                            this[keys[i]] = properties[keys[i]];\n            }\n\n            /**\n             * Sequence elemType.\n             * @member {onnx.ITypeProto|null|undefined} elemType\n             * @memberof onnx.TypeProto.Sequence\n             * @instance\n             */\n            Sequence.prototype.elemType = null;\n\n            /**\n             * Creates a new Sequence instance using the specified properties.\n             * @function create\n             * @memberof onnx.TypeProto.Sequence\n             * @static\n             * @param {onnx.TypeProto.ISequence=} [properties] Properties to set\n             * @returns {onnx.TypeProto.Sequence} Sequence instance\n             */\n            Sequence.create = function create(properties) {\n                return new Sequence(properties);\n            };\n\n            /**\n             * Encodes the specified Sequence message. Does not implicitly {@link onnx.TypeProto.Sequence.verify|verify} messages.\n             * @function encode\n             * @memberof onnx.TypeProto.Sequence\n             * @static\n             * @param {onnx.TypeProto.ISequence} message Sequence message or plain object to encode\n             * @param {$protobuf.Writer} [writer] Writer to encode to\n             * @returns {$protobuf.Writer} Writer\n             */\n            Sequence.encode = function encode(message, writer) {\n                if (!writer)\n                    writer = $Writer.create();\n                if (message.elemType != null && Object.hasOwnProperty.call(message, \"elemType\"))\n                    $root.onnx.TypeProto.encode(message.elemType, writer.uint32(/* id 1, wireType 2 =*/10).fork()).ldelim();\n                return writer;\n            };\n\n            /**\n             * Encodes the specified Sequence message, length delimited. Does not implicitly {@link onnx.TypeProto.Sequence.verify|verify} messages.\n             * @function encodeDelimited\n             * @memberof onnx.TypeProto.Sequence\n             * @static\n             * @param {onnx.TypeProto.ISequence} message Sequence message or plain object to encode\n             * @param {$protobuf.Writer} [writer] Writer to encode to\n             * @returns {$protobuf.Writer} Writer\n             */\n            Sequence.encodeDelimited = function encodeDelimited(message, writer) {\n                return this.encode(message, writer).ldelim();\n            };\n\n            /**\n             * Decodes a Sequence message from the specified reader or buffer.\n             * @function decode\n             * @memberof onnx.TypeProto.Sequence\n             * @static\n             * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n             * @param {number} [length] Message length if known beforehand\n             * @returns {onnx.TypeProto.Sequence} Sequence\n             * @throws {Error} If the payload is not a reader or valid buffer\n             * @throws {$protobuf.util.ProtocolError} If required fields are missing\n             */\n            Sequence.decode = function decode(reader, length) {\n                if (!(reader instanceof $Reader))\n                    reader = $Reader.create(reader);\n                var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.TypeProto.Sequence();\n                while (reader.pos < end) {\n                    var tag = reader.uint32();\n                    switch (tag >>> 3) {\n                    case 1: {\n                            message.elemType = $root.onnx.TypeProto.decode(reader, reader.uint32());\n                            break;\n                        }\n                    default:\n                        reader.skipType(tag & 7);\n                        break;\n                    }\n                }\n                return message;\n            };\n\n            /**\n             * Decodes a Sequence message from the specified reader or buffer, length delimited.\n             * @function decodeDelimited\n             * @memberof onnx.TypeProto.Sequence\n             * @static\n             * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n             * @returns {onnx.TypeProto.Sequence} Sequence\n             * @throws {Error} If the payload is not a reader or valid buffer\n             * @throws {$protobuf.util.ProtocolError} If required fields are missing\n             */\n            Sequence.decodeDelimited = function decodeDelimited(reader) {\n                if (!(reader instanceof $Reader))\n                    reader = new $Reader(reader);\n                return this.decode(reader, reader.uint32());\n            };\n\n            /**\n             * Verifies a Sequence message.\n             * @function verify\n             * @memberof onnx.TypeProto.Sequence\n             * @static\n             * @param {Object.<string,*>} message Plain object to verify\n             * @returns {string|null} `null` if valid, otherwise the reason why it is not\n             */\n            Sequence.verify = function verify(message) {\n                if (typeof message !== \"object\" || message === null)\n                    return \"object expected\";\n                if (message.elemType != null && message.hasOwnProperty(\"elemType\")) {\n                    var error = $root.onnx.TypeProto.verify(message.elemType);\n                    if (error)\n                        return \"elemType.\" + error;\n                }\n                return null;\n            };\n\n            /**\n             * Creates a Sequence message from a plain object. Also converts values to their respective internal types.\n             * @function fromObject\n             * @memberof onnx.TypeProto.Sequence\n             * @static\n             * @param {Object.<string,*>} object Plain object\n             * @returns {onnx.TypeProto.Sequence} Sequence\n             */\n            Sequence.fromObject = function fromObject(object) {\n                if (object instanceof $root.onnx.TypeProto.Sequence)\n                    return object;\n                var message = new $root.onnx.TypeProto.Sequence();\n                if (object.elemType != null) {\n                    if (typeof object.elemType !== \"object\")\n                        throw TypeError(\".onnx.TypeProto.Sequence.elemType: object expected\");\n                    message.elemType = $root.onnx.TypeProto.fromObject(object.elemType);\n                }\n                return message;\n            };\n\n            /**\n             * Creates a plain object from a Sequence message. Also converts values to other types if specified.\n             * @function toObject\n             * @memberof onnx.TypeProto.Sequence\n             * @static\n             * @param {onnx.TypeProto.Sequence} message Sequence\n             * @param {$protobuf.IConversionOptions} [options] Conversion options\n             * @returns {Object.<string,*>} Plain object\n             */\n            Sequence.toObject = function toObject(message, options) {\n                if (!options)\n                    options = {};\n                var object = {};\n                if (options.defaults)\n                    object.elemType = null;\n                if (message.elemType != null && message.hasOwnProperty(\"elemType\"))\n                    object.elemType = $root.onnx.TypeProto.toObject(message.elemType, options);\n                return object;\n            };\n\n            /**\n             * Converts this Sequence to JSON.\n             * @function toJSON\n             * @memberof onnx.TypeProto.Sequence\n             * @instance\n             * @returns {Object.<string,*>} JSON object\n             */\n            Sequence.prototype.toJSON = function toJSON() {\n                return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n            };\n\n            /**\n             * Gets the default type url for Sequence\n             * @function getTypeUrl\n             * @memberof onnx.TypeProto.Sequence\n             * @static\n             * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n             * @returns {string} The default type url\n             */\n            Sequence.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n                if (typeUrlPrefix === undefined) {\n                    typeUrlPrefix = \"type.googleapis.com\";\n                }\n                return typeUrlPrefix + \"/onnx.TypeProto.Sequence\";\n            };\n\n            return Sequence;\n        })();\n\n        TypeProto.Map = (function() {\n\n            /**\n             * Properties of a Map.\n             * @memberof onnx.TypeProto\n             * @interface IMap\n             * @property {number|null} [keyType] Map keyType\n             * @property {onnx.ITypeProto|null} [valueType] Map valueType\n             */\n\n            /**\n             * Constructs a new Map.\n             * @memberof onnx.TypeProto\n             * @classdesc Represents a Map.\n             * @implements IMap\n             * @constructor\n             * @param {onnx.TypeProto.IMap=} [properties] Properties to set\n             */\n            function Map(properties) {\n                if (properties)\n                    for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                        if (properties[keys[i]] != null)\n                            this[keys[i]] = properties[keys[i]];\n            }\n\n            /**\n             * Map keyType.\n             * @member {number} keyType\n             * @memberof onnx.TypeProto.Map\n             * @instance\n             */\n            Map.prototype.keyType = 0;\n\n            /**\n             * Map valueType.\n             * @member {onnx.ITypeProto|null|undefined} valueType\n             * @memberof onnx.TypeProto.Map\n             * @instance\n             */\n            Map.prototype.valueType = null;\n\n            /**\n             * Creates a new Map instance using the specified properties.\n             * @function create\n             * @memberof onnx.TypeProto.Map\n             * @static\n             * @param {onnx.TypeProto.IMap=} [properties] Properties to set\n             * @returns {onnx.TypeProto.Map} Map instance\n             */\n            Map.create = function create(properties) {\n                return new Map(properties);\n            };\n\n            /**\n             * Encodes the specified Map message. Does not implicitly {@link onnx.TypeProto.Map.verify|verify} messages.\n             * @function encode\n             * @memberof onnx.TypeProto.Map\n             * @static\n             * @param {onnx.TypeProto.IMap} message Map message or plain object to encode\n             * @param {$protobuf.Writer} [writer] Writer to encode to\n             * @returns {$protobuf.Writer} Writer\n             */\n            Map.encode = function encode(message, writer) {\n                if (!writer)\n                    writer = $Writer.create();\n                if (message.keyType != null && Object.hasOwnProperty.call(message, \"keyType\"))\n                    writer.uint32(/* id 1, wireType 0 =*/8).int32(message.keyType);\n                if (message.valueType != null && Object.hasOwnProperty.call(message, \"valueType\"))\n                    $root.onnx.TypeProto.encode(message.valueType, writer.uint32(/* id 2, wireType 2 =*/18).fork()).ldelim();\n                return writer;\n            };\n\n            /**\n             * Encodes the specified Map message, length delimited. Does not implicitly {@link onnx.TypeProto.Map.verify|verify} messages.\n             * @function encodeDelimited\n             * @memberof onnx.TypeProto.Map\n             * @static\n             * @param {onnx.TypeProto.IMap} message Map message or plain object to encode\n             * @param {$protobuf.Writer} [writer] Writer to encode to\n             * @returns {$protobuf.Writer} Writer\n             */\n            Map.encodeDelimited = function encodeDelimited(message, writer) {\n                return this.encode(message, writer).ldelim();\n            };\n\n            /**\n             * Decodes a Map message from the specified reader or buffer.\n             * @function decode\n             * @memberof onnx.TypeProto.Map\n             * @static\n             * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n             * @param {number} [length] Message length if known beforehand\n             * @returns {onnx.TypeProto.Map} Map\n             * @throws {Error} If the payload is not a reader or valid buffer\n             * @throws {$protobuf.util.ProtocolError} If required fields are missing\n             */\n            Map.decode = function decode(reader, length) {\n                if (!(reader instanceof $Reader))\n                    reader = $Reader.create(reader);\n                var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.TypeProto.Map();\n                while (reader.pos < end) {\n                    var tag = reader.uint32();\n                    switch (tag >>> 3) {\n                    case 1: {\n                            message.keyType = reader.int32();\n                            break;\n                        }\n                    case 2: {\n                            message.valueType = $root.onnx.TypeProto.decode(reader, reader.uint32());\n                            break;\n                        }\n                    default:\n                        reader.skipType(tag & 7);\n                        break;\n                    }\n                }\n                return message;\n            };\n\n            /**\n             * Decodes a Map message from the specified reader or buffer, length delimited.\n             * @function decodeDelimited\n             * @memberof onnx.TypeProto.Map\n             * @static\n             * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n             * @returns {onnx.TypeProto.Map} Map\n             * @throws {Error} If the payload is not a reader or valid buffer\n             * @throws {$protobuf.util.ProtocolError} If required fields are missing\n             */\n            Map.decodeDelimited = function decodeDelimited(reader) {\n                if (!(reader instanceof $Reader))\n                    reader = new $Reader(reader);\n                return this.decode(reader, reader.uint32());\n            };\n\n            /**\n             * Verifies a Map message.\n             * @function verify\n             * @memberof onnx.TypeProto.Map\n             * @static\n             * @param {Object.<string,*>} message Plain object to verify\n             * @returns {string|null} `null` if valid, otherwise the reason why it is not\n             */\n            Map.verify = function verify(message) {\n                if (typeof message !== \"object\" || message === null)\n                    return \"object expected\";\n                if (message.keyType != null && message.hasOwnProperty(\"keyType\"))\n                    if (!$util.isInteger(message.keyType))\n                        return \"keyType: integer expected\";\n                if (message.valueType != null && message.hasOwnProperty(\"valueType\")) {\n                    var error = $root.onnx.TypeProto.verify(message.valueType);\n                    if (error)\n                        return \"valueType.\" + error;\n                }\n                return null;\n            };\n\n            /**\n             * Creates a Map message from a plain object. Also converts values to their respective internal types.\n             * @function fromObject\n             * @memberof onnx.TypeProto.Map\n             * @static\n             * @param {Object.<string,*>} object Plain object\n             * @returns {onnx.TypeProto.Map} Map\n             */\n            Map.fromObject = function fromObject(object) {\n                if (object instanceof $root.onnx.TypeProto.Map)\n                    return object;\n                var message = new $root.onnx.TypeProto.Map();\n                if (object.keyType != null)\n                    message.keyType = object.keyType | 0;\n                if (object.valueType != null) {\n                    if (typeof object.valueType !== \"object\")\n                        throw TypeError(\".onnx.TypeProto.Map.valueType: object expected\");\n                    message.valueType = $root.onnx.TypeProto.fromObject(object.valueType);\n                }\n                return message;\n            };\n\n            /**\n             * Creates a plain object from a Map message. Also converts values to other types if specified.\n             * @function toObject\n             * @memberof onnx.TypeProto.Map\n             * @static\n             * @param {onnx.TypeProto.Map} message Map\n             * @param {$protobuf.IConversionOptions} [options] Conversion options\n             * @returns {Object.<string,*>} Plain object\n             */\n            Map.toObject = function toObject(message, options) {\n                if (!options)\n                    options = {};\n                var object = {};\n                if (options.defaults) {\n                    object.keyType = 0;\n                    object.valueType = null;\n                }\n                if (message.keyType != null && message.hasOwnProperty(\"keyType\"))\n                    object.keyType = message.keyType;\n                if (message.valueType != null && message.hasOwnProperty(\"valueType\"))\n                    object.valueType = $root.onnx.TypeProto.toObject(message.valueType, options);\n                return object;\n            };\n\n            /**\n             * Converts this Map to JSON.\n             * @function toJSON\n             * @memberof onnx.TypeProto.Map\n             * @instance\n             * @returns {Object.<string,*>} JSON object\n             */\n            Map.prototype.toJSON = function toJSON() {\n                return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n            };\n\n            /**\n             * Gets the default type url for Map\n             * @function getTypeUrl\n             * @memberof onnx.TypeProto.Map\n             * @static\n             * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n             * @returns {string} The default type url\n             */\n            Map.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n                if (typeUrlPrefix === undefined) {\n                    typeUrlPrefix = \"type.googleapis.com\";\n                }\n                return typeUrlPrefix + \"/onnx.TypeProto.Map\";\n            };\n\n            return Map;\n        })();\n\n        TypeProto.Optional = (function() {\n\n            /**\n             * Properties of an Optional.\n             * @memberof onnx.TypeProto\n             * @interface IOptional\n             * @property {onnx.ITypeProto|null} [elemType] Optional elemType\n             */\n\n            /**\n             * Constructs a new Optional.\n             * @memberof onnx.TypeProto\n             * @classdesc Represents an Optional.\n             * @implements IOptional\n             * @constructor\n             * @param {onnx.TypeProto.IOptional=} [properties] Properties to set\n             */\n            function Optional(properties) {\n                if (properties)\n                    for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                        if (properties[keys[i]] != null)\n                            this[keys[i]] = properties[keys[i]];\n            }\n\n            /**\n             * Optional elemType.\n             * @member {onnx.ITypeProto|null|undefined} elemType\n             * @memberof onnx.TypeProto.Optional\n             * @instance\n             */\n            Optional.prototype.elemType = null;\n\n            /**\n             * Creates a new Optional instance using the specified properties.\n             * @function create\n             * @memberof onnx.TypeProto.Optional\n             * @static\n             * @param {onnx.TypeProto.IOptional=} [properties] Properties to set\n             * @returns {onnx.TypeProto.Optional} Optional instance\n             */\n            Optional.create = function create(properties) {\n                return new Optional(properties);\n            };\n\n            /**\n             * Encodes the specified Optional message. Does not implicitly {@link onnx.TypeProto.Optional.verify|verify} messages.\n             * @function encode\n             * @memberof onnx.TypeProto.Optional\n             * @static\n             * @param {onnx.TypeProto.IOptional} message Optional message or plain object to encode\n             * @param {$protobuf.Writer} [writer] Writer to encode to\n             * @returns {$protobuf.Writer} Writer\n             */\n            Optional.encode = function encode(message, writer) {\n                if (!writer)\n                    writer = $Writer.create();\n                if (message.elemType != null && Object.hasOwnProperty.call(message, \"elemType\"))\n                    $root.onnx.TypeProto.encode(message.elemType, writer.uint32(/* id 1, wireType 2 =*/10).fork()).ldelim();\n                return writer;\n            };\n\n            /**\n             * Encodes the specified Optional message, length delimited. Does not implicitly {@link onnx.TypeProto.Optional.verify|verify} messages.\n             * @function encodeDelimited\n             * @memberof onnx.TypeProto.Optional\n             * @static\n             * @param {onnx.TypeProto.IOptional} message Optional message or plain object to encode\n             * @param {$protobuf.Writer} [writer] Writer to encode to\n             * @returns {$protobuf.Writer} Writer\n             */\n            Optional.encodeDelimited = function encodeDelimited(message, writer) {\n                return this.encode(message, writer).ldelim();\n            };\n\n            /**\n             * Decodes an Optional message from the specified reader or buffer.\n             * @function decode\n             * @memberof onnx.TypeProto.Optional\n             * @static\n             * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n             * @param {number} [length] Message length if known beforehand\n             * @returns {onnx.TypeProto.Optional} Optional\n             * @throws {Error} If the payload is not a reader or valid buffer\n             * @throws {$protobuf.util.ProtocolError} If required fields are missing\n             */\n            Optional.decode = function decode(reader, length) {\n                if (!(reader instanceof $Reader))\n                    reader = $Reader.create(reader);\n                var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.TypeProto.Optional();\n                while (reader.pos < end) {\n                    var tag = reader.uint32();\n                    switch (tag >>> 3) {\n                    case 1: {\n                            message.elemType = $root.onnx.TypeProto.decode(reader, reader.uint32());\n                            break;\n                        }\n                    default:\n                        reader.skipType(tag & 7);\n                        break;\n                    }\n                }\n                return message;\n            };\n\n            /**\n             * Decodes an Optional message from the specified reader or buffer, length delimited.\n             * @function decodeDelimited\n             * @memberof onnx.TypeProto.Optional\n             * @static\n             * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n             * @returns {onnx.TypeProto.Optional} Optional\n             * @throws {Error} If the payload is not a reader or valid buffer\n             * @throws {$protobuf.util.ProtocolError} If required fields are missing\n             */\n            Optional.decodeDelimited = function decodeDelimited(reader) {\n                if (!(reader instanceof $Reader))\n                    reader = new $Reader(reader);\n                return this.decode(reader, reader.uint32());\n            };\n\n            /**\n             * Verifies an Optional message.\n             * @function verify\n             * @memberof onnx.TypeProto.Optional\n             * @static\n             * @param {Object.<string,*>} message Plain object to verify\n             * @returns {string|null} `null` if valid, otherwise the reason why it is not\n             */\n            Optional.verify = function verify(message) {\n                if (typeof message !== \"object\" || message === null)\n                    return \"object expected\";\n                if (message.elemType != null && message.hasOwnProperty(\"elemType\")) {\n                    var error = $root.onnx.TypeProto.verify(message.elemType);\n                    if (error)\n                        return \"elemType.\" + error;\n                }\n                return null;\n            };\n\n            /**\n             * Creates an Optional message from a plain object. Also converts values to their respective internal types.\n             * @function fromObject\n             * @memberof onnx.TypeProto.Optional\n             * @static\n             * @param {Object.<string,*>} object Plain object\n             * @returns {onnx.TypeProto.Optional} Optional\n             */\n            Optional.fromObject = function fromObject(object) {\n                if (object instanceof $root.onnx.TypeProto.Optional)\n                    return object;\n                var message = new $root.onnx.TypeProto.Optional();\n                if (object.elemType != null) {\n                    if (typeof object.elemType !== \"object\")\n                        throw TypeError(\".onnx.TypeProto.Optional.elemType: object expected\");\n                    message.elemType = $root.onnx.TypeProto.fromObject(object.elemType);\n                }\n                return message;\n            };\n\n            /**\n             * Creates a plain object from an Optional message. Also converts values to other types if specified.\n             * @function toObject\n             * @memberof onnx.TypeProto.Optional\n             * @static\n             * @param {onnx.TypeProto.Optional} message Optional\n             * @param {$protobuf.IConversionOptions} [options] Conversion options\n             * @returns {Object.<string,*>} Plain object\n             */\n            Optional.toObject = function toObject(message, options) {\n                if (!options)\n                    options = {};\n                var object = {};\n                if (options.defaults)\n                    object.elemType = null;\n                if (message.elemType != null && message.hasOwnProperty(\"elemType\"))\n                    object.elemType = $root.onnx.TypeProto.toObject(message.elemType, options);\n                return object;\n            };\n\n            /**\n             * Converts this Optional to JSON.\n             * @function toJSON\n             * @memberof onnx.TypeProto.Optional\n             * @instance\n             * @returns {Object.<string,*>} JSON object\n             */\n            Optional.prototype.toJSON = function toJSON() {\n                return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n            };\n\n            /**\n             * Gets the default type url for Optional\n             * @function getTypeUrl\n             * @memberof onnx.TypeProto.Optional\n             * @static\n             * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n             * @returns {string} The default type url\n             */\n            Optional.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n                if (typeUrlPrefix === undefined) {\n                    typeUrlPrefix = \"type.googleapis.com\";\n                }\n                return typeUrlPrefix + \"/onnx.TypeProto.Optional\";\n            };\n\n            return Optional;\n        })();\n\n        TypeProto.SparseTensor = (function() {\n\n            /**\n             * Properties of a SparseTensor.\n             * @memberof onnx.TypeProto\n             * @interface ISparseTensor\n             * @property {number|null} [elemType] SparseTensor elemType\n             * @property {onnx.ITensorShapeProto|null} [shape] SparseTensor shape\n             */\n\n            /**\n             * Constructs a new SparseTensor.\n             * @memberof onnx.TypeProto\n             * @classdesc Represents a SparseTensor.\n             * @implements ISparseTensor\n             * @constructor\n             * @param {onnx.TypeProto.ISparseTensor=} [properties] Properties to set\n             */\n            function SparseTensor(properties) {\n                if (properties)\n                    for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                        if (properties[keys[i]] != null)\n                            this[keys[i]] = properties[keys[i]];\n            }\n\n            /**\n             * SparseTensor elemType.\n             * @member {number} elemType\n             * @memberof onnx.TypeProto.SparseTensor\n             * @instance\n             */\n            SparseTensor.prototype.elemType = 0;\n\n            /**\n             * SparseTensor shape.\n             * @member {onnx.ITensorShapeProto|null|undefined} shape\n             * @memberof onnx.TypeProto.SparseTensor\n             * @instance\n             */\n            SparseTensor.prototype.shape = null;\n\n            /**\n             * Creates a new SparseTensor instance using the specified properties.\n             * @function create\n             * @memberof onnx.TypeProto.SparseTensor\n             * @static\n             * @param {onnx.TypeProto.ISparseTensor=} [properties] Properties to set\n             * @returns {onnx.TypeProto.SparseTensor} SparseTensor instance\n             */\n            SparseTensor.create = function create(properties) {\n                return new SparseTensor(properties);\n            };\n\n            /**\n             * Encodes the specified SparseTensor message. Does not implicitly {@link onnx.TypeProto.SparseTensor.verify|verify} messages.\n             * @function encode\n             * @memberof onnx.TypeProto.SparseTensor\n             * @static\n             * @param {onnx.TypeProto.ISparseTensor} message SparseTensor message or plain object to encode\n             * @param {$protobuf.Writer} [writer] Writer to encode to\n             * @returns {$protobuf.Writer} Writer\n             */\n            SparseTensor.encode = function encode(message, writer) {\n                if (!writer)\n                    writer = $Writer.create();\n                if (message.elemType != null && Object.hasOwnProperty.call(message, \"elemType\"))\n                    writer.uint32(/* id 1, wireType 0 =*/8).int32(message.elemType);\n                if (message.shape != null && Object.hasOwnProperty.call(message, \"shape\"))\n                    $root.onnx.TensorShapeProto.encode(message.shape, writer.uint32(/* id 2, wireType 2 =*/18).fork()).ldelim();\n                return writer;\n            };\n\n            /**\n             * Encodes the specified SparseTensor message, length delimited. Does not implicitly {@link onnx.TypeProto.SparseTensor.verify|verify} messages.\n             * @function encodeDelimited\n             * @memberof onnx.TypeProto.SparseTensor\n             * @static\n             * @param {onnx.TypeProto.ISparseTensor} message SparseTensor message or plain object to encode\n             * @param {$protobuf.Writer} [writer] Writer to encode to\n             * @returns {$protobuf.Writer} Writer\n             */\n            SparseTensor.encodeDelimited = function encodeDelimited(message, writer) {\n                return this.encode(message, writer).ldelim();\n            };\n\n            /**\n             * Decodes a SparseTensor message from the specified reader or buffer.\n             * @function decode\n             * @memberof onnx.TypeProto.SparseTensor\n             * @static\n             * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n             * @param {number} [length] Message length if known beforehand\n             * @returns {onnx.TypeProto.SparseTensor} SparseTensor\n             * @throws {Error} If the payload is not a reader or valid buffer\n             * @throws {$protobuf.util.ProtocolError} If required fields are missing\n             */\n            SparseTensor.decode = function decode(reader, length) {\n                if (!(reader instanceof $Reader))\n                    reader = $Reader.create(reader);\n                var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.TypeProto.SparseTensor();\n                while (reader.pos < end) {\n                    var tag = reader.uint32();\n                    switch (tag >>> 3) {\n                    case 1: {\n                            message.elemType = reader.int32();\n                            break;\n                        }\n                    case 2: {\n                            message.shape = $root.onnx.TensorShapeProto.decode(reader, reader.uint32());\n                            break;\n                        }\n                    default:\n                        reader.skipType(tag & 7);\n                        break;\n                    }\n                }\n                return message;\n            };\n\n            /**\n             * Decodes a SparseTensor message from the specified reader or buffer, length delimited.\n             * @function decodeDelimited\n             * @memberof onnx.TypeProto.SparseTensor\n             * @static\n             * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n             * @returns {onnx.TypeProto.SparseTensor} SparseTensor\n             * @throws {Error} If the payload is not a reader or valid buffer\n             * @throws {$protobuf.util.ProtocolError} If required fields are missing\n             */\n            SparseTensor.decodeDelimited = function decodeDelimited(reader) {\n                if (!(reader instanceof $Reader))\n                    reader = new $Reader(reader);\n                return this.decode(reader, reader.uint32());\n            };\n\n            /**\n             * Verifies a SparseTensor message.\n             * @function verify\n             * @memberof onnx.TypeProto.SparseTensor\n             * @static\n             * @param {Object.<string,*>} message Plain object to verify\n             * @returns {string|null} `null` if valid, otherwise the reason why it is not\n             */\n            SparseTensor.verify = function verify(message) {\n                if (typeof message !== \"object\" || message === null)\n                    return \"object expected\";\n                if (message.elemType != null && message.hasOwnProperty(\"elemType\"))\n                    if (!$util.isInteger(message.elemType))\n                        return \"elemType: integer expected\";\n                if (message.shape != null && message.hasOwnProperty(\"shape\")) {\n                    var error = $root.onnx.TensorShapeProto.verify(message.shape);\n                    if (error)\n                        return \"shape.\" + error;\n                }\n                return null;\n            };\n\n            /**\n             * Creates a SparseTensor message from a plain object. Also converts values to their respective internal types.\n             * @function fromObject\n             * @memberof onnx.TypeProto.SparseTensor\n             * @static\n             * @param {Object.<string,*>} object Plain object\n             * @returns {onnx.TypeProto.SparseTensor} SparseTensor\n             */\n            SparseTensor.fromObject = function fromObject(object) {\n                if (object instanceof $root.onnx.TypeProto.SparseTensor)\n                    return object;\n                var message = new $root.onnx.TypeProto.SparseTensor();\n                if (object.elemType != null)\n                    message.elemType = object.elemType | 0;\n                if (object.shape != null) {\n                    if (typeof object.shape !== \"object\")\n                        throw TypeError(\".onnx.TypeProto.SparseTensor.shape: object expected\");\n                    message.shape = $root.onnx.TensorShapeProto.fromObject(object.shape);\n                }\n                return message;\n            };\n\n            /**\n             * Creates a plain object from a SparseTensor message. Also converts values to other types if specified.\n             * @function toObject\n             * @memberof onnx.TypeProto.SparseTensor\n             * @static\n             * @param {onnx.TypeProto.SparseTensor} message SparseTensor\n             * @param {$protobuf.IConversionOptions} [options] Conversion options\n             * @returns {Object.<string,*>} Plain object\n             */\n            SparseTensor.toObject = function toObject(message, options) {\n                if (!options)\n                    options = {};\n                var object = {};\n                if (options.defaults) {\n                    object.elemType = 0;\n                    object.shape = null;\n                }\n                if (message.elemType != null && message.hasOwnProperty(\"elemType\"))\n                    object.elemType = message.elemType;\n                if (message.shape != null && message.hasOwnProperty(\"shape\"))\n                    object.shape = $root.onnx.TensorShapeProto.toObject(message.shape, options);\n                return object;\n            };\n\n            /**\n             * Converts this SparseTensor to JSON.\n             * @function toJSON\n             * @memberof onnx.TypeProto.SparseTensor\n             * @instance\n             * @returns {Object.<string,*>} JSON object\n             */\n            SparseTensor.prototype.toJSON = function toJSON() {\n                return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n            };\n\n            /**\n             * Gets the default type url for SparseTensor\n             * @function getTypeUrl\n             * @memberof onnx.TypeProto.SparseTensor\n             * @static\n             * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n             * @returns {string} The default type url\n             */\n            SparseTensor.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n                if (typeUrlPrefix === undefined) {\n                    typeUrlPrefix = \"type.googleapis.com\";\n                }\n                return typeUrlPrefix + \"/onnx.TypeProto.SparseTensor\";\n            };\n\n            return SparseTensor;\n        })();\n\n        return TypeProto;\n    })();\n\n    onnx.OperatorSetIdProto = (function() {\n\n        /**\n         * Properties of an OperatorSetIdProto.\n         * @memberof onnx\n         * @interface IOperatorSetIdProto\n         * @property {string|null} [domain] OperatorSetIdProto domain\n         * @property {number|Long|null} [version] OperatorSetIdProto version\n         */\n\n        /**\n         * Constructs a new OperatorSetIdProto.\n         * @memberof onnx\n         * @classdesc Represents an OperatorSetIdProto.\n         * @implements IOperatorSetIdProto\n         * @constructor\n         * @param {onnx.IOperatorSetIdProto=} [properties] Properties to set\n         */\n        function OperatorSetIdProto(properties) {\n            if (properties)\n                for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                    if (properties[keys[i]] != null)\n                        this[keys[i]] = properties[keys[i]];\n        }\n\n        /**\n         * OperatorSetIdProto domain.\n         * @member {string} domain\n         * @memberof onnx.OperatorSetIdProto\n         * @instance\n         */\n        OperatorSetIdProto.prototype.domain = \"\";\n\n        /**\n         * OperatorSetIdProto version.\n         * @member {number|Long} version\n         * @memberof onnx.OperatorSetIdProto\n         * @instance\n         */\n        OperatorSetIdProto.prototype.version = $util.Long ? $util.Long.fromBits(0,0,false) : 0;\n\n        /**\n         * Creates a new OperatorSetIdProto instance using the specified properties.\n         * @function create\n         * @memberof onnx.OperatorSetIdProto\n         * @static\n         * @param {onnx.IOperatorSetIdProto=} [properties] Properties to set\n         * @returns {onnx.OperatorSetIdProto} OperatorSetIdProto instance\n         */\n        OperatorSetIdProto.create = function create(properties) {\n            return new OperatorSetIdProto(properties);\n        };\n\n        /**\n         * Encodes the specified OperatorSetIdProto message. Does not implicitly {@link onnx.OperatorSetIdProto.verify|verify} messages.\n         * @function encode\n         * @memberof onnx.OperatorSetIdProto\n         * @static\n         * @param {onnx.IOperatorSetIdProto} message OperatorSetIdProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        OperatorSetIdProto.encode = function encode(message, writer) {\n            if (!writer)\n                writer = $Writer.create();\n            if (message.domain != null && Object.hasOwnProperty.call(message, \"domain\"))\n                writer.uint32(/* id 1, wireType 2 =*/10).string(message.domain);\n            if (message.version != null && Object.hasOwnProperty.call(message, \"version\"))\n                writer.uint32(/* id 2, wireType 0 =*/16).int64(message.version);\n            return writer;\n        };\n\n        /**\n         * Encodes the specified OperatorSetIdProto message, length delimited. Does not implicitly {@link onnx.OperatorSetIdProto.verify|verify} messages.\n         * @function encodeDelimited\n         * @memberof onnx.OperatorSetIdProto\n         * @static\n         * @param {onnx.IOperatorSetIdProto} message OperatorSetIdProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        OperatorSetIdProto.encodeDelimited = function encodeDelimited(message, writer) {\n            return this.encode(message, writer).ldelim();\n        };\n\n        /**\n         * Decodes an OperatorSetIdProto message from the specified reader or buffer.\n         * @function decode\n         * @memberof onnx.OperatorSetIdProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @param {number} [length] Message length if known beforehand\n         * @returns {onnx.OperatorSetIdProto} OperatorSetIdProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        OperatorSetIdProto.decode = function decode(reader, length) {\n            if (!(reader instanceof $Reader))\n                reader = $Reader.create(reader);\n            var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.OperatorSetIdProto();\n            while (reader.pos < end) {\n                var tag = reader.uint32();\n                switch (tag >>> 3) {\n                case 1: {\n                        message.domain = reader.string();\n                        break;\n                    }\n                case 2: {\n                        message.version = reader.int64();\n                        break;\n                    }\n                default:\n                    reader.skipType(tag & 7);\n                    break;\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Decodes an OperatorSetIdProto message from the specified reader or buffer, length delimited.\n         * @function decodeDelimited\n         * @memberof onnx.OperatorSetIdProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @returns {onnx.OperatorSetIdProto} OperatorSetIdProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        OperatorSetIdProto.decodeDelimited = function decodeDelimited(reader) {\n            if (!(reader instanceof $Reader))\n                reader = new $Reader(reader);\n            return this.decode(reader, reader.uint32());\n        };\n\n        /**\n         * Verifies an OperatorSetIdProto message.\n         * @function verify\n         * @memberof onnx.OperatorSetIdProto\n         * @static\n         * @param {Object.<string,*>} message Plain object to verify\n         * @returns {string|null} `null` if valid, otherwise the reason why it is not\n         */\n        OperatorSetIdProto.verify = function verify(message) {\n            if (typeof message !== \"object\" || message === null)\n                return \"object expected\";\n            if (message.domain != null && message.hasOwnProperty(\"domain\"))\n                if (!$util.isString(message.domain))\n                    return \"domain: string expected\";\n            if (message.version != null && message.hasOwnProperty(\"version\"))\n                if (!$util.isInteger(message.version) && !(message.version && $util.isInteger(message.version.low) && $util.isInteger(message.version.high)))\n                    return \"version: integer|Long expected\";\n            return null;\n        };\n\n        /**\n         * Creates an OperatorSetIdProto message from a plain object. Also converts values to their respective internal types.\n         * @function fromObject\n         * @memberof onnx.OperatorSetIdProto\n         * @static\n         * @param {Object.<string,*>} object Plain object\n         * @returns {onnx.OperatorSetIdProto} OperatorSetIdProto\n         */\n        OperatorSetIdProto.fromObject = function fromObject(object) {\n            if (object instanceof $root.onnx.OperatorSetIdProto)\n                return object;\n            var message = new $root.onnx.OperatorSetIdProto();\n            if (object.domain != null)\n                message.domain = String(object.domain);\n            if (object.version != null)\n                if ($util.Long)\n                    (message.version = $util.Long.fromValue(object.version)).unsigned = false;\n                else if (typeof object.version === \"string\")\n                    message.version = parseInt(object.version, 10);\n                else if (typeof object.version === \"number\")\n                    message.version = object.version;\n                else if (typeof object.version === \"object\")\n                    message.version = new $util.LongBits(object.version.low >>> 0, object.version.high >>> 0).toNumber();\n            return message;\n        };\n\n        /**\n         * Creates a plain object from an OperatorSetIdProto message. Also converts values to other types if specified.\n         * @function toObject\n         * @memberof onnx.OperatorSetIdProto\n         * @static\n         * @param {onnx.OperatorSetIdProto} message OperatorSetIdProto\n         * @param {$protobuf.IConversionOptions} [options] Conversion options\n         * @returns {Object.<string,*>} Plain object\n         */\n        OperatorSetIdProto.toObject = function toObject(message, options) {\n            if (!options)\n                options = {};\n            var object = {};\n            if (options.defaults) {\n                object.domain = \"\";\n                if ($util.Long) {\n                    var long = new $util.Long(0, 0, false);\n                    object.version = options.longs === String ? long.toString() : options.longs === Number ? long.toNumber() : long;\n                } else\n                    object.version = options.longs === String ? \"0\" : 0;\n            }\n            if (message.domain != null && message.hasOwnProperty(\"domain\"))\n                object.domain = message.domain;\n            if (message.version != null && message.hasOwnProperty(\"version\"))\n                if (typeof message.version === \"number\")\n                    object.version = options.longs === String ? String(message.version) : message.version;\n                else\n                    object.version = options.longs === String ? $util.Long.prototype.toString.call(message.version) : options.longs === Number ? new $util.LongBits(message.version.low >>> 0, message.version.high >>> 0).toNumber() : message.version;\n            return object;\n        };\n\n        /**\n         * Converts this OperatorSetIdProto to JSON.\n         * @function toJSON\n         * @memberof onnx.OperatorSetIdProto\n         * @instance\n         * @returns {Object.<string,*>} JSON object\n         */\n        OperatorSetIdProto.prototype.toJSON = function toJSON() {\n            return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n        };\n\n        /**\n         * Gets the default type url for OperatorSetIdProto\n         * @function getTypeUrl\n         * @memberof onnx.OperatorSetIdProto\n         * @static\n         * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n         * @returns {string} The default type url\n         */\n        OperatorSetIdProto.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n            if (typeUrlPrefix === undefined) {\n                typeUrlPrefix = \"type.googleapis.com\";\n            }\n            return typeUrlPrefix + \"/onnx.OperatorSetIdProto\";\n        };\n\n        return OperatorSetIdProto;\n    })();\n\n    /**\n     * OperatorStatus enum.\n     * @name onnx.OperatorStatus\n     * @enum {number}\n     * @property {number} EXPERIMENTAL=0 EXPERIMENTAL value\n     * @property {number} STABLE=1 STABLE value\n     */\n    onnx.OperatorStatus = (function() {\n        var valuesById = {}, values = Object.create(valuesById);\n        values[valuesById[0] = \"EXPERIMENTAL\"] = 0;\n        values[valuesById[1] = \"STABLE\"] = 1;\n        return values;\n    })();\n\n    onnx.FunctionProto = (function() {\n\n        /**\n         * Properties of a FunctionProto.\n         * @memberof onnx\n         * @interface IFunctionProto\n         * @property {string|null} [name] FunctionProto name\n         * @property {Array.<string>|null} [input] FunctionProto input\n         * @property {Array.<string>|null} [output] FunctionProto output\n         * @property {Array.<string>|null} [attribute] FunctionProto attribute\n         * @property {Array.<onnx.IAttributeProto>|null} [attributeProto] FunctionProto attributeProto\n         * @property {Array.<onnx.INodeProto>|null} [node] FunctionProto node\n         * @property {string|null} [docString] FunctionProto docString\n         * @property {Array.<onnx.IOperatorSetIdProto>|null} [opsetImport] FunctionProto opsetImport\n         * @property {string|null} [domain] FunctionProto domain\n         */\n\n        /**\n         * Constructs a new FunctionProto.\n         * @memberof onnx\n         * @classdesc Represents a FunctionProto.\n         * @implements IFunctionProto\n         * @constructor\n         * @param {onnx.IFunctionProto=} [properties] Properties to set\n         */\n        function FunctionProto(properties) {\n            this.input = [];\n            this.output = [];\n            this.attribute = [];\n            this.attributeProto = [];\n            this.node = [];\n            this.opsetImport = [];\n            if (properties)\n                for (var keys = Object.keys(properties), i = 0; i < keys.length; ++i)\n                    if (properties[keys[i]] != null)\n                        this[keys[i]] = properties[keys[i]];\n        }\n\n        /**\n         * FunctionProto name.\n         * @member {string} name\n         * @memberof onnx.FunctionProto\n         * @instance\n         */\n        FunctionProto.prototype.name = \"\";\n\n        /**\n         * FunctionProto input.\n         * @member {Array.<string>} input\n         * @memberof onnx.FunctionProto\n         * @instance\n         */\n        FunctionProto.prototype.input = $util.emptyArray;\n\n        /**\n         * FunctionProto output.\n         * @member {Array.<string>} output\n         * @memberof onnx.FunctionProto\n         * @instance\n         */\n        FunctionProto.prototype.output = $util.emptyArray;\n\n        /**\n         * FunctionProto attribute.\n         * @member {Array.<string>} attribute\n         * @memberof onnx.FunctionProto\n         * @instance\n         */\n        FunctionProto.prototype.attribute = $util.emptyArray;\n\n        /**\n         * FunctionProto attributeProto.\n         * @member {Array.<onnx.IAttributeProto>} attributeProto\n         * @memberof onnx.FunctionProto\n         * @instance\n         */\n        FunctionProto.prototype.attributeProto = $util.emptyArray;\n\n        /**\n         * FunctionProto node.\n         * @member {Array.<onnx.INodeProto>} node\n         * @memberof onnx.FunctionProto\n         * @instance\n         */\n        FunctionProto.prototype.node = $util.emptyArray;\n\n        /**\n         * FunctionProto docString.\n         * @member {string} docString\n         * @memberof onnx.FunctionProto\n         * @instance\n         */\n        FunctionProto.prototype.docString = \"\";\n\n        /**\n         * FunctionProto opsetImport.\n         * @member {Array.<onnx.IOperatorSetIdProto>} opsetImport\n         * @memberof onnx.FunctionProto\n         * @instance\n         */\n        FunctionProto.prototype.opsetImport = $util.emptyArray;\n\n        /**\n         * FunctionProto domain.\n         * @member {string} domain\n         * @memberof onnx.FunctionProto\n         * @instance\n         */\n        FunctionProto.prototype.domain = \"\";\n\n        /**\n         * Creates a new FunctionProto instance using the specified properties.\n         * @function create\n         * @memberof onnx.FunctionProto\n         * @static\n         * @param {onnx.IFunctionProto=} [properties] Properties to set\n         * @returns {onnx.FunctionProto} FunctionProto instance\n         */\n        FunctionProto.create = function create(properties) {\n            return new FunctionProto(properties);\n        };\n\n        /**\n         * Encodes the specified FunctionProto message. Does not implicitly {@link onnx.FunctionProto.verify|verify} messages.\n         * @function encode\n         * @memberof onnx.FunctionProto\n         * @static\n         * @param {onnx.IFunctionProto} message FunctionProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        FunctionProto.encode = function encode(message, writer) {\n            if (!writer)\n                writer = $Writer.create();\n            if (message.name != null && Object.hasOwnProperty.call(message, \"name\"))\n                writer.uint32(/* id 1, wireType 2 =*/10).string(message.name);\n            if (message.input != null && message.input.length)\n                for (var i = 0; i < message.input.length; ++i)\n                    writer.uint32(/* id 4, wireType 2 =*/34).string(message.input[i]);\n            if (message.output != null && message.output.length)\n                for (var i = 0; i < message.output.length; ++i)\n                    writer.uint32(/* id 5, wireType 2 =*/42).string(message.output[i]);\n            if (message.attribute != null && message.attribute.length)\n                for (var i = 0; i < message.attribute.length; ++i)\n                    writer.uint32(/* id 6, wireType 2 =*/50).string(message.attribute[i]);\n            if (message.node != null && message.node.length)\n                for (var i = 0; i < message.node.length; ++i)\n                    $root.onnx.NodeProto.encode(message.node[i], writer.uint32(/* id 7, wireType 2 =*/58).fork()).ldelim();\n            if (message.docString != null && Object.hasOwnProperty.call(message, \"docString\"))\n                writer.uint32(/* id 8, wireType 2 =*/66).string(message.docString);\n            if (message.opsetImport != null && message.opsetImport.length)\n                for (var i = 0; i < message.opsetImport.length; ++i)\n                    $root.onnx.OperatorSetIdProto.encode(message.opsetImport[i], writer.uint32(/* id 9, wireType 2 =*/74).fork()).ldelim();\n            if (message.domain != null && Object.hasOwnProperty.call(message, \"domain\"))\n                writer.uint32(/* id 10, wireType 2 =*/82).string(message.domain);\n            if (message.attributeProto != null && message.attributeProto.length)\n                for (var i = 0; i < message.attributeProto.length; ++i)\n                    $root.onnx.AttributeProto.encode(message.attributeProto[i], writer.uint32(/* id 11, wireType 2 =*/90).fork()).ldelim();\n            return writer;\n        };\n\n        /**\n         * Encodes the specified FunctionProto message, length delimited. Does not implicitly {@link onnx.FunctionProto.verify|verify} messages.\n         * @function encodeDelimited\n         * @memberof onnx.FunctionProto\n         * @static\n         * @param {onnx.IFunctionProto} message FunctionProto message or plain object to encode\n         * @param {$protobuf.Writer} [writer] Writer to encode to\n         * @returns {$protobuf.Writer} Writer\n         */\n        FunctionProto.encodeDelimited = function encodeDelimited(message, writer) {\n            return this.encode(message, writer).ldelim();\n        };\n\n        /**\n         * Decodes a FunctionProto message from the specified reader or buffer.\n         * @function decode\n         * @memberof onnx.FunctionProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @param {number} [length] Message length if known beforehand\n         * @returns {onnx.FunctionProto} FunctionProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        FunctionProto.decode = function decode(reader, length) {\n            if (!(reader instanceof $Reader))\n                reader = $Reader.create(reader);\n            var end = length === undefined ? reader.len : reader.pos + length, message = new $root.onnx.FunctionProto();\n            while (reader.pos < end) {\n                var tag = reader.uint32();\n                switch (tag >>> 3) {\n                case 1: {\n                        message.name = reader.string();\n                        break;\n                    }\n                case 4: {\n                        if (!(message.input && message.input.length))\n                            message.input = [];\n                        message.input.push(reader.string());\n                        break;\n                    }\n                case 5: {\n                        if (!(message.output && message.output.length))\n                            message.output = [];\n                        message.output.push(reader.string());\n                        break;\n                    }\n                case 6: {\n                        if (!(message.attribute && message.attribute.length))\n                            message.attribute = [];\n                        message.attribute.push(reader.string());\n                        break;\n                    }\n                case 11: {\n                        if (!(message.attributeProto && message.attributeProto.length))\n                            message.attributeProto = [];\n                        message.attributeProto.push($root.onnx.AttributeProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 7: {\n                        if (!(message.node && message.node.length))\n                            message.node = [];\n                        message.node.push($root.onnx.NodeProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 8: {\n                        message.docString = reader.string();\n                        break;\n                    }\n                case 9: {\n                        if (!(message.opsetImport && message.opsetImport.length))\n                            message.opsetImport = [];\n                        message.opsetImport.push($root.onnx.OperatorSetIdProto.decode(reader, reader.uint32()));\n                        break;\n                    }\n                case 10: {\n                        message.domain = reader.string();\n                        break;\n                    }\n                default:\n                    reader.skipType(tag & 7);\n                    break;\n                }\n            }\n            return message;\n        };\n\n        /**\n         * Decodes a FunctionProto message from the specified reader or buffer, length delimited.\n         * @function decodeDelimited\n         * @memberof onnx.FunctionProto\n         * @static\n         * @param {$protobuf.Reader|Uint8Array} reader Reader or buffer to decode from\n         * @returns {onnx.FunctionProto} FunctionProto\n         * @throws {Error} If the payload is not a reader or valid buffer\n         * @throws {$protobuf.util.ProtocolError} If required fields are missing\n         */\n        FunctionProto.decodeDelimited = function decodeDelimited(reader) {\n            if (!(reader instanceof $Reader))\n                reader = new $Reader(reader);\n            return this.decode(reader, reader.uint32());\n        };\n\n        /**\n         * Verifies a FunctionProto message.\n         * @function verify\n         * @memberof onnx.FunctionProto\n         * @static\n         * @param {Object.<string,*>} message Plain object to verify\n         * @returns {string|null} `null` if valid, otherwise the reason why it is not\n         */\n        FunctionProto.verify = function verify(message) {\n            if (typeof message !== \"object\" || message === null)\n                return \"object expected\";\n            if (message.name != null && message.hasOwnProperty(\"name\"))\n                if (!$util.isString(message.name))\n                    return \"name: string expected\";\n            if (message.input != null && message.hasOwnProperty(\"input\")) {\n                if (!Array.isArray(message.input))\n                    return \"input: array expected\";\n                for (var i = 0; i < message.input.length; ++i)\n                    if (!$util.isString(message.input[i]))\n                        return \"input: string[] expected\";\n            }\n            if (message.output != null && message.hasOwnProperty(\"output\")) {\n                if (!Array.isArray(message.output))\n                    return \"output: array expected\";\n                for (var i = 0; i < message.output.length; ++i)\n                    if (!$util.isString(message.output[i]))\n                        return \"output: string[] expected\";\n            }\n            if (message.attribute != null && message.hasOwnProperty(\"attribute\")) {\n                if (!Array.isArray(message.attribute))\n                    return \"attribute: array expected\";\n                for (var i = 0; i < message.attribute.length; ++i)\n                    if (!$util.isString(message.attribute[i]))\n                        return \"attribute: string[] expected\";\n            }\n            if (message.attributeProto != null && message.hasOwnProperty(\"attributeProto\")) {\n                if (!Array.isArray(message.attributeProto))\n                    return \"attributeProto: array expected\";\n                for (var i = 0; i < message.attributeProto.length; ++i) {\n                    var error = $root.onnx.AttributeProto.verify(message.attributeProto[i]);\n                    if (error)\n                        return \"attributeProto.\" + error;\n                }\n            }\n            if (message.node != null && message.hasOwnProperty(\"node\")) {\n                if (!Array.isArray(message.node))\n                    return \"node: array expected\";\n                for (var i = 0; i < message.node.length; ++i) {\n                    var error = $root.onnx.NodeProto.verify(message.node[i]);\n                    if (error)\n                        return \"node.\" + error;\n                }\n            }\n            if (message.docString != null && message.hasOwnProperty(\"docString\"))\n                if (!$util.isString(message.docString))\n                    return \"docString: string expected\";\n            if (message.opsetImport != null && message.hasOwnProperty(\"opsetImport\")) {\n                if (!Array.isArray(message.opsetImport))\n                    return \"opsetImport: array expected\";\n                for (var i = 0; i < message.opsetImport.length; ++i) {\n                    var error = $root.onnx.OperatorSetIdProto.verify(message.opsetImport[i]);\n                    if (error)\n                        return \"opsetImport.\" + error;\n                }\n            }\n            if (message.domain != null && message.hasOwnProperty(\"domain\"))\n                if (!$util.isString(message.domain))\n                    return \"domain: string expected\";\n            return null;\n        };\n\n        /**\n         * Creates a FunctionProto message from a plain object. Also converts values to their respective internal types.\n         * @function fromObject\n         * @memberof onnx.FunctionProto\n         * @static\n         * @param {Object.<string,*>} object Plain object\n         * @returns {onnx.FunctionProto} FunctionProto\n         */\n        FunctionProto.fromObject = function fromObject(object) {\n            if (object instanceof $root.onnx.FunctionProto)\n                return object;\n            var message = new $root.onnx.FunctionProto();\n            if (object.name != null)\n                message.name = String(object.name);\n            if (object.input) {\n                if (!Array.isArray(object.input))\n                    throw TypeError(\".onnx.FunctionProto.input: array expected\");\n                message.input = [];\n                for (var i = 0; i < object.input.length; ++i)\n                    message.input[i] = String(object.input[i]);\n            }\n            if (object.output) {\n                if (!Array.isArray(object.output))\n                    throw TypeError(\".onnx.FunctionProto.output: array expected\");\n                message.output = [];\n                for (var i = 0; i < object.output.length; ++i)\n                    message.output[i] = String(object.output[i]);\n            }\n            if (object.attribute) {\n                if (!Array.isArray(object.attribute))\n                    throw TypeError(\".onnx.FunctionProto.attribute: array expected\");\n                message.attribute = [];\n                for (var i = 0; i < object.attribute.length; ++i)\n                    message.attribute[i] = String(object.attribute[i]);\n            }\n            if (object.attributeProto) {\n                if (!Array.isArray(object.attributeProto))\n                    throw TypeError(\".onnx.FunctionProto.attributeProto: array expected\");\n                message.attributeProto = [];\n                for (var i = 0; i < object.attributeProto.length; ++i) {\n                    if (typeof object.attributeProto[i] !== \"object\")\n                        throw TypeError(\".onnx.FunctionProto.attributeProto: object expected\");\n                    message.attributeProto[i] = $root.onnx.AttributeProto.fromObject(object.attributeProto[i]);\n                }\n            }\n            if (object.node) {\n                if (!Array.isArray(object.node))\n                    throw TypeError(\".onnx.FunctionProto.node: array expected\");\n                message.node = [];\n                for (var i = 0; i < object.node.length; ++i) {\n                    if (typeof object.node[i] !== \"object\")\n                        throw TypeError(\".onnx.FunctionProto.node: object expected\");\n                    message.node[i] = $root.onnx.NodeProto.fromObject(object.node[i]);\n                }\n            }\n            if (object.docString != null)\n                message.docString = String(object.docString);\n            if (object.opsetImport) {\n                if (!Array.isArray(object.opsetImport))\n                    throw TypeError(\".onnx.FunctionProto.opsetImport: array expected\");\n                message.opsetImport = [];\n                for (var i = 0; i < object.opsetImport.length; ++i) {\n                    if (typeof object.opsetImport[i] !== \"object\")\n                        throw TypeError(\".onnx.FunctionProto.opsetImport: object expected\");\n                    message.opsetImport[i] = $root.onnx.OperatorSetIdProto.fromObject(object.opsetImport[i]);\n                }\n            }\n            if (object.domain != null)\n                message.domain = String(object.domain);\n            return message;\n        };\n\n        /**\n         * Creates a plain object from a FunctionProto message. Also converts values to other types if specified.\n         * @function toObject\n         * @memberof onnx.FunctionProto\n         * @static\n         * @param {onnx.FunctionProto} message FunctionProto\n         * @param {$protobuf.IConversionOptions} [options] Conversion options\n         * @returns {Object.<string,*>} Plain object\n         */\n        FunctionProto.toObject = function toObject(message, options) {\n            if (!options)\n                options = {};\n            var object = {};\n            if (options.arrays || options.defaults) {\n                object.input = [];\n                object.output = [];\n                object.attribute = [];\n                object.node = [];\n                object.opsetImport = [];\n                object.attributeProto = [];\n            }\n            if (options.defaults) {\n                object.name = \"\";\n                object.docString = \"\";\n                object.domain = \"\";\n            }\n            if (message.name != null && message.hasOwnProperty(\"name\"))\n                object.name = message.name;\n            if (message.input && message.input.length) {\n                object.input = [];\n                for (var j = 0; j < message.input.length; ++j)\n                    object.input[j] = message.input[j];\n            }\n            if (message.output && message.output.length) {\n                object.output = [];\n                for (var j = 0; j < message.output.length; ++j)\n                    object.output[j] = message.output[j];\n            }\n            if (message.attribute && message.attribute.length) {\n                object.attribute = [];\n                for (var j = 0; j < message.attribute.length; ++j)\n                    object.attribute[j] = message.attribute[j];\n            }\n            if (message.node && message.node.length) {\n                object.node = [];\n                for (var j = 0; j < message.node.length; ++j)\n                    object.node[j] = $root.onnx.NodeProto.toObject(message.node[j], options);\n            }\n            if (message.docString != null && message.hasOwnProperty(\"docString\"))\n                object.docString = message.docString;\n            if (message.opsetImport && message.opsetImport.length) {\n                object.opsetImport = [];\n                for (var j = 0; j < message.opsetImport.length; ++j)\n                    object.opsetImport[j] = $root.onnx.OperatorSetIdProto.toObject(message.opsetImport[j], options);\n            }\n            if (message.domain != null && message.hasOwnProperty(\"domain\"))\n                object.domain = message.domain;\n            if (message.attributeProto && message.attributeProto.length) {\n                object.attributeProto = [];\n                for (var j = 0; j < message.attributeProto.length; ++j)\n                    object.attributeProto[j] = $root.onnx.AttributeProto.toObject(message.attributeProto[j], options);\n            }\n            return object;\n        };\n\n        /**\n         * Converts this FunctionProto to JSON.\n         * @function toJSON\n         * @memberof onnx.FunctionProto\n         * @instance\n         * @returns {Object.<string,*>} JSON object\n         */\n        FunctionProto.prototype.toJSON = function toJSON() {\n            return this.constructor.toObject(this, $protobuf.util.toJSONOptions);\n        };\n\n        /**\n         * Gets the default type url for FunctionProto\n         * @function getTypeUrl\n         * @memberof onnx.FunctionProto\n         * @static\n         * @param {string} [typeUrlPrefix] your custom typeUrlPrefix(default \"type.googleapis.com\")\n         * @returns {string} The default type url\n         */\n        FunctionProto.getTypeUrl = function getTypeUrl(typeUrlPrefix) {\n            if (typeUrlPrefix === undefined) {\n                typeUrlPrefix = \"type.googleapis.com\";\n            }\n            return typeUrlPrefix + \"/onnx.FunctionProto\";\n        };\n\n        return FunctionProto;\n    })();\n\n    return onnx;\n})();\n\nmodule.exports = $root;\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {flatbuffers} from 'flatbuffers';\nimport Long from 'long';\n\nimport {Graph} from './graph';\nimport {onnxruntime} from './ort-schema/flatbuffers/ort-generated';\nimport {onnx} from './ort-schema/protobuf/onnx';\nimport {Tensor} from './tensor';\n\n// check the inputs shape before running an OP.\n// return true when the inputs pass the check\n// return false when the inputs do not fit the requirement\n// throw exception when fatal error or not implemented\nexport function checkInputsShape(inputs: Tensor[], ...expectedDimensions: number[]): boolean {\n  if (!inputs || inputs.length !== expectedDimensions.length) {\n    return false;\n  }\n  for (let i = 0; i < inputs.length; i++) {\n    if (!inputs[i].dims || inputs[i].dims.length !== expectedDimensions[i]) {\n      return false;\n    }\n  }\n  return true;\n}\n\n// Evaluates the given expression and asserts error message if condition is unmet.\nexport function assert(expr: boolean, msg: () => string) {\n  if (!expr) {\n    throw new Error(typeof msg === 'string' ? msg : msg());\n  }\n}\n\nexport class ArrayUtil {\n  /**\n   * Verifies if 2 input arrays contain the same elements.\n   * @param n1 Array 1\n   * @param n2 Array 2\n   * @returns Whether these 2 are equal\n   */\n  static arraysEqual(\n      n1: readonly number[]|Int8Array|Uint8Array|Int16Array|Uint16Array|Int32Array|Uint32Array|Uint8ClampedArray|\n      Float32Array|Float64Array,\n      n2: readonly number[]|Int8Array|Uint8Array|Int16Array|Uint16Array|Int32Array|Uint32Array|Uint8ClampedArray|\n      Float32Array|Float64Array) {\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}\n\nexport class MatMulUtil {\n  /**\n   * Fix the input shapes for MatMul operation if they need fixing\n   * @param dimsA The shape of tensor A. Should be an array of positive integers\n   * @param dimsB The shape of tensor B. Should be an array of positive integers\n   * @returns A tuple containing the preprocessed input shapes as required by ONNX specifications\n   */\n  static preprocessInputShapes(dimsA: readonly number[], dimsB: readonly number[]):\n      [readonly number[], readonly number[]] {\n    // If the first argument is 1-D, it is promoted to a matrix by prepending\n    // a 1 to its dimensions. After matrix multiplication the prepended 1 is\n    // removed.\n    const a = (dimsA.length === 1) ? [1, dimsA[0]] : dimsA;\n\n    // If the second argument is 1-D, it is promoted to a matrix by appending\n    // a 1 to its dimensions. After matrix multiplication the appended 1 is\n    // removed.\n    const b = (dimsB.length === 1) ? [dimsB[0], 1] : dimsB;\n\n    return [a, b];\n  }\n\n  /**\n   * Fix the output shape computed for MatMul operation if it needs fixing\n   * @param outputShape The computed outputShape. Should be an array (atleast of length 2) of positive integers.\n   * This will be mutated.\n   * @param aRank The rank of tensor A.\n   * @param bRank The rank of tensor B.\n   */\n  static postprocessOutputShape(outputShape: number[], aRank: number, bRank: number) {\n    // Remove prepended dimension if first input is 1d\n    if (aRank === 1) {\n      // outputShape = outputShape.slice(0, outputShape.length - 2).concat(outputShape.slice(outputShape.length - 1));\n      outputShape.splice(outputShape.length - 2, 1);\n    }\n    // Remove appended dimension if second input is 1d\n    if (bRank === 1) {\n      outputShape.pop();\n    }\n  }\n\n  /**\n   * Calculate the expected shape when matrix multiplication\n   * @param a The shape of tensor A. Should be a tuple of 2 positive integers\n   * @param b The shape of tensor B. Should be a tuple of 2 positive integers\n   * @returns The expected shape of the result, or undefined if N/A\n   */\n  static calcMatMulShape(a: [number, number], b: [number, number]): [number, number]|undefined {\n    return (a[1] !== b[0]) ? undefined : [a[0], b[1]];\n  }\n}\n\nexport class BroadcastUtil {\n  /**\n   * Calculate the expected shape when broadcasting 2 tensors\n   * @param a The shape of tensor A. Should be an array of positive integers\n   * @param b The shape of tensor B. Should be an array of positive integers\n   * @param isMatMul Whether the operation is MatMul\n   * @returns The expected shape of the result, or undefined if N/A\n   */\n  static calcShape(adims: readonly number[], bdims: readonly number[], isMatMul = false): readonly number[]|undefined {\n    const arank = adims.length;\n    const brank = bdims.length;\n    if (arank === 0) {\n      return bdims;\n    }\n    if (brank === 0) {\n      return adims;\n    }\n    const crank = Math.max(adims.length, bdims.length);\n    const cdims = new Array<number>(crank);\n\n    // calculate the last 2 dimension if it is MatMul\n    if (isMatMul) {\n      if (arank < 2 || brank < 2) {\n        return undefined;\n      }\n      const cShapeMatMul =\n          MatMulUtil.calcMatMulShape([adims[arank - 2], adims[arank - 1]], [bdims[brank - 2], bdims[brank - 1]]);\n      if (cShapeMatMul === undefined) {\n        return undefined;\n      }\n      [cdims[crank - 2], cdims[crank - 1]] = cShapeMatMul;\n    }\n\n    for (let i = isMatMul ? 3 : 1; i <= crank; i++) {\n      const aLen = arank - i < 0 ? 1 : adims[arank - i];\n      const bLen = brank - i < 0 ? 1 : bdims[brank - i];\n\n      if (aLen !== bLen && aLen > 1 && bLen > 1) {\n        return undefined;\n      }\n      cdims[crank - i] = Math.max(aLen, bLen);\n    }\n\n    return cdims;\n  }\n\n  /**\n   * Given the indices of a broadcasted tensor, calculate the original indices\n   * @param broadcastedIndices The given indices of the broadcasted tensor.\n   * @param originalShape The original shape of the tensor before broadcas\n   * @returns The calculated indices that maps to the original tensor.\n   */\n  static index(broadcastedIndices: readonly number[], originalShape: readonly number[]): number[] {\n    // NOTE 1: we assume the parameter broadcastedIndices is valid. ie. it should have the same\n    // length as the broadcasted shape, and for each dimension the index should\n    // not be out of range.\n    const originalIndices = new Array(originalShape.length);\n    BroadcastUtil.fillIndex(broadcastedIndices, originalShape, originalIndices);\n    return originalIndices;\n  }\n\n  /**\n   * Given the indices of a broadcasted tensor, calculate the original indices\n   * @param broadcastedIndices The given indices of the broadcasted tensor.\n   * @param originalShape The original shape of the tensor before broadcast\n   * @param originalIndices The mapping of broadcastedIndices to the originalIndices (output parameter - will be\n   *     mutated).\n   */\n  static fillIndex(broadcastedIndices: readonly number[], originalShape: readonly number[], originalIndices: number[]) {\n    // NOTE 1: we assume the parameter broadcastedIndices is valid. ie. it should have the same length as the\n    // broadcasted shape, and for each dimension the index should not be out of range.\n    // NOTE 2: we assume the parameter originalIndices has the same length as the originalShape\n    const dimOffset = broadcastedIndices.length - originalShape.length;\n    for (let i = 0; i < originalShape.length; i++) {\n      originalIndices[i] = broadcastedIndices[dimOffset + i] % originalShape[i];\n    }\n  }\n\n  /**\n   * Perform the broadcasting operation on the specific operator\n   * @param a The input tensor A\n   * @param b The input tensor B\n   * @param op The operator lambda function\n   * @param inplace Whether to write the result back to A.\n   * @returns The result tensor, or undefined if input not broadcastable.\n   */\n  static calc(\n      a: Tensor, b: Tensor, op: (a: string|number, b: string|number) => (string | number), inplace: boolean,\n      resultType?: Tensor.DataType): Tensor|undefined {\n    const outputShape = BroadcastUtil.calcShape(a.dims, b.dims);\n\n    if (outputShape) {\n      if (inplace && !ShapeUtil.areEqual(outputShape, a.dims)) {\n        // B is not broadcastable to A, failed to calculate inplace.\n        return undefined;\n      }\n\n      const size = ShapeUtil.size(outputShape);\n      const c = inplace ? a : new Tensor(outputShape, resultType || a.type);\n\n      // both inputs are scalars\n      if (outputShape.length === 0) {\n        c.set([], op(a.get([]) as number, b.get([]) as number));\n      }\n\n      // atleast one input is a non-scalar\n      else {\n        const outputIndices = new Array<number>(outputShape.length);\n        const originalIndicesA = new Array(a.dims.length);\n        const originalIndicesB = new Array(b.dims.length);\n        let valA: string|number = 0;\n        let valB: string|number = 0;\n        let isAScalar = false;\n        let isBScalar = false;\n        if (a.dims.length === 0) {\n          valA = a.get([]) as number;\n          isAScalar = true;\n        }\n        if (b.dims.length === 0) {\n          valB = b.get([]) as number;\n          isBScalar = true;\n        }\n        let rest: number;\n        for (let i = 0; i < size; i++) {\n          // traversal indices\n          rest = i;\n          for (let j = outputShape.length - 1; j >= 0; j--) {\n            outputIndices[j] = rest % outputShape[j];\n            rest = Math.floor(rest / outputShape[j]);\n          }\n\n          if (!isAScalar) {\n            // map outputIndices (which is actually broadcasted) to the originalIndices\n            BroadcastUtil.fillIndex(outputIndices, a.dims, originalIndicesA);\n            valA = a.get(originalIndicesA) as number;\n          }\n          if (!isBScalar) {\n            BroadcastUtil.fillIndex(outputIndices, b.dims, originalIndicesB);\n            valB = b.get(originalIndicesB) as number;\n          }\n\n          c.set(outputIndices, op(valA, valB));\n        }\n      }\n\n      return c;\n    }\n\n    return undefined;\n  }\n\n  /**\n   * Determine if a shape is unidirectional broadcastable to another shape\n   * @param shape The input shape\n   * @param finalShape The desired shape after broadcasting\n   */\n  static isValidBroadcast(shape: readonly number[], finalShape: readonly number[]): boolean {\n    // align shape to the right\n    const inputRank = shape.length;\n    const finalRank = finalShape.length;\n    if (inputRank > finalRank) {\n      return false;\n    }\n    for (let i = 1; i <= inputRank; i++) {\n      if (shape[inputRank - i] !== 1 && shape[inputRank - i] !== finalShape[finalRank - i]) {\n        return false;\n      }\n    }\n    return true;\n  }\n\n  /**\n   * Determine the broadcasted dims in input shape based on the given output shape.\n   * Note that this function only returns the broadcasted dims.\n   * @param inputShape The input shape\n   * @param outputShape The output shape\n   * @returns The broadcasted dims in input shape.\n   */\n  static getBroadcastDims(inputShape: readonly number[], outputShape: readonly number[]): number[] {\n    const inRank = inputShape.length;\n    const dims: number[] = [];\n    for (let i = 0; i < inRank; i++) {\n      const dim = inRank - 1 - i;\n      const a = inputShape[dim] || 1;\n      const b = outputShape[outputShape.length - 1 - i] || 1;\n      if (b > 1 && a === 1) {\n        dims.unshift(dim);\n      }\n    }\n    return dims;\n  }\n}\n\n// copy array helper\n// mimics memcpy as much as possible\nexport function arrayCopyHelper(\n    target: number[]|Tensor.NumberType, source: number[]|Tensor.NumberType, targetIndex: number, sourceIndex: number,\n    blockSize: number) {\n  if (sourceIndex < 0 || sourceIndex >= source.length) {\n    throw new Error('sourceIndex out of bounds');\n  }\n  if (targetIndex < 0 || targetIndex >= target.length) {\n    throw new Error('targetIndex out of bounds');\n  }\n  if (sourceIndex + blockSize > source.length) {\n    throw new Error('source indices to be copied are outside bounds');\n  }\n  if (targetIndex + blockSize > target.length) {\n    throw new Error('target array is too small to hold result');\n  }\n\n  for (let offset = 0; offset < blockSize; offset++) {\n    target[targetIndex + offset] = source[sourceIndex + offset];\n  }\n}\n\nexport class GemmUtil {\n  // will make sure input shapes are compatible for this op\n  // and return back the shape of the output in the form of a tuple\n  // will throw exception if the input shapes are not compatible\n  static getShapeOfGemmResult(\n      leftShape: readonly number[], transLeft: boolean, rightShape: readonly number[], transRight: boolean,\n      biasShape?: readonly number[]): readonly number[] {\n    if (leftShape.length !== 2 || rightShape.length !== 2) {\n      throw new Error('shape need to be of size 2');\n    }\n\n    let M: number;\n    let K: number;\n    let N: number;\n\n    if (transLeft) {\n      M = leftShape[1];\n      K = leftShape[0];\n    } else {\n      M = leftShape[0];\n      K = leftShape[1];\n    }\n\n    let kDim = -1;\n\n    if (transRight) {\n      N = rightShape[0];\n      kDim = 1;\n    } else {\n      N = rightShape[1];\n      kDim = 0;\n    }\n\n    if (rightShape[kDim] !== K) {\n      throw new Error('dimension mismatch');\n    }\n\n    if (M <= 0 || N <= 0 || K <= 0) {\n      throw new Error('invalid shape specified');\n    }\n\n    if (biasShape && !BroadcastUtil.isValidBroadcast(biasShape, [M, N])) {\n      throw new Error('gemm: invalid bias shape for broadcast');\n    }\n\n    return [M, N, K];\n  }\n}\n\nexport class ProtoUtil {\n  static tensorDataTypeFromProto(typeProto: onnx.TensorProto.DataType|\n                                 onnxruntime.experimental.fbs.TensorDataType): Tensor.DataType {\n    switch (typeProto) {\n      case onnx.TensorProto.DataType.INT8:\n        return 'int8';\n      case onnx.TensorProto.DataType.UINT8:\n        return 'uint8';\n      case onnx.TensorProto.DataType.BOOL:\n        return 'bool';\n      case onnx.TensorProto.DataType.INT16:\n        return 'int16';\n      case onnx.TensorProto.DataType.UINT16:\n        return 'uint16';\n      case onnx.TensorProto.DataType.INT32:\n        return 'int32';\n      case onnx.TensorProto.DataType.UINT32:\n        return 'uint32';\n      case onnx.TensorProto.DataType.FLOAT:\n        return 'float32';\n      case onnx.TensorProto.DataType.DOUBLE:\n        return 'float64';\n      case onnx.TensorProto.DataType.STRING:\n        return 'string';\n\n      // For INT64/UINT64, reduce their value to 32-bits.\n      // Should throw exception when overflow\n      case onnx.TensorProto.DataType.INT64:\n        return 'int32';\n      case onnx.TensorProto.DataType.UINT64:\n        return 'uint32';\n\n      default:\n        throw new Error(`unsupported data type: ${onnx.TensorProto.DataType[typeProto]}`);\n    }\n  }\n\n  static tensorDataTypeStringToEnum(type: string): onnx.TensorProto.DataType {\n    switch (type) {\n      case 'int8':\n        return onnx.TensorProto.DataType.INT8;\n      case 'uint8':\n        return onnx.TensorProto.DataType.UINT8;\n      case 'bool':\n        return onnx.TensorProto.DataType.BOOL;\n      case 'int16':\n        return onnx.TensorProto.DataType.INT16;\n      case 'uint16':\n        return onnx.TensorProto.DataType.UINT16;\n      case 'int32':\n        return onnx.TensorProto.DataType.INT32;\n      case 'uint32':\n        return onnx.TensorProto.DataType.UINT32;\n      case 'float32':\n        return onnx.TensorProto.DataType.FLOAT;\n      case 'float64':\n        return onnx.TensorProto.DataType.DOUBLE;\n      case 'string':\n        return onnx.TensorProto.DataType.STRING;\n      case 'int64':\n        return onnx.TensorProto.DataType.INT64;\n      case 'uint64':\n        return onnx.TensorProto.DataType.UINT64;\n\n      default:\n        throw new Error(`unsupported data type: ${type}`);\n    }\n  }\n\n  static tensorDimsFromProto(dims: Array<number|Long>): number[] {\n    // get rid of Long type for dims\n    return dims.map(d => Long.isLong(d) ? d.toNumber() : d);\n  }\n\n  static tensorValueTypeFromProto(valueType: onnx.TypeProto.ITensor): Graph.ValueType {\n    return {\n      tensorType: ProtoUtil.tensorDataTypeFromProto(valueType.elemType!),\n      shape: {dims: ProtoUtil.tensorDimsFromProto(valueType.shape!.dim!.map(d => d.dimValue!))}\n    };\n  }\n\n  static tensorDimsFromORTFormat(tensor: onnxruntime.experimental.fbs.Tensor) {\n    const dims = [];\n    for (let i = 0; i < tensor.dimsLength(); i++) {\n      dims.push(LongUtil.longToNumber(tensor.dims(i)!));\n    }\n    return dims;\n  }\n\n  static tensorAttributesFromORTFormat(node: onnxruntime.experimental.fbs.Node) {\n    const attributes = [];\n    for (let i = 0; i < node.attributesLength(); i++) {\n      attributes.push(node.attributes(i)!);\n    }\n    return attributes;\n  }\n}\n\nexport class LongUtil {\n  // This function is called to get a number from long type of data for attribute, dim, and ir version,\n  // which values are signed integers.\n  // To make it more generic, add an optional parameter to convert to a unsigned number.\n  static longToNumber(n: Long|flatbuffers.Long|number, unsigned?: boolean) {\n    if (Long.isLong(n)) {\n      return n.toNumber();\n    } else if (n instanceof flatbuffers.Long) {\n      return Long.fromValue({low: n.low, high: n.high, unsigned: unsigned ?? false}).toNumber();\n    }\n    return n;\n  }\n  static isLong(n: unknown) {\n    return Long.isLong(n) || n instanceof flatbuffers.Long;\n  }\n}\n\nexport class ShapeUtil {\n  static size(dims: readonly number[]): number {\n    return ShapeUtil.getSizeFromDimensionRange(dims, 0, dims.length);\n  }\n\n  // `axis` inclusive\n  static sizeFromDimension(dims: readonly number[], axis: number): number {\n    if (axis < 0 || axis > dims.length) {\n      throw new Error(`invalid dimension of ${axis} for sizeFromDimension as Tensor has ${dims.length} dimensions.`);\n    }\n    return ShapeUtil.getSizeFromDimensionRange(dims, axis, dims.length);\n  }\n\n  // `axis` exclusive\n  static sizeToDimension(dims: readonly number[], axis: number): number {\n    if (axis < 0 || axis > dims.length) {\n      throw new Error(`invalid dimension of ${axis} for sizeToDimension as Tensor has ${dims.length} dimensions.`);\n    }\n    return ShapeUtil.getSizeFromDimensionRange(dims, 0, axis);\n  }\n\n  static getSizeFromDimensionRange(dims: readonly number[], start: number, end: number): number {\n    let size = 1;\n    for (let i = start; i < end; i++) {\n      // safety check as this method is called by multiple other methods requiring size.\n      // size cannot be 0 or negative.\n      if (dims[i] <= 0) {\n        throw new Error(\n            // eslint-disable-next-line max-len\n            'cannot get valid size from specified dimension range. Most likely the range contains 0 or negative values in them.');\n      }\n      size *= dims[i];\n    }\n    return size;\n  }\n\n  static computeStrides(dims: readonly number[]): readonly number[] {\n    const rank = dims.length;\n    if (rank === 0) {\n      return [];\n    } else if (rank === 1) {\n      return [1];\n    }\n    const strides = new Array(rank);\n    strides[rank - 1] = 1;\n    strides[rank - 2] = dims[rank - 1];\n    for (let i = rank - 3; i >= 0; --i) {\n      strides[i] = strides[i + 1] * dims[i + 1];\n    }\n    return strides;\n  }\n\n  static transpose(dims: readonly number[]): readonly number[] {\n    const copy = dims.slice();\n    return copy.reverse();\n  }\n\n  static indicesToOffset(indices: readonly number[], strides: readonly number[], axis?: number): number {\n    if (axis === undefined) {\n      axis = indices.length;\n    }\n    let offset = 0;\n    for (let i = 0; i < axis; ++i) {\n      offset += strides[i] * indices[i];\n    }\n    return offset;\n  }\n\n  static offsetToIndices(offset: number, strides: readonly number[]): readonly number[] {\n    const rank = strides.length;\n    if (rank === 0) {\n      return [];\n    } else if (rank === 1) {\n      return [offset * strides[0]];\n    }\n    const indices: number[] = new Array(strides.length);\n    for (let i = 0; i < indices.length - 1; ++i) {\n      indices[i] = Math.floor(offset / strides[i]);\n      offset -= indices[i] * strides[i];\n    }\n    indices[indices.length - 1] = offset;\n    return indices;\n  }\n\n  /**\n   * normailze axis of range [-r, r) into [0, r).\n   */\n  static normalizeAxis(axis: number, tensorRank: number): number {\n    if (axis < -tensorRank && axis >= tensorRank) {\n      throw new Error('unsupported axis for this operation.');\n    }\n    return axis < 0 ? axis + tensorRank : axis;\n  }\n\n  static normalizeAxes(axes: readonly number[], tensorRank: number): number[] {\n    return axes.map(x => this.normalizeAxis(x, tensorRank));\n  }\n\n  // Increment an index into a tensor (in lexicographic\n  // ordering), wrapping around the specified upper_bound.\n  /**\n   * Increment an index into a tensor (in lexicographic ordering), wrapping around the specified upper_bound.\n   * @param index Given index to increment (Will be mutated)\n   * @param dims The dimensions of the tensor for which the given index corresponds to\n   * @param axisToIncrementOn The 1-indexed axis to increment on. If undefined, axisToIncrementOn == rank\n   */\n  static incrementIndex(index: number[], dims: readonly number[], axisToIncrementOn?: number) {\n    if (dims.length === 0 || index.length === 0) {\n      throw new Error('Index incrementing unsupported for scalar Tensor');\n    }\n    if (axisToIncrementOn === undefined) {\n      axisToIncrementOn = dims.length;\n    } else {\n      if (axisToIncrementOn <= 0 || axisToIncrementOn > dims.length) {\n        throw new Error('Incorrect axis to increment on');\n      }\n    }\n\n    for (let k = axisToIncrementOn - 1; k >= 0; --k) {\n      index[k]++;\n      if (index[k] < dims[k]) {\n        break;\n      }\n      index[k] = 0;\n    }\n  }\n\n  /**\n   * Produces a new dimensions array based on the values in the 'originalDimensions' and 'shape' array\n   * Used in Reshape\n   * @param originalDims Original Shape array\n   * @param shapeHints array containing values to compute the new dimensions\n   * For example:\n   * originalDims = [2,2] and shapeHints = [0,-1] will return [2,2]\n   * originalDims = [2,2] and shapeHints = [4] will return [4]\n   * originalDims = [2,2] and shapeHints = [5] will throw an exception\n   * https://github.com/onnx/onnx/blob/main/docs/Operators.md#Reshape\n   */\n\n  static calculateReshapedDims(originalDims: readonly number[], shapeHints: ArrayLike<number>): number[] {\n    // reshape to a Scalar Tensor\n    if (shapeHints.length === 0) {\n      if (originalDims.length === 0 || ShapeUtil.size(originalDims) === 1) {\n        return [];\n      } else {\n        throw new Error('cannot reshape to a scalar Tensor');\n      }\n    }\n\n    const nDims = shapeHints.length;\n    const reshapedDims = new Array<number>(nDims);\n    let unknownDimension = -1;\n    let newTensorSize = 1;\n    for (let i = 0; i < nDims; i++) {\n      if (shapeHints[i] < -1) {\n        throw new Error('a dimension in shape hints cannot be less than -1');\n      }\n      if (shapeHints[i] === -1) {\n        if (unknownDimension !== -1) {\n          throw new Error('at most one dimension in shape hints can be -1');\n        }\n        unknownDimension = i;\n      } else {\n        if (shapeHints[i] === 0) {\n          if (i >= originalDims.length) {\n            throw new Error('the dimension with value zero exceeds the dimension size of the input tensor');\n          }\n          reshapedDims[i] = originalDims[i];\n        } else {\n          reshapedDims[i] = shapeHints[i];\n        }\n        newTensorSize *= reshapedDims[i];\n      }\n    }\n\n    const oldTensorSize = ShapeUtil.size(originalDims);\n    if (unknownDimension !== -1) {\n      if (oldTensorSize % newTensorSize !== 0) {\n        throw new Error(`the input tensor cannot be reshaped to the requested shape. Input shape: [${\n            originalDims}] Output shape: [${shapeHints}]`);\n      }\n      reshapedDims[unknownDimension] = oldTensorSize / newTensorSize;\n    }\n    // validate sizes from originalDims and reshapedDims match\n    else {\n      if (newTensorSize !== oldTensorSize) {\n        throw new Error('reshapedDims and originalDims don\\'t have matching sizes');\n      }\n    }\n    return reshapedDims;\n  }\n\n  /**\n   * Sorts a given array based on the indices in the Perm array\n   * Used in Transpose\n   * @param a Array to be sorted such as dims or strides\n   * @param perm Perm given; if null a will be reversed\n   */\n  static sortBasedOnPerm(a: readonly number[], perm?: readonly number[]): readonly number[] {\n    if (perm) {\n      return perm.map((v) => a[v]);\n    } else {\n      return a.slice().reverse();\n    }\n  }\n\n  /**\n   * Pads a given shape according to the padding values\n   * @param dims shape of the Tensor to be padded\n   * @param pad pad values\n   */\n  static padShape(dims: readonly number[], pad: readonly number[]): readonly number[] {\n    const rank = dims.length;\n    return dims.map((v, i) => v + pad[i] + pad[i + rank]);\n  }\n\n  /**\n   * Determines if the two shapes are identical\n   * @param shape1\n   * @param shape2\n   */\n  static areEqual(shape1: readonly number[], shape2: readonly number[]): boolean {\n    if (shape1.length !== shape2.length) {\n      return false;\n    }\n    return shape1.every((v, i) => v === shape2[i]);\n  }\n\n  /**\n   * Validates if the given `dims` or `shape` is valid in ONNX.js context and returns data size\n   * @param dims - input `dims` that needs to be checked\n   */\n  static validateDimsAndCalcSize(dims: readonly number[]): number {\n    if (dims.length > 6) {\n      throw new TypeError('Only rank 0 to 6 is supported for tensor shape.');\n    }\n    let size = 1;\n    for (const n of dims) {\n      if (!Number.isInteger(n)) {\n        throw new TypeError(`Invalid shape: ${n} is not an integer`);\n      }\n      if (n < 0 || n > 2147483647) {\n        throw new TypeError(`Invalid shape: length ${n} is not allowed`);\n      }\n      size *= n;\n    }\n    return size;\n  }\n\n  /**\n   * Determines the shape of output tensor y = flatten(x, axis)\n   * @param dims - shape of input tensor\n   * @param axis - flatten axis, in the range [-r, r]\n   */\n  static flattenShape(dims: readonly number[], axis: number): readonly number[] {\n    if (axis < 0) {\n      axis += dims.length;\n    }\n    const total = dims.reduce((x, y) => x * y, 1);\n    const right = dims.slice(axis).reduce((x, y) => x * y, 1);\n    const outputDims = [total / right, right];\n\n    return outputDims;\n  }\n\n  /**\n   * Determines the shape of output tensor y = squeeze(x, axes)\n   * @param dims - shape of input tensor\n   * @param axes - squeeze axes\n   */\n  static squeezeShape(dims: readonly number[], axes: readonly number[]): readonly number[] {\n    const outputDims = new Array<number>();\n\n    // sanity check\n    axes = ShapeUtil.normalizeAxes(axes, dims.length);\n\n    for (let i = 0; i < dims.length; i++) {\n      const inSqueezeList = axes.indexOf(i) >= 0;\n      if (inSqueezeList && dims[i] !== 1) {\n        throw new Error('squeeze an axis of size different than 1');\n      }\n\n      if ((axes.length === 0 && dims[i] > 1) || (axes.length > 0 && !inSqueezeList)) {\n        outputDims.push(dims[i]);\n      }\n    }\n\n    return outputDims;\n  }\n\n  /**\n   * Determines the shape of output tensor y = unsqueeze(x, axes)\n   * @param dims - shape of input tensor\n   * @param axes - unsqueeze axes\n   */\n  static unsqueezeShape(dims: readonly number[], axes: readonly number[]): readonly number[] {\n    const outputDims = new Array<number>(dims.length + axes.length);\n\n    // initialize the array elements to 0\n    outputDims.fill(0);\n\n    // set all axes indices to 1 in outputDims and check for duplicates\n    for (let i = 0; i < axes.length; i++) {\n      const axis = ShapeUtil.normalizeAxis(axes[i], outputDims.length);\n      if (axis >= outputDims.length) {\n        throw new Error('\\'axes\\' has an out of range axis');\n      }\n      if (outputDims[axis] !== 0) {\n        throw new Error('\\'axes\\' has a duplicate axis');\n      }\n\n      outputDims[axis] = 1;\n    }\n\n    // fill in the zero entries of outputDims with the input tensor's shape\n    let inputDimsIterator = 0;\n    for (let i = 0; i < outputDims.length; i++) {\n      if (outputDims[i] === 0) {\n        outputDims[i] = dims[inputDimsIterator++];\n      }\n    }\n\n    // sanity check assertion. 'inputDimsIterator'\n    // should be equal to the length of 'dims'\n    if (inputDimsIterator !== dims.length) {\n      throw new Error('the unsqueezed dimension could not be established');\n    }\n\n    return outputDims;\n  }\n}\n\n// bunch of helper methods that do a variety of math operations\nexport class MathUtil {\n  // y = (x*x) + y\n  static sqr(\n      target: number[]|Tensor.NumberType, source: number[]|Tensor.NumberType, targetIndex: number, sourceIndex: number,\n      blockSize: number) {\n    if (sourceIndex < 0 || sourceIndex >= source.length) {\n      throw new Error('sourceIndex out of bounds');\n    }\n    if (targetIndex < 0 || targetIndex >= target.length) {\n      throw new Error('targetIndex out of bounds');\n    }\n    if (sourceIndex + blockSize > source.length) {\n      throw new Error('source indices to be copied are outside bounds');\n    }\n    if (targetIndex + blockSize > target.length) {\n      throw new Error('target array is too small to hold result');\n    }\n\n    for (let offset = 0; offset < blockSize; offset++) {\n      target[targetIndex + offset] += Math.pow(source[sourceIndex + offset], 2);\n    }\n  }\n\n  // y = ax + y\n  static axpy(\n      target: number[]|Tensor.NumberType, source: number[]|Tensor.NumberType, targetIndex: number, sourceIndex: number,\n      blockSize: number, alpha: number) {\n    if (sourceIndex < 0 || sourceIndex >= source.length) {\n      throw new Error('sourceIndex out of bounds');\n    }\n    if (targetIndex < 0 || targetIndex >= target.length) {\n      throw new Error('targetIndex out of bounds');\n    }\n    if (sourceIndex + blockSize > source.length) {\n      throw new Error('source indices to be copied are outside bounds');\n    }\n    if (targetIndex + blockSize > target.length) {\n      throw new Error('target array is too small to hold result');\n    }\n\n    for (let offset = 0; offset < blockSize; offset++) {\n      target[targetIndex + offset] += (alpha * source[sourceIndex + offset]);\n    }\n  }\n\n  // y = pow(x, b)\n  static powx(\n      target: number[]|Tensor.NumberType, source: number[]|Tensor.NumberType, targetIndex: number, sourceIndex: number,\n      blockSize: number, b: number) {\n    if (sourceIndex < 0 || sourceIndex >= source.length) {\n      throw new Error('sourceIndex out of bounds');\n    }\n    if (targetIndex < 0 || targetIndex >= target.length) {\n      throw new Error('targetIndex out of bounds');\n    }\n    if (sourceIndex + blockSize > source.length) {\n      throw new Error('source indices to be copied are outside bounds');\n    }\n    if (targetIndex + blockSize > target.length) {\n      throw new Error('target array is too small to hold result');\n    }\n\n    for (let offset = 0; offset < blockSize; offset++) {\n      target[targetIndex + offset] = Math.pow(source[sourceIndex + offset], b);\n    }\n  }\n\n  // y = x * y\n  static mul(\n      target: number[]|Tensor.NumberType, source: number[]|Tensor.NumberType, targetIndex: number, sourceIndex: number,\n      blockSize: number) {\n    if (sourceIndex < 0 || sourceIndex >= source.length) {\n      throw new Error('sourceIndex out of bounds');\n    }\n    if (targetIndex < 0 || targetIndex >= target.length) {\n      throw new Error('targetIndex out of bounds');\n    }\n    if (sourceIndex + blockSize > source.length) {\n      throw new Error('source indices to be copied are outside bounds');\n    }\n    if (targetIndex + blockSize > target.length) {\n      throw new Error('target array is too small to hold result');\n    }\n\n    for (let offset = 0; offset < blockSize; offset++) {\n      target[targetIndex + offset] = (source[sourceIndex + offset] * target[targetIndex + offset]);\n    }\n  }\n}\n\nexport class SplitUtil {\n  /**\n   * Calculates new Shapes from existing one and the splits given along the axis provides\n   * @param dims Shape of the Tensor to be splitted into two or more Shapes\n   * @param axis The dimension along which the Tensor will be split\n   * @param splits Offsets for the start of each split\n   */\n  static splitShape(dims: readonly number[], axis: number, split: number[], numOutputs?: number):\n      [number[][], number[]] {\n    if (split.length === 0) {\n      if (!numOutputs) {\n        throw new Error('need to know number of outputs when the \\'split\\' attribute is not specified');\n      }\n      SplitUtil.determineSplit(dims[axis], numOutputs, split);\n    }\n\n    const shapes: number[][] = [];\n    const offsets = [0];\n    for (let i = 0; i < split.length; ++i) {\n      if (i !== 0) {\n        offsets.push(offsets[i - 1] + split[i - 1]);\n      }\n      const shape = dims.slice();\n      shape[axis] = split[i];\n      shapes.push(shape);\n    }\n    return [shapes, offsets];\n  }\n\n  static determineSplit(numElementsAlongAxis: number, numOutputs: number, split: number[]) {\n    // If 'split' is not specified by the user, we need to partition the number of elements equally among the outputs\n    if (numElementsAlongAxis % numOutputs !== 0) {\n      throw new Error('cannot split tensor to equal sized parts');\n    }\n    for (let i = 0; i < numOutputs; ++i) {\n      split.push(numElementsAlongAxis / numOutputs);\n    }\n  }\n}\n\nexport class ReduceUtil {\n  /**\n   * Perform reduce operations on the specific operator\n   * @param a Input tensor data\n   * @param axes The dimensions along which the Tensor will be reduced\n   * @param keepdims If set to true, the axes which are reduced are left in the\n   *    result as dimensions with size one.\n   * @param op1 The operation to be performed on each element in the tensor\n   * @param op2 The operation to be performed between elements in the tensor\n   */\n  static calcReduce(\n      a: Tensor, axes: number[], keepdims: boolean, op1: (b: number) => number,\n      op2: (a: number, b: number) => number): Tensor {\n    const dims = a.dims.slice(0);\n    // if axes is not set, perform reduce on all axes\n    if (axes.length === 0) {\n      dims.forEach((_d, ind) => axes.push(ind));\n    }\n    // get a temporary broadcastable output shape\n    const outputDims = ReduceUtil.calcReduceShape(dims, axes, true);\n\n    // loop through the output and calculate result one by one\n    const size = ShapeUtil.size(outputDims);\n    const y = new Tensor(outputDims, a.type);\n    const strides = ShapeUtil.computeStrides(outputDims);\n    const inputStrides = ShapeUtil.computeStrides(dims);\n    const indicesY = new Array(dims.length);\n    for (let i = 0; i < size; i++) {\n      const indices = ShapeUtil.offsetToIndices(i, strides);\n      // map index\n      BroadcastUtil.fillIndex(indices, dims, indicesY);\n      y.set(\n          indices,\n          ReduceUtil.calcReduceByAxis(\n              a.numberData, axes, dims, 0, ShapeUtil.indicesToOffset(indicesY, inputStrides), op1, op2));\n    }\n\n    if (keepdims) {\n      return y;\n    } else {\n      // keepdims == 0, calculate the expected shape\n      return new Tensor(\n          ReduceUtil.calcReduceShape(dims, axes, keepdims), y.type, undefined, undefined, y.data, y.dataId);\n    }\n  }\n\n  /**\n   * Perform reduce operations on the specific operator on specific axes\n   * @param a Input tensor data\n   * @param axes The dimensions along which the Tensor will be reduced\n   * @param dims The input dimension.\n   * @param curAxisInd Index in axes specifying the current dimension along\n   *      which the tensor will be reduced\n   * @param pos The current index of element to perform operation\n   * @param op1 The operation to be performed on each element in the tensor\n   * @param op2 The operation to be performed between elements in the tensor\n   */\n  static calcReduceByAxis(\n      input: Tensor.NumberType, axes: number[], dims: number[], curAxisInd: number, pos: number,\n      op1: (b: number) => number, op2: (a: number, b: number) => number): number {\n    let res = 0;\n    if (curAxisInd >= axes.length) {\n      return op1(input[pos]);\n    }\n    const axis = axes[curAxisInd];\n    const step = axis >= dims.length ? 1 : ShapeUtil.size(dims.slice(axis + 1));\n    for (let i = 0; i < dims[axis]; i++) {\n      res = i === 0 ? ReduceUtil.calcReduceByAxis(input, axes, dims, curAxisInd + 1, pos, op1, op2) :\n                      op2(res, ReduceUtil.calcReduceByAxis(input, axes, dims, curAxisInd + 1, pos, op1, op2));\n      pos += step;\n    }\n    return res;\n  }\n\n  /**\n   * Calculate the expected shape of a reduce operation\n   * @param dims The input tensor dimension\n   * @param axes The dimensions along which the Tensor will be reduced\n   * @param keepdims If set to true, the axes which are reduced are left in the\n   *    result as dimensions with size one.\n   */\n  static calcReduceShape(dims: readonly number[], axes: readonly number[], keepDims: boolean): number[] {\n    const outputDims = dims.slice();\n    for (let i = 0; i < axes.length; i++) {\n      if (keepDims) {\n        outputDims[axes[i]] = 1;\n      } else {\n        outputDims[axes[i]] = 0;\n      }\n    }\n    return outputDims.filter(dim => dim !== 0);\n  }\n}\n\nexport class PoolConvUtil {\n  /**\n   * Adjust the kernel, strides, pads to correct rank. Set to default value if not present\n   * @param isGlobalOperator If true, perform global pooling.\n   * @param inputDims The input tensor dimension.\n   * @param kernelShape The size of the kernel along each axis.\n   * @param strides Stride along each axis.\n   * @param dilations Dilation along each axis.\n   * @param pads Padding for the beginning and ending along each axis.\n   */\n  static adjustPoolAttributes(\n      isGlobalOperator: boolean, inputDims: readonly number[], kernelShape: number[], strides: number[],\n      dilations: number[], pads: number[]) {\n    if (!isGlobalOperator && kernelShape.length !== inputDims.length - 2) {\n      throw new Error('length of specified kernel shapes should be 2 less than length of input dimensions');\n    }\n\n    if (isGlobalOperator) {\n      // adjust kernel shape to cover the input dims\n      for (let dim = 0; dim < inputDims.length - 2; dim++) {\n        if (dim >= kernelShape.length) {\n          kernelShape.push(inputDims[dim + 2]);\n        } else {\n          kernelShape[dim] = inputDims[dim + 2];\n        }\n      }\n    }\n\n    // adjust strides length to match kernel shape length\n    for (let dim = 0; dim < kernelShape.length; dim++) {\n      if (dim < strides.length) {\n        if (strides[dim] < 0) {\n          throw new Error('strides should be greater than or equal to 1');\n        }\n      } else {\n        strides.push(1);\n      }\n    }\n\n    // adjust dilation value\n    for (let dim = 0; dim < kernelShape.length; dim++) {\n      if (dim < dilations.length) {\n        if (dilations[dim] < 0) {\n          throw new Error('dilations should be greater than or equal to 1');\n        }\n      } else {\n        dilations.push(1);\n      }\n    }\n\n    // adjust pads length to match 2 * kernel shape length\n    for (let dim = 0; dim < kernelShape.length * 2; dim++) {\n      if (dim < pads.length) {\n        if (pads[dim] < 0) {\n          throw new Error('pad should be greater than or equal to 1');\n        }\n      } else {\n        pads.push(0);\n      }\n    }\n\n    // sanity checks for values in kernel shapes and pads\n    for (let dim = 0; dim < kernelShape.length; dim++) {\n      if (kernelShape[dim] <= 0) {\n        throw new Error('kernel shapes need to be greater than 0');\n      }\n\n      if (pads[dim] >= kernelShape[dim] || pads[dim + kernelShape.length] >= kernelShape[dim]) {\n        throw new Error('pads should be smaller than kernel');\n      }\n    }\n  }\n\n  // adjust pad values based on 'autoPad' attribute\n  static adjustPadsBasedOnAutoPad(\n      inputDims: readonly number[], strides: readonly number[], dilations: readonly number[],\n      kernelShape: readonly number[], pads: number[], autoPad?: string) {\n    if (!autoPad) {\n      return;\n    }\n\n    if (pads.length !== 2 * (inputDims.length - 2)) {\n      throw new Error('length of pads should be twice the length of data dimensions');\n    }\n\n    if (strides.length !== (inputDims.length - 2)) {\n      throw new Error('length of strides should be the length of data dimensions');\n    }\n\n    if (kernelShape.length !== (inputDims.length - 2)) {\n      throw new Error('length of kernel shapes should be the length of data dimensions');\n    }\n\n    for (let dim = 0; dim < inputDims.length - 2; dim++) {\n      PoolConvUtil.adjustPadAndReturnShape(\n          inputDims[dim + 2], strides[dim], dilations[dim], kernelShape[dim], pads, dim, dim + inputDims.length - 2,\n          autoPad);\n    }\n  }\n\n  /**\n   * Calculate the output shape for Pool ops based on input attributes. (Should be used only for Pool ops)\n   * @param isGlobalOperator If true, perform global pooling.\n   * @param inputDims The input tensor dimension. (inputs[0].dims)\n   * @param strides Stride along each axis.\n   * @param dilations Dilation along each axis.\n   * @param kernelShape The size of the kernel along each axis.\n   * @param pads Padding for the beginning and ending along each axis.\n   * @param autoPad DEPRECATED attribute supported for legacy models. Specifies how to implicitly calculate pads in each\n   *     dimension. Can take values NOTSET, SAME_UPPER, SAME_LOWER, or VALID.\n   */\n  static computePoolOutputShape(\n      isGlobalOperator: boolean, inputDims: readonly number[], strides: number[], dilations: number[],\n      kernelShape: number[], pads: number[], autoPad?: string): number[] {\n    if (inputDims.length <= 0) {\n      throw new Error('input shape must be of size greater than 0');\n    }\n\n    // Add batch size and number of channels of output\n    const outputDims = [inputDims[0], inputDims[1]];\n\n    PoolConvUtil.computeShapeHelper(\n        isGlobalOperator, inputDims, outputDims, strides, dilations, kernelShape, pads, autoPad);\n    return outputDims;\n  }\n\n  /**\n   * Calculate the output shape for Conv op based on input attributes. (Should be used only for Conv op)\n   * @param inputDims The input tensor dimension. (inputs[0].dims)\n   * @param filterDims The filter tensor dimension. (inputs[1].dims)\n   * @param strides Stride along each axis.\n   * @param kernelShape The size of the kernel along each axis.\n   * @param pads Padding for the beginning and ending along each axis.\n   * @param autoPad DEPRECATED attribute supported for legacy models. Specifies how to implicitly calculate pads in each\n   *     dimension. Can take values NOTSET, SAME_UPPER, SAME_LOWER, or VALID.\n   */\n  static computeConvOutputShape(\n      inputDims: readonly number[], filterDims: readonly number[], strides: number[], dilations: number[],\n      kernelShape: number[], pads: number[], autoPad?: string): number[] {\n    if (inputDims.length <= 0 || filterDims.length <= 0) {\n      throw new Error('invalid input tensor dims or invalid filter tensor dims');\n    }\n\n    // Add batch size and number of channels of output\n    const outputDims = [inputDims[0], filterDims[0]];\n\n    PoolConvUtil.computeShapeHelper(false, inputDims, outputDims, strides, dilations, kernelShape, pads, autoPad);\n    return outputDims;\n  }\n\n  // will compute output shapes for data dimensions ONLY (i.e.) no batch size and channels\n  // called by computePoolOutputShape() and computeConvOutputShape()\n  // adjust pads based on 'autoPad' attribute prior to shape computation\n  private static computeShapeHelper(\n      isGlobalOperator: boolean, inputDims: readonly number[], outputDims: number[], strides: readonly number[],\n      dilations: readonly number[], kernelShape: readonly number[], pads: number[], autoPad?: string) {\n    if (isGlobalOperator) {\n      for (let dim = 0; dim < inputDims.length - 2; dim++) {\n        outputDims.push(1);\n      }\n    } else {\n      for (let dim = 0; dim < inputDims.length - 2; dim++) {\n        outputDims.push(PoolConvUtil.adjustPadAndReturnShape(\n            inputDims[dim + 2], strides[dim], dilations[dim], kernelShape[dim], pads, dim, dim + inputDims.length - 2,\n            autoPad));\n      }\n    }\n  }\n\n  // helper for computeShapeHelper() and adjustPadsBasedOnAutoPad()\n  // adjusts pad value for given 'autoPad' string and computes output shape along a particular dimension\n  private static adjustPadAndReturnShape(\n      inSize: number, stride: number, dilation: number, kernel: number, pads: number[], padHeadIndex: number,\n      padTailIndex: number, autoPad?: string): number {\n    const dkernel = dilation * (kernel - 1) + 1;\n    if (autoPad && autoPad !== 'NOTSET') {\n      switch (autoPad) {\n        case 'VALID':\n          pads[padHeadIndex] = 0;\n          pads[padTailIndex] = 0;\n          return Math.floor(((inSize - dkernel) / stride) + 1);\n        case 'SAME_LOWER':\n        case 'SAME_UPPER':\n          if (dilation !== 1) {\n            throw new Error('Dilation not supported for SAME_UPPER or SAME_LOWER');\n          } else {\n            const legacyTargetSize = (inSize + stride - 1) / stride;\n            const padNeeded = (legacyTargetSize - 1) * stride + kernel - inSize;\n            pads[padHeadIndex] =\n                (autoPad === 'SAME_LOWER') ? Math.floor((padNeeded + 1) / 2) : Math.floor(padNeeded / 2);\n            pads[padTailIndex] = padNeeded - pads[padHeadIndex];\n            return Math.floor(((inSize + padNeeded - kernel) / stride) + 1);\n          }\n        default:\n          throw new Error('Unsupported AutoPad type');\n      }\n    } else {\n      return Math.floor(((inSize + pads[padHeadIndex] + pads[padTailIndex] - dkernel) / stride) + 1);\n    }\n  }\n}\n\nexport const MIN_CLIP = -3.4028234663852886e+38;\nexport const MAX_CLIP = 3.4028234663852886e+38;\n\nexport function decodeUtf8String(buffer: Uint8Array): string {\n  return new TextDecoder().decode(buffer);\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Guid} from 'guid-typescript';\nimport Long from 'long';\n\nimport {onnxruntime} from './ort-schema/flatbuffers/ort-generated';\nimport {onnx} from './ort-schema/protobuf/onnx';\nimport {decodeUtf8String, ProtoUtil, ShapeUtil} from './util';\n\nimport ortFbs = onnxruntime.experimental.fbs;\n\nexport declare namespace Tensor {\n  export interface DataTypeMap {\n    bool: Uint8Array;\n    float32: Float32Array;\n    float64: Float64Array;\n    string: string[];\n    int8: Int8Array;\n    uint8: Uint8Array;\n    int16: Int16Array;\n    uint16: Uint16Array;\n    int32: Int32Array;\n    uint32: Uint32Array;\n    int64: BigInt64Array;\n  }\n\n  export type DataType = keyof DataTypeMap;\n\n  export type StringType = Tensor.DataTypeMap['string'];\n  export type BooleanType = Tensor.DataTypeMap['bool'];\n  export type IntegerType = Tensor.DataTypeMap['int8']|Tensor.DataTypeMap['uint8']|Tensor.DataTypeMap['int16']|\n                            Tensor.DataTypeMap['uint16']|Tensor.DataTypeMap['int32']|Tensor.DataTypeMap['uint32'];\n  export type FloatType = Tensor.DataTypeMap['float32']|Tensor.DataTypeMap['float64'];\n  export type NumberType = BooleanType|IntegerType|FloatType;\n\n  export type Id = Guid;\n}\n\ntype TensorData = Tensor.DataTypeMap[Tensor.DataType];\n\ntype DataProvider = (id: Tensor.Id) => TensorData;\ntype AsyncDataProvider = (id: Tensor.Id) => Promise<TensorData>;\n\nexport class Tensor {\n  /**\n   * get the underlying tensor data\n   */\n  get data(): TensorData {\n    if (this.cache === undefined) {\n      const data = this.dataProvider!(this.dataId);\n      if (data.length !== this.size) {\n        throw new Error('Length of data provided by the Data Provider is inconsistent with the dims of this Tensor.');\n      }\n      this.cache = data;\n    }\n    return this.cache;\n  }\n\n  /**\n   * get the underlying string tensor data. Should only use when type is STRING\n   */\n  get stringData() {\n    if (this.type !== 'string') {\n      throw new TypeError('data type is not string');\n    }\n\n    return this.data as Tensor.StringType;\n  }\n\n  /**\n   * get the underlying integer tensor data. Should only use when type is one of the following: (UINT8, INT8, UINT16,\n   * INT16, INT32, UINT32, BOOL)\n   */\n  get integerData() {\n    switch (this.type) {\n      case 'uint8':\n      case 'int8':\n      case 'uint16':\n      case 'int16':\n      case 'int32':\n      case 'uint32':\n      case 'bool':\n        return this.data as Tensor.IntegerType;\n\n      default:\n        throw new TypeError('data type is not integer (uint8, int8, uint16, int16, int32, uint32, bool)');\n    }\n  }\n\n  /**\n   * get the underlying float tensor data. Should only use when type is one of the following: (FLOAT, DOUBLE)\n   */\n  get floatData() {\n    switch (this.type) {\n      case 'float32':\n      case 'float64':\n        return this.data as Tensor.FloatType;\n\n      default:\n        throw new TypeError('data type is not float (float32, float64)');\n    }\n  }\n\n  /**\n   * get the underlying number tensor data. Should only use when type is one of the following: (UINT8, INT8, UINT16,\n   * INT16, INT32, UINT32, BOOL, FLOAT, DOUBLE)\n   */\n  get numberData() {\n    if (this.type !== 'string') {\n      return this.data as Tensor.NumberType;\n    }\n    throw new TypeError('type cannot be non-number (string)');\n  }\n\n  /**\n   * get value of an element at the given indices\n   */\n  get(indices: readonly number[]): Tensor.DataTypeMap[Tensor.DataType][number] {\n    return this.data[ShapeUtil.indicesToOffset(indices, this.strides)];\n  }\n\n  /**\n   * set value of an element at the given indices\n   */\n  set(indices: readonly number[], value: Tensor.DataTypeMap[Tensor.DataType][number]) {\n    this.data[ShapeUtil.indicesToOffset(indices, this.strides)] = value;\n  }\n\n  /**\n   * get the underlying tensor data asynchronously\n   */\n  async getData(): Promise<TensorData> {\n    if (this.cache === undefined) {\n      this.cache = await this.asyncDataProvider!(this.dataId);\n    }\n    return this.cache;\n  }\n\n  /**\n   * get the number of elements in the tensor\n   */\n  public readonly size: number;\n\n  private _strides: readonly number[];\n  /**\n   * get the strides for each dimension\n   */\n  get strides(): readonly number[] {\n    if (!this._strides) {\n      this._strides = ShapeUtil.computeStrides(this.dims);\n    }\n    return this._strides;\n  }\n\n  constructor(\n      /**\n       * get the dimensions of the tensor\n       */\n      public readonly dims: readonly number[],\n      /**\n       * get the type of the tensor\n       */\n      public readonly type: Tensor.DataType, private dataProvider?: DataProvider,\n      private asyncDataProvider?: AsyncDataProvider, private cache?: TensorData,\n      /**\n       * get the data ID that used to map to a tensor data\n       */\n      public readonly dataId: Guid = Guid.create()) {\n    this.size = ShapeUtil.validateDimsAndCalcSize(dims);\n    const size = this.size;\n    const empty = (dataProvider === undefined && asyncDataProvider === undefined && cache === undefined);\n\n    if (cache !== undefined) {\n      if (cache.length !== size) {\n        throw new RangeError('Input dims doesn\\'t match data length.');\n      }\n    }\n\n    if (type === 'string') {\n      if (cache !== undefined && (!Array.isArray(cache) || !cache.every(i => typeof i === 'string'))) {\n        throw new TypeError('cache should be a string array');\n      }\n\n      if (empty) {\n        this.cache = new Array<string>(size);\n      }\n    } else {\n      if (cache !== undefined) {\n        const constructor = dataviewConstructor(type);\n        if (!(cache instanceof constructor)) {\n          throw new TypeError(`cache should be type ${constructor.name}`);\n        }\n      }\n\n      if (empty) {\n        const buf = new ArrayBuffer(size * sizeof(type));\n        this.cache = createView(buf, type);\n      }\n    }\n  }\n\n  /**\n   * Construct new Tensor from a ONNX Tensor object\n   * @param tensorProto the ONNX Tensor\n   */\n  static fromProto(tensorProto: onnx.ITensorProto): Tensor {\n    if (!tensorProto) {\n      throw new Error('cannot construct Value from an empty tensor');\n    }\n    const type = ProtoUtil.tensorDataTypeFromProto(tensorProto.dataType!);\n    const dims = ProtoUtil.tensorDimsFromProto(tensorProto.dims!);\n\n    const value = new Tensor(dims, type);\n\n    if (type === 'string') {\n      // When it's STRING type, the value should always be stored in field\n      // 'stringData'\n      tensorProto.stringData!.forEach((str, i) => {\n        value.data[i] = decodeUtf8String(str);\n      });\n\n    } else if (\n        tensorProto.rawData && typeof tensorProto.rawData.byteLength === 'number' &&\n        tensorProto.rawData.byteLength > 0) {\n      // NOT considering segment for now (IMPORTANT)\n\n      // populate value from rawData\n      const dataDest = value.data;\n      const dataSource =\n          new DataView(tensorProto.rawData.buffer, tensorProto.rawData.byteOffset, tensorProto.rawData.byteLength);\n      const elementSize = sizeofProto(tensorProto.dataType!);\n      const length = tensorProto.rawData.byteLength / elementSize;\n\n      if (tensorProto.rawData.byteLength % elementSize !== 0) {\n        throw new Error('invalid buffer length');\n      }\n      if (dataDest.length !== length) {\n        throw new Error('buffer length mismatch');\n      }\n\n      for (let i = 0; i < length; i++) {\n        const n = readProto(dataSource, tensorProto.dataType!, i * elementSize);\n        dataDest[i] = n;\n      }\n    } else {\n      // populate value from array\n      let array: Array<number|Long>;\n      switch (tensorProto.dataType) {\n        case onnx.TensorProto.DataType.FLOAT:\n          array = tensorProto.floatData!;\n          break;\n        case onnx.TensorProto.DataType.INT32:\n        case onnx.TensorProto.DataType.INT16:\n        case onnx.TensorProto.DataType.UINT16:\n        case onnx.TensorProto.DataType.INT8:\n        case onnx.TensorProto.DataType.UINT8:\n        case onnx.TensorProto.DataType.BOOL:\n          array = tensorProto.int32Data!;\n          break;\n        case onnx.TensorProto.DataType.INT64:\n          array = tensorProto.int64Data!;\n          break;\n        case onnx.TensorProto.DataType.DOUBLE:\n          array = tensorProto.doubleData!;\n          break;\n        case onnx.TensorProto.DataType.UINT32:\n        case onnx.TensorProto.DataType.UINT64:\n          array = tensorProto.uint64Data!;\n          break;\n        default:\n          // should never run here\n          throw new Error('unspecific error');\n      }\n\n      if (array === null || array === undefined) {\n        throw new Error('failed to populate data from a tensorproto value');\n      }\n\n      const data = value.data;\n      if (data.length !== array.length) {\n        throw new Error('array length mismatch');\n      }\n\n      for (let i = 0; i < array.length; i++) {\n        const element = array[i];\n        if (Long.isLong(element)) {\n          data[i] = longToNumber(element, tensorProto.dataType);\n        } else {\n          data[i] = element;\n        }\n      }\n    }\n\n    return value;\n  }\n\n  /**\n   * Construct new Tensor from raw data\n   * @param data the raw data object. Should be a string array for 'string' tensor, and the corresponding typed array\n   * for other types of tensor.\n   * @param dims the dimensions of the tensor\n   * @param type the type of the tensor\n   */\n  static fromData(data: Tensor.DataTypeMap[Tensor.DataType], dims: readonly number[], type: Tensor.DataType) {\n    return new Tensor(dims, type, undefined, undefined, data);\n  }\n\n  static fromOrtTensor(ortTensor: ortFbs.Tensor) {\n    if (!ortTensor) {\n      throw new Error('cannot construct Value from an empty tensor');\n    }\n    const dims = ProtoUtil.tensorDimsFromORTFormat(ortTensor);\n    const type = ProtoUtil.tensorDataTypeFromProto(ortTensor.dataType());\n\n    const value = new Tensor(dims, type);\n\n    if (type === 'string') {\n      // When it's STRING type, the value should always be stored in field\n      // 'stringData'\n      for (let i = 0; i < ortTensor.stringDataLength(); i++) {\n        value.data[i] = ortTensor.stringData(i);\n      }\n\n    } else if (\n        ortTensor.rawDataArray() && typeof ortTensor.rawDataLength() === 'number' && ortTensor.rawDataLength() > 0) {\n      // NOT considering segment for now (IMPORTANT)\n\n      // populate value from rawData\n      const dataDest = value.data;\n      const dataSource = new DataView(\n          ortTensor.rawDataArray()!.buffer, ortTensor.rawDataArray()!.byteOffset, ortTensor.rawDataLength());\n      const elementSize = sizeofProto(ortTensor.dataType());\n      const length = ortTensor.rawDataLength() / elementSize;\n\n      if (ortTensor.rawDataLength() % elementSize !== 0) {\n        throw new Error('invalid buffer length');\n      }\n      if (dataDest.length !== length) {\n        throw new Error('buffer length mismatch');\n      }\n\n      for (let i = 0; i < length; i++) {\n        const n = readProto(dataSource, ortTensor.dataType(), i * elementSize);\n        dataDest[i] = n;\n      }\n    }\n    return value;\n  }\n}\n\nfunction sizeof(type: Tensor.DataType): number {\n  switch (type) {\n    case 'bool':\n    case 'int8':\n    case 'uint8':\n      return 1;\n    case 'int16':\n    case 'uint16':\n      return 2;\n    case 'int32':\n    case 'uint32':\n    case 'float32':\n      return 4;\n    case 'float64':\n      return 8;\n    default:\n      throw new Error(`cannot calculate sizeof() on type ${type}`);\n  }\n}\n\nfunction sizeofProto(type: onnx.TensorProto.DataType|ortFbs.TensorDataType): number {\n  switch (type) {\n    case onnx.TensorProto.DataType.UINT8:\n    case onnx.TensorProto.DataType.INT8:\n    case onnx.TensorProto.DataType.BOOL:\n      return 1;\n    case onnx.TensorProto.DataType.UINT16:\n    case onnx.TensorProto.DataType.INT16:\n      return 2;\n    case onnx.TensorProto.DataType.FLOAT:\n    case onnx.TensorProto.DataType.INT32:\n    case onnx.TensorProto.DataType.UINT32:\n      return 4;\n    case onnx.TensorProto.DataType.INT64:\n    case onnx.TensorProto.DataType.DOUBLE:\n    case onnx.TensorProto.DataType.UINT64:\n      return 8;\n    default:\n      throw new Error(`cannot calculate sizeof() on type ${onnx.TensorProto.DataType[type]}`);\n  }\n}\n\nfunction createView(dataBuffer: ArrayBuffer, type: Tensor.DataType) {\n  return new (dataviewConstructor(type))(dataBuffer);\n}\n\nfunction dataviewConstructor(type: Tensor.DataType) {\n  switch (type) {\n    case 'bool':\n    case 'uint8':\n      return Uint8Array;\n    case 'int8':\n      return Int8Array;\n    case 'int16':\n      return Int16Array;\n    case 'uint16':\n      return Uint16Array;\n    case 'int32':\n      return Int32Array;\n    case 'uint32':\n      return Uint32Array;\n    case 'int64':\n      return BigInt64Array;\n    case 'float32':\n      return Float32Array;\n    case 'float64':\n      return Float64Array;\n    default:\n      // should never run to here\n      throw new Error('unspecified error');\n  }\n}\n\n// convert a long number to a 32-bit integer (cast-down)\nfunction longToNumber(i: Long, type: onnx.TensorProto.DataType|ortFbs.TensorDataType): number {\n  // INT64, UINT32, UINT64\n  if (type === onnx.TensorProto.DataType.INT64 || type === ortFbs.TensorDataType.INT64) {\n    if (i.greaterThanOrEqual(2147483648) || i.lessThan(-2147483648)) {\n      throw new TypeError('int64 is not supported');\n    }\n  } else if (\n      type === onnx.TensorProto.DataType.UINT32 || type === ortFbs.TensorDataType.UINT32 ||\n      type === onnx.TensorProto.DataType.UINT64 || type === ortFbs.TensorDataType.UINT64) {\n    if (i.greaterThanOrEqual(4294967296) || i.lessThan(0)) {\n      throw new TypeError('uint64 is not supported');\n    }\n  } else {\n    throw new TypeError(`not a LONG type: ${onnx.TensorProto.DataType[type]}`);\n  }\n\n  return i.toNumber();\n}\n\n// read one value from TensorProto\nfunction readProto(view: DataView, type: onnx.TensorProto.DataType|ortFbs.TensorDataType, byteOffset: number): number {\n  switch (type) {\n    case onnx.TensorProto.DataType.BOOL:\n    case onnx.TensorProto.DataType.UINT8:\n      return view.getUint8(byteOffset);\n    case onnx.TensorProto.DataType.INT8:\n      return view.getInt8(byteOffset);\n    case onnx.TensorProto.DataType.UINT16:\n      return view.getUint16(byteOffset, true);\n    case onnx.TensorProto.DataType.INT16:\n      return view.getInt16(byteOffset, true);\n    case onnx.TensorProto.DataType.FLOAT:\n      return view.getFloat32(byteOffset, true);\n    case onnx.TensorProto.DataType.INT32:\n      return view.getInt32(byteOffset, true);\n    case onnx.TensorProto.DataType.UINT32:\n      return view.getUint32(byteOffset, true);\n    case onnx.TensorProto.DataType.INT64:\n      return longToNumber(\n          Long.fromBits(view.getUint32(byteOffset, true), view.getUint32(byteOffset + 4, true), false), type);\n    case onnx.TensorProto.DataType.DOUBLE:\n      return view.getFloat64(byteOffset, true);\n    case onnx.TensorProto.DataType.UINT64:\n      return longToNumber(\n          Long.fromBits(view.getUint32(byteOffset, true), view.getUint32(byteOffset + 4, true), true), type);\n    default:\n      throw new Error(`cannot read from DataView for type ${onnx.TensorProto.DataType[type]}`);\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\n/**\n * represent a version irrelevant abstraction of for GLSL source code\n */\nexport interface Glsl {\n  readonly version: string;\n  readonly attribute: string;\n  readonly varyingVertex: string;\n  readonly varyingFrag: string;\n  readonly texture2D: string;\n  readonly output: string;\n  readonly outputDeclaration: string;\n}\n\nconst GLSL_ES_2_0: Glsl = {\n  version: '',\n  attribute: 'attribute',\n  varyingVertex: 'varying',\n  varyingFrag: 'varying',\n  texture2D: 'texture2D',\n  output: 'gl_FragColor',\n  outputDeclaration: '',\n};\nconst GLSL_ES_3_0: Glsl = {\n  version: '#version 300 es',\n  attribute: 'in',\n  varyingVertex: 'out',\n  varyingFrag: 'in',\n  texture2D: 'texture',\n  output: 'outputColor',\n  outputDeclaration: 'out vec4 outputColor;',\n};\n\nexport function getGlsl(version: 1|2) {\n  return version === 1 ? GLSL_ES_2_0 : GLSL_ES_3_0;\n}\n\nexport function getVertexShaderSource(version: 1|2): string {\n  const glsl = getGlsl(version);\n  return `${glsl.version}\n      precision highp float;\n      ${glsl.attribute} vec3 position;\n      ${glsl.attribute} vec2 textureCoord;\n\n      ${glsl.varyingVertex} vec2 TexCoords;\n\n      void main()\n      {\n          gl_Position = vec4(position, 1.0);\n          TexCoords = textureCoord;\n      }`;\n}\n\nexport function getFragShaderPreamble(version: 1|2): string {\n  const glsl = getGlsl(version);\n  return `${glsl.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${glsl.varyingFrag} vec2 TexCoords;\n    ${glsl.outputDeclaration}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    // Custom vector types to handle higher dimenalities.\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    `;\n}\n\nexport function getDefaultFragShaderMain(version: 1|2, outputShapeLength: number): string {\n  const glsl = getGlsl(version);\n  return `\n  void main() {\n    int indices[${outputShapeLength}];\n    toVec(TexCoords, indices);\n    vec4 result = vec4(process(indices));\n    ${glsl.output} = result;\n  }\n  `;\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from '../../tensor';\n\n/**\n * Layout info is used for mapping n-dimensional array to 2D textures\n * The layout is created by the TextureLayoutStrategy based on\n * the Tensor's dimensions and strides\n */\nexport interface TextureLayout {\n  width: number;\n  height: number;\n  /**\n   * specify the number of value that encoded in a single pixel\n   */\n  channels: 1|2|3|4;\n  /**\n   * whether in packed mode or not\n   */\n  isPacked?: boolean;\n  /**\n   * the normalized shape\n   */\n  shape: readonly number[];\n  /**\n   * the stride of each dimensions, calculated according to shape\n   */\n  strides: readonly number[];\n  /**\n   * the original shape(dims) of the corresponding tensor\n   */\n  unpackedShape: readonly number[];\n\n  reversedWH?: boolean;\n}\nexport interface TextureData extends TextureLayout {\n  tensor: Tensor;\n  texture: WebGLTexture;\n}\n\nexport enum TextureType {\n  unpacked,              // <-- normal unpacked texture\n  unpackedReversed,      // <-- unpacked texture used in old ONNX.js implementation (deprecated)\n  packed,                // <-- normal packed texture\n  downloadUint8AsFloat,  // <-- ONLY used in texture downloading for iOS devices\n  packedLastDimension    // <-- ONLY used in old ONNX.js Conv implementation for input W (deprecated)\n}\n\nexport interface TensorInfo {\n  id?: Tensor.Id;\n  dims: readonly number[];\n  type: Tensor.DataType;\n  textureType: TextureType;\n}\n\nexport interface ProgramVariable {\n  type: 'float'|'int';\n  name: string;\n  arrayLength?: number;\n  data: number|number[];\n}\n\n/**\n * A set of metadata of a shader program.\n */\nexport interface ProgramMetadata {\n  /**\n   * the name of the program. used for debugging and profiling\n   */\n  name: string;\n  /**\n   * texture types for each input\n   */\n  inputTypes: TextureType[];\n  /**\n   * names of each input\n   */\n  inputNames: string[];\n  /**\n   * an optional string as a cache hint in the artifact cache\n   */\n  cacheHint?: string;\n}\n\n/**\n * A ProgramInfoLoader allows\n */\nexport interface ProgramInfoLoader extends ProgramMetadata {\n  /**\n   * a function to get the program info\n   */\n  get(): ProgramInfo;\n}\n\n/**\n * A set of data that represent a shader program\n */\nexport interface ProgramInfo extends ProgramMetadata {\n  /**\n   * information of uniform variables\n   */\n  variables?: ProgramVariable[];\n  /**\n   * tensor info for output\n   */\n  output: TensorInfo;\n  /**\n   * the shader's processing source code\n   */\n  shaderSource: string;\n  /**\n   * whether the shader source contains a customized main function implementation\n   */\n  hasMain?: boolean;\n}\n\nexport interface VariableInfo {\n  type: 'float'|'int';\n  name: string;\n  arrayLength?: number;\n}\n\nexport interface ProgramVariable {\n  type: 'float'|'int';\n  name: string;\n  arrayLength?: number;\n  data: number|number[];\n}\n\n/**\n * Information of uniforms that shader uses\n */\nexport interface UniformInfo {\n  type: 'sampler2D'|VariableInfo['type'];\n  name: string;\n  arrayLength?: number;\n}\n\nexport interface UniformLocation extends UniformInfo {\n  location: WebGLUniformLocation;\n}\n\n/**\n * Artifact is the result of compilation\n * It does not contain input of output data\n * However anything that could be run as a \"program\"\n */\nexport interface Artifact {\n  programInfo: ProgramInfo;\n  program: WebGLProgram;\n  uniformLocations: UniformLocation[];\n  attribLocations: {position: number; textureCoord: number};\n}\nexport declare namespace Artifact {\n  type UniformLocations = Artifact['uniformLocations'];\n  type AttribLocations = Artifact['attribLocations'];\n}\n\nexport interface UniformData {\n  [name: string]: number|number[];\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {assert} from '../../util';\n/**\n * Given a non RGBA shape calculate the R version\n * It is assumed that the dimensions are multiples of given channels\n * NOTE: it is always the last dim that gets packed.\n * @param unpackedShape original shape to create a packed version from\n */\nexport function getPackedShape(unpackedShape: readonly number[]): readonly number[] {\n  const len = unpackedShape.length;\n  return unpackedShape.slice(0, len - 1).concat(unpackedShape[len - 1] / 4);\n}\n\nexport async function repeatedTry(\n    checkFn: () => boolean, delayFn = (_counter: number) => 0, 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 * Generates the function name from an input sampler name.\n * @param samplerName Name of the sampler.\n */\nexport function generateShaderFuncNameFromInputSamplerName(samplerName: string): string {\n  assert(typeof samplerName !== 'undefined' && samplerName.length !== 0, () => 'empty string found for sampler name');\n  return 'get' + samplerName.charAt(0).toUpperCase() + samplerName.slice(1);\n}\n\n/**\n * Generates the function name from an input sampler name at output coordinates.\n * @param samplerName Name of the sampler.\n */\nexport function generateShaderFuncNameFromInputSamplerNameAtOutCoords(samplerName: string): string {\n  assert(typeof samplerName !== 'undefined' && samplerName.length !== 0, () => 'empty string found for sampler name');\n  return 'get' + samplerName.charAt(0).toUpperCase() + samplerName.slice(1) + 'AtOutCoords';\n}\n\n/** Returns a new input shape (a copy) that has a squeezed logical shape. */\nexport function squeezeInputShape(inputShape: readonly number[], squeezedShape: number[]): number[] {\n  // Deep copy.\n  let newInputShape: number[] = JSON.parse(JSON.stringify(inputShape));\n  newInputShape = squeezedShape;\n  return newInputShape;\n}\n\n/** Returns a list of squeezed parameters for shader functions */\nexport function getSqueezedParams(params: string[], keptDims: number[]): string {\n  return keptDims.map(d => params[d]).join(', ');\n}\n\n/** Returns the data type for different ranks. */\nexport function getCoordsDataType(rank: number): string {\n  if (rank <= 1) {\n    return 'int';\n  } else if (rank === 2) {\n    return 'ivec2';\n  } else if (rank === 3) {\n    return 'ivec3';\n  } else if (rank === 4) {\n    return 'ivec4';\n  } else if (rank === 5) {\n    return 'ivec5';\n  } else if (rank === 6) {\n    return 'ivec6';\n  } else {\n    throw Error(`GPU for rank ${rank} is not yet supported`);\n  }\n}\n\nexport function getGlChannels(rank = 6): string[] {\n  return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank);\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {getGlChannels} from '../utils';\n\nexport function getVecChannels(name: string, rank: number): string[] {\n  return getGlChannels(rank).map(d => `${name}.${d}`);\n}\n\nexport function getChannels(name: string, rank: number): string[] {\n  if (rank === 1) {\n    return [name];\n  }\n  return getVecChannels(name, rank);\n}\n\nexport function unpackFromChannel(): string {\n  return `\n    float getChannel(vec4 frag, int dim) {\n      int modCoord = imod(dim, 2);\n      return modCoord == 0 ? frag.r : frag.g;\n    }\n\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  `;\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from '../../../tensor';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, TextureType} from '../types';\nimport {getCoordsDataType} from '../utils';\n\nimport {getChannels} from './packing-utils';\n\nconst packProgramMetadata = {\n  name: 'pack',\n  inputNames: ['A'],\n  inputTypes: [TextureType.unpackedReversed]\n};\n\nconst createPackProgramInfo = (handler: WebGLInferenceHandler, input: Tensor): ProgramInfo => {\n  const glsl = getGlsl(handler.session.backend.glContext.version);\n  const inputShape = input.dims;\n\n  const inputRank = inputShape.length;\n  // createTextureLayoutFromShape won't change output rank. Need to verify by running tests\n  const outputRank = input.dims.length;\n\n  const coordsDataType = getCoordsDataType(outputRank);\n  const channels = getChannels('rc', outputRank);\n  const setup = getSetup(outputRank, channels, inputShape[inputShape.length - 2], inputShape[inputShape.length - 1]);\n\n  let reversedInputWH;\n  if (inputRank === 0) {\n    reversedInputWH = [1, 1];\n  } else if (inputRank === 1) {\n    reversedInputWH = [inputShape[0], 1];\n  } else {\n    reversedInputWH = [inputShape[outputRank - 1], inputShape[outputRank - 2]];\n  }\n  const outOfBoundsCondition = getOutOfBoundsCondition(outputRank, reversedInputWH, channels);\n  const output = getOutput(inputShape, channels);\n\n  const shaderSource = `\n        void main() {\n          ${coordsDataType} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            ${glsl.output} = vec4(0);\n          } else {\n            ${setup}\n\n            ${glsl.output} = vec4(${output});\n          }\n        }\n      `;\n  return {\n    ...packProgramMetadata,\n    hasMain: true,\n    output: {dims: input.dims, type: input.type, textureType: TextureType.packed},\n    shaderSource\n  };\n};\n\nexport const createPackProgramInfoLoader = (handler: WebGLInferenceHandler, input: Tensor): ProgramInfoLoader =>\n    ({...packProgramMetadata, get: () => createPackProgramInfo(handler, input)});\n\n/**\n * check output coordinate location and return false if it is outside input's width/height boundary\n */\nfunction getOutOfBoundsCondition(rank: number, shape: readonly number[], dims: string[]): string {\n  if (rank === 0) {\n    return 'false';\n  }\n  if (rank === 1) {\n    return `rc > ${shape[0]}`;\n  }\n\n  let cond = '';\n  for (let i = rank - 2; i < rank; i++) {\n    cond += `${dims[i]} >= ${shape[i - rank + 2]}`;\n    if (i < rank - 1) {\n      cond += '||';\n    }\n  }\n\n  return cond;\n}\n\n/**\n * code snippet to sample input texture with output coordinates\n */\nfunction getOutput(shape: readonly number[], dims: string[]): string {\n  const rank = shape.length;\n\n  if (rank === 0) {\n    return 'getA(), 0, 0, 0';\n  }\n\n  if (rank === 1) {\n    return `getA(rc),\n            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),\n            0, 0`;\n  }\n\n  const coord00 = 'r, c';\n  const coord01 = 'r, cp1';\n  const coord10 = 'rp1, c';\n  const coord11 = 'rp1, cp1';\n  let D = '';\n  if (rank > 2) {\n    for (let i = 0; i < rank - 2; ++i) {\n      D = D + `${dims[i]},`;\n    }\n  }\n  return `getA(${D}${coord00}),\n          rEdge ? 0. : getA(${D}${coord10}),\n          cEdge ? 0. : getA(${D}${coord01}),\n          rEdge || cEdge ? 0. : getA(${D}${coord11})`;\n}\n\n/**\n * code snippet to setup 4 coordinates and edge conditions\n */\nfunction getSetup(rank: number, dims: string[], rows: number, cols: number): string {\n  if (rank === 0 || rank === 1) {\n    return '';\n  }\n  // rank >= 2 for width+height pack.\n  else {\n    const setup = `\n    int r = ${dims[rank - 2]};\n    int c = ${dims[rank - 1]};\n    int rp1 = ${dims[rank - 2]} + 1;\n    int cp1 = ${dims[rank - 1]} + 1;\n    bool rEdge = rp1 >= ${cols};\n    bool cEdge = cp1 >= ${rows};\n    `;\n    return setup;\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from '../../../tensor';\nimport {ShapeUtil} from '../../../util';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';\n\nimport {unpackFromChannel} from './packing-utils';\n\nconst createPackedReshape3DProgramMetadata = (outputShape3D: readonly number[]) =>\n    ({name: 'Reshape (packed)', inputTypes: [TextureType.packed], inputNames: ['A'], cacheHint: `${outputShape3D}`});\n\nconst createPackedReshape3DProgramInfo =\n    (handler: WebGLInferenceHandler, input3D: Tensor, metadata: ProgramMetadata, outputShape3D: readonly number[]):\n        ProgramInfo => {\n          const inputShape3D = input3D.dims as [number, number, number];\n          const squeezedOutputShape = outputShape3D as [number, number, number];\n\n          let mainLoop = '';\n          for (let i = 0; i < 4; i++) {\n            let outputCoords = '';\n            switch (i) {\n              case 0:\n                outputCoords = 'outputCoords = rc;';\n                break;\n              case 1:\n                outputCoords = 'outputCoords = ivec3(rc.x, rc.y+1, rc.z);';\n                break;\n              case 2:\n                outputCoords = 'outputCoords = ivec3(rc.x, rc.y, rc.z+1);';\n                break;\n              case 3:\n                outputCoords = 'outputCoords = ivec3(rc.x, rc.y+1, rc.z+1);';\n                break;\n              default:\n                throw new Error();\n            }\n\n            mainLoop += `\n        ${outputCoords}\n        ${i > 0 ? 'if(outputCoords.y < rows && outputCoords.z < cols){' : ''}\n          int flattenedIndex = getFlattenedIndex(outputCoords);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flattenedIndex);\n          vec2 innerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), innerDims);\n\n        ${i > 0 ? '}' : ''}\n      `;\n          }\n          const glsl = getGlsl(handler.session.backend.glContext.version);\n\n          const shaderSource = `\n      ${getReshapedInputCoords(inputShape3D)}\n      ${getFlattenedIndexFrom3D(squeezedOutputShape)}\n      ${unpackFromChannel()}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.0);\n\n        ivec3 outputCoords;\n        int rows = ${squeezedOutputShape[2]};\n        int cols = ${squeezedOutputShape[1]};\n\n        ${mainLoop}\n        ${glsl.output} = result;\n      }\n    `;\n\n          return {\n            ...metadata,\n            output: {dims: squeezedOutputShape, type: input3D.type, textureType: TextureType.packed},\n            shaderSource,\n            hasMain: true\n          };\n        };\n\nexport const createPackedReshape3DProgramInfoLoader =\n    (handler: WebGLInferenceHandler, input3D: Tensor, outputShape3D: readonly number[]): ProgramInfoLoader => {\n      const metadata = createPackedReshape3DProgramMetadata(outputShape3D);\n      return {...metadata, get: () => createPackedReshape3DProgramInfo(handler, input3D, metadata, outputShape3D)};\n    };\n\nexport function processDims3D(shape: ArrayLike<number>): [number, number, number] {\n  if (shape.length === 0) {\n    return [1, 1, 1];\n  }\n  // TODO: squeeze other shapes to 2D case\n  let batch = 1;\n  for (let i = 0; i < shape.length - 2; ++i) {\n    batch *= shape[i];\n  }\n  return [batch, shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]];\n}\n\n// For packed reshape, we need to re-arrange texel data for output shape.\n// Our pack is designed to pack a 2x2 tile in last h and w dimension, so\n// for the reshaped new tensor, we just need to re-arrange the last h and\n// w dimension. For any shape that is not in 3D, i.e. [batch, W, H], we\n// first convert it to 3D by collapsing other dimension to batch dim, then\n// process with the last two dimensions.\n// Note: we only need the shape tensor to calculate output shape, so the\n// content in shape tensor is never uploaded to GPU. It is always kept in CPU.\n// TODO: optimize the algorithm -- in some cases, if the last two dims are\n// the same between input shape and output shape, the packed reshape can be\n// treated as no-op.\nexport function isReshapeCheap(dims: readonly number[], reshapedDims: readonly number[]) {\n  let isCheapReshape = false;\n  if (dims.length === 0 || reshapedDims.length === 0) {  // scalar\n    isCheapReshape = true;\n  } else if (dims.length < 2 || reshapedDims.length < 2) {  // 1D\n    isCheapReshape = dims[dims.length - 1] === reshapedDims[reshapedDims.length - 1];\n  } else {  // 2D +\n    isCheapReshape = dims[dims.length - 1] === reshapedDims[reshapedDims.length - 1] &&\n        dims[dims.length - 2] === reshapedDims[reshapedDims.length - 2];\n  }\n\n  return isCheapReshape;\n}\n\nfunction getReshapedInputCoords(shape: [number, number, number]): string {\n  const strides = ShapeUtil.computeStrides(shape);\n  const coords = ['b', 'r', 'c'];\n  const index = 'index';\n  const coordsFromIndexSnippet = 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\n  return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(b, r, c);\n    }\n  `;\n}\n\nfunction getFlattenedIndexFrom3D(shape: [number, number, number]): string {\n  const strides = ShapeUtil.computeStrides(shape);\n\n  return `\n  int getFlattenedIndex(ivec3 coords) {\n    // reverse y, z order\n    return coords.x * ${strides[0]} + coords.z * ${strides[1]} + coords.y;\n  }\n`;\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {TextureData, TextureType} from '../types';\n\nexport const encodeAsUint8 = (inferenceHandler: WebGLInferenceHandler, input: TextureData): TextureData => {\n  const outputShape = input.shape;\n  const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);\n  /**\n   * https://github.com/tensorflow/tfjs-core/blob/master/src/kernels/webgl/encode_float_gpu.ts\n   */\n  const shaderSource = `\n    const float FLOAT_MAX = 1.70141184e38;\n    const float FLOAT_MIN = 1.17549435e-38;\n\n    bool isNaN(float val) {\n      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\n    }\n\n    highp vec4 encodeAsUint8(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    void main() {\n      float value = ${glsl.texture2D}(X,TexCoords).r;\n      ${glsl.output} = encodeAsUint8(value);\n    }`;\n  const programInfo = {\n    name: 'Uint8Encode',\n    inputTypes: [TextureType.unpacked],\n    inputNames: ['X'],\n    output: {dims: outputShape, type: input.tensor.type, textureType: TextureType.downloadUint8AsFloat},\n    shaderSource,\n    hasMain: true\n  };\n  return inferenceHandler.executeProgram(programInfo, [input.tensor]);\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from '../../../tensor';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, TextureType} from '../types';\nimport {getCoordsDataType} from '../utils';\n\nimport {getChannels, unpackFromChannel} from './packing-utils';\n\nconst unpackProgramMetadata = {\n  name: 'unpack',\n  inputNames: ['A'],\n  inputTypes: [TextureType.packed]\n};\n\nexport const createUnpackProgramInfo = (handler: WebGLInferenceHandler, input: Tensor): ProgramInfo => {\n  const rank = input.dims.length;\n\n  const channels = getChannels('rc', rank);\n  const innerDims = channels.slice(-2);\n  const coordsDataType = getCoordsDataType(rank);\n  const unpackChannel = unpackFromChannel();\n  const isScalar = (input.dims.length === 0);\n  const sourceCoords = isScalar ? '' : getSourceCoords(rank, channels);\n  const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;\n  const glsl = getGlsl(handler.session.backend.glContext.version);\n  const shaderSource = `\n    ${unpackChannel}\n    void main() {\n      ${coordsDataType} rc = getOutputCoords();\n\n       // Sample the texture with the coords to get the rgba channel value.\n       vec4 packedInput = getA(${sourceCoords});\n\n       ${glsl.output} = vec4(getChannel(packedInput, ${coords}), 0, 0, 0);\n     }\n   `;\n\n  return {\n    ...unpackProgramMetadata,\n    hasMain: true,\n    output: {dims: input.dims, type: input.type, textureType: TextureType.unpacked},\n    shaderSource\n  };\n};\n\nexport const createUnpackProgramInfoLoader = (handler: WebGLInferenceHandler, input: Tensor): ProgramInfoLoader =>\n    ({...unpackProgramMetadata, get: () => createUnpackProgramInfo(handler, input)});\n\nfunction getSourceCoords(rank: number, dims: string[]): string {\n  if (rank === 1) {\n    return 'rc';\n  }\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","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Logger} from '../../instrument';\n\nexport declare namespace Encoder {\n  export interface DataTypeMap {\n    float: Float32Array;\n    byte: Uint8Array;\n    int: Uint32Array;\n  }\n  export type DataType = keyof DataTypeMap;\n  type DataArrayType = DataTypeMap[DataType];\n}\n\n/* eslint-disable @typescript-eslint/naming-convention */\nexport const enum EncoderUsage {\n  Default = 0,\n  UploadOnly,\n  Download4BytesAsFloat32,\n}\n/* eslint-enable @typescript-eslint/naming-convention */\n\n/**\n * Abstraction for mapping data types to texture texlets\n * Encoding means how a Float32 is mapped to 1 or 4 channels for each texlet\n * Decoding means how a texlet's channels are mapped to a resulting Float32\n */\nexport interface DataEncoder {\n  internalFormat: number;\n  format: number;\n  textureType: number;\n  channelSize: number;\n  encode(src: Encoder.DataArrayType, textureSize: number): Encoder.DataArrayType;\n  allocate(size: number): Encoder.DataArrayType;\n  decode(buffer: Encoder.DataArrayType, dataSize: number): Encoder.DataArrayType;\n}\n/**\n * WebGL2 data encoder\n * Uses R32F as the format for texlet\n */\nexport class RedFloat32DataEncoder implements DataEncoder {\n  internalFormat: number;\n  format: number;\n  textureType: number;\n  channelSize: number;\n  constructor(gl: WebGL2RenderingContext, channels = 1) {\n    if (channels === 1) {\n      this.internalFormat = gl.R32F;\n      this.format = gl.RED;\n      this.textureType = gl.FLOAT;\n      this.channelSize = channels;\n    } else if (channels === 4) {\n      this.internalFormat = gl.RGBA32F;\n      this.format = gl.RGBA;\n      this.textureType = gl.FLOAT;\n      this.channelSize = channels;\n    } else {\n      throw new Error(`Invalid number of channels: ${channels}`);\n    }\n  }\n  encode(src: Encoder.DataArrayType, textureSize: number): Encoder.DataArrayType {\n    let result: Float32Array;\n    let source: Float32Array;\n    if (src.constructor !== Float32Array) {\n      Logger.warning('Encoder', 'data was not of type Float32; creating new Float32Array');\n      source = new Float32Array(src);\n    }\n    if (textureSize * this.channelSize > src.length) {\n      Logger.warning('Encoder', 'Source data too small. Allocating larger array');\n      source = src as Float32Array;\n      result = this.allocate(textureSize * this.channelSize) as Float32Array;\n      source.forEach((v, i) => result[i] = v);\n    } else {\n      source = src as Float32Array;\n      result = source;\n    }\n    return result;\n  }\n  allocate(size: number): Encoder.DataArrayType {\n    return new Float32Array(size * 4);\n  }\n  decode(buffer: Encoder.DataArrayType, dataSize: number): Float32Array {\n    if (this.channelSize === 1) {\n      const filteredData = (buffer as Float32Array).filter((_value, index) => index % 4 === 0).subarray(0, dataSize);\n      return filteredData;\n    }\n    return buffer.subarray(0, dataSize) as Float32Array;\n  }\n}\n/**\n * Data encoder for WebGL 1 with support for floating point texture\n */\nexport class RGBAFloatDataEncoder implements DataEncoder {\n  internalFormat: number;\n  format: number;\n  textureType: number;\n  channelSize: number;\n  constructor(gl: WebGLRenderingContext, channels = 1, textureType?: number) {\n    if (channels !== 1 && channels !== 4) {\n      throw new Error(`Invalid number of channels: ${channels}`);\n    }\n    this.internalFormat = gl.RGBA;\n    this.format = gl.RGBA;\n    this.channelSize = channels;\n    this.textureType = textureType || gl.FLOAT;\n  }\n  encode(src: Float32Array, textureSize: number): Encoder.DataArrayType {\n    let dest = src;\n    if (this.channelSize === 1) {\n      Logger.verbose('Encoder', 'Exploding into a larger array');\n      dest = this.allocate(textureSize) as Float32Array;\n      src.forEach((v, i) => dest[i * 4] = v);\n    }\n    return dest;\n  }\n  allocate(size: number): Encoder.DataArrayType {\n    return new Float32Array(size * 4);\n  }\n  decode(buffer: Encoder.DataArrayType, dataSize: number): Float32Array {\n    if (this.channelSize === 1) {\n      const filteredData = (buffer as Float32Array).filter((_value, index) => index % 4 === 0).subarray(0, dataSize);\n      return filteredData;\n    }\n    return buffer.subarray(0, dataSize) as Float32Array;\n  }\n}\n\nexport class Uint8DataEncoder implements DataEncoder {\n  internalFormat: number;\n  format: number;\n  textureType: number;\n  channelSize = 4;\n  constructor(gl: WebGLRenderingContext, channels = 1) {\n    if (channels === 1) {\n      this.internalFormat = gl.ALPHA;\n      this.format = gl.ALPHA;  // not tested\n      this.textureType = gl.UNSIGNED_BYTE;\n      this.channelSize = channels;\n    } else if (channels === 4) {\n      this.internalFormat = gl.RGBA;\n      this.format = gl.RGBA;\n      this.textureType = gl.UNSIGNED_BYTE;\n      this.channelSize = channels;\n    } else {\n      throw new Error(`Invalid number of channels: ${channels}`);\n    }\n  }\n  encode(src: Uint8Array, _textureSize: number): Encoder.DataArrayType {\n    return new Uint8Array(src.buffer, src.byteOffset, src.byteLength);\n  }\n  allocate(size: number): Encoder.DataArrayType {\n    return new Uint8Array(size * this.channelSize);\n  }\n  decode(buffer: Encoder.DataArrayType, dataSize: number): Uint8Array {\n    if (buffer instanceof Uint8Array) {\n      return buffer.subarray(0, dataSize);\n    }\n    throw new Error(`Invalid array type: ${buffer.constructor}`);\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {ShapeUtil} from '../../util';\n\nimport {TextureLayoutStrategy, WidthHeightPrefs} from './texture-layout-strategy';\nimport {TextureLayout, TextureType} from './types';\n\nexport const createTextureLayoutFromTextureType =\n    (textureLayoutStrategy: TextureLayoutStrategy, shape: readonly number[],\n     textureType: TextureType): TextureLayout => {\n      const channel = (textureType === TextureType.unpacked || textureType === TextureType.unpackedReversed) ? 1 : 4;\n      const isPacked = textureType === TextureType.packed;\n      const reverseWH = (textureType === TextureType.unpackedReversed || textureType === TextureType.packed);\n      const breakAxis = textureType === TextureType.packedLastDimension ? shape.length - 1 : undefined;\n      const unpackedShape = textureType === TextureType.packedLastDimension ?\n          shape.map((d, i) => i === shape.length - 1 ? d * 4 : d) :\n          undefined;\n      return createTextureLayoutFromShape(\n          textureLayoutStrategy, shape, channel, unpackedShape, {isPacked, reverseWH, breakAxis});\n    };\n\nexport const calculateTextureWidthAndHeight =\n    (textureLayoutStrategy: TextureLayoutStrategy, shape: readonly number[], textureType: TextureType):\n        [number, number] => {\n          const layout = createTextureLayoutFromTextureType(textureLayoutStrategy, shape, textureType);\n          return [layout.width, layout.height];\n        };\n\n/**\n * Create a TextureLayout object from shape.\n */\nexport const createTextureLayoutFromShape =\n    (textureLayoutStrategy: TextureLayoutStrategy, shape: readonly number[], channels: 1|4 = 1,\n     unpackedShape?: readonly number[], prefs?: WidthHeightPrefs): TextureLayout => {\n      const isPacked = !!(prefs && prefs.isPacked);\n      const [width, height] = textureLayoutStrategy.computeTextureWH(isPacked ? unpackedShape || shape : shape, prefs);\n      const rank = shape.length;\n      let inferredDims = shape.slice(0);\n      if (rank === 0) {\n        inferredDims = [1];\n      }\n      if (channels === 1) {\n        // unpackedShape will take `shape` and not `inferredDims` so as to create a scalar Tensor if need be\n        unpackedShape = shape;\n      } else if (isPacked) {\n        if (channels !== 4) {\n          throw new Error('a packed texture must be 4-channel');\n        }\n        unpackedShape = shape;\n        if (rank > 0) {\n          inferredDims[rank - 1] = Math.ceil(inferredDims[rank - 1] / 2);\n        }\n        if (rank > 1) {\n          inferredDims[rank - 2] = Math.ceil(inferredDims[rank - 2] / 2);\n        }\n      } else if (!unpackedShape) {\n        throw new Error('Unpacked shape is needed when using channels > 1');\n      }\n      return {\n        width,\n        height,\n        channels,\n        isPacked,\n        shape: inferredDims,\n        strides: ShapeUtil.computeStrides(inferredDims),\n        unpackedShape,\n        reversedWH: (prefs && prefs.reverseWH)\n      };\n    };\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {InferenceHandler} from '../../backend';\nimport {Logger} from '../../instrument';\nimport {Tensor} from '../../tensor';\nimport {ShapeUtil} from '../../util';\n\nimport {createPackProgramInfoLoader} from './ops/pack';\nimport {createPackedReshape3DProgramInfoLoader, isReshapeCheap, processDims3D} from './ops/reshape-packed';\nimport {encodeAsUint8} from './ops/uint8-encode';\nimport {createUnpackProgramInfoLoader} from './ops/unpack';\nimport {WebGLSessionHandler} from './session-handler';\nimport {EncoderUsage} from './texture-data-encoder';\nimport {calculateTextureWidthAndHeight, createTextureLayoutFromShape, createTextureLayoutFromTextureType} from './texture-layout';\nimport {Artifact, ProgramInfo, ProgramInfoLoader, TextureData, TextureLayout, TextureType} from './types';\n\nconst getProgramInfoUniqueKey =\n    (programInfo: ProgramInfo|ProgramInfoLoader, inputTextureDatas: TextureData[]): string => {\n      const inputs =\n          inputTextureDatas.map(texture => `${texture.unpackedShape.join(',')};${texture.width}x${texture.height}`)\n              .join('_');\n      let key = programInfo.name;\n      if (programInfo.cacheHint) {\n        key += '[' + programInfo.cacheHint + ']';\n      }\n      key += ':' + inputs;\n      return key;\n    };\n\nexport class WebGLInferenceHandler implements InferenceHandler {\n  private packedTextureDataCache: Map<Tensor.Id, TextureData>;\n  private unpackedTextureDataCache: Map<Tensor.Id, TextureData>;\n  constructor(public session: WebGLSessionHandler) {\n    this.packedTextureDataCache = new Map();\n    this.unpackedTextureDataCache = new Map();\n  }\n\n  /**\n   * @returns [width, height]\n   */\n  calculateTextureWidthAndHeight(shape: readonly number[], textureType: TextureType): [number, number] {\n    return calculateTextureWidthAndHeight(this.session.layoutStrategy, shape, textureType);\n  }\n\n  executeProgram(program: ProgramInfo|ProgramInfoLoader, inputs: readonly Tensor[]): TextureData {\n    if (inputs.length < program.inputNames.length) {\n      throw new Error(`Input size mustn't be less than ${program.inputNames.length}.`);\n    }\n    if (program.inputNames.length !== program.inputTypes.length) {\n      throw new Error('input names size does not match input types');\n    }\n\n    // create texture info for input\n    const inputTextureDatas: TextureData[] = [];\n    for (let i = 0; i < program.inputNames.length; ++i) {\n      inputTextureDatas[i] = this.getOrCreateTextureData(inputs[i], program.inputTypes[i]);\n    }\n\n    const key = getProgramInfoUniqueKey(program, inputTextureDatas);\n    let artifact = this.session.programManager.getArtifact(key);\n    const programInfo = artifact ?\n        artifact.programInfo :\n        (typeof (program as ProgramInfoLoader).get === 'function' ? (program as ProgramInfoLoader).get() :\n                                                                    (program as ProgramInfo));\n\n    // create texture info for output\n    const outputTextureLayout = createTextureLayoutFromTextureType(\n        this.session.layoutStrategy, programInfo.output.dims, programInfo.output.textureType);\n    const outputTextureData = this.createTextureData(outputTextureLayout, programInfo.output.type);\n\n    if (!artifact) {\n      artifact = this.session.programManager.build(programInfo, inputTextureDatas, outputTextureData);\n      this.session.programManager.setArtifact(key, artifact);\n    }\n\n    this.runProgram(artifact, inputTextureDatas, outputTextureData);\n    return outputTextureData;\n  }\n\n  run(program: ProgramInfoLoader, inputs: readonly Tensor[]): Tensor {\n    const outputTextureData = this.executeProgram(program, inputs);\n    return outputTextureData.tensor;\n  }\n\n  private runProgram(artifact: Artifact, inputs: TextureData[], output: TextureData): void {\n    // input should match\n    for (let i = 0; i < inputs.length; ++i) {\n      if (!!inputs[i].isPacked !== (artifact.programInfo.inputTypes[i] === TextureType.packed)) {\n        throw new Error(`input[${i}] property packed inconsistent`);\n      }\n    }\n\n    // output should match\n    if (!!output.isPacked !== (artifact.programInfo.output.textureType === TextureType.packed)) {\n      throw new Error('output property packed inconsistent');\n    }\n\n    this.session.programManager.run(artifact, inputs, output);\n  }\n\n  /**\n   * Create a TextureData object from a tensor.\n   * Usage = EncoderUsage.UploadOnly.\n   * If a related texture data is found in cache, returns it;\n   * Otherwise:\n   *   Creates a new texture layout if not provided;\n   *   Creates WebGLTexture with the layout;\n   *   Upload tensor data to the texture;\n   *   Creates a texture data object associated with the given tensor.\n   * @param tensor the tensor with data to upload\n   */\n  private getOrCreateTextureData(tensor: Tensor, textureType: TextureType) {\n    let td = this.getTextureData(tensor.dataId, textureType === TextureType.packed);\n\n    if (!td) {\n      // check if we have texture data in different type\n      td = this.getTextureData(tensor.dataId, textureType !== TextureType.packed);\n      if (td) {\n        if (textureType === TextureType.packed) {\n          return this.pack(td);\n        } else {\n          return this.unpack(td);\n        }\n      }\n    }\n\n    if (!td) {\n      const layout = createTextureLayoutFromTextureType(this.session.layoutStrategy, tensor.dims, textureType);\n\n      if (textureType === TextureType.packedLastDimension) {\n        const group = 1;\n        const channels = 4;\n        const shape = tensor.dims;\n        if (shape.length === 4) {\n          // pre-processing for kernel data of Conv.\n          //\n          // TODO: currently this is a hacking to overwrite Conv's weight. The correct way to do this should be:\n          // 1. implement texture based const-folding\n          // 2. create a WebGL program \"preprocessConvWeight\" to do the same work as below\n          // 3. run the program before dotProduct.\n          //\n          const adjustedKernelShape = [shape[0], Math.ceil((shape[1] * shape[2] * shape[3]) / channels)];\n          const adjustedLayout =\n              createTextureLayoutFromTextureType(this.session.layoutStrategy, adjustedKernelShape, textureType);\n          let buffer = tensor.numberData;\n          if (shape[1] * shape[2] * shape[3] % channels !== 0) {\n            const numFeatureMaps = shape[0];\n            const oldRowSize = shape[1] * shape[2] * shape[3];\n            const newRowSize = Math.ceil(oldRowSize * group / channels) * channels;\n            const newSize = numFeatureMaps * newRowSize;\n            buffer = new Float32Array(newSize);\n            for (let f = 0; f < numFeatureMaps; ++f) {\n              const oldOffset = f * oldRowSize;\n              const newOffset = f * newRowSize + f % group * oldRowSize;\n              buffer.set(tensor.numberData.subarray(oldOffset, oldOffset + oldRowSize), newOffset);\n            }\n          }\n          return this.createTextureData(adjustedLayout, tensor.type, buffer, tensor, EncoderUsage.UploadOnly);\n        }\n      }\n\n      if (textureType === TextureType.packed) {\n        const unpackedTextureLayout =\n            createTextureLayoutFromShape(this.session.layoutStrategy, tensor.dims, 1, [], {reverseWH: true});\n        const unpackedTextureData = this.createTextureData(\n            unpackedTextureLayout, tensor.type, tensor.numberData, tensor, EncoderUsage.UploadOnly);\n        td = this.pack(unpackedTextureData);\n      } else {\n        td = this.createTextureData(layout, tensor.type, tensor.numberData, tensor, EncoderUsage.UploadOnly);\n      }\n    }\n    return td;\n  }\n\n  /**\n   * Create a TextureData object using the given data and bind to the given tensor.\n   * Usage = EncoderUsage.UploadOnly.\n   * NOTE: this function is a hack for Conv implementation. should remove this function, after rewriting Conv\n   * implementation by Graph.Transformer\n   * @param dataType the tensor data type\n   * @param data the actual data to upload\n   * @param tensor the tensor to bind. tensor's data is ignored.\n   */\n  createTextureDataFromLayoutBindTensor(\n      layout: TextureLayout, dataType: Tensor.DataType, data: Tensor.NumberType, tensor: Tensor): TextureData {\n    return this.createTextureData(layout, dataType, data, tensor, EncoderUsage.UploadOnly);\n  }\n\n  private createTextureData(\n      layout: TextureLayout, dataType: Tensor.DataType, data?: Tensor.NumberType, tensor?: Tensor,\n      usage?: EncoderUsage): TextureData {\n    Logger.verbose('InferenceHandler', `Creating TextureData: layout:[${JSON.stringify(layout)}]`);\n    const texture = this.session.textureManager.createTextureFromLayout(dataType, layout, data, usage);\n    return this.createTextureDataFromTexture(layout, dataType, texture, tensor);\n  }\n\n  reshapeUnpacked(input: Tensor, reshapedDims: readonly number[]): Tensor {\n    const inputTD = this.getOrCreateTextureData(input, TextureType.unpacked);\n    const newTextureLayout: TextureLayout = {\n      channels: inputTD.channels,\n      height: inputTD.height,\n      width: inputTD.width,\n      // handle reshaping into scalar Tensors\n      shape: reshapedDims.length !== 0 ? reshapedDims : [1],\n      strides: ShapeUtil.computeStrides(reshapedDims),\n      unpackedShape: reshapedDims,\n    };\n    const newTextureData = this.createTextureDataFromTexture(newTextureLayout, input.type, inputTD.texture);\n    return newTextureData.tensor;\n  }\n\n  reshapePacked(input: Tensor, reshapedDims: readonly number[]): Tensor {\n    const inputTD = this.getOrCreateTextureData(input, TextureType.packed);\n\n    // check if the reshape is 'cheap'\n    if (isReshapeCheap(input.dims, reshapedDims)) {\n      const newTextureLayout: TextureLayout = {\n        channels: inputTD.channels,\n        height: inputTD.height,\n        width: inputTD.width,\n        // handle reshaping into scalar Tensors\n        shape: reshapedDims.length !== 0 ? reshapedDims : [1],\n        strides: ShapeUtil.computeStrides(reshapedDims),\n        unpackedShape: reshapedDims,\n        isPacked: true\n      };\n      const newTextureData = this.createTextureDataFromTexture(newTextureLayout, input.type, inputTD.texture);\n      return newTextureData.tensor;\n    }\n\n    const squeezedInputShape = processDims3D(input.dims);\n    const squeezedOutputShape = processDims3D(reshapedDims);\n\n    const squeezedInputTensor = this.reshapePacked(input, squeezedInputShape);\n    const squeezedOutputTensor = this.run(\n        createPackedReshape3DProgramInfoLoader(this, squeezedInputTensor, squeezedOutputShape), [squeezedInputTensor]);\n    const outputTensor = this.reshapePacked(squeezedOutputTensor, reshapedDims);\n    return outputTensor;\n  }\n\n  cast(input: Tensor, type: Tensor.DataType): Tensor {\n    const inputTD = this.getOrCreateTextureData(input, TextureType.unpacked);\n    const newTextureData = this.createTextureDataFromTexture(inputTD as TextureLayout, type, inputTD.texture);\n    return newTextureData.tensor;\n  }\n\n  private createTextureDataFromTexture(\n      layout: TextureLayout, dataType: Tensor.DataType, texture: WebGLTexture, tensor?: Tensor, tensorId?: Tensor.Id) {\n    const textureData: TextureData = {\n      ...layout,\n      tensor: tensor ||\n          new Tensor(\n                  layout.unpackedShape, dataType, (_id: Tensor.Id) => this.readTexture(textureData),\n                  async (_id: Tensor.Id) => this.readTextureAsync(textureData), undefined, tensorId),\n      texture\n    };\n    this.setTextureData(textureData.tensor.dataId, textureData, layout.isPacked);\n    return textureData;\n  }\n\n  private getTextureData(tensorId: Tensor.Id, isPacked = false): TextureData|undefined {\n    return this.session.isInitializer(tensorId) ? this.session.getTextureData(tensorId, isPacked) :\n        isPacked                                ? this.packedTextureDataCache.get(tensorId) :\n                                                  this.unpackedTextureDataCache.get(tensorId);\n  }\n  setTextureData(tensorId: Tensor.Id, td: TextureData, isPacked = false): void {\n    if (this.session.isInitializer(tensorId)) {\n      this.session.setTextureData(tensorId, td, isPacked);\n    } else {\n      (isPacked ? this.packedTextureDataCache : this.unpackedTextureDataCache).set(tensorId, td);\n    }\n  }\n  isTextureLayoutCached(tensor: Tensor, isPacked = false): boolean {\n    return !!this.getTextureData(tensor.dataId, isPacked);\n  }\n\n  dispose(): void {\n    this.session.textureManager.clearActiveTextures();\n    this.packedTextureDataCache.forEach(td => this.session.textureManager.releaseTexture(td));\n    this.packedTextureDataCache = new Map();\n    this.unpackedTextureDataCache.forEach(td => this.session.textureManager.releaseTexture(td));\n    this.unpackedTextureDataCache = new Map();\n  }\n\n  readTexture(textureData: TextureData): Tensor.NumberType {\n    if (textureData.isPacked) {\n      return this.readTexture(this.unpack(textureData));\n    }\n    if (!this.session.backend.glContext.isFloat32DownloadSupported) {\n      return this.session.textureManager.readUint8TextureAsFloat(encodeAsUint8(this, textureData));\n    }\n    return this.session.textureManager.readTexture(textureData, textureData.tensor.type, textureData.channels);\n  }\n\n  async readTextureAsync(textureData: TextureData): Promise<Tensor.NumberType> {\n    if (textureData.isPacked) {\n      return this.readTextureAsync(this.unpack(textureData));\n    }\n    if (!this.session.backend.glContext.isFloat32DownloadSupported) {\n      return this.session.textureManager.readUint8TextureAsFloat(encodeAsUint8(this, textureData));\n    }\n    return this.session.textureManager.readTextureAsync(textureData, textureData.tensor.type, textureData.channels);\n  }\n\n  pack(input: TextureData): TextureData {\n    const outputTextureData = this.executeProgram(createPackProgramInfoLoader(this, input.tensor), [input.tensor]);\n    return outputTextureData;\n  }\n\n  unpack(input: TextureData): TextureData {\n    const outputTextureData = this.executeProgram(createUnpackProgramInfoLoader(this, input.tensor), [input.tensor]);\n    return outputTextureData;\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nclass AttributeWithCacheKeyImpl {\n  constructor(attribute: Record<string, unknown>) {\n    Object.assign(this, attribute);\n  }\n\n  private key: string;\n  public get cacheKey(): string {\n    if (!this.key) {\n      this.key =\n          Object.getOwnPropertyNames(this).sort().map(name => `${(this as Record<string, unknown>)[name]}`).join(';');\n    }\n    return this.key;\n  }\n}\n\nexport interface AttributeWithCacheKey {\n  readonly cacheKey: string;\n}\n\nexport const createAttributeWithCacheKey = <T extends Record<string, unknown>>(attribute: T): T&AttributeWithCacheKey =>\n    new AttributeWithCacheKeyImpl(attribute) as unknown as T & AttributeWithCacheKey;\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, TextureType} from '../types';\n\nexport interface BatchNormalizationAttributes extends AttributeWithCacheKey {\n  epsilon: number;\n  momentum: number;\n  spatial: number;\n}\n\nconst batchNormalizationProgramMetadata = {\n  name: 'BatchNormalization',\n  inputNames: ['A', 'Scale', 'B', 'Mean', 'Variance'],\n  inputTypes:\n      [TextureType.unpacked, TextureType.unpacked, TextureType.unpacked, TextureType.unpacked, TextureType.unpacked]\n};\n\nexport const batchNormalization: OperatorImplementation<BatchNormalizationAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: BatchNormalizationAttributes): Tensor[] => {\n      validateInputs(inputs);\n      const output = inferenceHandler.run(\n          {\n            ...batchNormalizationProgramMetadata,\n            cacheHint: attributes.cacheKey,\n            get: () => createBatchNormalizationProgramInfo(inferenceHandler, inputs, attributes)\n          },\n          inputs);\n      return [output];\n    };\n\nexport const parseBatchNormalizationAttributes: OperatorInitialization<BatchNormalizationAttributes> =\n    (node: Graph.Node): BatchNormalizationAttributes => {\n      const epsilon = node.attributes.getFloat('epsilon', 1e-5);\n      const momentum = node.attributes.getFloat('momentum', 0.9);\n      const spatial = node.attributes.getInt('spatial', 1);\n      return createAttributeWithCacheKey({epsilon, momentum, spatial});\n    };\n\nconst createBatchNormalizationProgramInfo =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: BatchNormalizationAttributes):\n        ProgramInfo => {\n          const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);\n          const rank = inputs[0].dims.length;\n          const [scaleWidth, scaleHeight] =\n              inferenceHandler.calculateTextureWidthAndHeight(inputs[1].dims, TextureType.unpacked);\n          const shaderSource = `\n  float process(int[${rank}] indices) {\n    vec2 position = offsetToCoords(indices[1], ${scaleWidth}, ${scaleHeight});\n    float scale = getColorAsFloat(${glsl.texture2D}(Scale, position));\n    float mean = getColorAsFloat(${glsl.texture2D}(Mean, position));\n    float variance = getColorAsFloat(${glsl.texture2D}(Variance, position));\n    float b = getColorAsFloat(${glsl.texture2D}(B, position));\n\n    return scale * ( (_A(indices) - mean) / sqrt(variance + float(${attributes.epsilon})) ) + b;\n  }`;\n          return {\n            ...batchNormalizationProgramMetadata,\n            output: {dims: inputs[0].dims, type: inputs[0].type, textureType: TextureType.unpacked},\n            shaderSource\n          };\n        };\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 5) {\n    throw new Error('BatchNormalization requires 5 inputs.');\n  }\n\n  const X = inputs[0];\n  const scale = inputs[1];\n  const B = inputs[2];\n  const mean = inputs[3];\n  const var_ = inputs[4];\n\n  // input should atleast have three dimensions - N,C,dim1,...,dimn\n  // other inputs can have only one dimensions\n  if (X.dims.length < 3 || scale.dims.length !== 1 || B.dims.length !== 1 || mean.dims.length !== 1 ||\n      var_.dims.length !== 1) {\n    throw new Error('invalid input shape.');\n  }\n  if (scale.dims[0] !== X.dims[1] || B.dims[0] !== X.dims[1] || mean.dims[0] !== X.dims[1] ||\n      var_.dims[0] !== X.dims[1]) {\n    throw new Error('invalid input shape.');\n  }\n  if ((X.type !== 'float32' && X.type !== 'float64') || (scale.type !== 'float32' && scale.type !== 'float64') ||\n      (B.type !== 'float32' && B.type !== 'float64') || (mean.type !== 'float32' && mean.type !== 'float64') ||\n      (var_.type !== 'float32' && var_.type !== 'float64')) {\n    throw new Error('invalid input tensor types.');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {ProgramInfo, TextureLayout} from './types';\nimport {WebGLContext} from './webgl-context';\n\n/* eslint-disable @typescript-eslint/naming-convention */\nexport enum FunctionType {\n  ValueBased,\n  Positional\n}\nexport interface GlslFunction<T extends FunctionType> {\n  body: string;\n  name: string;\n  type: T;\n}\nexport type GlslValueFunction = GlslFunction<FunctionType.ValueBased>;\nexport interface GlslPositionalFunction extends GlslFunction<FunctionType.Positional> {\n  inputShape: readonly number[];\n  outputShape: readonly number[];\n}\n\nexport class GlslContext {\n  constructor(\n      public glContext: WebGLContext, public programInfo: ProgramInfo, public inputTextureLayouts: TextureLayout[],\n      public outputTextureLayout: TextureLayout) {}\n}\nexport abstract class GlslLib {\n  constructor(public context: GlslContext) {}\n  abstract getFunctions(): {[name: string]: GlslLibRoutine};\n  abstract getCustomTypes(): {[name: string]: string};\n}\n\n// abstraction to represent a GLSL library routine and it's dependencies\nexport class GlslLibRoutine {\n  constructor(public routineBody: string, public dependencies?: string[]) {}\n}\n\n// abstraction to represent a GLSL library routine and it's dependencies AS GRAPH Nodes\n// this level of abstraction is used to topologically sort routines before fragment shade inclusion\nexport class GlslLibRoutineNode {\n  dependencies: GlslLibRoutineNode[];\n  routineBody: string;\n  constructor(public name: string, routineBody?: string, dependencies?: GlslLibRoutineNode[]) {\n    if (dependencies) {\n      this.dependencies = dependencies;\n    } else {\n      this.dependencies = [];\n    }\n\n    if (routineBody) {\n      this.routineBody = routineBody;\n    }\n  }\n  addDependency(node: GlslLibRoutineNode) {\n    if (node) {\n      this.dependencies.push(node);\n    }\n  }\n}\n\n// topologically sort GLSL library routines (graph nodes abstraction) before shader script inclusion\nexport class TopologicalSortGlslRoutines {\n  static returnOrderedNodes(nodes: GlslLibRoutineNode[]): GlslLibRoutineNode[] {\n    if (!nodes || nodes.length === 0) {\n      return [];\n    }\n\n    if (nodes.length === 1) {\n      return nodes;\n    }\n\n    const cycleCheck = new Set<string>();\n    const alreadyTraversed = new Set<string>();\n    const result = new Array<GlslLibRoutineNode>();\n\n    this.createOrderedNodes(nodes, cycleCheck, alreadyTraversed, result);\n    return result;\n  }\n\n  private static createOrderedNodes(\n      graphNodes: GlslLibRoutineNode[], cycleCheck: Set<string>, alreadyTraversed: Set<string>,\n      result: GlslLibRoutineNode[]) {\n    for (let i = 0; i < graphNodes.length; ++i) {\n      this.dfsTraverse(graphNodes[i], cycleCheck, alreadyTraversed, result);\n    }\n  }\n\n  private static dfsTraverse(\n      root: GlslLibRoutineNode, cycleCheck: Set<string>, alreadyTraversed: Set<string>, result: GlslLibRoutineNode[]) {\n    // if this root has already been traversed return\n    if (!root || alreadyTraversed.has(root.name)) {\n      return;\n    }\n\n    // cyclic dependency has been detected\n    if (cycleCheck.has(root.name)) {\n      throw new Error('Cyclic dependency detected. Can\\'t topologically sort routines needed for shader.');\n    }\n\n    // hold this node to detect cycles if any\n    cycleCheck.add(root.name);\n\n    // traverse children in a dfs fashion\n    const dependencies = root.dependencies;\n    if (dependencies && dependencies.length > 0) {\n      for (let i = 0; i < dependencies.length; ++i) {\n        this.dfsTraverse(dependencies[i], cycleCheck, alreadyTraversed, result);\n      }\n    }\n\n    // add to result holder\n    result.push(root);\n\n    // mark this node as traversed so that we don't traverse from this again\n    alreadyTraversed.add(root.name);\n\n    // release the hold\n    cycleCheck.delete(root.name);\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from '../../../tensor';\nimport {BroadcastUtil, ShapeUtil} from '../../../util';\nimport {FunctionType, GlslValueFunction} from '../glsl-definitions';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, TextureType} from '../types';\n\nexport function glslAdd(): GlslValueFunction {\n  const name = 'add_';\n  const body = `\n  float ${name}(float a, float b) {\n    return a + b;\n  }\n  vec4 ${name}(vec4 v1, vec4 v2) {\n    return v1 + v2;\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslDiv(): GlslValueFunction {\n  const name = 'div_';\n  const body = `\n  float ${name}(float a, float b) {\n    return a / b;\n  }\n  vec4 ${name}(vec4 v1, vec4 v2) {\n    return v1 / v2;\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslMul(): GlslValueFunction {\n  const name = 'mul_';\n  const body = `\n  float ${name}(float a, float b) {\n    return a * b;\n  }\n  vec4 ${name}(vec4 v1, vec4 v2) {\n    return v1 * v2;\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslSub(): GlslValueFunction {\n  const name = 'sub_';\n  const body = `\n  float ${name}(float a, float b) {\n    return a - b;\n  }\n  vec4 ${name}(vec4 v1, vec4 v2) {\n    return v1 - v2;\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslEqual(): GlslValueFunction {\n  const name = 'equal_';\n  const body = `\n  float ${name}(float a, float b) {\n    return float(a == b);\n  }\n  vec4 ${name}(vec4 v1, vec4 v2) {\n    return vec4(equal(v1, v2));\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslGreater(): GlslValueFunction {\n  const name = 'greater_';\n  const body = `\n  float ${name}(float a, float b) {\n    return float(a > b);\n  }\n  vec4 ${name}(vec4 v1, vec4 v2) {\n    return vec4( v1.r > v2.r ,\n      v1.g > v2.g,\n      v1.b > v2.b,\n      v1.a > v2.a );\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslLess(): GlslValueFunction {\n  const name = 'less_';\n  const body = `\n  float ${name}(float a, float b) {\n    return float(a < b);\n  }\n  vec4 ${name}(vec4 v1, vec4 v2) {\n    return vec4( v1.r < v2.r ,\n                v1.g < v2.g,\n                v1.b < v2.b,\n                v1.a < v2.a );\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslAnd(): GlslValueFunction {\n  const name = 'and_';\n  const body = `\n  float ${name}(float a, float b) {\n    return float( bool(a) && bool(b) );\n  }\n  vec4 ${name}(vec4 v1, vec4 v2) {\n    bvec4 b1 = bvec4(v1);\n    bvec4 b2 = bvec4(v2);\n    return vec4( b1.r && b2.r ,\n                b1.g && b2.g,\n                b1.b && b2.b,\n                b1.a && b2.a );\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslOr(): GlslValueFunction {\n  const name = 'or_';\n  const body = `\n  float ${name}(float a, float b) {\n    return float( bool(a) || bool(b) );\n  }\n  vec4 ${name}(vec4 v1, vec4 v2) {\n    bvec4 b1 = bvec4(v1);\n    bvec4 b2 = bvec4(v2);\n    return vec4( b1.r || b2.r ,\n                b1.g || b2.g,\n                b1.b || b2.b,\n                b1.a || b2.a );\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslXor(): GlslValueFunction {\n  const name = 'xor_';\n  const body = `\n  float ${name}(float a, float b) {\n    return float( bool(a) ^^ bool(b) );\n  }\n  vec4 ${name}(vec4 v1, vec4 v2) {\n    bvec4 b1 = bvec4(v1);\n    bvec4 b2 = bvec4(v2);\n    return vec4( b1.r ^^ b2.r ,\n                b1.g ^^ b2.g,\n                b1.b ^^ b2.b,\n                b1.a ^^ b2.a );\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslPow(): GlslValueFunction {\n  return glslBuiltinBinary('pow');\n}\nexport function glslPRelu(): GlslValueFunction {\n  const name = 'prelu_';\n  const body = `\n  float ${name}(float a, float b) {\n    return a < 0.0 ? a * b: a;\n  }\n  vec4 ${name}(vec4 v1, vec4 v2) {\n    return vec4(\n      v1.r < 0.0 ? v1.r * v2.r: v1.r,\n      v1.g < 0.0 ? v1.g * v2.g: v1.g,\n      v1.b < 0.0 ? v1.b * v2.b: v1.b,\n      v1.a < 0.0 ? v1.a * v2.a: v1.a\n      );\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\n\nfunction glslBuiltinBinary(fname: string): GlslValueFunction {\n  const name = `${fname}_`;\n  const body = `\n  float ${name}(float a, float b) {\n    return ${fname}(a, b);\n  }\n  vec4 ${name}(vec4 v1, vec4 v2) {\n    return ${fname}(v1, v2);\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\n\nconst createBinaryProgramInfoLoader =\n    (handler: WebGLInferenceHandler, inputs: Tensor[], glslFunc: GlslValueFunction,\n     outputTensorType: Tensor.DataType = inputs[0].type, cacheKey?: string): ProgramInfoLoader => {\n      const textureType = handler.session.pack ? TextureType.packed : TextureType.unpacked;\n      return {\n        name: glslFunc.name,\n        inputNames: ['A', 'B'],\n        inputTypes: [textureType, textureType],\n        cacheHint: cacheKey,\n        get: () => createBinaryProgramInfo(handler, inputs, glslFunc, outputTensorType)\n      };\n    };\n\nconst createBinaryProgramInfo =\n    (handler: WebGLInferenceHandler, inputs: Tensor[], glslFunc: GlslValueFunction,\n     outputTensorType: Tensor.DataType = inputs[0].type): ProgramInfo => {\n      const textureType = handler.session.pack ? TextureType.packed : TextureType.unpacked;\n      const isBroadcast = !ShapeUtil.areEqual(inputs[0].dims, inputs[1].dims);\n      let outputShape = inputs[0].dims;\n\n      const usePackedTexture = handler.session.pack;\n\n      if (isBroadcast) {\n        const calculatedShape = BroadcastUtil.calcShape(inputs[0].dims, inputs[1].dims, false);\n        if (!calculatedShape) {\n          throw new Error('Can\\'t perform binary op on the given tensors');\n        }\n        outputShape = calculatedShape;\n        const outputRank = outputShape.length;\n        const aRank = inputs[0].dims.length !== 0 ? inputs[0].dims.length : 1;\n        const bRank = inputs[1].dims.length !== 0 ? inputs[1].dims.length : 1;\n        const aBcast = inputs[0].dims.length !== 0 ? 'bcastIndices_A(indices, aindices);' : 'aindices[0] = 0;';\n        const bBcast = inputs[1].dims.length !== 0 ? 'bcastIndices_B(indices, bindices);' : 'bindices[0] = 0;';\n\n        const glsl = getGlsl(handler.session.backend.glContext.version);\n        const shaderSource = usePackedTexture ? `\n      ${glslFunc.body}\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n        vec4 result = ${glslFunc.name}(a, b);\n        ${glsl.output} = result;\n      }` :\n                                                `\n      ${glslFunc.body}\n      float process(int indices[${outputRank}]) {\n        int aindices[${aRank}];\n        int bindices[${bRank}];\n        ${aBcast}\n        ${bBcast}\n        return ${glslFunc.name}(_A(aindices), _B(bindices));\n      }`;\n\n        return {\n          name: glslFunc.name,\n          inputNames: ['A', 'B'],\n          inputTypes: [textureType, textureType],\n          output: {dims: outputShape, type: outputTensorType, textureType},\n          shaderSource,\n          hasMain: usePackedTexture\n        };\n      }\n      const glsl = getGlsl(handler.session.backend.glContext.version);\n      const shaderSource = `\n    ${glslFunc.body}\n    void main() {\n      vec4 v1 = ${glsl.texture2D}(A, TexCoords);\n      vec4 v2 = ${glsl.texture2D}(B, TexCoords);\n      vec4 result = ${glslFunc.name}(v1, v2);\n      ${glsl.output} = result;\n    }\n    `;\n\n      return {\n        name: glslFunc.name,\n        inputNames: ['A', 'B'],\n        inputTypes: [textureType, textureType],\n        output: {dims: inputs[0].dims, type: outputTensorType, textureType},\n        shaderSource,\n        hasMain: true\n      };\n    };\n\nexport const add = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createBinaryProgramInfoLoader(handler, inputs, glslAdd()), inputs)];\n\nexport const and = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createBinaryProgramInfoLoader(handler, inputs, glslAnd(), 'bool'), inputs)];\n\nexport const div = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createBinaryProgramInfoLoader(handler, inputs, glslDiv()), inputs)];\n\nexport const equal = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createBinaryProgramInfoLoader(handler, inputs, glslEqual(), 'bool'), inputs)];\n\nexport const greater = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createBinaryProgramInfoLoader(handler, inputs, glslGreater(), 'bool'), inputs)];\n\nexport const less = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createBinaryProgramInfoLoader(handler, inputs, glslLess(), 'bool'), inputs)];\n\nexport const mul = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createBinaryProgramInfoLoader(handler, inputs, glslMul()), inputs)];\n\nexport const or = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createBinaryProgramInfoLoader(handler, inputs, glslOr(), 'bool'), inputs)];\n\nexport const pow = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createBinaryProgramInfoLoader(handler, inputs, glslPow()), inputs)];\n\nexport const pRelu = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createBinaryProgramInfoLoader(handler, inputs, glslPRelu()), inputs)];\n\nexport const sub = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createBinaryProgramInfoLoader(handler, inputs, glslSub()), inputs)];\n\nexport const xor = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createBinaryProgramInfoLoader(handler, inputs, glslXor(), 'bool'), inputs)];\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {ProtoUtil} from '../../../util';\nimport {WebGLInferenceHandler} from '../inference-handler';\n\nexport const cast: OperatorImplementation<Tensor.DataType> =\n    (handler: WebGLInferenceHandler, inputs: Tensor[], to: Tensor.DataType): Tensor[] => {\n      validateInputs(inputs);\n      return [handler.cast(inputs[0], to)];\n    };\n\nexport const parseCastAttributes: OperatorInitialization<Tensor.DataType> = (node: Graph.Node): Tensor.DataType =>\n    ProtoUtil.tensorDataTypeFromProto(node.attributes.getInt('to'));\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 1) {\n    throw new Error('Cast requires 1 input.');\n  }\n\n  if (inputs[0].type === 'string') {\n    throw new Error('Invalid input type.');\n  }\n};","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from '../../../tensor';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';\nimport {getCoordsDataType, getGlChannels} from '../utils';\n\nimport {ConcatAttributes} from './concat';\nimport {getChannels, unpackFromChannel} from './packing-utils';\n\nconst createPackedConcatProgramMetadata = (inputCount: number, cacheHint: string) => ({\n  name: 'Concat (packed)',\n  inputNames: Array.from({length: inputCount}, (_v, i) => `X${i}`),\n  inputTypes: Array(inputCount).fill(TextureType.packed),\n  cacheHint\n});\n\nconst createPackedConcatProgramInfo =\n    (handler: WebGLInferenceHandler, metadata: ProgramMetadata, inputs: Tensor[], axis: number): ProgramInfo => {\n      const inputShape = inputs[0].dims.slice();\n      if (axis >= inputShape.length || axis < (-1 * inputShape.length)) {\n        throw new Error('axis specified for concat doesn\\'t match input dimensionality');\n      }\n      if (axis < 0) {\n        axis = inputShape.length + axis;\n      }\n      // ensure all of the non-concatenated axes match each other\n      // calculate the shape of the output tensor while we do that\n      const outputShape = inputShape.slice(0);\n      for (let i = 1; i < inputs.length; i++) {\n        const dataNShape = inputs[i].dims.slice();\n        for (let axisIndex = 0; axisIndex < inputShape.length; axisIndex++) {\n          // add to the placeholder for computing output shape\n          if (axisIndex === axis) {\n            outputShape[axis] += dataNShape[axisIndex];\n          }\n          // ensure all non-cancatenated axes match each other\n          else if (inputShape[axisIndex] !== dataNShape[axisIndex]) {\n            throw new Error('non concat dimensions must match');\n          }\n        }\n      }\n\n      const rank = outputShape.length;\n      const coords = getChannels('coords', rank);\n      const dtype = getCoordsDataType(rank);\n      const unpackChannel = unpackFromChannel();\n\n      const shapes = inputs.map(i => i.dims);\n      const channels = getGlChannels(rank);\n      const offsets: number[] = new Array(shapes.length - 1);\n\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\n      const channel = channels[axis];\n      const lastChannels = channels.slice(-2);\n      const allChannels = channels.join();\n\n      let getValueSnippet = `if (${channel} < ${offsets[0]}) {\n        return getChannel(\n            getX0(${allChannels}), vec2(${lastChannels.join()}));\n        }`;\n      for (let i = 1; i < offsets.length; i++) {\n        const shift = offsets[i - 1];\n        getValueSnippet += `\n            if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {\n              return getChannel(\n                getX${i}(${getShiftedChannelsSnippet(channels, channel, shift)}),\n                vec2(${getShiftedChannelsSnippet(lastChannels, channel, shift)}));\n            }`;\n      }\n      const lastIndex = offsets.length;\n      const shift = offsets[offsets.length - 1];\n      getValueSnippet += `\n            return getChannel(\n              getX${lastIndex}(${getShiftedChannelsSnippet(channels, channel, shift)}),\n              vec2(${getShiftedChannelsSnippet(lastChannels, channel, shift)}));`;\n\n      const glsl = getGlsl(handler.session.backend.glContext.version);\n\n      const shaderSource = `\n          ${unpackChannel}\n          float getValue(${channels.map(x => 'int ' + x)}) {\n            ${getValueSnippet}\n          }\n\n          void main() {\n            ${dtype} coords = getOutputCoords();\n            int lastDim = coords.${channels[rank - 1]};\n            coords.${channels[rank - 1]} = coords.${channels[rank - 2]};\n            coords.${channels[rank - 2]} = lastDim;\n\n            vec4 result = vec4(getValue(${coords}), 0., 0., 0.);\n\n            ${coords[rank - 1]} = ${coords[rank - 1]} + 1;\n            if (${coords[rank - 1]} < ${outputShape[rank - 1]}) {\n              result.g = getValue(${coords});\n            }\n\n            ${coords[rank - 2]} = ${coords[rank - 2]} + 1;\n            if (${coords[rank - 2]} < ${outputShape[rank - 2]}) {\n              result.a = getValue(${coords});\n            }\n\n            ${coords[rank - 1]} = ${coords[rank - 1]} - 1;\n            if (${coords[rank - 2]} < ${outputShape[rank - 2]} &&\n                ${coords[rank - 1]} < ${outputShape[rank - 1]}) {\n              result.b = getValue(${coords});\n            }\n            ${glsl.output} = result;\n          }\n        `;\n\n      return {\n        ...metadata,\n        output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.packed},\n        shaderSource,\n        hasMain: true,\n      };\n    };\n\nexport const createPackedConcatProgramInfoLoader =\n    (handler: WebGLInferenceHandler, inputs: Tensor[], attributes: ConcatAttributes): ProgramInfoLoader => {\n      const metadata = createPackedConcatProgramMetadata(inputs.length, attributes.cacheKey);\n      return {...metadata, get: () => createPackedConcatProgramInfo(handler, metadata, inputs, attributes.axis)};\n    };\n\nconst getShiftedChannelsSnippet = (channels: string[], channel: string, shift: number): string => {\n  const channelIdx = channels.indexOf(channel);\n  const res = channels.map((c, idx) => {\n    if (idx === channelIdx) {\n      return `${c} - ${shift}`;\n    } else {\n      return c;\n    }\n  });\n  return res.join();\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';\n\nimport {createPackedConcatProgramInfoLoader} from './concat-packed';\n\nexport interface ConcatAttributes extends AttributeWithCacheKey {\n  readonly axis: number;\n}\n\nexport const concat: OperatorImplementation<ConcatAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: ConcatAttributes): Tensor[] => {\n      validateInputs(inputs);\n      if (inferenceHandler.session.pack && inputs[0].dims.length > 1) {\n        const output =\n            inferenceHandler.run(createPackedConcatProgramInfoLoader(inferenceHandler, inputs, attributes), inputs);\n        return [output];\n      } else {\n        const output =\n            inferenceHandler.run(createUnpackedConcatProgramInfoLoader(inferenceHandler, inputs, attributes), inputs);\n        return [output];\n      }\n    };\n\nconst createUnpackedConcatProgramMetadata = (inputCount: number, cacheHint: string) => ({\n  name: 'Concat',\n  inputNames: Array.from({length: inputCount}, (_v, i) => `X${i}`),\n  inputTypes: Array(inputCount).fill(TextureType.unpacked),\n  cacheHint\n});\n\nconst createUnpackedConcatProgramInfo =\n    (_handler: WebGLInferenceHandler, metadata: ProgramMetadata, inputs: Tensor[], axis: number): ProgramInfo => {\n      const inputShape = inputs[0].dims.slice();\n      if (axis >= inputShape.length || axis < (-1 * inputShape.length)) {\n        throw new Error('axis specified for concat doesn\\'t match input dimensionality');\n      }\n      if (axis < 0) {\n        axis = inputShape.length + axis;\n      }\n      // ensure all of the non-concatenated axes match each other\n      // calculate the shape of the output tensor while we do that\n      const outputShape = inputShape.slice(0);\n      for (let i = 1; i < inputs.length; i++) {\n        const dataNShape = inputs[i].dims.slice();\n        for (let axisIndex = 0; axisIndex < inputShape.length; axisIndex++) {\n          // add to the placeholder for computing output shape\n          if (axisIndex === axis) {\n            outputShape[axis] += dataNShape[axisIndex];\n          }\n          // ensure all non-cancatenated axes match each other\n          else if (inputShape[axisIndex] !== dataNShape[axisIndex]) {\n            throw new Error('non concat dimensions must match');\n          }\n        }\n      }\n\n      const rank = outputShape.length;\n\n      const sizeInConcatAxis = new Array<number>(inputs.length);\n      let previousSum = 0;\n      for (let i = 0; i < sizeInConcatAxis.length; ++i) {\n        previousSum += inputs[i].dims[axis];\n        sizeInConcatAxis[i] = previousSum;\n      }\n\n      let getTextureIndexWhereDataResidesMethod = '';\n      // in most cases linear search is sufficient, as in most scenarios, only 2 tensors are concatenated\n      if (inputs.length < 5) {\n        getTextureIndexWhereDataResidesMethod = getTextureIndexWhereDataResidesLinearSearch(sizeInConcatAxis);\n      } else {\n        getTextureIndexWhereDataResidesMethod = getTextureIndexWhereDataResidesBinarySearch(sizeInConcatAxis);\n      }\n\n      const fetchDataFromCorrectTextureMethod = getFetchDataFromCorrectTextureMethod(inputs.length, rank);\n      const getSizeInConcatAxisValueFromIndexMethod = getGetSizeInConcatAxisValueFromIndexMethod(sizeInConcatAxis);\n      const shaderSource = `\n        ${fetchDataFromCorrectTextureMethod}\n        ${getSizeInConcatAxisValueFromIndexMethod}\n        ${getTextureIndexWhereDataResidesMethod}\n        float process(int indices[${rank}]) {\n          int textureIndex = getTextureWhereDataResides (indices[${axis}]);\n\n          if(textureIndex != 0) {\n            indices[${axis}] = indices[${axis}] - int(getSizeInConcatAxisValueFromIndex(textureIndex-int(1)));\n          }\n\n          return fetchDataFromCorrectTexture(textureIndex, indices);\n        }`;\n      return {\n        ...metadata,\n        output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},\n        shaderSource,\n      };\n    };\n\nconst createUnpackedConcatProgramInfoLoader =\n    (handler: WebGLInferenceHandler, inputs: Tensor[], attributes: ConcatAttributes): ProgramInfoLoader => {\n      const metadata = createUnpackedConcatProgramMetadata(inputs.length, attributes.cacheKey);\n      return {...metadata, get: () => createUnpackedConcatProgramInfo(handler, metadata, inputs, attributes.axis)};\n    };\n\nconst getTextureIndexWhereDataResidesLinearSearch = (sizeInConcatAxis: number[]): string => {\n  const searchAxis = sizeInConcatAxis.map((size, i) => `if(index<${size}) {return ${i};}\n`);\n  return `int getTextureWhereDataResides(int index) {\n      ${searchAxis.join('')}\n    }`;\n};\n\n// TODO: Implement BinarySearch in GLSL\nconst getTextureIndexWhereDataResidesBinarySearch = (sizeInConcatAxis: number[]): string =>\n    getTextureIndexWhereDataResidesLinearSearch(sizeInConcatAxis);\n\nconst getFetchDataFromCorrectTextureMethod = (numberOfTensors: number, tensorRank: number) => {\n  const codeLines: string[] = [`float fetchDataFromCorrectTexture(int textureIndex, int indices[${tensorRank}]) {`];\n  for (let i = 0; i < numberOfTensors; ++i) {\n    if (i === 0) {\n      codeLines.push(\n          '\\t' +\n          `if (textureIndex == ${i}) { return _X${i}(indices); }`);\n    } else if (i === numberOfTensors - 1) {\n      codeLines.push(\n          '\\t' +\n          `else { return _X${i}(indices); }`);\n    } else {\n      codeLines.push(\n          '\\t' +\n          `else if (textureIndex == ${i}) { return _X${i}(indices); }`);\n    }\n  }\n  codeLines.push(\n      '\\t' +\n      '}');\n  return codeLines.join('\\n');\n};\n\nconst getGetSizeInConcatAxisValueFromIndexMethod = (sizeInConcatAxis: number[]): string => {\n  const codeLines: string[] = ['int getSizeInConcatAxisValueFromIndex(int index) {'];\n  for (let i = 0; i < sizeInConcatAxis.length; ++i) {\n    if (i === 0) {\n      codeLines.push(\n          '\\t' +\n          `if (index == ${i}) { return ${sizeInConcatAxis[i]}; }`);\n    } else if (i === sizeInConcatAxis.length - 1) {\n      codeLines.push(\n          '\\t' +\n          `else { return ${sizeInConcatAxis[i]}; }`);\n    } else {\n      codeLines.push(\n          '\\t' +\n          `else if (index == ${i}) { return ${sizeInConcatAxis[i]}; }`);\n    }\n  }\n  codeLines.push(\n      '\\t' +\n      '}');\n\n  return codeLines.join('\\n');\n};\n\nexport const parseConcatAttributes: OperatorInitialization<ConcatAttributes> = (node: Graph.Node): ConcatAttributes =>\n    createAttributeWithCacheKey({axis: node.attributes.getInt('axis')});\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length < 1) {\n    throw new Error('too few inputs');\n  }\n\n  const inputType = inputs[0].type;\n  const inputDimensionality = inputs[0].dims.length;\n\n  // TODO: Support string concat\n  if (inputType === 'string') {\n    throw new Error('string tensor is not supported yet');\n  }\n\n  for (const input of inputs) {\n    // make sure types of all inputs match\n    if (input.type !== inputType) {\n      throw new Error('input tensors should be one type');\n    }\n\n    // make sure the dimensionality of all inputs are the same\n    if (input.dims.length !== inputDimensionality) {\n      throw new Error('input tensors should have the same shape');\n    }\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {Graph} from '../../../graph';\nimport {Tensor} from '../../../tensor';\nimport {MAX_CLIP, MIN_CLIP} from '../../../util';\nimport {FunctionType, GlslValueFunction} from '../glsl-definitions';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';\n\nexport function glslAbs(): GlslValueFunction {\n  return glslBuiltinUnary('abs');\n}\nexport function glslAcos(): GlslValueFunction {\n  return glslBuiltinUnary('acos');\n}\nexport function glslAsin(): GlslValueFunction {\n  return glslBuiltinUnary('asin');\n}\nexport function glslAtan(): GlslValueFunction {\n  return glslBuiltinUnary('atan');\n}\nexport function glslCeil(): GlslValueFunction {\n  return glslBuiltinUnary('ceil');\n}\nexport function glslCos(): GlslValueFunction {\n  return glslBuiltinUnary('cos');\n}\nexport function glslElu(alpha: number): GlslValueFunction {\n  const name = 'elu';\n  const body = `\n  const float alpha = float(${alpha});\n\n  float ${name}_(float a) {\n    return a >= 0.0 ? a: (exp(a) - 1.0) * alpha;\n  }\n  vec4 ${name}_(vec4 v) {\n    return vec4(${name}_(v.x), ${name}_(v.y), ${name}_(v.z), ${name}_(v.w));\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslExp(): GlslValueFunction {\n  return glslBuiltinUnary('exp');\n}\nexport function glslFloor(): GlslValueFunction {\n  return glslBuiltinUnary('floor');\n}\nexport function glslClip(min: number, max: number): GlslValueFunction {\n  const name = 'clip';\n  const body = `\n  const float min = float(${min});\n  const float max = float(${max});\n\n  float ${name}_(float a) {\n    return clamp(a, min, max);\n  }\n  vec4 ${name}_(vec4 v) {\n    return clamp(v, min, max);\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslIdentity(): GlslValueFunction {\n  const name = 'indentity';\n  const body = `\n  float ${name}_(float a) {\n    return a;\n  }\n  vec4 ${name}_(vec4 v) {\n    return v;\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslLeakyRelu(alpha: number): GlslValueFunction {\n  const name = 'leakyRelu';\n  const body = `\n  const float alpha = float(${alpha});\n\n  float ${name}_(float a) {\n    return a < 0.0 ? a * alpha : a;\n  }\n  vec4 ${name}_(vec4 v) {\n    return vec4(${name}_(v.x), ${name}_(v.y), ${name}_(v.z), ${name}_(v.w));\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslLog(): GlslValueFunction {\n  return glslBuiltinUnary('log');\n}\nexport function glslNeg(): GlslValueFunction {\n  const name = 'neg';\n  const body = `\n  float ${name}_(float a) {\n    return -a;\n  }\n  vec4 ${name}_(vec4 v) {\n    return -v;\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslNot(): GlslValueFunction {\n  const name = 'not';\n  const body = `\n  float ${name}_(float a) {\n    return float( ! bool(a) );\n  }\n  bool ${name}_(bool a) {\n    return !a;\n  }\n  vec4 ${name}_(vec4 v) {\n    return vec4(!bool(v.x), !bool(v.y), !bool(v.z), !bool(v.w));\n  }\n  bvec4 ${name}_(bvec4 v) {\n    return bvec4(!v.x, !v.y, !v.z, !v.w);\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslSin(): GlslValueFunction {\n  return glslBuiltinUnary('sin');\n}\nexport function glslRelu(): GlslValueFunction {\n  const name = 'relu';\n  const body = `\n  float ${name}_(float a) {\n    return max( a, 0.0 );\n  }\n  vec4 ${name}_(vec4 v) {\n    return max( v, 0.0 );\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslSigmoid(): GlslValueFunction {\n  const name = 'sigmoid';\n  const body = `\n  float ${name}_(float a) {\n    return 1.0 / (1.0 + exp(-a));\n  }\n  vec4 ${name}_(vec4 v) {\n    return 1.0 / (1.0 + exp(-v));\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nexport function glslSqrt(): GlslValueFunction {\n  return glslBuiltinUnary('sqrt');\n}\nexport function glslTan(): GlslValueFunction {\n  return glslBuiltinUnary('tan');\n}\nexport function glslTanh(): GlslValueFunction {\n  const name = 'tanh';\n  const body = `\n  float ${name}_(float a) {\n    a = clamp(a, -10., 10.);\n    a = exp(2.*a);\n    return (a - 1.) / (a + 1.);\n  }\n  vec4 ${name}_(vec4 v) {\n    v = clamp(v, -10., 10.);\n    v = exp(2.*v);\n    return (v - 1.) / (v + 1.);\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\nfunction glslBuiltinUnary(name: string): GlslValueFunction {\n  const body = `\n  float ${name}_(float a) {\n    return ${name}(a);\n  }\n  vec4 ${name}_(vec4 v) {\n    return ${name}(v);\n  }\n  `;\n  return {body, name, type: FunctionType.ValueBased};\n}\n\n/////\n/////\n/////\n\nconst createElementwiseProgramInfo =\n    (handler: WebGLInferenceHandler, metadata: ProgramMetadata, input: Tensor, glslFunc: GlslValueFunction):\n        ProgramInfo => {\n          const textureType = handler.session.pack ? TextureType.packed : TextureType.unpacked;\n          const glsl = getGlsl(handler.session.backend.glContext.version);\n          return {\n            ...metadata,\n            output: {dims: input.dims, type: input.type, textureType},\n            shaderSource: `\n     ${glslFunc.body}\n     void main() {\n       vec4 v = ${glsl.texture2D}(A, TexCoords);\n       v = ${glslFunc.name}_(v);\n       ${glsl.output} = v;\n     }\n     `,\n            hasMain: true\n          };\n        };\n\nconst createElementwiseProgramInfoLoader =\n    (handler: WebGLInferenceHandler, input: Tensor, glslFunc: GlslValueFunction, cacheKey?: string):\n        ProgramInfoLoader => {\n          const textureType = handler.session.pack ? TextureType.packed : TextureType.unpacked;\n          const metadata = {name: glslFunc.name, inputTypes: [textureType], inputNames: ['A'], cacheHint: cacheKey};\n          return {...metadata, get: () => createElementwiseProgramInfo(handler, metadata, input, glslFunc)};\n        };\n\nexport const abs = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslAbs()), inputs)];\n\nexport const acos = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslAcos()), inputs)];\n\nexport const asin = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslAsin()), inputs)];\n\nexport const atan = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslAtan()), inputs)];\n\nexport interface ClipAttributes extends AttributeWithCacheKey {\n  readonly min: number;\n  readonly max: number;\n}\n\nexport const clip =\n    (handler: WebGLInferenceHandler, inputs: Tensor[], attributes: ClipAttributes): Tensor[] => [handler.run(\n        createElementwiseProgramInfoLoader(\n            handler, inputs[0], glslClip(attributes.min, attributes.max), attributes.cacheKey),\n        inputs)];\n\nexport const parseClipAttributes = (node: Graph.Node): ClipAttributes => createAttributeWithCacheKey(\n    {min: node.attributes.getFloat('min', MIN_CLIP), max: node.attributes.getFloat('max', MAX_CLIP)});\n\nexport const clipV11 = (handler: WebGLInferenceHandler, inputs: Tensor[]): Tensor[] => {\n  const attributes = generateClipAttributesFromInputs(handler, inputs);\n  return clip(handler, [inputs[0]], attributes);\n};\n\nconst generateClipAttributesFromInputs = (handler: WebGLInferenceHandler, inputs: Tensor[]): ClipAttributes => {\n  if (inputs.length >= 3 &&\n      (!handler.session.isInitializer(inputs[1].dataId) || !handler.session.isInitializer(inputs[2].dataId))) {\n    throw new Error('dynamic clip attributes are not allowed');\n  }\n\n  const min = (inputs.length >= 3) ? inputs[1].numberData[0] : MIN_CLIP;\n  const max = (inputs.length >= 3) ? inputs[2].numberData[0] : MAX_CLIP;\n  return createAttributeWithCacheKey({min, max});\n};\n\nexport const ceil = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslCeil()), inputs)];\n\nexport const cos = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslCos()), inputs)];\n\nexport interface EluAttributes extends AttributeWithCacheKey {\n  readonly alpha: number;\n}\n\nexport const elu =\n    (handler: WebGLInferenceHandler, inputs: Tensor[], attributes: EluAttributes): Tensor[] => [handler.run(\n        createElementwiseProgramInfoLoader(handler, inputs[0], glslElu(attributes.alpha), attributes.cacheKey),\n        inputs)];\n\nexport const parseEluAttributes = (node: Graph.Node): EluAttributes =>\n    createAttributeWithCacheKey({alpha: node.attributes.getFloat('alpha', 1.0)});\n\nexport const exp = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslExp()), inputs)];\n\nexport const floor = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslFloor()), inputs)];\n\nexport const identity = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslIdentity()), inputs)];\n\nexport interface LeakyReluAttributes extends AttributeWithCacheKey {\n  readonly alpha: number;\n}\n\nexport const leakyRelu =\n    (handler: WebGLInferenceHandler, inputs: Tensor[], attributes: LeakyReluAttributes): Tensor[] => [handler.run(\n        createElementwiseProgramInfoLoader(handler, inputs[0], glslLeakyRelu(attributes.alpha), attributes.cacheKey),\n        inputs)];\n\nexport const parseLeakyReluAttributes = (node: Graph.Node): LeakyReluAttributes =>\n    createAttributeWithCacheKey({alpha: node.attributes.getFloat('alpha', 0.01)});\n\nexport const log = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslLog()), inputs)];\n\nexport const neg = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslNeg()), inputs)];\n\nexport const not = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslNot()), inputs)];\n\nexport const relu = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslRelu()), inputs)];\n\nexport const sigmoid = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslSigmoid()), inputs)];\n\nexport const sin = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslSin()), inputs)];\n\nexport const sqrt = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslSqrt()), inputs)];\n\nexport const tan = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslTan()), inputs)];\n\nexport const tanh = (handler: WebGLInferenceHandler, inputs: Tensor[]):\n    Tensor[] => [handler.run(createElementwiseProgramInfoLoader(handler, inputs[0], glslTanh()), inputs)];\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Attribute} from '../../../attribute';\nimport {MAX_CLIP, MIN_CLIP} from '../../../util';\nimport {GlslValueFunction} from '../glsl-definitions';\n\nimport {glslClip, glslRelu, glslSigmoid} from './unary-op';\n\nexport interface InternalActivationAttributes {\n  readonly activation: string;\n  readonly clipMin?: number;\n  readonly clipMax?: number;\n  readonly activationCacheKey: string;\n}\n\nexport function getActivationSnippet(attributes: InternalActivationAttributes) {\n  let func: GlslValueFunction;\n  switch (attributes.activation) {\n    case 'Relu':\n      func = glslRelu();\n      break;\n    case 'Sigmoid':\n      func = glslSigmoid();\n      break;\n    case 'Clip':\n      func = glslClip(attributes.clipMin!, attributes.clipMax!);\n      break;\n    // TODO: adding other activations that can be fused.\n    default:\n      return {activationFunction: '', applyActivation: ''};\n  }\n\n  const activationName = func.name;\n  const activationFunction = func.body;\n  const applyActivation = `value = ${activationName}_(value);`;\n  return {activationFunction, applyActivation};\n}\n\nexport const parseInternalActivationAttributes = (attributes: Attribute): InternalActivationAttributes => {\n  const activation = attributes.getString('activation', '');\n\n  if (activation === 'Clip') {\n    const [clipMin, clipMax] = attributes.getFloats('activation_params', [MIN_CLIP, MAX_CLIP]);\n    return {activation, clipMax, clipMin, activationCacheKey: `${activation}:${clipMin},${clipMax}`};\n  }\n  return {activation, activationCacheKey: activation};\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Logger} from '../../../instrument';\nimport {Tensor} from '../../../tensor';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';\n\nimport {calculateOutputShape, ConvAttributes} from './conv';\nimport {getActivationSnippet} from './fuse-utils';\n\nconst createUnpackedGroupedConvProgramMetadata = (hasBias: boolean, cacheHint: string): ProgramMetadata => ({\n  name: 'GroupedConv',\n  inputNames: hasBias ? ['X', 'W', 'Bias'] : ['X', 'W'],\n  inputTypes: hasBias ? [TextureType.unpacked, TextureType.unpacked, TextureType.unpacked] :\n                        [TextureType.unpacked, TextureType.unpacked],\n  cacheHint\n});\n\nconst createUnpackedGroupedConvProgramInfo =\n    (inferenceHandler: WebGLInferenceHandler, inputs: readonly Tensor[], metadata: ProgramMetadata,\n     attributes: ConvAttributes): ProgramInfo => {\n      const hasBias = inputs.length > 2;\n      const processBias = hasBias ? 'value += getBias(output_channel);' : '';\n      const xShape = inputs[0].dims.slice();\n      const wShape = inputs[1].dims.slice();\n      const outputChannelsPerGroup = wShape[0] / attributes.group;\n      Logger.verbose(\n          'GroupedConv',\n          `autpPad:${attributes.autoPad}, dilations:${attributes.dilations}, group:${attributes.group}, kernelShape:${\n              attributes.kernelShape}, pads:${attributes.pads}, strides:${attributes.strides}`);\n      const outputShape =\n          calculateOutputShape(xShape, wShape, attributes.dilations, attributes.pads, attributes.strides);\n      const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);\n      const {activationFunction, applyActivation} = getActivationSnippet(attributes);\n\n      const shaderSource = `\n  const ivec2 strides = ivec2(${attributes.strides[0]}, ${attributes.strides[1]});\n  const ivec2 pads = ivec2(${attributes.pads[0]}, ${attributes.pads[1]});\n  ${activationFunction}\n  void main() {\n    ivec4 coords = getOutputCoords();\n    int batch = coords.x;\n    int output_channel = coords.y;\n    ivec2 xRCCorner = coords.zw * strides - pads;\n    int group_id = output_channel / ${outputChannelsPerGroup};\n\n    float value = 0.0;\n    for (int wInChannel = 0; wInChannel < ${wShape[1]}; wInChannel++) {\n      int input_channel = group_id * ${wShape[1]} + wInChannel;\n      for (int wHeight = 0; wHeight < ${wShape[2]}; wHeight++) {\n        int xHeight = xRCCorner.x + wHeight * ${attributes.dilations[0]};\n\n        if (xHeight < 0 || xHeight >= ${xShape[2]}) {\n          continue;\n        }\n\n        for (int wWidth = 0; wWidth < ${wShape[3]}; wWidth++) {\n          int xWidth = xRCCorner.y + wWidth * ${attributes.dilations[1]};\n          if (xWidth < 0 || xWidth >= ${xShape[3]}) {\n            continue;\n          }\n\n          float xVal = getX(batch, input_channel, xWidth, xHeight);\n          float wVal = getW(output_channel, wInChannel, wWidth, wHeight);\n          value += xVal*wVal;\n        }\n      }\n    }\n    ${processBias}\n    ${applyActivation}\n    ${glsl.output} = vec4(value, .0, .0, .0);\n  }\n`;\n      return {\n        ...metadata,\n        output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},\n        shaderSource,\n        hasMain: true,\n      };\n    };\n\nexport const createUnpackedGroupedConvProgramInfoLoader =\n    (inferenceHandler: WebGLInferenceHandler, inputs: readonly Tensor[], attributes: ConvAttributes):\n        ProgramInfoLoader => {\n          const metadata = createUnpackedGroupedConvProgramMetadata(inputs.length > 2, attributes.cacheKey);\n          return {\n            ...metadata,\n            get: () => createUnpackedGroupedConvProgramInfo(inferenceHandler, inputs, metadata, attributes)\n          };\n        };\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from '../../../tensor';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';\n\nimport {ConvAttributes} from './conv';\nimport {unpackFromChannel} from './packing-utils';\n\nconst createPackedIm2ColProgramMetadata = (cacheHint: string) => ({\n  name: 'Im2Col (packed)',\n  inputNames: ['A'],\n  inputTypes: [TextureType.packed],\n  cacheHint,\n});\n\nconst createPackedIm2ColProgramInfo =\n    (inferenceHandler: WebGLInferenceHandler, metadata: ProgramMetadata, x: Tensor, w: Tensor,\n     outputShape: readonly number[], attributes: ConvAttributes): ProgramInfo => {\n      const xshape = x.dims;\n      const wshape = w.dims;\n      const rowDim = 2;\n      const colDim = 3;\n      const rank = outputShape.length;\n      const im2colShape = [wshape[1] * wshape[2] * wshape[3], outputShape[2] * outputShape[3]];\n      const kernelSize = wshape[2] * wshape[3];\n      const unpackChannel = unpackFromChannel();\n      const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);\n      let unrolled = '';\n\n      for (let row = 0; row <= 1; row++) {\n        for (let col = 0; col <= 1; col++) {\n          unrolled += `\n            blockIndex = rc.x + ${col};\n            pos = rc.y + ${row};\n\n            if(blockIndex < ${im2colShape[1]} && pos < ${im2colShape[0]}) {\n              offsetY = int(blockIndex / (${outputShape[rank - 1]})) * ${attributes.strides[0]} -\n                ${attributes.pads[0]};\n              d0 = offsetY + ${attributes.dilations[0]} * (imod(pos, ${kernelSize}) / ${wshape[2]});\n\n              if(d0 < ${xshape[rowDim]} && d0 >= 0) {\n                offsetX = imod(blockIndex, ${outputShape[rank - 1]}) * ${attributes.strides[1]} -\n                  ${attributes.pads[1]};\n                d1 = offsetX + ${attributes.dilations[1]} * imod(imod(pos, ${kernelSize}), ${wshape[2]});\n\n                if(d1 < ${xshape[colDim]} && d1 >= 0) {\n\n                  ch = int(float(pos)/ ${kernelSize}.);\n                    innerDims = vec2(d0, d1);\n                    result[${row * 2 + col}] = getChannel(\n                      getA(0, ch, int(innerDims.x),\n                      int(innerDims.y)), innerDims);\n                }\n              }\n            }\n\n          `;\n        }\n      }\n\n      const shaderSource = `\n      ${unpackChannel}\n\n      void main() {\n        ivec2 rc = getOutputCoords();\n          vec4 result = vec4(0.0);\n          int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n          vec2 innerDims;\n          ${unrolled}\n          ${glsl.output} = result;\n      }\n            `;\n      return {\n        ...metadata,\n        output: {dims: im2colShape, type: x.type, textureType: TextureType.packed},\n        shaderSource,\n        hasMain: true\n      };\n    };\n\nexport const createPackedIm2ColProgramInfoLoader =\n    (inferenceHandler: WebGLInferenceHandler, x: Tensor, w: Tensor, outputShape: readonly number[],\n     attributes: ConvAttributes): ProgramInfoLoader => {\n      const metadata = createPackedIm2ColProgramMetadata(attributes.cacheKey);\n      return {\n        ...metadata,\n        get: () => createPackedIm2ColProgramInfo(inferenceHandler, metadata, x, w, outputShape, attributes)\n      };\n    };\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {BroadcastUtil, ShapeUtil} from '../../../util';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';\nimport {getCoordsDataType, getGlChannels} from '../utils';\n\nimport {getActivationSnippet, InternalActivationAttributes, parseInternalActivationAttributes} from './fuse-utils';\nimport {createPackedMatmulProgramInfoLoader} from './matmul-pack';\n\nexport const matMul: OperatorImplementation<InternalActivationAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: InternalActivationAttributes): Tensor[] => {\n      validateInputs(inputs);\n\n      if (inferenceHandler.session.pack) {\n        return [inferenceHandler.run(\n            createPackedMatmulProgramInfoLoader(inferenceHandler, inputs, attributes), inputs)];\n      } else {\n        return [inferenceHandler.run(createMatmulProgramInfoLoader(inputs, attributes), inputs)];\n      }\n    };\n\nexport const parseMatMulAttributes: OperatorInitialization<InternalActivationAttributes> =\n    (node: Graph.Node): InternalActivationAttributes => parseInternalActivationAttributes(node.attributes);\n\nconst createMatmulProgramMetadata = (hasBias: boolean, cacheHint: string) => ({\n  name: 'MatMul',\n  inputNames: hasBias ? ['A', 'B', 'Bias'] : ['A', 'B'],\n  inputTypes: hasBias ? [TextureType.unpacked, TextureType.unpacked, TextureType.unpacked] :\n                        [TextureType.unpacked, TextureType.unpacked],\n  cacheHint\n});\n\nfunction createMatmulProgramInfo(\n    metadata: ProgramMetadata, inputs: Tensor[], activationAttributes: InternalActivationAttributes): ProgramInfo {\n  const aShape = inputs[0].dims;\n  const bShape = inputs[1].dims;\n  const outputShape = BroadcastUtil.calcShape(aShape, bShape, true);\n  if (!outputShape) {\n    throw new Error('Can\\'t use matmul on the given tensors');\n  }\n  const coordsDataType = getCoordsDataType(outputShape.length);\n  const allGlChannels = getGlChannels();\n  const {activationFunction, applyActivation} = getActivationSnippet(activationAttributes);\n\n  const hasBias = inputs.length > 2;\n  const processBias = hasBias ? 'value += getBiasForMatmul();' : '';\n  const getBiasForMatmulSnippet =\n      hasBias ? `${getBiasForMatmul(coordsDataType, allGlChannels, inputs[2].dims, outputShape, false)}` : '';\n\n  const rank = outputShape.length;\n  const arank = aShape.length;\n  const brank = bShape.length;\n  const sharedDim = aShape[aShape.length - 1];\n  const shaderSource = `\n    ${activationFunction}\n    ${getBiasForMatmulSnippet}\n    float process(int indices[${rank}]) {\n        int a[${arank}];\n        int b[${brank}];\n        bcastMatmulIndices_A(indices, a);\n        bcastMatmulIndices_B(indices, b);\n\n        float value;\n        for (int k=0; k<${sharedDim}; ++k) {\n            a[${arank - 1}] = k;\n            b[${brank - 2}] = k;\n            value += _A(a) * _B(b);\n        }\n        ${processBias}\n        ${applyActivation}\n        return value;\n    }`;\n  return {\n    ...metadata,\n    output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},\n    shaderSource,\n  };\n}\n\nexport function createMatmulProgramInfoLoader(\n    inputs: Tensor[], activationAttributes: InternalActivationAttributes): ProgramInfoLoader {\n  const metadata = createMatmulProgramMetadata(inputs.length > 2, activationAttributes.activationCacheKey);\n  return {...metadata, get: () => createMatmulProgramInfo(metadata, inputs, activationAttributes)};\n}\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 2) {\n    throw new Error('MatMul requires 2 inputs.');\n  }\n\n  if (inputs[0].dims[inputs[0].dims.length - 1] !== inputs[1].dims[inputs[1].dims.length - 2]) {\n    throw new Error('shared dimension does not match.');\n  }\n\n  if ((inputs[0].type !== 'float32' && inputs[0].type !== 'float64') ||\n      (inputs[1].type !== 'float32' && inputs[1].type !== 'float64')) {\n    throw new Error('inputs should be float type');\n  }\n\n  if (inputs[0].type !== inputs[1].type) {\n    throw new Error('inputs types should match');\n  }\n};\n\nexport function getBiasForMatmul(\n    coordsDataType: string, allGlChannels: readonly string[], inShape: readonly number[], outShape: readonly number[],\n    isPacked: boolean): string {\n  let unpackedCoordsSnippet = '';\n  const inRank = inShape.length;\n  const outRank = outShape.length;\n  const rankDiff = outRank - inRank;\n  if (outRank < 2 && inRank > 0) {\n    unpackedCoordsSnippet = 'coords';\n  } else {\n    unpackedCoordsSnippet = inShape.map((_s, i) => `coords.${allGlChannels[i + rankDiff]}`).join(', ');\n  }\n  const broadcastDims = BroadcastUtil.getBroadcastDims(inShape, outShape);\n  const coordsSnippet = broadcastDims.map(d => `coords.${allGlChannels[d + rankDiff]} = 0;`).join('\\n');\n  const inSize = ShapeUtil.size(inShape);\n  const isInputScalar = inSize === 1;\n  let output = 'vec4(outputValue.xx, outputValue.yy)';\n  if (isInputScalar) {\n    output = 'vec4(outputValue.x)';\n  }\n  const getBiasForMatmulSource = isPacked ? `\nvec4 getBiasForMatmul() {\n  ${coordsDataType} coords = getOutputCoords();\n  ${coordsSnippet}\n  vec4 outputValue = getBias(${unpackedCoordsSnippet});\n  return ${output};\n}` :\n                                            `\nfloat getBiasForMatmul() {\n  ${coordsDataType} coords = getOutputCoords();\n  ${coordsSnippet}\n  return getBias(coords.x);\n}`;\n\n  return getBiasForMatmulSource;\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from '../../../tensor';\nimport {BroadcastUtil, ShapeUtil} from '../../../util';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';\nimport {getCoordsDataType, getGlChannels} from '../utils';\n\nimport {getActivationSnippet, InternalActivationAttributes} from './fuse-utils';\nimport {getBiasForMatmul} from './matmul';\n\nconst createPackedMatmulProgramMetadata = (hasBias: boolean, cacheHint: string) => ({\n  name: 'MatMul (packed)',\n  inputNames: hasBias ? ['A', 'B', 'Bias'] : ['A', 'B'],\n  inputTypes: hasBias ? [TextureType.packed, TextureType.packed, TextureType.packed] :\n                        [TextureType.packed, TextureType.packed],\n  cacheHint\n});\n\nconst createPackedMatmulProgramInfo =\n    (inferenceHandler: WebGLInferenceHandler, metadata: ProgramMetadata, inputs: Tensor[],\n     activationAttributes: InternalActivationAttributes): ProgramInfo => {\n      const hasBias = inputs.length > 2;\n      const processBias = hasBias ? 'value += getBiasForMatmul();' : '';\n      const aShape = inputs[0].dims;\n      const bShape = inputs[1].dims;\n      const outputShape = BroadcastUtil.calcShape(aShape, bShape, true);\n      const isBroadcast = !ShapeUtil.areEqual(inputs[0].dims, inputs[1].dims);\n\n      if (!outputShape) {\n        throw new Error('Can\\'t use matmul on the given tensors');\n      }\n      const sharedDim = aShape[aShape.length - 1];\n      const sharedDimIndex = Math.ceil(sharedDim / 2);\n      const aRank = aShape.length;\n      const bRank = bShape.length;\n\n      const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);\n      const coordsDataType = getCoordsDataType(outputShape.length);\n      const outRank = outputShape.length;\n      const allGlChannels = getGlChannels();\n      const {activationFunction, applyActivation} = getActivationSnippet(activationAttributes);\n\n      const getBiasForMatmulSnippet =\n          hasBias ? `${getBiasForMatmul(coordsDataType, allGlChannels, inputs[2].dims, outputShape, true)}` : '';\n\n      const getBcastedSamplerForMatmulSnippet =\n          isBroadcast ? `${getBcastSamplerForMatmul(coordsDataType, allGlChannels, inputs, outputShape)}` : '';\n\n      const getSamplerAInLoopSnippet = isBroadcast ? 'getAAtOutCoordsMatmul(i)' : `getA(${getA(allGlChannels, aRank)})`;\n      const getSamplerBInLoopSnippet = isBroadcast ? 'getBAtOutCoordsMatmul(i)' : `getB(${getB(allGlChannels, bRank)})`;\n      const getOutputCoordsSnippet = isBroadcast ? '' : `${coordsDataType} rc =\n          getOutputCoords(); int lastDim = rc.${allGlChannels[outRank - 1]}; rc.${allGlChannels[outRank - 1]} =\n          rc.${allGlChannels[outRank - 2]}; rc.${allGlChannels[outRank - 2]} = lastDim;\n      `;\n      const shaderSource = `\n            ${getBcastedSamplerForMatmulSnippet}\n            ${getBiasForMatmulSnippet}\n            ${activationFunction}\n            void main() {\n              ${getOutputCoordsSnippet}\n\n              vec4 value = vec4(0);\n              for (int i = 0; i < ${sharedDimIndex}; i++) {\n                vec4 a = ${getSamplerAInLoopSnippet};\n                vec4 b = ${getSamplerBInLoopSnippet};\n\n                value += (a.rrbb * b.rgrg);\n                value += (a.ggaa * b.baba);\n              }\n              ${processBias}\n              ${applyActivation}\n              ${glsl.output} = value;\n            }`;\n      return {\n        ...metadata,\n        output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.packed},\n        shaderSource,\n        hasMain: true\n      };\n    };\n\nexport const createPackedMatmulProgramInfoLoader =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[],\n     activationAttributes: InternalActivationAttributes): ProgramInfoLoader => {\n      const metadata = createPackedMatmulProgramMetadata(inputs.length > 2, activationAttributes.activationCacheKey);\n      return {\n        ...metadata,\n        get: () => createPackedMatmulProgramInfo(inferenceHandler, metadata, inputs, activationAttributes)\n      };\n    };\n\nfunction getBcastSamplerForMatmul(\n    coordsDataType: string, allGlChannels: readonly string[], inputs: Tensor[], outShape: readonly number[]): string {\n  let unpackedACoordsSnippet = [];\n  let unpackedBCoordsSnippet = [];\n\n  const inAShape = inputs[0].dims;\n  const inBShape = inputs[1].dims;\n\n  const inARank = inAShape.length;\n  const inBRank = inBShape.length;\n\n  const outRank = outShape.length;\n  const rankADiff = outRank - inARank;\n  const rankBDiff = outRank - inBRank;\n\n  unpackedACoordsSnippet = inAShape.map((_s, i) => `coords.${allGlChannels[i + rankADiff]}`);\n  unpackedACoordsSnippet[inARank - 1] = 'i*2';\n  unpackedACoordsSnippet.join(', ');\n  unpackedBCoordsSnippet = inBShape.map((_s, i) => `coords.${allGlChannels[i + rankBDiff]}`);\n  unpackedBCoordsSnippet[inBRank - 2] = 'i*2';\n  unpackedBCoordsSnippet.join(', ');\n\n  const broadcastADims = BroadcastUtil.getBroadcastDims(inAShape, outShape);\n  const broadcastBDims = BroadcastUtil.getBroadcastDims(inBShape, outShape);\n\n  const coordsASnippet = broadcastADims.map(d => `coords.${allGlChannels[d + rankADiff]} = 0;`).join('\\n');\n  const coordsBSnippet = broadcastBDims.map(d => `coords.${allGlChannels[d + rankBDiff]} = 0;`).join('\\n');\n  const swapDimSnippet = `int lastDim = coords.${allGlChannels[outRank - 1]};\n  coords.${allGlChannels[outRank - 1]} = coords.${allGlChannels[outRank - 2]};\n  coords.${allGlChannels[outRank - 2]} = lastDim;`;\n\n  const getBcastSamplerMatmulSource = `\nvec4 getAAtOutCoordsMatmul(int i) {\n  ${coordsDataType} coords = getOutputCoords();\n  ${swapDimSnippet}\n  ${coordsASnippet}\n  vec4 outputValue = getA(${unpackedACoordsSnippet});\n  return outputValue;\n}\n\nvec4 getBAtOutCoordsMatmul(int i) {\n  ${coordsDataType} coords = getOutputCoords();\n  ${swapDimSnippet}\n  ${coordsBSnippet}\n  vec4 outputValue = getB(${unpackedBCoordsSnippet});\n  return outputValue;\n}`;\n\n  return getBcastSamplerMatmulSource;\n}\n\nfunction getA(allGlChannels: string[], rank: number): string {\n  let res = '';\n  for (let i = 0; i < rank - 2; i++) {\n    res += `rc.${allGlChannels[i]}, `;\n  }\n  res += `rc.${allGlChannels[rank - 2]}, ` +\n      'i*2';\n  return res;\n}\n\nfunction getB(allGlChannels: string[], rank: number): string {\n  let res = '';\n  for (let i = 0; i < rank - 2; i++) {\n    res += `rc.${allGlChannels[i]}, `;\n  }\n  res += 'i*2, ' +\n      `rc.${allGlChannels[rank - 1]}`;\n  return res;\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from '../../../tensor';\nimport {WebGLInferenceHandler} from '../inference-handler';\n\nimport {calculateOutputShape, ConvAttributes} from './conv';\nimport {createPackedIm2ColProgramInfoLoader} from './im2col-pack';\nimport {createPackedMatmulProgramInfoLoader} from './matmul-pack';\n\nexport const conv2DPackedPointwise =\n    (inferenceHandler: WebGLInferenceHandler, inputs: readonly Tensor[], attributes: ConvAttributes): Tensor => {\n      const xshape = inputs[0].dims;\n      const kshape = inputs[1].dims;\n      const outputShape =\n          calculateOutputShape(xshape, kshape, attributes.dilations, attributes.pads, attributes.strides);\n      const reshapedX = inferenceHandler.reshapePacked(inputs[0], [xshape[1], xshape[2] * xshape[3]]);\n      const reshapedK = inferenceHandler.reshapePacked(inputs[1], [kshape[0], kshape[1]]);\n\n      const matmulInputs = inputs.length > 2 ? [reshapedK, reshapedX, inputs[2]] : [reshapedK, reshapedX];\n      const matmulOutput = inferenceHandler.run(\n          createPackedMatmulProgramInfoLoader(inferenceHandler, matmulInputs, attributes), matmulInputs);\n      return inferenceHandler.reshapePacked(matmulOutput, outputShape);\n    };\n\nexport const conv2DPacked =\n    (inferenceHandler: WebGLInferenceHandler, inputs: readonly Tensor[], attributes: ConvAttributes): Tensor => {\n      const xshape = inputs[0].dims;\n      const kshape = inputs[1].dims;\n      const outputShape =\n          calculateOutputShape(xshape, kshape, attributes.dilations, attributes.pads, attributes.strides);\n\n      // run im2col\n      const im2colOutput = inferenceHandler.run(\n          createPackedIm2ColProgramInfoLoader(inferenceHandler, inputs[0], inputs[1], outputShape, attributes),\n          [inputs[0]]);\n\n      // reshape kernel\n      const kernelReshaped = inferenceHandler.reshapePacked(inputs[1], [kshape[0], kshape[1] * kshape[2] * kshape[3]]);\n\n      // run matmul\n      const matmulInputs =\n          (inputs.length === 3) ? [kernelReshaped, im2colOutput, inputs[2]] : [kernelReshaped, im2colOutput];\n      const matmulOutput = inferenceHandler.run(\n          createPackedMatmulProgramInfoLoader(inferenceHandler, matmulInputs, attributes), matmulInputs);\n\n      // reshape output\n      const outputReshaped = inferenceHandler.reshapePacked(matmulOutput, outputShape);\n      return outputReshaped;\n    };\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from '../../../tensor';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';\n\nimport {ConvAttributes} from './conv';\n\nconst createIm2ColProgramMetadata = (cacheHint: string) => ({\n  name: 'Im2Col',\n  inputNames: ['X'],\n  inputTypes: [TextureType.unpacked],\n  cacheHint,\n});\n\nconst createIm2ColProgramInfo =\n    (_inferenceHandler: WebGLInferenceHandler, metadata: ProgramMetadata, x: Tensor, w: Tensor,\n     outputShape: readonly number[], attributes: ConvAttributes): ProgramInfo => {\n      const xshape = x.dims;\n      const wshape = w.dims;\n\n      const rank = outputShape.length;\n      const im2colDims = calculateIm2ColDims(xshape, wshape, outputShape, 4);\n\n      const shaderSource = `\n        const int XC = ${xshape[1]};\n        const int XH = ${xshape[2]};\n        const int XW = ${xshape[3]};\n        const int KH = ${attributes.kernelShape[0]};\n        const int KW = ${attributes.kernelShape[1]};\n        const int dilationH = ${attributes.dilations[0]};\n        const int dilationW = ${attributes.dilations[1]};\n        const int strideH = ${attributes.strides[0]};\n        const int strideW = ${attributes.strides[1]};\n        const int padH = ${attributes.pads[0]};\n        const int padW = ${attributes.pads[1]};\n        const int KHKW = KH*KW;\n        const int XCKHKW = XC * KHKW;\n        const int outputChannels = 4;\n        vec4 process(int indices[${rank}]) {\n          int b  = indices[0]; // batch size\n          int oh = indices[1] * strideH - padH; //output height\n          int ow = indices[2] * strideW - padW; //output width\n          int p = indices[3] * outputChannels; //patch\n          vec4 value = vec4(0.0);\n          for(int i=0; i < outputChannels; ++i) {\n            if(p < XCKHKW) {\n              int patchC = p / KHKW;\n              int patchH = (p - patchC*KHKW) / KW;\n              int patchW = (p - patchC*KHKW) - patchH * KW;\n              int xh2 = oh + patchH * dilationH;\n              int xw2 = ow + patchW * dilationW;\n              int x[${xshape.length}];\n              x[0] = b;\n              x[1] = patchC;\n              x[2] = xh2;\n              x[3] = xw2;\n              if(xh2 >= 0 &&\n                  xh2 < XH &&\n                  xw2 >= 0 &&\n                  xw2 < XW) {\n                value[i] = _X(x);\n              }\n            }\n            ++p;\n          }\n          return value;\n        }\n        `;\n      return {\n        ...metadata,\n        output: {dims: im2colDims, type: x.type, textureType: TextureType.packedLastDimension},\n        shaderSource\n      };\n    };\n\nexport const createIm2ColProgramInfoLoader =\n    (inferenceHandler: WebGLInferenceHandler, x: Tensor, w: Tensor, outputShape: readonly number[],\n     attributes: ConvAttributes): ProgramInfoLoader => {\n      const metadata = createIm2ColProgramMetadata(attributes.cacheKey);\n      return {\n        ...metadata,\n        get: () => createIm2ColProgramInfo(inferenceHandler, metadata, x, w, outputShape, attributes)\n      };\n    };\n\n\nexport const calculateIm2ColDims =\n    (inputShape: readonly number[], kernelShape: readonly number[], outputShape: readonly number[], channels = 4):\n        number[] =>\n            [outputShape[0], outputShape[2], outputShape[3],\n             Math.ceil(inputShape[1] * kernelShape[2] * kernelShape[3] / channels)];\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from '../../../tensor';\nimport {ShapeUtil} from '../../../util';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';\n\nimport {getActivationSnippet, InternalActivationAttributes} from './fuse-utils';\nimport {calculateIm2ColDims} from './im2col';\n\nconst createDotProductProgramMetadata = (hasBias: boolean, attributes: InternalActivationAttributes) => ({\n  name: 'ConvDotProduct',\n  inputNames: hasBias ? ['Im2Col', 'K', 'B'] : ['Im2Col', 'K'],\n  inputTypes: hasBias ? [TextureType.unpacked, TextureType.packedLastDimension, TextureType.unpacked] :\n                        [TextureType.unpacked, TextureType.packedLastDimension],\n  cacheKey: attributes.activationCacheKey\n});\n\nconst createDotProductProgramInfo =\n    (inferenceHandler: WebGLInferenceHandler, metadata: ProgramMetadata, inputs: readonly Tensor[],\n     outputShape: number[], attributes: InternalActivationAttributes): ProgramInfo => {\n      const xshape = inputs[0].dims;\n      const kshape = inputs[1].dims;\n      const adjustedKernelShape = [kshape[0], Math.ceil((xshape[1] * kshape[2] * kshape[3]) / 4)];\n      const im2colShape = calculateIm2ColDims(xshape, kshape, outputShape);\n      const [kWidth, kHeight] =\n          inferenceHandler.calculateTextureWidthAndHeight(adjustedKernelShape, TextureType.packedLastDimension);\n\n      const im2colStrides = ShapeUtil.computeStrides(im2colShape);\n      const [im2colWidth, im2colHeight] =\n          inferenceHandler.calculateTextureWidthAndHeight(im2colShape, TextureType.packedLastDimension);\n      const rank = outputShape.length;\n\n      const initValue = (inputs.length < 3) ? '0.0' : '_B(b)';\n      const sharedDim = Math.ceil(xshape[1] * kshape[2] * kshape[3] / 4);\n      const {activationFunction, applyActivation} = getActivationSnippet(attributes);\n      const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);\n      const shaderSource = `\n${activationFunction}\nfloat process(int indices[${rank}]) {\n  int b[1];\n  b[0] = indices[1];\n  int im2col[4];\n  im2col[0] = indices[0];\n  im2col[1] = indices[2];\n  im2col[2] = indices[3];\n  int im2colOffset = im2col[0] * ${im2colStrides[0]} + im2col[1] * ${im2colStrides[1]} + im2col[2] * ${\n          im2colStrides[2]};\n  int kernelOffset = indices[1] * ${adjustedKernelShape[1]};\n  float value = ${initValue};\n  for (int i = 0; i < ${sharedDim}; ++i) {\n    vec2 im2colCoords = offsetToCoords(im2colOffset, ${im2colWidth}, ${im2colHeight});\n    vec2 kernelCoords = offsetToCoords(kernelOffset, ${kWidth}, ${kHeight});\n    value += dot(${glsl.texture2D}(Im2Col, im2colCoords), ${glsl.texture2D}(K, kernelCoords));\n    ++im2colOffset;\n    ++kernelOffset;\n  }\n  ${applyActivation}\n  return value;\n}`;\n      return {\n        ...metadata,\n        output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},\n        shaderSource\n      };\n    };\n\nexport const createDotProductProgramInfoLoader =\n    (inferenceHandler: WebGLInferenceHandler, inputs: readonly Tensor[], outputShape: number[],\n     attributes: InternalActivationAttributes): ProgramInfoLoader => {\n      const metadata = createDotProductProgramMetadata(inputs.length > 2, attributes);\n      return {\n        ...metadata,\n        get: () => createDotProductProgramInfo(inferenceHandler, metadata, inputs, outputShape, attributes)\n      };\n    };\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {InferenceHandler} from '../../../backend';\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {PoolConvUtil} from '../../../util';\nimport {WebGLInferenceHandler} from '../inference-handler';\n\nimport {createUnpackedGroupedConvProgramInfoLoader} from './conv-grouped';\nimport {conv2DPacked} from './conv-pack';\nimport {createDotProductProgramInfoLoader} from './dot-product';\nimport {InternalActivationAttributes, parseInternalActivationAttributes} from './fuse-utils';\nimport {createIm2ColProgramInfoLoader} from './im2col';\nimport {createMatmulProgramInfoLoader} from './matmul';\n\n\nexport const calculateOutputShape =\n    (inputShape: readonly number[], kernelShape: readonly number[], dilations: readonly number[],\n     adjustPads: readonly number[], strides: readonly number[]): number[] => {\n      const batchSize = inputShape[0];\n      const inputSpatialShape = inputShape.slice(2);\n      const spatialRank = inputSpatialShape.length;\n      const outChannels = kernelShape[0];\n      const kernelSpatialShape = kernelShape.slice(2);\n      const dilatedKernelShape = kernelSpatialShape.map((v, i) => v + (v - 1) * (dilations[i] - 1));\n      const inputSpatialShapeWithPad = inputSpatialShape.map((v, i) => v + adjustPads[i] + adjustPads[i + spatialRank]);\n      const outputSpatialShape =\n          inputSpatialShapeWithPad.map((v, i) => Math.floor((v - dilatedKernelShape[i] + strides[i]) / strides[i]));\n      const outputShape = [batchSize, outChannels].concat(...outputSpatialShape);\n      return outputShape;\n    };\n\nexport interface ConvAttributes extends InternalActivationAttributes, AttributeWithCacheKey {\n  readonly autoPad: string;\n  readonly dilations: readonly number[];\n  readonly group: number;\n  readonly kernelShape: readonly number[];\n  readonly pads: readonly number[];\n  readonly strides: readonly number[];\n}\n\nexport const conv: OperatorImplementation<ConvAttributes> =\n    (inferenceHandler: InferenceHandler, inputs: Tensor[], attributes: ConvAttributes): Tensor[] => {\n      validateInputs(inputs, attributes);  // currently will fail if not conv2D\n      return conv2d(inferenceHandler, inputs, attributes);\n    };\n\nconst conv2d: OperatorImplementation<ConvAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: ConvAttributes): Tensor[] => {\n      const adjustedAttributes = getAdjustedConvAttributes(attributes, inputs);\n      const packMode = inferenceHandler.session.pack;\n      const isPointwise = adjustedAttributes.kernelShape[0] === 1 && adjustedAttributes.kernelShape[1] === 1;\n      if (adjustedAttributes.group > 1) {\n        const result = inferenceHandler.run(\n            createUnpackedGroupedConvProgramInfoLoader(inferenceHandler, inputs, adjustedAttributes), inputs);\n        return [result];\n      } else if (isPointwise && packMode) {\n        return [conv2DUnpackedPointwise(inferenceHandler, inputs, adjustedAttributes)];\n      } else if (packMode && inputs[0].dims.length === 4 && inputs[0].dims[0] === 1 && !isPointwise) {\n        return [conv2DPacked(inferenceHandler, inputs, adjustedAttributes)];\n      } else {\n        return [conv2DUnpacked(inferenceHandler, inputs, adjustedAttributes)];\n      }\n    };\n\nconst conv2DUnpackedPointwise =\n    (inferenceHandler: WebGLInferenceHandler, inputs: readonly Tensor[], attributes: ConvAttributes): Tensor => {\n      const xshape = inputs[0].dims;\n      const kshape = inputs[1].dims;\n      const outputShape =\n          calculateOutputShape(xshape, kshape, attributes.dilations, attributes.pads, attributes.strides);\n      const reshapedX = inferenceHandler.reshapeUnpacked(inputs[0], [xshape[1], xshape[2] * xshape[3]]);\n      const reshapedK = inferenceHandler.reshapeUnpacked(inputs[1], [kshape[0], kshape[1]]);\n\n      const matmulInputs = inputs.length > 2 ? [reshapedK, reshapedX, inputs[2]] : [reshapedK, reshapedX];\n      const matmulOutput = inferenceHandler.run(createMatmulProgramInfoLoader(matmulInputs, attributes), matmulInputs);\n      return inferenceHandler.reshapeUnpacked(matmulOutput, outputShape);\n    };\n\nconst conv2DUnpacked =\n    (inferenceHandler: WebGLInferenceHandler, inputs: readonly Tensor[], attributes: ConvAttributes): Tensor => {\n      const xshape = inputs[0].dims;\n      const kshape = inputs[1].dims;\n      const outputShape =\n          calculateOutputShape(xshape, kshape, attributes.dilations, attributes.pads, attributes.strides);\n      const xIm2Col = inferenceHandler.run(\n          createIm2ColProgramInfoLoader(inferenceHandler, inputs[0], inputs[1], outputShape, attributes), [inputs[0]]);\n\n      const dotProductInputs = inputs.length === 3 ? [xIm2Col, inputs[1], inputs[2]] : [xIm2Col, inputs[1]];\n      const output = inferenceHandler.run(\n          createDotProductProgramInfoLoader(inferenceHandler, inputs, outputShape, attributes), dotProductInputs);\n      return output;\n    };\n\nconst getAdjustedConvAttributes = <T extends ConvAttributes>(attributes: T, inputs: Tensor[]): T => {\n  const kernelShape = attributes.kernelShape.slice();\n  // if kernelShape is not specified in the attributes of this op, infer it from the weight tensor dims\n  if (attributes.kernelShape.length === 0) {\n    for (let i = 2; i < inputs[1].dims.length; ++i) {\n      kernelShape.push(inputs[1].dims[i]);\n    }\n  }\n  const pads = attributes.pads.slice();\n  PoolConvUtil.adjustPadsBasedOnAutoPad(\n      inputs[0].dims, attributes.strides, attributes.dilations, kernelShape, pads, attributes.autoPad);\n\n  // always return a new object so does not modify the original attributes\n  const newAttributes: T = Object.assign({}, attributes);\n  Object.assign(newAttributes, {kernelShape, pads, cacheKey: attributes.cacheKey});\n  return newAttributes;\n};\n\nexport const parseConvAttributes: OperatorInitialization<ConvAttributes> = (node: Graph.Node): ConvAttributes => {\n  const attributes = node.attributes;\n  const activationAttributes = parseInternalActivationAttributes(attributes);\n  // TODO : Make this generic enough to compute default attributes for multi-dimensional conv\n  const autoPad = attributes.getString('auto_pad', 'NOTSET');\n  const dilations = attributes.getInts('dilations', [1, 1]);\n  const group = attributes.getInt('group', 1);\n  const kernelShape = attributes.getInts('kernel_shape', []);\n  const pads = attributes.getInts('pads', [0, 0, 0, 0]);\n  const strides = attributes.getInts('strides', [1, 1]);\n\n  return createAttributeWithCacheKey({autoPad, dilations, group, kernelShape, pads, strides, ...activationAttributes});\n};\n\nconst validateInputs = (inputs: Tensor[], attributes: ConvAttributes): void => {\n  // Refer to the below link for all input checks\n  // https://github.com/onnx/onnx/blob/main/docs/Operators.md#Conv\n  if (!inputs || (inputs.length !== 2 && inputs.length !== 3)) {\n    throw new Error('Conv requires 2 or 3 inputs');\n  }\n\n  // TODO : Need to add support for multi-dimensional conv\n  if (inputs[0].dims.length !== 4 || inputs[1].dims.length !== 4) {\n    throw new Error('currently only support 2-dimensional conv');\n  }\n\n  // FILTER_IN_CHANNEL should be equal to DATA_CHANNEL\n  const dataChannel = inputs[0].dims[1];\n  const filterInChannel = inputs[1].dims[1] * attributes.group;\n  if (dataChannel !== filterInChannel) {\n    throw new Error('FILTER_IN_CHANNEL should be equal to DATA_CHANNEL');\n  }\n\n  // if bias is provided it should be 1D and the number of elements should be equal to the number of feature maps\n  if (inputs.length === 3 && (inputs[2].dims.length !== 1 || inputs[1].dims[0] !== inputs[2].dims[0])) {\n    throw new Error('invalid bias');\n  }\n\n  const spatialRank = inputs[0].dims.length - 2;\n  // wrong dilations dimension\n  if (attributes.dilations.length !== spatialRank) {\n    throw new Error(`dilations should be ${spatialRank}D`);\n  }\n\n  // Wrong strides dimension\n  if (attributes.strides.length !== spatialRank) {\n    throw new Error(`strides should be ${spatialRank}D`);\n  }\n\n  // Wrong pads dimension\n  if (attributes.pads.length !== spatialRank * 2) {\n    throw new Error(`pads should be ${spatialRank * 2}D`);\n  }\n\n  // if kernelShape is specified, it's data length must be 2 less than dims length of the weights tensor\n  // (the first 2 dims are batch_size and channels)\n  if (attributes.kernelShape.length !== 0 && attributes.kernelShape.length !== inputs[1].dims.length - 2) {\n    throw new Error('invalid kernel shape');\n  }\n\n  // TODO : Need to add support for float64\n  if (inputs[0].type !== 'float32' || inputs[1].type !== 'float32') {\n    throw new Error('Conv input(X,W) should be float tensor');\n  }\n\n  if (inputs.length === 3 && inputs[2].type !== 'float32') {\n    throw new Error('Conv input(bias) should be float tensor');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {InferenceHandler} from '../../../backend';\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';\n\nimport {ConvAttributes} from './conv';\nimport {getActivationSnippet, parseInternalActivationAttributes} from './fuse-utils';\n\nconst computeTotalPad =\n    (inDim: number, stride: number, adj: number, kernel: number, dilation: number, outSize: number) =>\n        (inDim - 1) * stride + adj + (kernel - 1) * dilation + 1 - outSize;\n\nconst distributePadding = (totalPad: number, autoPad: string, pads: number[], head: number, tail: number) => {\n  const smallPad = Math.floor(totalPad / 2);\n  if (autoPad === 'SAME_UPPER') {\n    pads[head] = smallPad;\n    pads[tail] = totalPad - smallPad;\n  } else if (autoPad === 'SAME_LOWER') {\n    pads[head] = totalPad - smallPad;\n    pads[tail] = smallPad;\n  }\n};\n\nconst calculateOutputShapeAndPads =\n    (inputShape: readonly number[], kernelShape: readonly number[], dilations: readonly number[], autoPad: string,\n     pads: number[], strides: readonly number[], outputPadding: readonly number[], outputShape: number[]) => {\n      const spatialRank = inputShape.length - 2;\n      const updateShape = outputShape.length === 0;\n      for (let i = 0; i < spatialRank; ++i) {\n        const outSize = updateShape ? inputShape[i + 2] * strides[i] : outputShape[i];\n        const totalPad = computeTotalPad(inputShape[i + 2], strides[i], pads[i], kernelShape[i], dilations[i], outSize);\n        distributePadding(totalPad, autoPad, pads, i, i + spatialRank);\n        if (updateShape) {\n          outputShape.push(\n              strides[i] * (inputShape[i + 2] - 1) + outputPadding[i] + (kernelShape[i] - 1) * dilations[i] + 1 -\n              pads[i] - pads[i + spatialRank]);\n        }\n      }\n    };\n\nexport interface ConvTransposeAttributes extends ConvAttributes {\n  readonly outputPadding: readonly number[];\n  readonly outputShape: readonly number[];\n}\n\nexport const convTranspose: OperatorImplementation<ConvTransposeAttributes> =\n    (inferenceHandler: InferenceHandler, inputs: Tensor[], attributes: ConvTransposeAttributes): Tensor[] => {\n      validateInputs(inputs, attributes);  // currently will fail if not convTranspose2D\n      return convTranspose2d(inferenceHandler, inputs, attributes);\n    };\n\nconst convTranspose2d: OperatorImplementation<ConvTransposeAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: ConvTransposeAttributes): Tensor[] => {\n      const adjustedAttributes = getAdjustedConvTransposeAttributes(attributes, inputs);\n      return [convTranspose2DUnpacked(inferenceHandler, inputs, adjustedAttributes)];\n    };\n\nconst createConvTransposeProgramMetadata = (hasBias: boolean, cacheHint: string) => ({\n  name: 'ConvTranspose',\n  inputNames: hasBias ? ['X', 'W', 'B'] : ['X', 'W'],\n  inputTypes: hasBias ? [TextureType.unpacked, TextureType.unpacked, TextureType.unpacked] :\n                        [TextureType.unpacked, TextureType.unpacked],\n  cacheHint\n});\n\nconst createUnpackedConvTransposeProgramInfo =\n    (inferenceHandler: WebGLInferenceHandler, inputs: readonly Tensor[], metadata: ProgramMetadata,\n     attributes: ConvTransposeAttributes): ProgramInfo => {\n      const hasBias = inputs.length > 2;\n      const valueInit = hasBias ? 'getB(output_channel)' : '0.0';\n      const xShape = inputs[0].dims;\n      const wShape = inputs[1].dims;\n      const outputChannelsPerGroup = wShape[1];\n      const inputChannelsPerGroup = wShape[0] / attributes.group;\n      const outputShape = [inputs[0].dims[0], inputs[1].dims[1] * attributes.group, ...attributes.outputShape];\n      const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);\n      const {activationFunction, applyActivation} = getActivationSnippet(attributes);\n\n      const shaderSource = `\n  const ivec2 strides = ivec2(${attributes.strides[0]}, ${attributes.strides[1]});\n  const ivec2 pads = ivec2(${attributes.pads[0]}, ${attributes.pads[1]});\n  ${activationFunction}\n  void main() {\n    ivec4 coords = getOutputCoords();\n    int batch = coords.x;\n    int output_channel = coords.y;\n\n    ivec2 loc = coords.zw + pads;\n\n    int group_id = output_channel / ${outputChannelsPerGroup};\n    int wOutChannel = output_channel - group_id * ${outputChannelsPerGroup};\n\n    float value = ${valueInit};\n    for (int inChannelOffset = 0; inChannelOffset < ${inputChannelsPerGroup}; inChannelOffset++) {\n      int input_channel = group_id * ${inputChannelsPerGroup} + inChannelOffset;\n      for (int wWOff = 0; wWOff < ${wShape[2]}; wWOff++) {\n        for (int wHOff = 0; wHOff < ${wShape[3]}; wHOff++) {\n          ivec2 wOff = ivec2(wWOff * ${attributes.dilations[0]}, wHOff * ${attributes.dilations[1]});\n          ivec2 wLoc = loc - wOff;\n          ivec2 wLocIn = wLoc / strides;\n          if (\n            wLocIn * strides == wLoc &&\n            wLocIn.x >= 0 && wLocIn.x < ${xShape[2]} &&\n            wLocIn.y >= 0 && wLocIn.y < ${xShape[3]}\n          ) {\n            float xVal = getX(batch, input_channel, wLocIn.y, wLocIn.x);\n            float wVal = getW(input_channel, wOutChannel, wHOff, wWOff);\n            value += xVal * wVal;\n          }\n        }\n      }\n    }\n    ${applyActivation}\n    ${glsl.output} = vec4(value, .0, .0, .0);\n  }\n`;\n      return {\n        ...metadata,\n        output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},\n        shaderSource,\n        hasMain: true,\n      };\n    };\n\nconst createUnpackedConvTransposeProgramInfoLoader =\n    (inferenceHandler: WebGLInferenceHandler, inputs: readonly Tensor[], attributes: ConvTransposeAttributes):\n        ProgramInfoLoader => {\n          const metadata = createConvTransposeProgramMetadata(inputs.length > 2, attributes.cacheKey);\n          return {\n            ...metadata,\n            get: () => createUnpackedConvTransposeProgramInfo(inferenceHandler, inputs, metadata, attributes)\n          };\n        };\n\n\nconst convTranspose2DUnpacked =\n    (inferenceHandler: WebGLInferenceHandler, inputs: readonly Tensor[], attributes: ConvTransposeAttributes):\n        Tensor => {\n          const result = inferenceHandler.run(\n              createUnpackedConvTransposeProgramInfoLoader(inferenceHandler, inputs, attributes), inputs);\n          return result;\n        };\n\nconst getAdjustedConvTransposeAttributes = <T extends ConvTransposeAttributes>(attributes: T, inputs: Tensor[]): T => {\n  const kernelShape = attributes.kernelShape.slice();\n  // if kernelShape is not specified in the attributes of this op, infer it from the weight tensor dims\n  if (attributes.kernelShape.length === 0) {\n    for (let i = 2; i < inputs[1].dims.length; ++i) {\n      kernelShape.push(inputs[1].dims[i]);\n    }\n  }\n\n  const pads = attributes.pads.slice();\n  const outputShape = attributes.outputShape.slice();\n  const inputShape = inputs[0].dims;\n  // If outputShape is not specified in the attributes of this op, infer it from the parameters\n  // Similarly, automatically infer pads if not specified\n  calculateOutputShapeAndPads(\n      inputShape, kernelShape, attributes.dilations, attributes.autoPad, pads, attributes.strides,\n      attributes.outputPadding, outputShape);\n\n  // always return a new object so does not modify the original attributes\n  const newAttributes: T = Object.assign({}, attributes);\n  Object.assign(newAttributes, {kernelShape, pads, outputShape, cacheKey: attributes.cacheKey});\n  return newAttributes;\n};\n\nexport const parseConvTransposeAttributes: OperatorInitialization<ConvTransposeAttributes> =\n    (node: Graph.Node): ConvTransposeAttributes => {\n      const attributes = node.attributes;\n      const activationAttributes = parseInternalActivationAttributes(attributes);\n      // TODO : Make this generic enough to compute default attributes for multi-dimensional conv\n      const autoPad = attributes.getString('auto_pad', 'NOTSET');\n      const dilations = attributes.getInts('dilations', [1, 1]);\n      const group = attributes.getInt('group', 1);\n      const kernelShape = attributes.getInts('kernel_shape', []);\n      const outputPadding = attributes.getInts('output_padding', [0, 0]);\n      const outputShape = attributes.getInts('output_shape', []);\n      const pads = attributes.getInts('pads', [0, 0, 0, 0]);\n      const strides = attributes.getInts('strides', [1, 1]);\n\n      return createAttributeWithCacheKey(\n          {autoPad, dilations, group, kernelShape, outputPadding, outputShape, pads, strides, ...activationAttributes});\n    };\n\nconst validateInputs = (inputs: Tensor[], attributes: ConvTransposeAttributes): void => {\n  // Refer to the below link for all input checks\n  // https://github.com/onnx/onnx/blob/main/docs/Operators.md#Conv\n  if (!inputs || (inputs.length !== 2 && inputs.length !== 3)) {\n    throw new Error('Conv requires 2 or 3 inputs');\n  }\n\n  // TODO : Need to add support for multi-dimensional conv\n  if (inputs[0].dims.length !== 4 || inputs[1].dims.length !== 4) {\n    throw new Error('currently only support 2-dimensional conv');\n  }\n\n  // FILTER_IN_CHANNEL should be equal to DATA_CHANNEL\n  const dataChannel = inputs[0].dims[1];\n  const filterInChannel = inputs[1].dims[0];\n  if (dataChannel !== filterInChannel) {\n    throw new Error('FILTER_IN_CHANNEL should be equal to DATA_CHANNEL');\n  }\n\n  const featureMaps = inputs[1].dims[1] * attributes.group;\n\n  // if bias is provided it should be 1D and the number of elements should be equal to the number of feature maps\n  if (inputs.length === 3 && (inputs[2].dims.length !== 1 || inputs[2].dims[0] !== featureMaps)) {\n    throw new Error('invalid bias');\n  }\n\n  const spatialRank = inputs[0].dims.length - 2;\n  // wrong dilations dimension\n  if (attributes.dilations.length !== spatialRank) {\n    throw new Error(`dilations should be ${spatialRank}D`);\n  }\n\n  // Wrong strides dimension\n  if (attributes.strides.length !== spatialRank) {\n    throw new Error(`strides should be ${spatialRank}D`);\n  }\n\n  // Wrong pads dimension\n  if (attributes.pads.length !== spatialRank * 2) {\n    throw new Error(`pads should be ${spatialRank * 2}D`);\n  }\n\n  // Wrong output padding dimension\n  if (attributes.outputPadding.length !== spatialRank) {\n    throw new Error(`output_padding should be ${spatialRank}D`);\n  }\n\n  // if kernelShape is specified, it's data length must be 2 less than dims length of the weights tensor\n  // (the first 2 dims are batch_size and channels)\n  if (attributes.kernelShape.length !== 0 && attributes.kernelShape.length !== inputs[1].dims.length - 2) {\n    throw new Error('invalid kernel shape');\n  }\n\n  // as with kernelShape, must have same number of spatial dims as input\n  if (attributes.outputShape.length !== 0 && attributes.outputShape.length !== inputs[0].dims.length - 2) {\n    throw new Error('invalid output shape');\n  }\n\n  // TODO : Need to add support for float64\n  if (inputs[0].type !== 'float32' || inputs[1].type !== 'float32') {\n    throw new Error('ConvTranspose input(X,W) should be float tensor');\n  }\n\n  if (inputs.length === 3 && inputs[2].type !== 'float32') {\n    throw new Error('ConvTranspose input(bias) should be float tensor');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {ShapeUtil} from '../../../util';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, TextureType} from '../types';\n\nexport interface TransposeAttributes extends AttributeWithCacheKey {\n  readonly perm: number[];\n}\n\nconst transposeProgramMetadata = {\n  name: 'Transpose',\n  inputNames: ['A'],\n  inputTypes: [TextureType.unpacked]\n};\n\nexport const transpose: OperatorImplementation<TransposeAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: TransposeAttributes): Tensor[] => {\n      validateInputs(inputs);\n      const output = inferenceHandler.run(\n          {\n            ...transposeProgramMetadata,\n            cacheHint: attributes.cacheKey,\n            get: () => createTransposeProgramInfo(inferenceHandler, inputs[0], attributes.perm)\n          },\n          inputs);\n      return [output];\n    };\n\nexport const parseTransposeAttributes: OperatorInitialization<TransposeAttributes> =\n    (node: Graph.Node): TransposeAttributes => createAttributeWithCacheKey({perm: node.attributes.getInts('perm', [])});\n\nconst createTransposeProgramInfo =\n    (_inferenceHandler: WebGLInferenceHandler, input: Tensor, perm: number[]): ProgramInfo => {\n      const inputShape = input.dims;\n      perm = getAdjustedPerm(inputShape, perm);\n      const unpackedOutputShape = getOutputShape(inputShape, perm);\n      const rank = inputShape.length;\n      // A dims=[${inputs[0].dims.toString()}]\n      // out Dims=[${unpackedOutputShape.toString()}]\n      // based on perm=[${perm.toString()}]\n      const shaderSource = `\n      ${getPermFunctionBody('perm', perm, rank)}\n      float process(int indices[${rank}]) {\n        int a[${rank}];\n        perm(a, indices);\n        return _A(a);\n      }`;\n      return {\n        ...transposeProgramMetadata,\n        output: {dims: unpackedOutputShape, type: input.type, textureType: TextureType.unpacked},\n        shaderSource\n      };\n    };\n\nconst getAdjustedPerm = (inputShape: readonly number[], perm: number[]): number[] => {\n  if (perm && perm.length !== inputShape.length) {\n    perm = [...(inputShape.keys())].reverse();\n  }\n  return perm;\n};\n\nconst getOutputShape = (inputShape: readonly number[], perm: number[]): readonly number[] => {\n  perm = getAdjustedPerm(inputShape, perm);\n  return ShapeUtil.sortBasedOnPerm(inputShape, perm);\n};\n\nconst getPermFunctionBody = (name: string, perm: number[], rank: number): string => {\n  const reverseFunc = [];\n  reverseFunc.push(`void ${name}(out int a[${rank}], int src[${rank}]) {`);\n  for (let i = 0; i < rank; ++i) {\n    reverseFunc.push(`\\ta[${perm[i]}]=src[${i}];`);\n  }\n  reverseFunc.push('\\t}');\n  return reverseFunc.join('\\n');\n};\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 1) {\n    throw new Error('Transpose requires 1 input.');\n  }\n\n  if (inputs[0].type !== 'float32' && inputs[0].type !== 'float64') {\n    throw new Error('input should be float tensor');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {WebGLInferenceHandler} from '../inference-handler';\n\nimport {transpose, TransposeAttributes} from './transpose';\n\nexport interface DepthToSpaceAttributes {\n  mode: 'DCR'|'CRD';\n  blocksize: number;\n}\n\nexport const depthToSpace: OperatorImplementation<DepthToSpaceAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: DepthToSpaceAttributes): Tensor[] => {\n      validateInputs(inputs);\n      const blocksize = attributes.blocksize;\n      const blocksizeSqr = blocksize * blocksize;\n      const transposePerm = attributes.mode === 'DCR' ? [0, 3, 4, 1, 5, 2] : [0, 1, 4, 2, 5, 3];\n      const firstReshapeShape = attributes.mode === 'DCR' ?\n          [\n            inputs[0].dims[0], blocksize, blocksize, inputs[0].dims[1] / blocksizeSqr, inputs[0].dims[2],\n            inputs[0].dims[3]\n          ] :\n          [\n            inputs[0].dims[0], inputs[0].dims[1] / blocksizeSqr, blocksize, blocksize, inputs[0].dims[2],\n            inputs[0].dims[3]\n          ];\n\n      // const transpose = new WebGLTranspose();\n      // const attributes = new Attribute(undefined);\n      // attributes.set('perm', 'ints', transposePerm);\n      // transpose.initialize(attributes);\n\n      // First reshape\n      const firstReshapedTensor = inferenceHandler.reshapeUnpacked(inputs[0], firstReshapeShape);\n\n      // transpose\n      const transposeAttributes: TransposeAttributes = {perm: transposePerm, cacheKey: `${transposePerm}`};\n      const [transposeOutput] = transpose(inferenceHandler, [firstReshapedTensor], transposeAttributes);\n\n      // Second reshape\n      const secondReshapeShape = [\n        inputs[0].dims[0], inputs[0].dims[1] / blocksizeSqr, inputs[0].dims[2] * blocksize,\n        inputs[0].dims[3] * blocksize\n      ];\n      const result = inferenceHandler.reshapeUnpacked(transposeOutput, secondReshapeShape);\n      return [result];\n    };\n\nexport const parseDepthToSpaceAttributes: OperatorInitialization<DepthToSpaceAttributes> =\n    (node: Graph.Node): DepthToSpaceAttributes => {\n      // processing node attributes\n      const blocksize = node.attributes.getInt('blocksize');\n      if (blocksize < 1) {\n        throw new Error(`blocksize must be >= 1, but got : ${blocksize} for DepthToSpace`);\n      }\n      const mode = node.attributes.getString('mode', 'DCR');\n      if (mode !== 'DCR' && mode !== 'CRD') {\n        throw new Error(`unrecognized mode: ${mode} for DepthToSpace`);\n      }\n      return {mode, blocksize};\n    };\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (inputs.length !== 1) {\n    throw new Error(`DepthToSpace expect 1 inputs, but got ${inputs.length}`);\n  }\n\n  // Input has to be a 4-D tensor\n  // TODO: Support string depth-to-space.\n  if (inputs[0].type === 'string' || inputs[0].dims.length !== 4) {\n    throw new TypeError('DepthToSpace input should be a 4-D numeric tensor');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {ShapeUtil} from '../../../util';\nimport {WebGLInferenceHandler} from '../inference-handler';\n\nexport const flatten: OperatorImplementation<number> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], axis: number): Tensor[] => {\n      validateInputs(inputs, axis);\n\n      const outputDims = ShapeUtil.flattenShape(inputs[0].dims, axis);\n      return [inferenceHandler.reshapeUnpacked(inputs[0], outputDims)];\n    };\n\nexport const parseFlattenAttributes: OperatorInitialization<number> = (node: Graph.Node): number =>\n    node.attributes.getInt('axis', 1);  // default axis is 1\n\nconst validateInputs = (inputs: Tensor[], axis: number): void => {\n  if (!inputs || inputs.length !== 1) {\n    throw new Error('Flatten requires 1 input.');\n  }\n\n  const r = inputs[0].dims.length;\n  if (r === 0) {\n    throw new Error('scalar tensor is not supported.');\n  }\n\n  if (axis < -r || axis > r) {\n    throw new Error('Invalid axis');\n  }\n\n  // TODO: Support string type\n  if (inputs[0].type === 'string') {\n    throw new Error('string tensor is not supported.');\n  }\n};","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {InferenceHandler} from './backend';\nimport {Graph} from './graph';\nimport {Tensor} from './tensor';\n\nexport type OperatorImplementation<T> = (inferenceHandler: InferenceHandler, inputs: Tensor[], context: T) => Tensor[];\nexport type OperatorInitialization<T> = (node: Graph.Node, graph: Graph) => T;\n\nexport interface Operator {\n  readonly impl: OperatorImplementation<unknown>;\n  readonly context: Graph.Node|unknown;\n}\n\nexport const NUMBER_TYPES: readonly Tensor.DataType[] =\n    ['float32', 'float64', 'int32', 'int16', 'int8', 'uint16', 'uint32', 'uint8'];\nexport const INT_TYPES: readonly Tensor.DataType[] = ['int32', 'int16', 'int8', 'uint16', 'uint32', 'uint8'];\nexport const FLOAT_TYPES: readonly Tensor.DataType[] = ['float32', 'float64'];\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {Graph} from '../../../graph';\nimport {NUMBER_TYPES, OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {ShapeUtil} from '../../../util';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';\n\ninterface GatherAttributes extends AttributeWithCacheKey {\n  readonly axis: number;\n}\n\nexport const gather: OperatorImplementation<GatherAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: GatherAttributes): Tensor[] => {\n      validateInputs(inputs, attributes.axis);\n      const output = inferenceHandler.run(createGatherProgramInfoLoader(inferenceHandler, inputs, attributes), inputs);\n      return [output];\n    };\n\nexport const parseGatherAttributes: OperatorInitialization<GatherAttributes> = (node: Graph.Node): GatherAttributes =>\n    createAttributeWithCacheKey({axis: node.attributes.getInt('axis', 0)});\n\nconst gatherProgramMetadata = {\n  name: 'Gather',\n  inputNames: ['A', 'B'],\n  inputTypes: [TextureType.unpacked, TextureType.unpacked],\n};\n\nconst createGatherProgramInfo =\n    (_handler: WebGLInferenceHandler, metadata: ProgramMetadata, inputs: Tensor[], axis: number): ProgramInfo => {\n      const inputShape = inputs[0].dims.slice();\n      const indexDataShape = inputs[1].dims.slice();\n      const outputShape = new Array(inputShape.length + indexDataShape.length - 1);\n\n      axis = ShapeUtil.normalizeAxis(axis, inputShape.length);\n      const indexCopyOps: string[] = [];\n      for (let i = 0; i < outputShape.length; i++) {\n        // outputShape is divided into three parts: A, B, C\n        // |0        axis|  axis + indexDataShape.length |          end|\n        // |     A       |             B                 |      C      |\n        //\n        // inputIdx: [A, inputs[1][B], C]\n        if (i < axis) {  // A\n          outputShape[i] = inputShape[i];\n          indexCopyOps.push(`inputIdx[${i}] = outputIdx[${i}];`);\n        } else {\n          if (i < axis + indexDataShape.length) {  // B\n            outputShape[i] = indexDataShape[i - axis];\n            indexCopyOps.push(`indexDataIdx[${i - axis}] = outputIdx[${i}];`);\n          } else {                                                       // C\n            outputShape[i] = inputShape[i - indexDataShape.length + 1];  // skip 1 for axis\n            indexCopyOps.push(`inputIdx[${i - indexDataShape.length + 1}] = outputIdx[${i}];`);\n          }\n        }\n      }\n\n      const orank = outputShape.length || 1;\n      const irank = inputShape.length;\n      const iDrank = indexDataShape.length || 1;\n      const shaderSource = `\n      float process(int outputIdx[${orank}]) {\n        int inputIdx[${irank}];\n        int indexDataIdx[${iDrank}];\n        indexDataIdx[0] = 0;\n        ${indexCopyOps.join('\\n        ')}\n        int idx = int(_B(indexDataIdx));\n        inputIdx[${axis}] = idx < 0 ? idx + ${inputShape[axis]} : idx;\n        return _A(inputIdx);\n      }`;\n      return {\n        ...metadata,\n        output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},\n        shaderSource\n      };\n    };\n\nconst createGatherProgramInfoLoader =\n    (handler: WebGLInferenceHandler, inputs: Tensor[], attributes: GatherAttributes): ProgramInfoLoader => {\n      const metadata = {...gatherProgramMetadata, cacheHint: attributes.cacheKey};\n      return {...metadata, get: () => createGatherProgramInfo(handler, metadata, inputs, attributes.axis)};\n    };\n\nconst validateInputs = (inputs: Tensor[], axis: number): void => {\n  if (!inputs || inputs.length !== 2) {\n    throw new Error('Gather requires 2 inputs.');\n  }\n  const tensorRank = inputs[0].dims.length;\n  if (tensorRank < 1) {\n    throw new Error('Invalid input shape.');\n  }\n  if (axis < -tensorRank || axis > tensorRank - 1) {\n    throw new Error('Invalid axis.');\n  }\n  if (NUMBER_TYPES.indexOf(inputs[0].type) === -1) {\n    throw new Error('Invaid input type.');\n  }\n  if (inputs[1].type !== 'int32' && inputs[1].type !== 'int16') {\n    throw new Error('Invaid input type.');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {GemmUtil} from '../../../util';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';\n\nexport interface GemmAttributes extends AttributeWithCacheKey {\n  transA: boolean;\n  transB: boolean;\n  alpha: number;\n  beta: number;\n  isOptionalC: boolean;  // in opset 11, C becomes optional\n}\n\nexport const gemm: OperatorImplementation<GemmAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: GemmAttributes): Tensor[] => {\n      validateInputs(inputs, attributes);\n      const output = inferenceHandler.run(createGemmProgramInfoLoader(inputs, attributes), inputs);\n      return [output];\n    };\n\nconst parseGemmAttributes = (node: Graph.Node, isOptionalC: boolean): GemmAttributes => {\n  const transA = node.attributes.getInt('transA', 0) !== 0;\n  const transB = node.attributes.getInt('transB', 0) !== 0;\n  const alpha = node.attributes.getFloat('alpha', 1.0);\n  const beta = node.attributes.getFloat('beta', 1.0);\n  return createAttributeWithCacheKey({transA, transB, alpha, beta, isOptionalC});\n};\n\nexport const parseGemmAttributesV7: OperatorInitialization<GemmAttributes> = (node: Graph.Node): GemmAttributes =>\n    parseGemmAttributes(node, false);\n\nexport const parseGemmAttributesV11: OperatorInitialization<GemmAttributes> = (node: Graph.Node): GemmAttributes =>\n    parseGemmAttributes(node, true);\n\nconst createGemmProgramInfoLoader = (inputs: Tensor[], attributes: GemmAttributes): ProgramInfoLoader => {\n  const metadata = {\n    name: 'Gemm',\n    inputNames: inputs.length === 3 ? ['A', 'B', 'C'] : ['A', 'B'],\n    inputTypes: inputs.length === 3 ? [TextureType.unpacked, TextureType.unpacked, TextureType.unpacked] :\n                                      [TextureType.unpacked, TextureType.unpacked],\n    key: attributes.cacheKey\n  };\n\n  return {...metadata, get: () => createGemmProgramInfo(metadata, inputs, attributes)};\n};\n\nconst createGemmProgramInfo =\n    (metadata: ProgramMetadata, inputs: Tensor[], attributes: GemmAttributes): ProgramInfo => {\n      const aShape = inputs[0].dims.slice();\n      const bShape = inputs[1].dims.slice();\n      const [M, N] = GemmUtil.getShapeOfGemmResult(\n          aShape, attributes.transA, bShape, attributes.transB, inputs.length === 3 ? inputs[2].dims : undefined);\n      const outputShape = [M, N];\n      if (!outputShape) {\n        throw new Error('Can\\'t use gemm on the given tensors');\n      }\n      let sharedDim = aShape[aShape.length - 1];\n      let line = '';\n      if (attributes.transA) {\n        sharedDim = aShape[0];\n      }\n      if (attributes.transA && attributes.transB) {\n        line = 'value += _A_T(a) * _B_T(b);';\n      } else if (attributes.transA && !attributes.transB) {\n        line = 'value += _A_T(a) * _B(b);';\n      } else if (!attributes.transA && attributes.transB) {\n        line = 'value += _A(a) * _B_T(b);';\n      } else if (!attributes.transA && !attributes.transB) {\n        line = 'value += _A(a) * _B(b);';\n      }\n      const rank = outputShape.length;\n      const declareC = inputs.length === 3 ? `int c[${inputs[2].dims.length}];` : '';\n      const broadcastC = inputs.length === 3 ? 'bcastIndices_C(indices, c);' : '';\n      const calculateC = inputs.length === 3 ? 'value += beta * _C(c);' : '';\n      const shaderSource = `\n      float process(int indices[${rank}]) {\n          int a[${rank}];\n          int b[${rank}];\n          ${declareC}\n\n          copyVec(indices, a);\n          copyVec(indices, b);\n          ${broadcastC}\n\n          float value = 0.0;\n          for (int k=0; k<${sharedDim}; ++k) {\n              a[${rank - 1}] = k;\n              b[${rank - 2}] = k;\n              ${line}\n          }\n\n          value = value * alpha;\n          ${calculateC}\n          return value;\n      }`;\n      return {\n        ...metadata,\n        output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},\n        variables: [\n          {name: 'alpha', type: 'float', data: attributes.alpha}, {name: 'beta', type: 'float', data: attributes.beta}\n        ],\n        shaderSource\n      };\n    };\n\nconst validateInputs = (inputs: Tensor[], attributes: GemmAttributes): void => {\n  if (!inputs) {\n    throw new Error('Input is missing');\n  }\n  if (attributes.isOptionalC && (inputs.length < 2 || inputs.length > 3)) {\n    throw new Error('Invaid input shape.');\n  }\n  if (!attributes.isOptionalC && inputs.length !== 3) {\n    throw new Error('Gemm requires 3 inputs');\n  }\n\n  // 'C' can be of dimensionality 1 or 2 only\n  if (inputs.length === 3 && inputs[2].dims.length !== 1 && inputs[2].dims.length !== 2) {\n    throw new Error('Invalid input shape of C');\n  }\n\n  if ((inputs[0].type !== 'float32' && inputs[0].type !== 'float64') ||\n      (inputs[1].type !== 'float32' && inputs[1].type !== 'float64') ||\n      (inputs.length === 3 && inputs[2].type !== 'float32' && inputs[2].type !== 'float64')) {\n    throw new Error('Invalid input type.');\n  }\n\n  if ((inputs[0].type !== inputs[1].type) || (inputs.length === 3 && inputs[0].type !== inputs[2].type)) {\n    throw new Error('Input types are mismatched');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';\n\nexport interface ImageScalerAttributes extends AttributeWithCacheKey {\n  scale: number;\n  bias: number[];\n}\n\nexport const imageScaler: OperatorImplementation<ImageScalerAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: ImageScalerAttributes): Tensor[] => {\n      validateInputs(inputs);\n      const output =\n          inferenceHandler.run(createImageScalerProgramInfoLoader(inferenceHandler, inputs, attributes), inputs);\n      return [output];\n    };\n\nexport const parseImageScalerAttributes: OperatorInitialization<ImageScalerAttributes> =\n    (node: Graph.Node): ImageScalerAttributes => {\n      const scale = node.attributes.getFloat('scale');\n      const bias = node.attributes.getFloats('bias');\n      return createAttributeWithCacheKey({scale, bias});\n    };\n\nconst imageScalerProgramMetadata = {\n  name: 'ImageScaler',\n  inputNames: ['X'],\n  inputTypes: [TextureType.unpacked],\n};\n\nconst createImageScalerProgramInfo =\n    (_handler: WebGLInferenceHandler, metadata: ProgramMetadata, inputs: Tensor[], attributes: ImageScalerAttributes):\n        ProgramInfo => {\n          const outputShape = inputs[0].dims.slice();\n          const rank = outputShape.length;\n          const getBiasMethod = createGetBiasMethod(attributes.bias.length);\n          const shaderSource = `\n      ${getBiasMethod}\n      float process(int indices[${rank}]) {\n        return _X(indices) * scale + getBias(bias, indices[1]);\n      }`;\n          return {\n            ...metadata,\n            output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},\n            variables: [\n              {name: 'bias', type: 'float', arrayLength: attributes.bias.length, data: attributes.bias},\n              {name: 'scale', type: 'float', data: attributes.scale}\n            ],\n            shaderSource\n          };\n        };\n\nconst createImageScalerProgramInfoLoader =\n    (handler: WebGLInferenceHandler, inputs: Tensor[], attributes: ImageScalerAttributes): ProgramInfoLoader => {\n      const metadata = {...imageScalerProgramMetadata, cacheHint: attributes.cacheKey};\n      return {...metadata, get: () => createImageScalerProgramInfo(handler, metadata, inputs, attributes)};\n    };\n\nconst createGetBiasMethod = (numChannels: number): string => {\n  const codeLines: string[] = [`float getBias(float bias[${numChannels}], int channel) {`];\n  for (let i = 0; i < numChannels; ++i) {\n    if (i === 0) {\n      codeLines.push(\n          '\\t' +\n          `if (channel == ${i}) { return bias[${i}]; }`);\n    } else if (i === numChannels - 1) {\n      codeLines.push(\n          '\\t' +\n          `else { return bias[${i}]; }`);\n    } else {\n      codeLines.push(\n          '\\t' +\n          `else if (channel == ${i}) { return bias[${i}]; }`);\n    }\n  }\n  codeLines.push(\n      '\\t' +\n      '}');\n  return codeLines.join('\\n');\n};\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 1) {\n    throw new Error('ImageScaler requires 1 input.');\n  }\n  if (inputs[0].dims.length !== 4) {\n    throw new Error('Invalid input shape.');\n  }\n  if (inputs[0].type !== 'float32' && inputs[0].type !== 'float64') {\n    throw new Error('Invalid input type.');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, ProgramMetadata, TextureType} from '../types';\n\nexport const instanceNormalization: OperatorImplementation<number> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], epsilon: number): Tensor[] => {\n      validateInputs(inputs);\n\n      const meanAndVariance = inferenceHandler.run(createMeanAndVarianceProgramInfoLoader(inputs[0]), inputs);\n      const output = inferenceHandler.run(\n          createComputeOutputProgramInfoLoader(inferenceHandler, inputs[0], epsilon, meanAndVariance.dims),\n          [inputs[0], meanAndVariance, inputs[1], inputs[2]]);\n      return [output];\n    };\n\nexport const parseInstanceNormalizationAttributes: OperatorInitialization<number> = (node: Graph.Node): number =>\n    node.attributes.getFloat('epsilon', 1e-5);\n\nconst meanAndVarianceProgramMetadata = {\n  name: 'InstanceNormalization_MeanAndVariance',\n  inputNames: ['X'],\n  inputTypes: [TextureType.unpacked],\n};\n\nconst createMeanAndVarianceProgramInfo = (metadata: ProgramMetadata, input: Tensor): ProgramInfo => {\n  const xDims = input.dims.slice();\n  const channel = xDims[1];\n  const channelSize = xDims[2] * xDims[3];\n  const outputShape = [xDims[0], channel];\n\n  const shaderSource = `\n      vec4 process(int[2] indices) {\n        vec4 v = vec4(0.0);\n        int a[4];\n        a[0] = indices[0];\n        a[1] = indices[1];\n        float temp = 0.0;\n        for(int a2=0; a2<${xDims[2]}; a2++) {\n          a[2] = a2;\n          for(int a3=0; a3<${xDims[3]}; a3++) {\n            a[3] = a3;\n            float x = _X(a);\n            temp += x;\n          }\n        }\n        float mean = temp / float(${channelSize});\n        temp = 0.0;\n        for(int a2=0; a2<${xDims[2]}; a2++) {\n          a[2] = a2;\n          for(int a3=0; a3<${xDims[3]}; a3++) {\n            a[3] = a3;\n            float x = _X(a);\n            temp += (x - mean) * (x - mean);\n          }\n        }\n        v.r = mean;\n        v.g = temp / float(${channelSize});\n\n        return v;\n      }`;\n  return {\n    ...metadata,\n    output: {dims: outputShape, type: input.type, textureType: TextureType.packedLastDimension},\n    shaderSource\n  };\n};\n\nconst createMeanAndVarianceProgramInfoLoader = (input: Tensor): ProgramInfoLoader => ({\n  ...meanAndVarianceProgramMetadata,\n  get: () => createMeanAndVarianceProgramInfo(meanAndVarianceProgramMetadata, input)\n});\n\nconst computeOutputProgramMetadata = {\n  name: 'InstanceNormalization_ComputeOutput',\n  inputNames: ['X', 'MeanAndVariance', 'Scale', 'B'],\n  inputTypes: [TextureType.unpacked, TextureType.packedLastDimension, TextureType.unpacked, TextureType.unpacked],\n};\n\nconst createComputeOutputProgramInfo =\n    (inferenceHandler: WebGLInferenceHandler, metadata: ProgramMetadata, input: Tensor, epsilon: number,\n     meanAndVarianceShape: readonly number[]): ProgramInfo => {\n      const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);\n      const [textureWidth, textureHeight] =\n          inferenceHandler.calculateTextureWidthAndHeight(meanAndVarianceShape, TextureType.packedLastDimension);\n      const [meanAndVarianceWidth, meanAndVarianceHeight] = [textureWidth / 4, textureHeight];\n      const shaderSource = `\n      vec4 get_MeanAndVariance(int[2] mv) {\n        int offset = indicesToOffset_MeanAndVariance(mv);\n        vec2 coords = offsetToCoords(offset, ${meanAndVarianceWidth}, ${meanAndVarianceHeight});\n        return ${glsl.texture2D}(MeanAndVariance, coords);\n      }\n\n      float process(int[4] indices) {\n        int mv[2];\n        mv[0] = indices[0];\n        mv[1] = indices[1];\n        vec4 mean_and_variance = get_MeanAndVariance(mv);\n        float mean = mean_and_variance.r;\n        float variance = mean_and_variance.g;\n\n        int sb[1];\n        sb[0] = indices[1];\n        float scale = _Scale(sb);\n        float b = _B(sb);\n\n        return scale * (_X(indices) - mean) / sqrt(variance + epsilon) + b;\n      }`;\n      return {\n        ...metadata,\n        output: {dims: input.dims, type: input.type, textureType: TextureType.unpacked},\n        variables: [{name: 'epsilon', type: 'float', data: epsilon}],\n        shaderSource\n      };\n    };\n\nconst createComputeOutputProgramInfoLoader =\n    (inferenceHandler: WebGLInferenceHandler, input: Tensor, epsilon: number, meanAndVarianceShape: readonly number[]):\n        ProgramInfoLoader => {\n          const metadata = {...computeOutputProgramMetadata, cacheHint: `${epsilon}`};\n          return {\n            ...metadata,\n            get: () => createComputeOutputProgramInfo(inferenceHandler, metadata, input, epsilon, meanAndVarianceShape)\n          };\n        };\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 3) {\n    throw new Error('InstanceNormalization requires 3 inputs.');\n  }\n\n  const X = inputs[0];\n  const scale = inputs[1];\n  const B = inputs[2];\n\n  // input should at least have three dimensions - N,C,dim1,...,dimn\n  // other inputs can have only one dimensions\n  if (X.dims.length < 3 || scale.dims.length !== 1 || B.dims.length !== 1) {\n    throw new Error('Invalid input shape.');\n  }\n  if (scale.dims[0] !== X.dims[1] || B.dims[0] !== X.dims[1]) {\n    throw new Error('Input shapes are mismatched.');\n  }\n  if ((X.type !== 'float32' && X.type !== 'float64') || (scale.type !== 'float32' && scale.type !== 'float64') ||\n      (B.type !== 'float32' && B.type !== 'float64')) {\n    throw new Error('Invalid input type.');\n  }\n  if (inputs[0].dims.length !== 4) {\n    throw new Error('Only support 4-D input shape.');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramInfoLoader, TextureType} from '../types';\n\nexport interface LrnAttributes extends AttributeWithCacheKey {\n  alpha: number;\n  beta: number;\n  bias: number;\n  size: number;\n}\n\nexport const lrn: OperatorImplementation<LrnAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: LrnAttributes): Tensor[] => {\n      validateInputs(inputs);\n\n      // if (inferenceHandler.session.pack) {\n      //   return [inferenceHandler.run(createPackedLrnProgramInfoLoader(inferenceHandler, inputs, attributes),\n      //   inputs)];\n      // } else {\n      return [inferenceHandler.run(createLrnProgramInfoLoader(inputs, attributes), inputs)];\n      //}\n    };\n\nexport const parseLrnAttributes: OperatorInitialization<LrnAttributes> = (node: Graph.Node): LrnAttributes => {\n  const alpha = node.attributes.getFloat('alpha', 0.0001);\n  const beta = node.attributes.getFloat('beta', 0.75);\n  const bias = node.attributes.getFloat('bias', 1.0);\n  const size = node.attributes.getInt('size');\n\n  return createAttributeWithCacheKey({alpha, beta, bias, size});\n};\n\nconst lrnProgramMetadata = {\n  name: 'LRN',\n  inputNames: ['X'],\n  inputTypes: [TextureType.unpacked]\n};\n\nfunction createLrnProgramInfo(inputs: Tensor[], attributes: LrnAttributes): ProgramInfo {\n  const C = inputs[0].dims[1];\n  const rank = inputs[0].dims.length;\n  const from = -Math.floor((attributes.size - 1) / 2);\n  const to = Math.ceil((attributes.size - 1) / 2);\n  const alpha = `float(${attributes.alpha}) / float(${attributes.size})`;\n  const bias = `float(${attributes.bias})`;\n  const beta = `float(${attributes.beta})`;\n\n  const shaderSource = `\n    float process(int indices[${rank}]) {\n        int c = indices[1];\n        float x = _X(indices);\n        float square_sum = 0.0;\n\n        for (int i = ${from}; i <= ${to}; i++) {\n          int idx = c + i;\n          if (c >= 0 && c < ${C}) {\n            indices[1] = idx;\n            float j = _X(indices);\n            square_sum += j * j;\n          }\n        }\n        return x / pow(${bias} + ${alpha} * square_sum, ${beta});\n    }`;\n  return {\n    ...lrnProgramMetadata,\n    cacheHint: attributes.cacheKey,\n    output: {dims: inputs[0].dims, type: inputs[0].type, textureType: TextureType.unpacked},\n    shaderSource,\n  };\n}\n\nexport function createLrnProgramInfoLoader(inputs: Tensor[], attributes: LrnAttributes): ProgramInfoLoader {\n  return {...lrnProgramMetadata, cacheHint: attributes.cacheKey, get: () => createLrnProgramInfo(inputs, attributes)};\n}\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 1) {\n    throw new Error('LRN requires 1 input.');\n  }\n  if (inputs[0].dims.length !== 4) {\n    throw new Error('currently only support LRN for input with \"NCHW\" format');\n  }\n  if (inputs[0].type !== 'float32') {\n    throw new Error('input should be float type');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {ShapeUtil} from '../../../util';\nimport {getGlsl, Glsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, TextureType} from '../types';\n\nexport interface PadAttributes extends AttributeWithCacheKey {\n  readonly mode: string;\n  readonly pads: number[];\n  readonly value: number;\n}\n\nconst padProgramMetadata = {\n  name: 'Pad',\n  inputNames: ['A'],\n  inputTypes: [TextureType.unpacked],\n};\n\nexport const padV2: OperatorImplementation<PadAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: PadAttributes): Tensor[] => {\n      validateInputsV2(inputs);\n      const output = inferenceHandler.run(\n          {\n            ...padProgramMetadata,\n            cacheHint: attributes.cacheKey,\n            get: () => createPadProgramInfo(inferenceHandler, inputs[0], attributes)\n          },\n          inputs);\n      return [output];\n    };\n\nexport const parsePadAttributesV2: OperatorInitialization<PadAttributes> = (node: Graph.Node): PadAttributes => {\n  const mode = node.attributes.getString('mode', 'constant');\n  const value = node.attributes.getFloat('value', 0.0);\n  const pads = node.attributes.getInts('pads');\n  return createAttributeWithCacheKey({mode, value, pads});\n};\n\nexport const padV11: OperatorImplementation<string> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], mode: string): Tensor[] => {\n      validateInputsV11(inputs);\n      const attrubutes = generatePadAttributesFromInputs(inferenceHandler, inputs, mode);\n      return padV2(inferenceHandler, [inputs[0]], attrubutes);\n    };\n\nexport const parsePadAttributesV11: OperatorInitialization<string> = (node: Graph.Node): string =>\n    node.attributes.getString('mode', 'constant');\n\nconst generatePadAttributesFromInputs =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], mode: string): PadAttributes => {\n      if (!inferenceHandler.session.isInitializer(inputs[1].dataId) ||\n          (inputs.length >= 3 && !inferenceHandler.session.isInitializer(inputs[2].dataId))) {\n        throw new Error('dynamic pad attributes are not allowed');\n      }\n\n      const pads = Array.from(inputs[1].integerData);\n      const value = (inputs.length >= 3) ? inputs[2].floatData[0] : 0.0;\n\n      return createAttributeWithCacheKey({mode, pads, value});\n    };\n\nconst createPadProgramInfo =\n    (inferenceHandler: WebGLInferenceHandler, input: Tensor, attributes: PadAttributes): ProgramInfo => {\n      const outputShape = ShapeUtil.padShape(input.dims.slice(), attributes.pads);\n      const rank = outputShape.length;\n      const padFunction = getPadFunction(inferenceHandler, input, attributes);\n      const shaderSource = `\n      ${padFunction}\n      float process(int[${rank}] indices) {\n          return padA(indices);\n      }`;\n      return {\n        name: 'Pad',\n        inputNames: ['A'],\n        inputTypes: [TextureType.unpacked],\n        output: {dims: outputShape, type: input.type, textureType: TextureType.unpacked},\n        shaderSource\n      };\n    };\n\nconst validateInputsV2 = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 1) {\n    throw new Error('Pad requires 1 input');\n  }\n  if (inputs[0].type !== 'float32' && inputs[0].type !== 'float64') {\n    throw new Error('Invalid input type.');\n  }\n};\n\nconst validateInputsV11 = (inputs: Tensor[]): void => {\n  if (!inputs || (inputs.length !== 2 && inputs.length !== 3)) {\n    throw new Error('Pad requires 2 or 3 inputs');\n  }\n  if (inputs[1].type !== 'int32') {\n    throw new Error('Invalid input type.');\n  }\n  if (inputs.length >= 3 && inputs[2].type === 'string') {\n    throw new Error('Invalid input type.');\n  }\n};\n\nconst getPadFunction = (inferenceHandler: WebGLInferenceHandler, input: Tensor, attributes: PadAttributes): string => {\n  const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);\n  const [width, height] = inferenceHandler.calculateTextureWidthAndHeight(input.dims, TextureType.unpacked);\n  const strides = ShapeUtil.computeStrides(input.dims);\n\n  switch (attributes.mode) {\n    case 'constant':\n      return getPadConstant(glsl, input.dims, strides, width, height, attributes.pads, attributes.value);\n    case 'reflect':\n      return getPadReflect(glsl, input.dims, strides, width, height, attributes.pads);\n    case 'edge':\n      return getPadEdge(glsl, input.dims, strides, width, height, attributes.pads);\n    default:\n      throw new Error('Invalid mode');\n  }\n};\n\nconst getPadConstant =\n    (glsl: Glsl, shape: readonly number[], strides: readonly number[], width: number, height: number, pads: number[],\n     value: number): string => {\n      const rank = shape.length;\n      let block = '';\n      for (let i = rank - 1; i >= 0; --i) {\n        block += `\n        k = m[${i}] - ${pads[i]};\n        if (k < 0)  return constant;\n        if (k >= ${shape[i]}) return constant;\n        offset += k * ${strides[i]};\n        `;\n      }\n      return `\n      float padA(int m[${rank}]) {\n        const float constant = float(${value});\n        int offset = 0;\n        int k = 0;\n        ${block}\n        vec2 coords = offsetToCoords(offset, ${width}, ${height});\n        float value = getColorAsFloat(${glsl.texture2D}(A, coords));\n        return value;\n      }\n      `;\n    };\n\nconst getPadReflect =\n    (glsl: Glsl, shape: readonly number[], strides: readonly number[], width: number, height: number, pads: number[]):\n        string => {\n          const rank = shape.length;\n\n          let block = '';\n          for (let i = rank - 1; i >= 0; --i) {\n            block += `\n        k = m[${i}] - ${pads[i]};\n        if (k < 0) { k = -k; }\n        {\n          const int _2n_1 = ${2 * (shape[i] - 1)};\n          k = int( mod( float(k), float(_2n_1) ) ) ;\n          if(k >= ${shape[i]}) { k = _2n_1 - k; }\n        }\n        offset += k * ${strides[i]};\n        `;\n          }\n          return `\n      float padA(int m[${rank}]) {\n        int offset = 0;\n        int k = 0;\n        ${block}\n        vec2 coords = offsetToCoords(offset, ${width}, ${height});\n        float value = getColorAsFloat(${glsl.texture2D}(A, coords));\n        return value;\n      }\n      `;\n        };\n\nconst getPadEdge =\n    (glsl: Glsl, shape: readonly number[], strides: readonly number[], width: number, height: number, pads: number[]):\n        string => {\n          const rank = shape.length;\n\n          let block = '';\n          for (let i = rank - 1; i >= 0; --i) {\n            block += `\n        k = m[${i}] - ${pads[i]};\n        if (k < 0)  k = 0;\n        if (k >= ${shape[i]}) k = ${shape[i] - 1};\n        offset += k * ${strides[i]};\n      `;\n          }\n          return `\n      float padA(int m[${rank}]) {\n        int offset = 0;\n        int k = 0;\n        ${block}\n        vec2 coords = offsetToCoords(offset, ${width}, ${height});\n        float value = getColorAsFloat(${glsl.texture2D}(A, coords));\n        return value;\n      }\n      `;\n        };\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {PoolConvUtil, ShapeUtil} from '../../../util';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramMetadata, TextureType} from '../types';\n\nexport interface AveragePoolAttributes extends AttributeWithCacheKey {\n  readonly autoPad: string;\n  readonly ceilMode: number;\n  readonly countIncludePad: boolean;\n  readonly kernelShape: readonly number[];\n  readonly strides: readonly number[];\n  readonly pads: readonly number[];\n}\n\nexport const averagePool: OperatorImplementation<AveragePoolAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: AveragePoolAttributes): Tensor[] => {\n      validateInputs(inputs);\n      const metadata =\n          {name: 'AveragePool', inputNames: ['X'], inputTypes: [TextureType.unpacked], cacheHint: attributes.cacheKey};\n      const output = inferenceHandler.run(\n          {...metadata, get: () => createAveragePoolProgramInfo(inputs, metadata, false, attributes)}, inputs);\n      return [output];\n    };\n\nexport const parseAveragePoolAttributes: OperatorInitialization<AveragePoolAttributes> =\n    (node: Graph.Node): AveragePoolAttributes => {\n      const autoPad = node.attributes.getString('auto_pad', 'NOTSET');\n      const ceilMode = node.attributes.getInt('ceil_mode', 0);\n      const countIncludePad = (node.attributes.getInt('count_include_pad', 0) === 0 ? false : true);\n      const kernelShape = node.attributes.getInts('kernel_shape');\n      const strides = node.attributes.getInts('strides', []);\n      const pads = node.attributes.getInts('pads', []);\n\n      // TODO: support attribute 'ceil_mode'\n      if (ceilMode !== 0) {\n        throw new Error('using ceil() in shape computation is not yet supported for AveragePool');\n      }\n\n      return createAttributeWithCacheKey({autoPad, ceilMode, countIncludePad, kernelShape, strides, pads});\n    };\n\nconst createAveragePoolProgramInfo =\n    (inputs: Tensor[], metadata: ProgramMetadata, isGlobalOperator: boolean, attributes: AveragePoolAttributes):\n        ProgramInfo => {\n          const [adjustedAttributes, outputShape] =\n              getAdjustedPoolAttributesAndOutputShape(inputs, attributes, isGlobalOperator);\n          const kernelSize = ShapeUtil.size(adjustedAttributes.kernelShape);\n          const op1 = 'value += _X(x);';\n          let op2 = '';\n          if (adjustedAttributes.countIncludePad) {\n            op2 += `value /= float(${kernelSize});`;\n          } else {\n            op2 += `value /= float(${kernelSize} - pad);`;\n          }\n          const poolingCode = generatePoolingCode(inputs[0].dims, adjustedAttributes, op1, op2, '0.0');\n          const shaderSource = `\n        ${poolingCode}\n      `;\n          return {\n            ...metadata,\n            output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},\n            shaderSource\n          };\n        };\n\nexport const globalAveragePool: OperatorImplementation<AveragePoolAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: AveragePoolAttributes): Tensor[] => {\n      validateInputs(inputs);\n      const metadata = {\n        name: 'GlobalAveragePool',\n        inputNames: ['X'],\n        inputTypes: [TextureType.unpacked],\n        cacheHint: `${attributes.countIncludePad}`\n      };\n      const output = inferenceHandler.run(\n          {...metadata, get: () => createAveragePoolProgramInfo(inputs, metadata, true, attributes)}, inputs);\n      return [output];\n    };\n\nexport const parseGlobalAveragePoolAttributes: OperatorInitialization<AveragePoolAttributes> =\n    (node: Graph.Node): AveragePoolAttributes => {\n      const countIncludePad = (node.attributes.getInt('count_include_pad', 0) === 0 ? false : true);\n      return createAttributeWithCacheKey(\n          {autoPad: '', ceilMode: 0, countIncludePad, kernelShape: [], strides: [], pads: []});\n    };\n\nexport interface MaxPoolAttributes extends AveragePoolAttributes {\n  readonly storageOrder: number;\n  readonly dilations: number[];\n}\n\nexport const maxPool: OperatorImplementation<MaxPoolAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: MaxPoolAttributes): Tensor[] => {\n      validateInputs(inputs);\n      const metadata =\n          {name: 'MaxPool', inputNames: ['X'], inputTypes: [TextureType.unpacked], cacheHint: attributes.cacheKey};\n      const output = inferenceHandler.run(\n          {...metadata, get: () => createMaxPoolProgramInfo(inputs, metadata, false, attributes)}, inputs);\n      return [output];\n    };\n\nexport const parseMaxPoolAttributes: OperatorInitialization<MaxPoolAttributes> =\n    (node: Graph.Node): MaxPoolAttributes => {\n      const autoPad = node.attributes.getString('auto_pad', 'NOTSET');\n      const ceilMode = node.attributes.getInt('ceil_mode', 0);\n      const kernelShape = node.attributes.getInts('kernel_shape');\n      const strides = node.attributes.getInts('strides', []);\n      const pads = node.attributes.getInts('pads', []);\n      const storageOrder = node.attributes.getInt('storage_order', 0);\n      const dilations = node.attributes.getInts('dilations', []);\n\n      // TODO: support attribute 'ceil_mode' and 'storage_order'\n      if (storageOrder !== 0) {\n        throw new Error('column major storage order is not yet supported for MaxPool');\n      }\n      if (ceilMode !== 0) {\n        throw new Error('using ceil() in shape computation is not yet supported for MaxPool');\n      }\n\n      return createAttributeWithCacheKey(\n          {autoPad, ceilMode, countIncludePad: false, kernelShape, strides, pads, storageOrder, dilations});\n    };\n\nconst createMaxPoolProgramInfo =\n    (inputs: Tensor[], metadata: ProgramMetadata, isGlobalOperator: boolean, attributes: MaxPoolAttributes):\n        ProgramInfo => {\n          const [adjustedAttributes, outputShape] =\n              getAdjustedPoolAttributesAndOutputShape(inputs, attributes, isGlobalOperator);\n          const op1 = `\n      value = max(_X(x), value);\n    `;\n          const op2 = '';\n          const poolingCode = generatePoolingCode(inputs[0].dims, adjustedAttributes, op1, op2, '-1e5');\n          const shaderSource = `\n      ${poolingCode}\n    `;\n          return {\n            ...metadata,\n            output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},\n            shaderSource\n          };\n        };\n\nconst getAdjustedPoolAttributesAndOutputShape =\n    (inputs: Tensor[], attributes: AveragePoolAttributes|MaxPoolAttributes, isGlobalOperator: boolean):\n        [AveragePoolAttributes|MaxPoolAttributes, number[]] => {\n          const inputShape = inputs[0].dims.slice();\n          const hasDilations = Object.hasOwnProperty.call(attributes, 'dilations');\n          const kernelShape = attributes.kernelShape.slice();\n          const strides = attributes.strides.slice();\n          const dilations: number[] = hasDilations ? (attributes as MaxPoolAttributes).dilations.slice() : [];\n          const pads = attributes.pads.slice();\n          PoolConvUtil.adjustPoolAttributes(isGlobalOperator, inputShape, kernelShape, strides, dilations, pads);\n\n          const outputShape = PoolConvUtil.computePoolOutputShape(\n              isGlobalOperator, inputShape, strides, dilations, kernelShape, pads, attributes.autoPad);\n\n          const newAttributes = Object.assign({}, attributes);\n          if (hasDilations) {\n            Object.assign(newAttributes, {kernelShape, strides, pads, dilations, cacheKey: attributes.cacheKey});\n          } else {\n            Object.assign(newAttributes, {kernelShape, strides, pads, cacheKey: attributes.cacheKey});\n          }\n          return [newAttributes, outputShape];\n        };\n\nconst globalMaxPoolAttributes = {\n  autoPad: '',\n  ceilMode: 0,\n  countIncludePad: false,\n  kernelShape: [],\n  strides: [],\n  pads: [],\n  storageOrder: 0,\n  dilations: [],\n  cacheKey: ''\n};\n\nconst globalMaxPoolMetadata = {\n  name: 'GlobalMaxPool',\n  inputNames: ['X'],\n  inputTypes: [TextureType.unpacked]\n};\n\nexport const globalMaxPool = (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[]): Tensor[] => {\n  validateInputs(inputs);\n  const output = inferenceHandler.run(\n      {\n        ...globalMaxPoolMetadata,\n        get: () => createMaxPoolProgramInfo(inputs, globalMaxPoolMetadata, true, globalMaxPoolAttributes)\n      },\n      inputs);\n  return [output];\n};\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 1) {\n    throw new Error('Pool ops requires 1 input.');\n  }\n  if (inputs[0].type !== 'float32' && inputs[0].type !== 'float64') {\n    throw new Error('Invalid input type.');\n  }\n};\n\nconst generatePoolingCode =\n    (inputDims: readonly number[], attributes: AveragePoolAttributes, op1: string, op2: string, start: string):\n        string => {\n          const rank = inputDims.length;\n          if (attributes.kernelShape.length <= 2) {\n            const kw = attributes.kernelShape[attributes.kernelShape.length - 1];\n            const sw = attributes.strides[attributes.strides.length - 1];\n            const pwStart = attributes.pads[attributes.pads.length / 2 - 1];\n            const pwEnd = attributes.pads[attributes.pads.length - 1];\n            const dimW = inputDims[rank - 1];\n            let codeW = '';\n            let codeH = '';\n            let codeHEnd = '';\n            if (pwStart + pwEnd !== 0) {\n              codeW = `\n          for (int i = 0; i < ${kw}; i++) {\n            x[${rank} - 1] = indices[${rank} - 1] * ${sw} - ${pwStart} + i;\n            if (x[${rank} - 1] < 0 || x[${rank} - 1] >= ${dimW}) {\n              pad++;\n              continue;\n            }\n            ${op1}\n          }`;\n            } else {\n              codeW = `\n          for (int i = 0; i < ${kw}; i++) {\n            x[${rank} - 1] = indices[${rank} - 1] * ${sw} - ${pwStart} + i;\n            ${op1}\n          }`;\n            }\n\n            if (attributes.kernelShape.length === 2) {\n              const kh = attributes.kernelShape[attributes.kernelShape.length - 2];\n              const sh = attributes.strides[attributes.strides.length - 2];\n              const phStart = attributes.pads[attributes.pads.length / 2 - 2];\n              const phEnd = attributes.pads[attributes.pads.length - 2];\n              const dimH = inputDims[rank - 2];\n              if (phStart + phEnd !== 0) {\n                codeH = `\n            for (int j = 0; j < ${kh}; j++) {\n              x[${rank} - 2] = indices[${rank} - 2] * ${sh} - ${phStart} + j;\n              if (x[${rank} - 2] < 0 || x[${rank} - 2] >= ${dimH}) {\n                pad+= ${kw};\n                continue;\n              }\n          `;\n              } else {\n                codeH = `\n            for (int j = 0; j < ${kh}; j++) {\n              x[${rank} - 2] = indices[${rank} - 2] * ${sh} - ${phStart} + j;\n            `;\n              }\n              codeHEnd = `\n          }\n        `;\n            }\n\n            const poolingCode = `\n        float process(int indices[${rank}]) {\n          int x[${rank}];\n          copyVec(indices, x);\n\n          float value = ${start};\n          int pad = 0;\n          ${codeH}\n          ${codeW}\n          ${codeHEnd}\n          ${op2}\n          return value;\n        }\n      `;\n            return poolingCode;\n          } else {\n            const kernelSize = ShapeUtil.size(attributes.kernelShape);\n            const kernelStrides = ShapeUtil.computeStrides(attributes.kernelShape);\n            const stridesRank = kernelStrides.length;\n            const padsRank = attributes.pads.length;\n            const offsetToIndicesFunction = offsetToIndices(stridesRank);\n            const copyInputDims = copyArray(inputDims, 'inputDims');\n            const copyPads = copyArray(attributes.pads, 'pads');\n            const copyKernelStrides = copyArray(kernelStrides, 'kernelStrides');\n            const copyStrides = copyArray(attributes.strides, 'strides');\n            const hasPads = attributes.pads.reduce((sum, cur) => sum + cur);\n            let padCode = '';\n            if (hasPads) {\n              padCode = `\n            if (x[j] >= inputDims[j] || x[j] < 0) {\n              pad++;\n              isPad = true;\n              break;\n            }\n          }\n          if (!isPad) {\n            ${op1}\n          }`;\n            } else {\n              padCode = `\n          }\n          ${op1}\n        `;\n            }\n            const poolingCode = `\n        ${offsetToIndicesFunction}\n        float process(int indices[${rank}]) {\n          int x[${rank}];\n          copyVec(indices, x);\n          int offset[${stridesRank}];\n          int pads[${padsRank}];\n          int inputDims[${rank}];\n          int kernelStrides[${stridesRank}];\n          int strides[${stridesRank}];\n          ${copyPads}\n          ${copyInputDims}\n          ${copyStrides}\n          ${copyKernelStrides}\n\n          float value = ${start};\n          int pad = 0;\n          bool isPad = false;\n          for (int i = 0; i < ${kernelSize}; i++) {\n            offsetToIndices(i, kernelStrides, offset);\n            isPad = false;\n            for (int j = ${rank} - ${stridesRank}; j < ${rank}; j++) {\n              x[j] = indices[j] * strides[j - ${rank} + ${stridesRank}]\n                + offset[j - ${rank} + ${stridesRank}] - pads[j - 2];\n              ${padCode}\n          }\n          ${op2}\n\n          return value;\n        }\n      `;\n            return poolingCode;\n          }\n        };\n\nconst copyArray = (array: readonly number[], arrayName: string): string => {\n  let block = '';\n  for (let i = 0; i < array.length; i++) {\n    block += `\n      ${arrayName}[${i}] = ${array[i]};\n    `;\n  }\n  return block;\n};\n\nconst offsetToIndices = (rank: number): string => `\n  void offsetToIndices(int offset, int[${rank}] strides, out int[${rank}] indices) {\n    if (${rank} == 0) {\n      return;\n    }\n    for (int i = 0; i < ${rank} - 1; ++i) {\n      indices[i] = offset / strides[i];\n      offset -= indices[i] * strides[i];\n    }\n    indices[${rank} - 1] = offset;\n  }`;\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {Graph} from '../../../graph';\nimport {NUMBER_TYPES, OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {ShapeUtil} from '../../../util';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramMetadata, TextureType} from '../types';\n\nexport interface ReduceAttributes extends AttributeWithCacheKey {\n  readonly axes: number[];\n  readonly keepDims: boolean;\n}\n\n// return [init ops, reduce ops, final ops]\ntype ReduceOp = (inputs: Tensor[], axes: number[]) => string[];\n\nconst reduce =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: ReduceAttributes, name: string,\n     reduceOp: ReduceOp): Tensor[] => {\n      validateInputs(inputs);\n\n      const reduceProgramMetadata = {\n        name,\n        inputNames: ['A'],\n        inputTypes: [TextureType.unpacked],\n      };\n\n      const output = inferenceHandler.run(\n          {\n            ...reduceProgramMetadata,\n            cacheHint: attributes.cacheKey,\n            get: () =>\n                createReduceProgramInfo(inferenceHandler, inputs, attributes, name, reduceOp, reduceProgramMetadata)\n          },\n          inputs);\n      return [output];\n    };\n\nexport const parseReduceAttributes: OperatorInitialization<ReduceAttributes> = (node: Graph.Node): ReduceAttributes => {\n  const axes = node.attributes.getInts('axes', []);\n  const keepDims = node.attributes.getInt('keepdims', 1) === 1;\n  return createAttributeWithCacheKey({axes, keepDims});\n};\n\nconst createReduceProgramInfo =\n    (_handler: WebGLInferenceHandler, inputs: Tensor[], attributes: ReduceAttributes, _name: string, reduceOp: ReduceOp,\n     reduceProgramMetadata: ProgramMetadata): ProgramInfo => {\n      const outputShape: number[] = [];\n      const iRank = inputs[0].dims.length || 1;\n\n      const idxCopy = [];  // copy output indexes to input indexes\n\n      const axes = ShapeUtil.normalizeAxes(attributes.axes, inputs[0].dims.length);\n      const ops = reduceOp(inputs, axes);\n      let reduceOps = ops[1];\n\n      for (let k = 0; k < inputs[0].dims.length; k++) {\n        // if this axis is reduced\n        if (axes.indexOf(k) >= 0 || axes.length === 0) {\n          if (attributes.keepDims) {\n            outputShape.push(1);\n          }  // else { remove the axis from outputShape; }\n\n          // loop over the d-th axis\n          reduceOps = `\n          for(int j${k} = 0; j${k} < ${inputs[0].dims[k]}; j${k}++) {\n            inputIdx[${k}] = j${k};\n            ${reduceOps}\n          }`;\n        } else {\n          idxCopy.push(`inputIdx[${k}] = outputIdx[${outputShape.length}];`);\n\n          outputShape.push(inputs[0].dims[k]);\n        }\n      }\n\n      const oRank = outputShape.length || 1;\n\n      const shaderSource = `\n      float process(int outputIdx[${oRank}]) {\n        float value;                 // final result\n        int inputIdx[${iRank}];      // addressing input data\n        ${idxCopy.join('\\n')}\n        ${ops[0]}       // init ops for reduce max/min\n        ${reduceOps}\n        ${ops[2]}       // final computation for reduce mean\n        return value;\n      }`;\n\n      return {\n        ...reduceProgramMetadata,\n        output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},\n        shaderSource\n      };\n    };\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  // TODO: support Reduce* operators with 2 inputs.\n  if (!inputs || inputs.length !== 1) {\n    throw new Error('Reduce op requires 1 input.');\n  }\n\n  if (NUMBER_TYPES.indexOf(inputs[0].type) === -1) {\n    throw new Error('Invalid input type.');\n  }\n};\n\nexport const reduceSum: OperatorImplementation<ReduceAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: ReduceAttributes): Tensor[] => {\n      const reduceOp: ReduceOp = (): string[] => ['value = 0.0;', 'value += _A(inputIdx);', ''];\n      return reduce(inferenceHandler, inputs, attributes, 'ReduceSum', reduceOp);\n    };\n\nexport const reduceMean: OperatorImplementation<ReduceAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: ReduceAttributes): Tensor[] => {\n      const reduceOp: ReduceOp = (inputs: Tensor[], axes: number[]): string[] => {\n        let size = 1.0;\n        for (let k = 0; k < inputs[0].dims.length; k++) {\n          if (axes.indexOf(k) >= 0 || axes.length === 0) {\n            size *= inputs[0].dims[k];\n          }\n        }\n\n        return ['value = 0.0;', 'value += _A(inputIdx);', `value /= ${size}.;`];  // ensure real number with `.`\n      };\n      return reduce(inferenceHandler, inputs, attributes, 'ReduceMean', reduceOp);\n    };\n\nexport const reduceMax: OperatorImplementation<ReduceAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: ReduceAttributes): Tensor[] => {\n      const reduceOp: ReduceOp = (inputs: Tensor[], axes: number[]): string[] => {\n        const idxZero = [];\n        for (let k = 0; k < inputs[0].dims.length; k++) {\n          if (axes.indexOf(k) >= 0 || axes.length === 0) {\n            idxZero.push(`inputIdx[${k}] = 0;`);  // first element\n          }\n        }\n\n        return [`${idxZero.join('\\n')}\\nvalue = _A(inputIdx);`, 'value = max(value, _A(inputIdx));', ''];\n      };\n      return reduce(inferenceHandler, inputs, attributes, 'ReduceMax', reduceOp);\n    };\n\nexport const reduceMin: OperatorImplementation<ReduceAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: ReduceAttributes): Tensor[] => {\n      const reduceOp: ReduceOp = (inputs: Tensor[], axes: number[]): string[] => {\n        const idxZero = [];\n        for (let k = 0; k < inputs[0].dims.length; k++) {\n          if (axes.indexOf(k) >= 0 || axes.length === 0) {\n            idxZero.push(`inputIdx[${k}] = 0;`);  // first element\n          }\n        }\n\n        return [`${idxZero.join('\\n')}\\nvalue = _A(inputIdx);`, 'value = min(value, _A(inputIdx));', ''];\n      };\n      return reduce(inferenceHandler, inputs, attributes, 'ReduceMin', reduceOp);\n    };\n\nexport const reduceProd: OperatorImplementation<ReduceAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: ReduceAttributes): Tensor[] => {\n      const reduceOp: ReduceOp = (): string[] => ['value = 1.0;', 'value *= _A(inputIdx);', ''];\n      return reduce(inferenceHandler, inputs, attributes, 'ReduceProd', reduceOp);\n    };\n\nexport const reduceLogSum: OperatorImplementation<ReduceAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: ReduceAttributes): Tensor[] => {\n      const reduceOp: ReduceOp = (): string[] => ['value = 0.0;', 'value += _A(inputIdx);', 'value = log(value);'];\n      return reduce(inferenceHandler, inputs, attributes, 'ReduceLogSum', reduceOp);\n    };\n\nexport const reduceLogSumSquare: OperatorImplementation<ReduceAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: ReduceAttributes): Tensor[] => {\n      const reduceOp: ReduceOp = (): string[] => ['float t; value = 0.0;', 't = _A(inputIdx); value += t * t;', ''];\n      return reduce(inferenceHandler, inputs, attributes, 'ReduceLogSumSquare', reduceOp);\n    };\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from '../../../tensor';\nimport {ShapeUtil} from '../../../util';\nimport {WebGLInferenceHandler} from '../inference-handler';\n\nexport const reshape = (handler: WebGLInferenceHandler, inputs: Tensor[]): Tensor[] => {\n  const reshapedDims = ShapeUtil.calculateReshapedDims(inputs[0].dims, inputs[1].integerData);\n  if (handler.session.pack) {\n    return [handler.reshapePacked(inputs[0], reshapedDims)];\n  } else {\n    return [handler.reshapeUnpacked(inputs[0], reshapedDims)];\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, TextureType} from '../types';\n\nexport interface UpsampleAttributes extends AttributeWithCacheKey {\n  readonly opset: number;\n  readonly isResize: boolean;\n  readonly mode: string;\n  readonly scales: number[];\n  readonly extrapolationValue: number;\n  readonly coordinateTransformMode: string;\n  readonly useExtrapolation: boolean;\n  readonly needRoiInput: boolean;\n  readonly nearestMode: string;\n  readonly cubicCoefficientA: number;\n  readonly excludeOutside: boolean;\n  readonly useNearest2xOptimization: boolean;\n  readonly roiInputIdx: number;\n  readonly scalesInputIdx: number;\n  readonly sizesInputIdx: number;\n}\n\nconst upsampleProgramMetadata = {\n  name: 'Upsample',\n  inputNames: ['X'],\n  inputTypes: [TextureType.unpacked],\n};\n\nexport const upsample: OperatorImplementation<UpsampleAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: UpsampleAttributes): Tensor[] => {\n      validateInputs(inputs, attributes);\n      const output = inferenceHandler.run(\n          {\n            ...upsampleProgramMetadata,\n            cacheHint: attributes.cacheKey,\n            get: () => createUpsampleProgramInfo(inferenceHandler, inputs, attributes)\n          },\n          inputs);\n      return [output];\n    };\n\nexport const parseUpsampleAttributesV7: OperatorInitialization<UpsampleAttributes> =\n    (node: Graph.Node): UpsampleAttributes => parseUpsampleAttributes(node, 7);\n\nexport const parseUpsampleAttributesV9: OperatorInitialization<UpsampleAttributes> =\n    (node: Graph.Node): UpsampleAttributes => parseUpsampleAttributes(node, 9);\n\nexport const parseUpsampleAttributes = (node: Graph.Node, opset: number): UpsampleAttributes => {\n  const isResize = (opset >= 10);\n\n  // processing node attributes\n  const mode = node.attributes.getString('mode', 'nearest');\n  if (mode !== 'nearest' && mode !== 'linear' && (opset < 11 || mode !== 'cubic')) {\n    throw new Error(`unrecognized mode: ${mode}`);\n  }\n\n  let scales: number[] = [];\n  if (opset < 9) {\n    scales = node.attributes.getFloats('scales');\n    scalesValidation(scales, mode, isResize);\n  }\n\n  const extrapolationValue = node.attributes.getFloat('extrapolation_value', 0.0);\n\n  const coordinateTransformMode =\n      opset > 10 ? node.attributes.getString('coordinate_transformation_mode', 'half_pixel') : 'asymmetric';\n  if ([\n        'asymmetric', 'pytorch_half_pixel', 'tf_half_pixel_for_nn', 'align_corners', 'tf_crop_and_resize', 'half_pixel'\n      ].indexOf(coordinateTransformMode) === -1) {\n    throw new Error(`coordinate_transform_mode '${coordinateTransformMode}' is not supported`);\n  }\n  const needRoiInput = (coordinateTransformMode === 'tf_crop_and_resize');\n  const useExtrapolation = needRoiInput;\n\n  const nearestMode =\n      (mode === 'nearest' && opset >= 11) ? node.attributes.getString('nearest_mode', 'round_prefer_floor') : '';\n  if (['round_prefer_floor', 'round_prefer_ceil', 'floor', 'ceil', ''].indexOf(nearestMode) === -1) {\n    throw new Error(`nearest_mode '${nearestMode}' is not supported`);\n  }\n\n  const cubicCoefficientA = node.attributes.getFloat('cubic_coeff_a', -0.75);\n  const excludeOutside = node.attributes.getInt('exclude_outside', 0) !== 0;\n  if (excludeOutside && mode !== 'cubic') {\n    throw new Error('exclude_outside can be set to 1 only when mode is CUBIC.');\n  }\n\n  const useNearest2xOptimization =\n      (opset < 11) ? true : (mode === 'nearest' && coordinateTransformMode === 'asymmetric' && nearestMode === 'floor');\n\n  let roiInputIdx = 0;\n  let scalesInputIdx = 0;\n  let sizesInputIdx = 0;\n\n  if (opset > 10) {\n    // handle when roiInput is not given\n    if (node.inputs.length > 2) {\n      roiInputIdx = 1;\n      scalesInputIdx = 2;\n      sizesInputIdx = 3;\n    } else {\n      scalesInputIdx = 1;\n      sizesInputIdx = 2;\n    }\n  } else if (opset === 9) {\n    scalesInputIdx = 1;\n  }\n\n  return createAttributeWithCacheKey({\n    opset,\n    isResize,\n    mode,\n    scales,\n    extrapolationValue,\n    coordinateTransformMode,\n    useExtrapolation,\n    needRoiInput,\n    nearestMode,\n    cubicCoefficientA,\n    excludeOutside,\n    useNearest2xOptimization,\n    roiInputIdx,\n    scalesInputIdx,\n    sizesInputIdx\n  });\n};\n\nconst createUpsampleProgramInfo =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: UpsampleAttributes): ProgramInfo => {\n      const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);\n      const [inputWidth, inputHeight] =\n          inferenceHandler.calculateTextureWidthAndHeight(inputs[0].dims, TextureType.unpacked);\n\n      const outputShape = inputs[0].dims.map((dim, i) => Math.floor(dim * attributes.scales[i]));\n      const [outputWidth, outputHeight] =\n          inferenceHandler.calculateTextureWidthAndHeight(outputShape, TextureType.unpacked);\n      const dim = outputShape.length;\n\n      const outputPitches = new Array<number>(dim);\n      const inputPitches = new Array<number>(dim);\n      let precalculatedPitches = `\n      int output_pitches[${dim}];\n      int input_pitches[${dim}];\n      `;\n      for (let d = dim - 1; d >= 0; d--) {\n        outputPitches[d] = (d === dim - 1) ? 1 : outputPitches[d + 1] * outputShape[d + 1];\n        inputPitches[d] = (d === dim - 1) ? 1 : inputPitches[d + 1] * inputs[0].dims[d + 1];\n\n        precalculatedPitches += `\n        output_pitches[${d}] = ${outputPitches[d]};\n        input_pitches[${d}] = ${inputPitches[d]};\n        `;\n      }\n      const getInputFloatFunction = `\n      float getInputFloat(int index) {\n        vec2 coords = offsetToCoords(index, ${inputWidth}, ${inputHeight});\n        float value = getColorAsFloat(${glsl.texture2D}(X, coords));\n        return value;\n      }\n      `;\n\n      const shaderSource = attributes.mode === 'nearest' ?\n          // nearest\n          `\n    ${getInputFloatFunction}\n    float process(int indices[${dim}]) {\n      int input_index = 0;\n      int output_index = coordsToOffset(TexCoords, ${outputWidth}, ${outputHeight});\n\n      ${precalculatedPitches}\n\n      int d, m;\n      for (int dim = 0; dim < ${dim}; ++dim) {\n        d = output_index / output_pitches[dim];\n        m = output_index - d * output_pitches[dim];\n        output_index = m;\n\n        if (scales[dim] != 1 && d > 0) {\n          int d2 = d / scales[dim];\n          m = d - d2 * scales[dim];\n          d = d2;\n        }\n        input_index += input_pitches[dim] * d;\n      }\n\n      return getInputFloat(input_index);\n    }` :\n          dim === 4 ?\n          // bilinear 4D\n              `\n    ${getInputFloatFunction}\n    float process(int indices[4]) {\n      int input_index = 0;\n      int output_index = coordsToOffset(TexCoords, ${outputWidth}, ${outputHeight});\n\n      ${precalculatedPitches}\n\n      int m;\n      int index_of_dim0, index_of_dim1, index_of_dim2, index_of_dim3;\n      index_of_dim0 = output_index / output_pitches[0];\n      m = output_index - index_of_dim0 * output_pitches[0];\n      index_of_dim1 = m / output_pitches[1];\n      m = m - index_of_dim1 * output_pitches[1];\n      index_of_dim2 = m / output_pitches[2];\n      m = m - index_of_dim2 * output_pitches[2];\n      index_of_dim3 = m;\n\n      int index_of_input_dim2, index_of_input_dim3, x_offset, y_offset;\n      index_of_input_dim2 = index_of_dim2 / scales[2];\n      y_offset = index_of_dim2 - index_of_input_dim2 * scales[2];\n      index_of_input_dim3 = index_of_dim3 / scales[3];\n      x_offset = index_of_dim3 - index_of_input_dim3 * scales[3];\n\n      input_index = index_of_dim0 * input_pitches[0] +\n            index_of_dim1 * input_pitches[1] +\n            index_of_input_dim2 * input_pitches[2] +\n            index_of_input_dim3;\n\n      float x00 = getInputFloat(input_index);\n      float x10, x01, x11;\n\n      bool end_of_dim2 = false;\n      if (index_of_input_dim2 == (${inputs[0].dims[2]} - 1)) {\n        // It's the end in dimension 2\n        x01 = x00;\n        end_of_dim2 = true;\n      } else {\n        x01 = getInputFloat(input_index + input_pitches[2]);\n      }\n\n      if (index_of_input_dim3 == (input_pitches[2] - 1)) {\n        // It's the end in dimension 3\n        x10 = x00;\n        x11 = x01;\n      }\n      else {\n        x10 = getInputFloat(input_index + 1);\n        x11 = end_of_dim2 ? x10 : getInputFloat(input_index + input_pitches[2] + 1);\n      }\n\n      float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[2]);\n      float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[2]);\n      return y0 + float(x_offset) * (y1 - y0) / float(scales[3]);\n    }` :\n              // bilinear 2D\n              `\n    ${getInputFloatFunction}\n    float process(int indices[2]) {\n      int input_index = 0;\n      int output_index = coordsToOffset(TexCoords, ${outputWidth}, ${outputHeight});\n\n      ${precalculatedPitches}\n\n      int m;\n      int index_of_dim0, index_of_dim1;\n      index_of_dim0 = output_index / output_pitches[0];\n      m = output_index - index_of_dim0 * output_pitches[0];\n      index_of_dim1 = m;\n\n      int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset;\n      index_of_input_dim0 = index_of_dim0 / scales[0];\n      y_offset = index_of_dim0 - index_of_input_dim0 * scales[0];\n      index_of_input_dim1 = index_of_dim1 / scales[1];\n      x_offset = index_of_dim1 - index_of_input_dim1 * scales[1];\n\n      input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1;\n\n      float x00 = getInputFloat(input_index);\n      float x10, x01, x11;\n\n      bool end_of_dim0 = false;\n      if (index_of_input_dim0 == (${inputs[0].dims[0]} - 1)) {\n        // It's the end in dimension 0\n        x01 = x00;\n        end_of_dim0 = true;\n      } else {\n        x01 = getInputFloat(input_index + input_pitches[0]);\n      }\n\n      if (index_of_input_dim1 == (input_pitches[0] - 1)) {\n        // It's the end in dimension 1\n        x10 = x00;\n        x11 = x01;\n      }\n      else {\n        x10 = getInputFloat(input_index + 1);\n        x11 = end_of_dim0 ? x10 : getInputFloat(input_index + input_pitches[0] + 1);\n      }\n\n      float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[0]);\n      float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[0]);\n      return y0 + float(x_offset) * (y1 - y0) / float(scales[1]);\n    }`;\n      return {\n        ...upsampleProgramMetadata,\n        output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},\n        shaderSource,\n        variables: [{\n          name: 'scales',\n          type: 'int',\n          arrayLength: attributes.scales.length,\n          data: attributes.scales.map(x => Math.ceil(x))\n        }]\n      };\n    };\n\nexport const validateInputs = (inputs: Tensor[], attribute: UpsampleAttributes): void => {\n  if (!inputs || (attribute.opset < 9 && inputs.length !== 1) ||\n      (attribute.opset >= 9 && attribute.opset < 11 && inputs.length !== 2) ||\n      (attribute.opset >= 11 && inputs.length < 2)) {\n    throw new Error('invalid inputs.');\n  }\n\n  if (attribute.scales.length > 0 && inputs[0].dims.length !== attribute.scales.length) {\n    throw new Error('Invalid input shape.');\n  }\n\n  if (inputs[0].type === 'string') {\n    throw new Error('Invalid input tensor types.');\n  }\n};\n\nexport const scalesValidation = (scales: number[], mode: string, isResize: boolean): void => {\n  if (!isResize) {\n    for (const scale of scales) {\n      if (scale < 1) {\n        throw new Error('Scale value should be greater than or equal to 1.');\n      }\n    }\n  } else {\n    for (const scale of scales) {\n      if (scale <= 0) {\n        throw new Error('Scale value should be greater than 0.');\n      }\n    }\n  }\n  if (mode === 'linear' || mode === 'cubic') {\n    if (scales.length !== 2 && (scales.length !== 4 || scales[0] !== 1 || scales[1] !== 1)) {\n      throw new Error(`'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') \\\n        or 4-D inputs with the corresponding outermost 2 scale values being 1 \\\n        in the ${isResize ? 'Resize' : 'Upsample'} opeartor.`);\n    }\n  }\n};","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, TextureType} from '../types';\nimport {getCoordsDataType} from '../utils';\n\nimport {unpackFromChannel} from './packing-utils';\nimport {parseUpsampleAttributes, scalesValidation, UpsampleAttributes, validateInputs} from './upsample';\n\nconst resizeProgramMetadata = {\n  name: 'Resize',\n  inputNames: ['A'],\n  inputTypes: [TextureType.packed]\n};\n\nexport const resize: OperatorImplementation<UpsampleAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: UpsampleAttributes): Tensor[] => {\n      validateInputs(inputs, attributes);\n      const output = inferenceHandler.run(\n          {\n            ...resizeProgramMetadata,\n            cacheHint: attributes.cacheKey,\n            get: () => createPackedResizeProgramInfo(inferenceHandler, inputs, attributes)\n          },\n          inputs);\n      return [output];\n    };\n\nexport const parseResizeAttributesV10: OperatorInitialization<UpsampleAttributes> =\n    (node: Graph.Node): UpsampleAttributes => parseUpsampleAttributes(node, 10);\n\nexport const parseResizeAttributesV11: OperatorInitialization<UpsampleAttributes> =\n    (node: Graph.Node): UpsampleAttributes => parseUpsampleAttributes(node, 11);\n\nconst createPackedResizeProgramInfo =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: UpsampleAttributes): ProgramInfo => {\n      const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);\n      const [scales, outputShape] = prepareInputs(inputs, attributes);\n\n      const isSame =\n          scales.every((s: number) => s === 1) && attributes.coordinateTransformMode !== 'tf_crop_and_resize';\n      if (isSame) {\n        return {\n          ...resizeProgramMetadata,\n          output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.packed},\n          hasMain: true,\n          shaderSource: `void main() {\n                    vec4 v = ${glsl.texture2D}(X, TexCoords);\n                    ${glsl.output} = v;\n                }`\n        };\n      }\n\n      const dim = outputShape.length;\n      if (dim < 2) {\n        throw new Error(`output dimension should be at least 2, but got ${dim}`);\n      }\n\n      const outputHeight = outputShape[dim - 2];\n      const outputWidth = outputShape[dim - 1];\n\n      const inputShape = inputs[0].dims;\n      if (dim !== inputShape.length) {\n        throw new Error(`output dimension should match input ${inputShape.length}, but got ${dim}`);\n      }\n      const inputHeight = inputShape[dim - 2];\n      const inputWidth = inputShape[dim - 1];\n\n      const scalesHeight = scales[dim - 2];\n      const scalesWidth = scales[dim - 1];\n\n      let getSourceFracIndex = '';\n\n      if (attributes.mode !== 'linear') {\n        // TODO: support other modes\n        throw new Error(`resize (packed) does not support mode: '${attributes.mode}'`);\n      }\n      switch (attributes.coordinateTransformMode) {\n        case 'asymmetric':\n          getSourceFracIndex = `\n                    vec4 getSourceFracIndex(ivec4 coords) {\n                        return vec4(coords) / scaleWHWH;\n                    }\n                `;\n          break;\n        case 'half_pixel':\n          getSourceFracIndex = `\n                    vec4 getSourceFracIndex(ivec4 coords) {\n                        return (vec4(coords) + 0.5) / scaleWHWH - 0.5;\n                    }\n                `;\n          break;\n        case 'pytorch_half_pixel':\n          getSourceFracIndex = `\n                    vec4 getSourceFracIndex(ivec4 coords) {\n                        vec4 fcoords = vec4(coords);\n                        return vec4(\n                            ${outputWidth}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,\n                            ${outputHeight}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,\n                            ${outputWidth}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,\n                            ${outputHeight}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0\n                          );\n                    }\n                `;\n          break;\n        case 'align_corners':\n          getSourceFracIndex = `\n                    vec4 getSourceFracIndex(ivec4 coords) {\n                        vec4 resized = vec4(${outputWidth}.0 - 1.0, ${outputHeight}.0 - 1.0, ${outputWidth}.0 - 1.0,\n                            ${outputHeight}.0 - 1.0);\n                        vec4 original = vec4(${inputWidth}.0 - 1.0, ${inputHeight}.0 - 1.0, ${inputWidth}.0 - 1.0,\n                            ${inputHeight}.0 - 1.0);\n                        vec4 new_scale = original / resized;\n                        return vec4(coords) * new_scale;\n                    }\n                `;\n          break;\n        default:\n          // TODO:supporting other coordinateTransformModes\n          throw new Error(`resize (packed) does not support coordinateTransformMode: \\\n                                '${attributes.coordinateTransformMode}'`);\n      }\n\n      const coordsDataType = getCoordsDataType(dim);\n      const unpackChannel = unpackFromChannel();\n      const shaderSource = `\n            const vec2 inputWH = vec2(${inputHeight}.0, ${inputWidth}.0);\n            const vec4 scaleWHWH = vec4(float(${scalesHeight}), float(${scalesWidth}), float(${scalesHeight}), float(${\n          scalesWidth}));\n            ${unpackChannel}\n            ${getSourceFracIndex}\n            float getAValue(int x10, int r, int c, int d) {\n                return getChannel(getA(x10, r, c, d), vec2(c, d));\n            }\n            void main() {\n                ${coordsDataType} rc = getOutputCoords();\n\n                int batch = rc[0];\n                int depth = rc[1];\n\n                // retrieve the 4 coordinates that is used in the 4 packed output values.\n                ivec4 coords = ivec4(rc.wz, rc.w + 1, rc.z + 1);\n\n                // calculate the source index in fraction\n                vec4 sourceFrac = getSourceFracIndex(coords);\n\n                // get the lower and upper bound of the 4 values that will be packed into one texel.\n                ivec4 x00 = ivec4(max(sourceFrac.xy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xy)));\n                ivec4 x01 = ivec4(max(sourceFrac.xw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xw)));\n                ivec4 x10 = ivec4(max(sourceFrac.zy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zy)));\n                ivec4 x11 = ivec4(max(sourceFrac.zw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zw)));\n\n                bool hasNextRow = rc.w < ${outputHeight - 1};\n                bool hasNextCol = rc.z < ${outputWidth - 1};\n\n                // pack x00, x01, x10, x11's top-left corner into one vec4 structure\n                vec4 topLeft = vec4(\n                    getAValue(batch, depth, x00.x, x00.y),\n                    hasNextCol ? getAValue(batch, depth, x01.x, x01.y) : 0.0,\n                    hasNextRow ? getAValue(batch, depth, x10.x, x10.y) : 0.0,\n                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.y) : 0.0);\n\n                // pack x00, x01, x10, x11's top-right corner into one vec4 structure\n                vec4 topRight = vec4(\n                    getAValue(batch, depth, x00.x, x00.w),\n                    hasNextCol ? getAValue(batch, depth, x01.x, x01.w) : 0.0,\n                    hasNextRow ? getAValue(batch, depth, x10.x, x10.w) : 0.0,\n                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.w) : 0.0);\n\n                // pack x00, x01, x10, x11's bottom-left corner into one vec4 structure\n                vec4 bottomLeft = vec4(\n                    getAValue(batch, depth, x00.z, x00.y),\n                    hasNextCol ? getAValue(batch, depth, x01.z, x01.y) : 0.0,\n                    hasNextRow ? getAValue(batch, depth, x10.z, x10.y) : 0.0,\n                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.y) : 0.0);\n\n                // pack x00, x01, x10, x11's bottom-right corner into one vec4 structure\n                vec4 bottomRight = vec4(\n                    getAValue(batch, depth, x00.z, x00.w),\n                    hasNextCol ? getAValue(batch, depth, x01.z, x01.w) : 0.0,\n                    hasNextRow ? getAValue(batch, depth, x10.z, x10.w) : 0.0,\n                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.w) : 0.0);\n\n                // calculate the interpolation fraction on u and v direction\n                vec4 frac = vec4(sourceFrac) - floor(sourceFrac);\n                vec4 clampFrac = clamp(frac, vec4(0.0), vec4(1.0));\n\n                vec4 top = mix(topLeft, topRight, clampFrac.ywyw);\n                vec4 bottom = mix(bottomLeft, bottomRight, clampFrac.ywyw);\n                vec4 newValue = mix(top, bottom, clampFrac.xxzz);\n\n                ${glsl.output} = vec4(newValue);\n            }\n        `;\n      return {\n        ...resizeProgramMetadata,\n        output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.packed},\n        hasMain: true,\n        shaderSource\n      };\n    };\n\n\nconst prepareInputs = (inputs: Tensor[], attributes: UpsampleAttributes): [readonly number[], readonly number[]] => {\n  const x = inputs[0];\n  const xDims = x.dims;\n\n  let scales = attributes.scales;\n  let outputSizes: number[]|undefined;\n  if (scales.length === 0) {\n    const scalesTensor = inputs[attributes.scalesInputIdx];\n    if (scalesTensor && scalesTensor.size !== 0) {\n      if (inputs[attributes.sizesInputIdx]) {\n        throw new Error('Only one of scales or sizes must be provided as input.');\n      }\n      scales = parseScalesData(scalesTensor, attributes.mode, attributes.isResize);\n    } else {\n      const sizesTensor = inputs[attributes.sizesInputIdx];\n      if (!sizesTensor || sizesTensor.size === 0) {\n        throw new Error('Either scales or sizes MUST be provided as input.');\n      }\n\n      outputSizes = Array.from(sizesTensor.integerData);\n      scales = parseScalesDataFromOutputSize(outputSizes, xDims, attributes.mode, attributes.isResize);\n    }\n  } else {\n    if (inputs[attributes.sizesInputIdx]) {\n      throw new Error('Only one of scales or sizes must be provided as input.');\n    }\n  }\n\n  const yDims = outputSizes || (xDims.map((dim, i) => Math.floor(dim * scales[i])));\n\n  return [scales, yDims];\n};\n\nconst parseScalesData = (scale: Tensor, mode: string, isResize: boolean): number[] => {\n  const scales = Array.from(scale.floatData);\n  scalesValidation(scales, mode, isResize);\n  return scales;\n};\n\nconst parseScalesDataFromOutputSize =\n    (yDims: readonly number[], xDims: readonly number[], mode: string, isResize: boolean): number[] => {\n      const length = xDims.length;\n      const scales = new Array<number>(length);\n\n      for (let i = 0, end = length; i < end; i++) {\n        if (xDims[i] === 0) {\n          if (yDims[i] !== 0) {\n            throw new Error('Input dim is zero but required output dim is non-zero.');\n          }\n          scales[i] = 1;\n        } else {\n          scales[i] = yDims[i] / xDims[i];\n        }\n      }\n      scalesValidation(scales, mode, isResize);\n      return scales;\n    };\n\n// roi data is not used yet. but leave here for future usage.\n// const getRoi = (inputs: Tensor[], attributes: UpsampleAttributes) : number[] => {\n//     let roi: number[] = [];\n//     if (attributes.needRoiInput) {\n//         if (attributes.roiInputIdx <= 0) {\n//             throw new Error('Invalid roi input index.');\n//         }\n//         const roiTensor = inputs[attributes.roiInputIdx];\n//         roi = roiTensor.size > 0 ? Array.from(roiTensor.floatData) : [];\n//     } else {\n//         roi = new Array(inputs[0].dims.length * 2).fill(0);\n//     }\n//     return roi;\n// };\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from '../../../tensor';\nimport {WebGLInferenceHandler} from '../inference-handler';\n\nexport const shape = (_inferenceHandler: WebGLInferenceHandler, inputs: Tensor[]): Tensor[] => {\n  validateInputs(inputs);\n  return [new Tensor([inputs[0].dims.length], 'int32', undefined, undefined, new Int32Array(inputs[0].dims))];\n};\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 1) {\n    throw new Error('Shape requires 1 input.');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {Graph} from '../../../graph';\nimport {NUMBER_TYPES, OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {ShapeUtil} from '../../../util';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, TextureType} from '../types';\n\nexport interface SliceAttributes extends AttributeWithCacheKey {\n  readonly axes: number[];\n  readonly ends: number[];\n  readonly starts: number[];\n}\n\nconst sliceProgramMetadata = {\n  name: 'Slice',\n  inputNames: ['A'],\n  inputTypes: [TextureType.unpacked]\n};\n\nexport const slice: OperatorImplementation<SliceAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: SliceAttributes): Tensor[] => {\n      validateInputs(inputs);\n      const output = inferenceHandler.run(\n          {\n            ...sliceProgramMetadata,\n            cacheHint: attributes.cacheKey,\n            get: () => createSliceProgramInfo(inferenceHandler, inputs[0], attributes)\n          },\n          inputs);\n      return [output];\n    };\n\nexport const parseSliceAttributes: OperatorInitialization<SliceAttributes> = (node: Graph.Node): SliceAttributes => {\n  const starts = node.attributes.getInts('starts');\n  const ends = node.attributes.getInts('ends');\n  const axes = node.attributes.getInts('axes', []);\n  return createAttributeWithCacheKey({starts, ends, axes});\n};\n\nconst createSliceProgramInfo =\n    (_inferenceHandler: WebGLInferenceHandler, input: Tensor, attributes: SliceAttributes): ProgramInfo => {\n      const axes = (attributes.axes.length === 0) ? input.dims.slice(0).map((_val, i) => i) : attributes.axes;\n      const normalizedAxes = ShapeUtil.normalizeAxes(axes, input.dims.length);\n      const starts = attributes.starts.map((start, i) => {\n        if (start > input.dims[normalizedAxes[i]] - 1) {\n          return input.dims[normalizedAxes[i]];\n        }\n        return ShapeUtil.normalizeAxis(start, input.dims[normalizedAxes[i]]);\n      });\n      const ends = attributes.ends.map((end, i) => {\n        if (end > input.dims[normalizedAxes[i]] - 1) {\n          return input.dims[normalizedAxes[i]];\n        }\n        return ShapeUtil.normalizeAxis(end, input.dims[normalizedAxes[i]]);\n      });\n\n      const outputShape = input.dims.slice();\n\n      const sliceOps: string[] = [];\n      for (let i = 0; i < normalizedAxes.length; i++) {\n        outputShape[normalizedAxes[i]] = ends[i] - starts[i];\n        if (starts[i] > 0) {\n          sliceOps.push(`outputIdx[${normalizedAxes[i]}] += ${starts[i]};`);\n        }  // else { sliceOps.push(`outputIdx[${normalizedAxes[i]}] += 0;`); }\n      }\n\n      const rank = outputShape.length;\n      const shaderSource = `\n      float process(int outputIdx[${rank}]) {\n        ${sliceOps.join('\\n      ')}\n        return _A(outputIdx);\n      }`;\n      return {\n        ...sliceProgramMetadata,\n        output: {dims: outputShape, type: input.type, textureType: TextureType.unpacked},\n        shaderSource\n      };\n    };\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 1) {\n    throw new Error('Slice requires 1 input.');\n  }\n  if (NUMBER_TYPES.indexOf(inputs[0].type) === -1) {\n    throw new Error('Invalid input type.');\n  }\n};\n\nexport const sliceV10 = (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[]): Tensor[] => {\n  validateInputsV10(inputs);\n  const attributes = generateSliceAttributesFromInputs(inferenceHandler, inputs);\n  const output = inferenceHandler.run(\n      {\n        ...sliceProgramMetadata,\n        cacheHint: attributes.cacheKey,\n        get: () => createSliceProgramInfo(inferenceHandler, inputs[0], attributes)\n      },\n      [inputs[0]]);\n  return [output];\n};\n\nconst generateSliceAttributesFromInputs =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[]): SliceAttributes => {\n      if (!inferenceHandler.session.isInitializer(inputs[1].dataId) ||\n          !inferenceHandler.session.isInitializer(inputs[2].dataId) ||\n          (inputs.length >= 4 && !inferenceHandler.session.isInitializer(inputs[3].dataId)) ||\n          (inputs.length >= 5 && !inferenceHandler.session.isInitializer(inputs[4].dataId))) {\n        throw new Error('dynamic slice attributes are not allowed');\n      }\n\n      if (inputs.length >= 5 && inputs[4].integerData.some((i: number) => i !== 1)) {\n        throw new Error('currently non-1 steps is not supported for Slice');\n      }\n\n      const starts = Array.from(inputs[1].integerData);\n      const ends = Array.from(inputs[2].integerData);\n      const axes = inputs.length >= 4 ? Array.from(inputs[3].integerData) : [];\n      const cacheKey = `${axes};${starts};${ends}`;\n      return {starts, ends, axes, cacheKey};\n    };\n\nconst validateInputsV10 = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length < 3 || inputs.length > 5) {\n    throw new Error('Invalid input number.');\n  }\n  if (inputs[1].type !== 'int32' || inputs[1].dims.length !== 1) {\n    throw new Error('Invalid input type.');\n  }\n  if (inputs[2].type !== 'int32' || inputs[2].dims.length !== 1) {\n    throw new Error('Invalid input type.');\n  }\n  if (inputs.length >= 4 && (inputs[3].type !== 'int32' || inputs[3].dims.length !== 1)) {\n    throw new Error('Invalid input type.');\n  }\n  if (inputs.length >= 5 && (inputs[4].type !== 'int32' || inputs[4].dims.length !== 1)) {\n    throw new Error('Invalid input type.');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {ShapeUtil} from '../../../util';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, TextureType} from '../types';\n\nimport {transpose, TransposeAttributes} from './transpose';\n\nexport interface SoftmaxAttributes extends AttributeWithCacheKey {\n  readonly axis: number;\n}\n\nconst softmaxComputeMaxProgramMetadata = {\n  name: 'SoftmaxComputeMax',\n  inputNames: ['A'],\n  inputTypes: [TextureType.unpacked],\n};\n\nconst softmaxComputeScaleProgramMetadata = {\n  name: 'SoftmaxComputeScale',\n  inputNames: ['A', 'Max'],\n  inputTypes: [TextureType.unpacked, TextureType.unpacked],\n};\n\nconst softmaxProgramMetadata = {\n  name: 'SoftMax',\n  inputNames: ['A', 'Max', 'Norm'],\n  inputTypes: [TextureType.unpacked, TextureType.unpacked, TextureType.unpacked],\n};\n\nexport const softmax: OperatorImplementation<SoftmaxAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: SoftmaxAttributes): Tensor[] => {\n      validateInputs(inputs);\n\n      const inputShape = inputs[0].dims.slice();\n      const axis = ShapeUtil.normalizeAxis(attributes.axis, inputShape.length);\n      const logicalRowCount = ShapeUtil.sizeToDimension(inputShape, axis);\n      const featureCount = ShapeUtil.sizeFromDimension(inputShape, axis);\n\n      const output = computeSoftmax(inferenceHandler, inputs, attributes, logicalRowCount, featureCount);\n      return output;\n    };\n\nexport const parseSoftmaxAttributes: OperatorInitialization<SoftmaxAttributes> =\n    (node: Graph.Node): SoftmaxAttributes => createAttributeWithCacheKey({axis: node.attributes.getInt('axis', 1)});\n\nexport const parseSoftmaxAttributesV13: OperatorInitialization<SoftmaxAttributes> =\n    (node: Graph.Node): SoftmaxAttributes => createAttributeWithCacheKey({axis: node.attributes.getInt('axis', -1)});\n\n// The \"semantic\" meaning of axis has changed in opset-13.\n// Please compare: https://github.com/onnx/onnx/blob/main/docs/Operators.md#Softmax\n// with https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Softmax-11 for detailed explanations\n// To account for the opset-13 behavior, our plan will be to transpose the \"axis\" dim to the innermost dim\n// and perform softmax and then reverse the transpose. We can skip the transposing aspect if the axis is already\n// the innermost dim\nexport const softmaxV13: OperatorImplementation<SoftmaxAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: SoftmaxAttributes): Tensor[] => {\n      validateInputs(inputs);\n\n      const inputShape = inputs[0].dims.slice();\n      const axis = ShapeUtil.normalizeAxis(attributes.axis, inputShape.length);\n      const rank = inputShape.length;\n\n      const isTransposeRequired = (axis !== rank - 1) ? true : false;\n      const transposedInputShape: number[] = [];\n      let perm: number[] = [];\n      let transposedInputs: Tensor[] = [];\n      let transposeAttribute: TransposeAttributes;\n\n      if (isTransposeRequired) {\n        perm = Array.from({length: rank}).map((_, i) => i);\n\n        // swap the innermost dim with the dim corresponding to axis\n        perm[axis] = rank - 1;\n        perm[rank - 1] = axis;\n\n        perm.map(p => transposedInputShape.push(inputShape[p]));\n\n        transposeAttribute = createAttributeWithCacheKey({perm});\n        transposedInputs = transpose(inferenceHandler, inputs, transposeAttribute);\n      }\n\n      const logicalRowCount = isTransposeRequired ? ShapeUtil.sizeToDimension(transposedInputShape, rank - 1) :\n                                                    ShapeUtil.sizeToDimension(inputShape, rank - 1);\n      const featureCount = isTransposeRequired ? ShapeUtil.sizeFromDimension(transposedInputShape, rank - 1) :\n                                                 ShapeUtil.sizeFromDimension(inputShape, rank - 1);\n\n      const output = computeSoftmax(\n          inferenceHandler, isTransposeRequired ? transposedInputs : inputs, attributes, logicalRowCount, featureCount);\n\n      if (isTransposeRequired) {\n        const reversedOutput = transpose(inferenceHandler, output, transposeAttribute!);\n        return reversedOutput;\n      } else {\n        return output;\n      }\n    };\n\nconst computeSoftmax =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: SoftmaxAttributes, logicalRowCount: number,\n     featureCount: number): Tensor[] => {\n      const computeMaxProgramInfo =\n          createComputeMaxProgramInfo(inferenceHandler, inputs[0], logicalRowCount, featureCount, [logicalRowCount]);\n      const max = inferenceHandler.run(\n          {...softmaxComputeMaxProgramMetadata, cacheHint: attributes.cacheKey, get: () => computeMaxProgramInfo},\n          inputs);\n\n      const computeScaleProgramInfo = createComputScaleProgramInfo(\n          inferenceHandler, inputs[0], logicalRowCount, featureCount, computeMaxProgramInfo.output.dims,\n          [logicalRowCount]);\n      const scale = inferenceHandler.run(\n          {...softmaxComputeScaleProgramMetadata, cacheHint: attributes.cacheKey, get: () => computeScaleProgramInfo},\n          [inputs[0], max]);\n\n      const softMaxProgramInfo = createSoftMaxProgramInfo(\n          inferenceHandler, inputs[0], logicalRowCount, featureCount, computeMaxProgramInfo.output.dims,\n          computeScaleProgramInfo.output.dims);\n      const output = inferenceHandler.run(\n          {...softmaxProgramMetadata, cacheHint: attributes.cacheKey, get: () => softMaxProgramInfo},\n          [inputs[0], max, scale]);\n      return [output];\n    };\n\n/**\n * Create a texture that contains the maximum value of each of the 'N' rows\n */\nconst createComputeMaxProgramInfo =\n    (inferenceHandler: WebGLInferenceHandler, input: Tensor, logicalRowCount: number, featureCount: number,\n     outputShape: number[]): ProgramInfo => {\n      const [textureWidth, textureHeight] =\n          inferenceHandler.calculateTextureWidthAndHeight(input.dims, TextureType.unpacked);\n      const rank = outputShape.length;\n\n      if (logicalRowCount < 1 || featureCount < 1) {\n        throw new Error('Logical row count N and feature count D must be greater than or equal to 1');\n      }\n\n      if (outputShape.length !== 1) {\n        throw new Error('Dimensionality of the output should be 1');\n      }\n\n      if (outputShape[0] !== logicalRowCount) {\n        throw new Error('Shape of the output should be equal to logical row count');\n      }\n\n      const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);\n      const shaderSource = `\n      float process(int[${rank}] indices) {\n        int logical_row_start_offset = indices[0] * ${featureCount};\n\n        float max = getColorAsFloat(${glsl.texture2D}(A, offsetToCoords(logical_row_start_offset, ${textureWidth},\n        ${textureHeight} )));\n        for(int i=1; i<${featureCount}; ++i)\n        {\n          float current = getColorAsFloat(${glsl.texture2D}(A, offsetToCoords(logical_row_start_offset + i,\n            ${textureWidth}, ${textureHeight})));\n          if(current > max)\n          max = current;\n        }\n\n        return max;\n      }`;\n      return {\n        ...softmaxComputeMaxProgramMetadata,\n        output: {dims: outputShape, type: input.type, textureType: TextureType.unpacked},\n        shaderSource\n      };\n    };\n\n/**\n * Create a texture that contains the normalization factor for each of the 'N' rows\n */\nconst createComputScaleProgramInfo =\n    (inferenceHandler: WebGLInferenceHandler, input: Tensor, logicalRowCount: number, featureCount: number,\n     maxElementPerLogicalRow: readonly number[], outputShape: number[]): ProgramInfo => {\n      const [textureWidth, textureHeight] =\n          inferenceHandler.calculateTextureWidthAndHeight(input.dims, TextureType.unpacked);\n      const rank = outputShape.length;\n\n      if (logicalRowCount < 1 || featureCount < 1) {\n        throw new Error('Logical row count N and feature count D must be greater than or equal to 1');\n      }\n\n      if (outputShape.length !== 1) {\n        throw new Error('Dimensionality of the output should be 1');\n      }\n\n      if (outputShape[0] !== logicalRowCount) {\n        throw new Error('Shape of the output should be equal to logical row count');\n      }\n\n      if (maxElementPerLogicalRow.length !== 1) {\n        throw new Error('Dimensionality of the intermediate results should be 1');\n      }\n\n      if (maxElementPerLogicalRow[0] !== logicalRowCount) {\n        throw new Error('Shape of the intermediate results should be equal to logical row count');\n      }\n\n      const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);\n      const shaderSource = `\n      float process(int[${rank}] indices) {\n        int logical_row_start_offset = indices[0] * ${featureCount};\n\n        float norm_factor = 0.0;\n        float max = _Max(indices);\n        for(int i=0; i<${featureCount}; ++i)\n        {\n          norm_factor += exp(getColorAsFloat(${glsl.texture2D}(A, offsetToCoords(logical_row_start_offset + i,\n            ${textureWidth}, ${textureHeight}))) - max);\n        }\n\n        return norm_factor;\n      }`;\n      return {\n        ...softmaxComputeScaleProgramMetadata,\n        output: {dims: outputShape, type: input.type, textureType: TextureType.unpacked},\n        shaderSource\n      };\n    };\n\nconst createSoftMaxProgramInfo =\n    (inferenceHandler: WebGLInferenceHandler, input: Tensor, logicalRowCount: number, featureCount: number,\n     maxElementPerLogicalRow: readonly number[], normalizationPerLogicalRow: readonly number[]): ProgramInfo => {\n      const [textureWidth, textureHeight] =\n          inferenceHandler.calculateTextureWidthAndHeight(input.dims, TextureType.unpacked);\n      const rank = input.dims.length;\n\n      if (logicalRowCount < 1 || featureCount < 1) {\n        throw new Error('Logical row count N and feature count D must be greater than or equal to 1');\n      }\n\n      if (maxElementPerLogicalRow.length !== 1 || normalizationPerLogicalRow.length !== 1) {\n        throw new Error('Dimensionality of the intermediate results should be 1');\n      }\n\n      if (maxElementPerLogicalRow[0] !== logicalRowCount || normalizationPerLogicalRow[0] !== logicalRowCount) {\n        throw new Error('Shape of the intermediate results should be equal to logical row count');\n      }\n\n      const shaderSource = `\n      float process(int[${rank}] indices) {\n\n      // get offset of current logical tensor index from the 2-D texture coordinates (TexCoords)\n      int offset = coordsToOffset(TexCoords, ${textureWidth}, ${textureHeight});\n\n      //determine the logical row for this index\n      int logical_row_index[1];\n      logical_row_index[0] = offset / ${featureCount};\n\n      float norm_factor = _Norm(logical_row_index);\n\n      // avoid possible division by 0\n      // if norm_facor is 0, all elements are zero\n      // if so, return 0\n      if(norm_factor == 0.0)\n        return 0.0;\n\n      return exp(_A(indices) - _Max(logical_row_index)) / norm_factor;\n    }`;\n      return {\n        ...softmaxProgramMetadata,\n        output: {dims: input.dims, type: input.type, textureType: TextureType.unpacked},\n        shaderSource\n      };\n    };\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 1) {\n    throw new Error('Softmax requires 1 input.');\n  }\n\n  if (inputs[0].type !== 'float32' && inputs[0].type !== 'float64') {\n    throw new Error('Invalid input type');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {ShapeUtil, SplitUtil} from '../../../util';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, TextureType} from '../types';\n\nexport interface SplitAttributes extends AttributeWithCacheKey {\n  readonly axis: number;\n  readonly split: number[];\n  readonly numOutputs: number;\n}\n\nconst splitProgramMetadata = {\n  name: 'Split',\n  inputNames: ['A'],\n  inputTypes: [TextureType.unpacked],\n};\n\nexport const split: OperatorImplementation<SplitAttributes> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: SplitAttributes): Tensor[] => {\n      validateInputs(inputs);\n\n      const axis = ShapeUtil.normalizeAxis(attributes.axis, inputs[0].dims.length);\n      const count = getProgramCount(inferenceHandler, inputs, axis, attributes);\n      const output: Tensor[] = [];\n      for (let i = 0; i < count; ++i) {\n        output.push(inferenceHandler.run(\n            {\n              ...splitProgramMetadata,\n              cacheHint: `${attributes.cacheKey};${i}`,\n              get: () => createSplitProgramInfo(inferenceHandler, inputs[0], attributes, axis, i)\n            },\n            inputs));\n      }\n\n      return output;\n    };\n\nexport const parseSplitAttributes: OperatorInitialization<SplitAttributes> = (node: Graph.Node): SplitAttributes => {\n  const axis = node.attributes.getInt('axis', 0);\n  const split = node.attributes.getInts('split', []);\n  const numOutputs = node.outputs.length;\n  return createAttributeWithCacheKey({axis, split, numOutputs});\n};\n\nconst getProgramCount =\n    (_inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], axis: number, attributes: SplitAttributes): number => {\n      const [, offsets] = SplitUtil.splitShape(inputs[0].dims, axis, attributes.split, attributes.numOutputs);\n      return offsets.length;\n    };\n\nconst createSplitProgramInfo =\n    (_inferenceHandler: WebGLInferenceHandler, input: Tensor, attributes: SplitAttributes, axis: number, index: number):\n        ProgramInfo => {\n          const [shapes, offsets] = SplitUtil.splitShape(input.dims, axis, attributes.split, attributes.numOutputs);\n          const offset = offsets[index];\n          const outputShape = shapes[index];\n          const rank = outputShape.length;\n          const shaderSource = `\n      float process(int indices[${rank}]) {\n        indices[${axis}] += ${offset};\n        return _A(indices);\n      }\n    `;\n          return {\n            ...splitProgramMetadata,\n            cacheHint: `${attributes.cacheKey}:${index}`,\n            output: {dims: outputShape, type: input.type, textureType: TextureType.unpacked},\n            shaderSource\n          };\n        };\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 1) {\n    throw new Error('Split requires one input.');\n  }\n\n  if (inputs[0].type !== 'int8' && inputs[0].type !== 'uint8' && inputs[0].type !== 'int16' &&\n      inputs[0].type !== 'uint16' && inputs[0].type !== 'int32' && inputs[0].type !== 'uint32' &&\n      inputs[0].type !== 'float32' && inputs[0].type !== 'float64' && inputs[0].type !== 'bool') {\n    throw new Error('Invalid input type.');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {ShapeUtil} from '../../../util';\nimport {WebGLInferenceHandler} from '../inference-handler';\n\nexport const squeeze: OperatorImplementation<number[]> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], axes: number[]): Tensor[] => {\n      validateInputs(inputs);\n      const outputShape = ShapeUtil.squeezeShape(inputs[0].dims, axes);\n      const output = inferenceHandler.reshapeUnpacked(inputs[0], outputShape);\n      return [output];\n    };\n\nexport const squeezeV13 = (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[]): Tensor[] => {\n  validateInputsV13(inputs);\n  return squeeze(inferenceHandler, [inputs[0]], Array.from(inputs[1].integerData));\n};\n\nexport const parseSqueezeAttributes: OperatorInitialization<number[]> = (node: Graph.Node): number[] =>\n    node.attributes.getInts('axes');\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 1) {\n    throw new Error('Squeeze requires 1 input.');\n  }\n\n  if (inputs[0].type === 'string') {\n    throw new Error('invalid input tensor types.');\n  }\n};\n\nconst validateInputsV13 = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 2) {\n    throw new Error('Squeeze requires 2 inputs.');\n  }\n\n  if (inputs[1].type !== 'int32') {\n    throw new Error('Invalid input type.');\n  }\n};","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from '../../../tensor';\nimport {getGlsl} from '../glsl-source';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramMetadata, TextureType} from '../types';\n\nexport const sum = (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[]): Tensor[] => {\n  validateInputs(inputs);\n\n  const sumProgramMetadata = {\n    name: 'Sum',\n    inputNames: inputs.map((_v, i) => `X${i}`),\n    inputTypes: new Array(inputs.length).fill(TextureType.unpacked)\n  };\n\n  const output = inferenceHandler.run(\n      {...sumProgramMetadata, get: () => createSumProgramInfo(inferenceHandler, inputs, sumProgramMetadata)}, inputs);\n  return [output];\n};\n\nconst createSumProgramInfo =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], sumProgramMetadata: ProgramMetadata): ProgramInfo => {\n      const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);\n      const outputShape = inputs[0].dims.slice();\n      const sumLine = inputs.map((_v, i) => `${glsl.texture2D}(X${i},TexCoords)`).join(' + ');\n      const shaderSource = `\n      void main() {\n        vec4 result = ${sumLine};\n        ${glsl.output} = result;\n      }\n    `;\n      return {\n        ...sumProgramMetadata,\n        output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},\n        hasMain: true,\n        shaderSource\n      };\n    };\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length === 0) {\n    throw new Error('Sum requires inputs.');\n  }\n\n  const length = inputs[0].dims.length;\n  for (let i = 1; i < inputs.length; i++) {\n    if (length !== inputs[i].dims.length) {\n      throw new Error('Input shapes are mismatched.');\n    }\n\n    for (let j = 0; j < length; j++) {\n      if (inputs[0].dims[j] !== inputs[i].dims[j]) {\n        throw new Error('Input shapes are not matched.');\n      }\n    }\n  }\n\n  if (inputs[0].type !== 'float32' && inputs[0].type !== 'float64') {\n    throw new Error('Invalid input type.');\n  }\n  for (let i = 1; i < inputs.length; i++) {\n    if (inputs[0].type !== inputs[i].type) {\n      throw new Error('Input types are not matched.');\n    }\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {NUMBER_TYPES} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {WebGLInferenceHandler} from '../inference-handler';\nimport {ProgramInfo, ProgramMetadata, TextureType} from '../types';\n\nexport const tile = (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[]): Tensor[] => {\n  validateInputs(inputs);\n\n  const tileProgramMetadata = {\n    name: 'Tile',\n    inputNames: ['A'],\n    inputTypes: [TextureType.unpacked],\n  };\n\n  const output = inferenceHandler.run(\n      {...tileProgramMetadata, get: () => createTileProgramInfo(inferenceHandler, inputs, tileProgramMetadata)},\n      inputs);\n  return [output];\n};\n\nconst createTileProgramInfo =\n    (_handler: WebGLInferenceHandler, inputs: Tensor[], tileProgramMetadata: ProgramMetadata): ProgramInfo => {\n      const inputShape = inputs[0].dims.slice();\n      const outputShape = new Array(inputShape.length);\n\n      const tileOps: string[] = [];\n      for (let i = 0; i < inputShape.length; i++) {\n        outputShape[i] = inputShape[i] * inputs[1].numberData[i];\n        tileOps.push(`inputIdx[${i}] = int(mod(float(outputIdx[${i}]), ${inputShape[i]}.));`);\n      }\n\n      const rank = outputShape.length;\n      const shaderSource = `\n      float process(int outputIdx[${rank}]) {\n        int inputIdx[${rank}];\n        ${tileOps.join('\\n')}\n        return _A(inputIdx);\n      }\n    `;\n      return {\n        ...tileProgramMetadata,\n        output: {dims: outputShape, type: inputs[0].type, textureType: TextureType.unpacked},\n        shaderSource\n      };\n    };\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 2) {\n    throw new Error('Tile requires 2 input.');\n  }\n  if (inputs[1].dims.length !== 1) {\n    throw new Error('The second input shape must 1 dimension.');\n  }\n  if (inputs[1].dims[0] !== inputs[0].dims.length) {\n    throw new Error('Invalid input shape.');\n  }\n  if (NUMBER_TYPES.indexOf(inputs[0].type) === -1) {\n    throw new Error('Invalid input type.');\n  }\n  if (inputs[1].type !== 'int32' && inputs[1].type !== 'int16') {\n    throw new Error('Invalid repeat type.');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Graph} from '../../../graph';\nimport {OperatorImplementation, OperatorInitialization} from '../../../operators';\nimport {Tensor} from '../../../tensor';\nimport {ShapeUtil} from '../../../util';\nimport {WebGLInferenceHandler} from '../inference-handler';\n\nexport const unsqueeze: OperatorImplementation<number[]> =\n    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], axes: number[]): Tensor[] => {\n      validateInputs(inputs);\n      const outputShape = ShapeUtil.unsqueezeShape(inputs[0].dims, axes);\n      const output = inferenceHandler.reshapeUnpacked(inputs[0], outputShape);\n      return [output];\n    };\n\nexport const unsqueezeV13 = (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[]): Tensor[] => {\n  validateInputsV13(inputs);\n  return unsqueeze(inferenceHandler, [inputs[0]], Array.from(inputs[1].integerData));\n};\n\nexport const parseUnsqueezeAttributes: OperatorInitialization<number[]> = (node: Graph.Node): number[] =>\n    node.attributes.getInts('axes');\n\nconst validateInputs = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 1) {\n    throw new Error('Unsqueeze requires 1 input.');\n  }\n\n  if (inputs[0].type === 'string') {\n    throw new Error('invalid input tensor types.');\n  }\n};\n\nconst validateInputsV13 = (inputs: Tensor[]): void => {\n  if (!inputs || inputs.length !== 2) {\n    throw new Error('Unsqueeze requires 2 inputs.');\n  }\n\n  if (inputs[1].type !== 'int32') {\n    throw new Error('Invalid input type.');\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {OpSet} from '../../opset';\n\nimport {batchNormalization, parseBatchNormalizationAttributes} from './ops/batch-normalization';\nimport * as binaryOps from './ops/binary-op';\nimport {cast, parseCastAttributes} from './ops/cast';\nimport {concat, parseConcatAttributes} from './ops/concat';\nimport {conv, parseConvAttributes} from './ops/conv';\nimport {convTranspose, parseConvTransposeAttributes} from './ops/conv-transpose';\nimport {depthToSpace, parseDepthToSpaceAttributes} from './ops/depth-to-space';\nimport {flatten, parseFlattenAttributes} from './ops/flatten';\nimport {gather, parseGatherAttributes} from './ops/gather';\nimport {gemm, parseGemmAttributesV11, parseGemmAttributesV7} from './ops/gemm';\nimport {imageScaler, parseImageScalerAttributes} from './ops/image-scaler';\nimport {instanceNormalization, parseInstanceNormalizationAttributes} from './ops/instance-normalization';\nimport {lrn, parseLrnAttributes} from './ops/lrn';\nimport {matMul, parseMatMulAttributes} from './ops/matmul';\nimport {padV11, padV2, parsePadAttributesV11, parsePadAttributesV2} from './ops/pad';\nimport {averagePool, globalAveragePool, globalMaxPool, maxPool, parseAveragePoolAttributes, parseGlobalAveragePoolAttributes, parseMaxPoolAttributes} from './ops/pool';\nimport {parseReduceAttributes, reduceLogSum, reduceLogSumSquare, reduceMax, reduceMean, reduceMin, reduceProd, reduceSum} from './ops/reduce';\nimport {reshape} from './ops/reshape';\nimport {parseResizeAttributesV10, parseResizeAttributesV11, resize} from './ops/resize-packed';\nimport {shape} from './ops/shape';\nimport {parseSliceAttributes, slice, sliceV10} from './ops/slice';\nimport {parseSoftmaxAttributes, parseSoftmaxAttributesV13, softmax, softmaxV13} from './ops/softmax';\nimport {parseSplitAttributes, split} from './ops/split';\nimport {parseSqueezeAttributes, squeeze, squeezeV13} from './ops/squeeze';\nimport {sum} from './ops/sum';\nimport {tile} from './ops/tile';\nimport {parseTransposeAttributes, transpose} from './ops/transpose';\nimport * as unaryOps from './ops/unary-op';\nimport {parseUnsqueezeAttributes, unsqueeze, unsqueezeV13} from './ops/unsqueeze';\nimport {parseUpsampleAttributesV7, parseUpsampleAttributesV9, upsample} from './ops/upsample';\n\nexport const WEBGL_OP_RESOLVE_RULES: readonly OpSet.ResolveRule[] = [\n  ['Abs', '', '6+', unaryOps.abs],\n  ['Acos', '', '7+', unaryOps.acos],\n  ['Add', '', '7+', binaryOps.add],\n  ['And', '', '7+', binaryOps.and],\n  ['Asin', '', '7+', unaryOps.asin],\n  ['Atan', '', '7+', unaryOps.atan],\n  // TODO: support new attributes for AveragePool-10\n  ['AveragePool', '', '7+', averagePool, parseAveragePoolAttributes],\n  ['BatchNormalization', '', '7+', batchNormalization, parseBatchNormalizationAttributes],\n  ['Cast', '', '6+', cast, parseCastAttributes],\n  ['Ceil', '', '6+', unaryOps.ceil],\n  ['Clip', '', '6-10', unaryOps.clip, unaryOps.parseClipAttributes],\n  ['Clip', '', '11+', unaryOps.clipV11],\n  ['Concat', '', '4+', concat, parseConcatAttributes],\n  ['Conv', '', '1+', conv, parseConvAttributes],\n  ['ConvTranspose', '', '1+', convTranspose, parseConvTransposeAttributes],\n  ['Cos', '', '7+', unaryOps.cos],\n  ['Div', '', '7+', binaryOps.div],\n  ['Dropout', '', '7+', unaryOps.identity],\n  ['DepthToSpace', '', '1+', depthToSpace, parseDepthToSpaceAttributes],\n  ['Equal', '', '7+', binaryOps.equal],\n  ['Elu', '', '6+', unaryOps.elu, unaryOps.parseEluAttributes],\n  ['Exp', '', '6+', unaryOps.exp],\n  ['Flatten', '', '1+', flatten, parseFlattenAttributes],\n  ['Floor', '', '6+', unaryOps.floor],\n  ['FusedConv', 'com.microsoft', '1+', conv, parseConvAttributes],\n  ['Gather', '', '1+', gather, parseGatherAttributes],\n  ['Gemm', '', '7-10', gemm, parseGemmAttributesV7],\n  ['Gemm', '', '11+', gemm, parseGemmAttributesV11],\n  ['GlobalAveragePool', '', '1+', globalAveragePool, parseGlobalAveragePoolAttributes],\n  ['GlobalMaxPool', '', '1+', globalMaxPool],\n  ['Greater', '', '7+', binaryOps.greater],\n  ['Identity', '', '1+', unaryOps.identity],\n  ['ImageScaler', '', '1+', imageScaler, parseImageScalerAttributes],\n  ['InstanceNormalization', '', '6+', instanceNormalization, parseInstanceNormalizationAttributes],\n  ['LeakyRelu', '', '6+', unaryOps.leakyRelu, unaryOps.parseLeakyReluAttributes],\n  ['Less', '', '7+', binaryOps.less],\n  ['LRN', '', '1+', lrn, parseLrnAttributes],\n  ['Log', '', '6+', unaryOps.log],\n  ['MatMul', '', '1+', matMul, parseMatMulAttributes],\n  // TODO: support new attributes for MaxPool-8 and MaxPool-10\n  ['MaxPool', '', '1+', maxPool, parseMaxPoolAttributes],\n  ['Mul', '', '7+', binaryOps.mul],\n  ['Neg', '', '6+', unaryOps.neg],\n  ['Not', '', '1+', unaryOps.not],\n  ['Or', '', '7+', binaryOps.or],\n  ['Pad', '', '2-10', padV2, parsePadAttributesV2],\n  ['Pad', '', '11+', padV11, parsePadAttributesV11],\n  ['Pow', '', '7+', binaryOps.pow],\n  ['PRelu', '', '7+', binaryOps.pRelu],\n  ['ReduceLogSum', '', '1+', reduceLogSum, parseReduceAttributes],\n  ['ReduceMax', '', '1+', reduceMax, parseReduceAttributes],\n  ['ReduceMean', '', '1+', reduceMean, parseReduceAttributes],\n  ['ReduceMin', '', '1+', reduceMin, parseReduceAttributes],\n  ['ReduceProd', '', '1+', reduceProd, parseReduceAttributes],\n  ['ReduceSum', '', '1-12', reduceSum, parseReduceAttributes],\n  ['ReduceSumSquare', '', '1+', reduceLogSumSquare, parseReduceAttributes],\n  ['Relu', '', '6+', unaryOps.relu],\n  ['Reshape', '', '5+', reshape],\n  ['Resize', '', '10', resize, parseResizeAttributesV10],\n  ['Resize', '', '11+', resize, parseResizeAttributesV11],\n  ['Shape', '', '1+', shape],\n  ['Sigmoid', '', '6+', unaryOps.sigmoid],\n  ['Sin', '', '7+', unaryOps.sin],\n  ['Slice', '', '10+', sliceV10],  // TODO: support 'steps' for Slice-10\n  ['Slice', '', '1-9', slice, parseSliceAttributes],\n  // The \"semantic\" meaning of axis has changed in opset-13.\n  ['Softmax', '', '1-12', softmax, parseSoftmaxAttributes],\n  ['Softmax', '', '13+', softmaxV13, parseSoftmaxAttributesV13],\n  // 'Split' operator has an optional attribute 'split'\n  // this attribute determines how the specified axis of input data is split.\n  // When the attribute is missing, we need the count of number of outputs\n  // so that we can determine the 'split' attribute from the runtime input to the Operator\n  ['Split', '', '2-12', split, parseSplitAttributes],\n  ['Sqrt', '', '6+', unaryOps.sqrt],\n  ['Squeeze', '', '1-12', squeeze, parseSqueezeAttributes],\n  ['Squeeze', '', '13+', squeezeV13],\n  ['Sub', '', '7+', binaryOps.sub],\n  ['Sum', '', '6+', sum],\n  ['Tan', '', '7+', unaryOps.tan],\n  ['Tanh', '', '6+', unaryOps.tanh],\n  ['Tile', '', '6+', tile],\n  ['Transpose', '', '1+', transpose, parseTransposeAttributes],\n  ['Upsample', '', '7-8', upsample, parseUpsampleAttributesV7],\n  ['Upsample', '', '9', upsample, parseUpsampleAttributesV9],\n  ['Unsqueeze', '', '1-12', unsqueeze, parseUnsqueezeAttributes],\n  ['Unsqueeze', '', '13+', unsqueezeV13],\n  ['Xor', '', '7+', binaryOps.xor],\n];\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nconst INLINE_FUNC_DEF_REGEX = /@inline[\\s\\n\\r]+(\\w+)[\\s\\n\\r]+([0-9a-zA-Z_]+)\\s*\\(([^)]*)\\)\\s*{(([^}]|[\\n\\r])*)}/gm;\nconst FUNC_CALL_REGEX = '(\\\\w+)?\\\\s+([_0-9a-zA-Z]+)\\\\s+=\\\\s+__FUNC__\\\\((.*)\\\\)\\\\s*;';\n/**\n * GLSL preprocessor responsible for resolving @inline directives\n */\nexport function replaceInlines(script: string): string {\n  const inlineDefs: {[name: string]: {params: Array<{type: string; name: string}|null>; body: string}} = {};\n  let match;\n  while ((match = INLINE_FUNC_DEF_REGEX.exec(script)) !== null) {\n    const params = match[3]\n                       .split(',')\n                       .map(s => {\n                         const tokens = s.trim().split(' ');\n                         if (tokens && tokens.length === 2) {\n                           return {type: tokens[0], name: tokens[1]};\n                         }\n                         return null;\n                       })\n                       .filter(v => v !== null);\n    inlineDefs[match[2]] = {params, body: match[4]};\n  }\n  for (const name in inlineDefs) {\n    const regexString = FUNC_CALL_REGEX.replace('__FUNC__', name);\n    const regex = new RegExp(regexString, 'gm');\n    while ((match = regex.exec(script)) !== null) {\n      const type = match[1];\n      const variable = match[2];\n      const params = match[3].split(',');\n      const declLine = (type) ? `${type} ${variable};` : '';\n      let newBody: string = inlineDefs[name].body;\n      let paramRedecLine = '';\n      inlineDefs[name].params.forEach((v, i) => {\n        if (v) {\n          paramRedecLine += `${v.type} ${v.name} = ${params[i]};\\n`;\n        }\n      });\n      newBody = `${paramRedecLine}\\n ${newBody}`;\n      newBody = newBody.replace('return', `${variable} = `);\n      const replacement = `\n      ${declLine}\n      {\n        ${newBody}\n      }\n      `;\n      script = script.replace(match[0], replacement);\n    }\n  }\n  script = script.replace(INLINE_FUNC_DEF_REGEX, '');\n  return script;\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Logger} from '../../instrument';\nimport {assert} from '../../util';\n\n/** Layout preferences */\nexport interface WidthHeightPrefs {\n  breakAxis?: number;\n  isPacked?: boolean;\n  reverseWH?: boolean;\n}\n/**\n * TextureLayoutStrategy is an abstraction for different plans\n * for mapping n-dimensional arrays to 2D textures (and back)\n */\nexport interface TextureLayoutStrategy {\n  computeTextureWH(shape: readonly number[], prefs?: WidthHeightPrefs): [number, number];\n}\n\n/**\n * This strategy try to find the minimal max(W,H) that fulfills (W * H == totalSize)\n */\nexport class AlwaysKeepOriginalSizeStrategy implements TextureLayoutStrategy {\n  constructor(public maxTextureSize: number) {}\n  computeTextureWH(shape: readonly number[], prefs?: WidthHeightPrefs): [number, number] {\n    // scalar tensor\n    if (shape.length === 0) {\n      return [1, 1];\n    }\n    const maxTextureSize = this.maxTextureSize;\n    if (prefs && prefs.breakAxis !== undefined) {\n      // check to see if dims fit\n      const wsize = prefs.breakAxis >= shape.length ? 1 : shape.slice(prefs.breakAxis).reduce((a, b) => a * b);\n      const hsize = prefs.breakAxis <= 0 ? 1 : shape.slice(0, prefs.breakAxis).reduce((a, b) => a * b);\n      if (wsize > maxTextureSize || hsize > maxTextureSize) {\n        // ignore preferences\n        // continue with default layout\n        Logger.verbose(\n            'TextureLayout',\n            `Given width/height preferences were unattainable: shape:${shape}, breakAxis:${prefs.breakAxis}`);\n      } else {\n        return [wsize, hsize];\n      }\n    }\n    const totalSize = shape.reduce((a, b) => a * b);\n\n    let width = Math.floor(Math.sqrt(totalSize));\n\n    for (; width < maxTextureSize && width < totalSize; width++) {\n      if (totalSize % width === 0) {\n        break;\n      }\n    }\n\n    if (width >= maxTextureSize || totalSize % width !== 0) {\n      throw new Error(`The given dimensions are outside this GPU's boundaries: ${shape}`);\n    }\n    return [width, totalSize / width];\n  }\n}\n\nexport class PreferLogicalStrategy implements TextureLayoutStrategy {\n  constructor(public maxTextureSize: number) {}\n  computeTextureWH(shape: readonly number[], prefs?: WidthHeightPrefs): [number, number] {\n    const wh = this.computeTexture(shape, prefs);\n    if (prefs && prefs.isPacked) {\n      wh[0] /= 2;\n      wh[1] /= 2;\n    }\n    if (prefs && prefs.reverseWH) {\n      return [wh[1], wh[0]];\n    }\n    return wh;\n  }\n\n  computeTexture(shape: readonly number[], prefs?: WidthHeightPrefs): [number, number] {\n    const isPacked = prefs && prefs.isPacked;\n    // scalar tensor\n    if (shape.length === 0) {\n      return isPacked ? [2, 2] : [1, 1];\n    }\n    let maxTextureSize = this.maxTextureSize;\n    if (prefs && prefs.breakAxis !== undefined) {\n      // check to see if dims fit\n      const wsize = prefs.breakAxis >= shape.length ? 1 : shape.slice(prefs.breakAxis).reduce((a, b) => a * b);\n      const hsize = prefs.breakAxis <= 0 ? 1 : shape.slice(0, prefs.breakAxis).reduce((a, b) => a * b);\n      if (wsize > maxTextureSize || hsize > maxTextureSize) {\n        // ignore preferences\n        // continue with default layout\n        Logger.verbose(\n            'TextureLayout',\n            `Given width/height preferences were unattainable: shape:${shape}, breakAxis:${prefs.breakAxis}`);\n      } else {\n        return [wsize, hsize];\n      }\n    }\n    let logShape = shape.slice(0);\n    if (isPacked) {\n      maxTextureSize = maxTextureSize * 2;\n\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(\n          (_d, i) => i >= logShape.length - 2 ? (logShape[i] % 2 === 0 ? logShape[i] : logShape[i] + 1) : logShape[i]);\n\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\n    // If logical shape is 2, we don't squeeze, since we want to match physical.\n    if (logShape.length !== 2) {\n      const squeezeResult = squeezeShape(logShape);\n      logShape = squeezeResult.newShape;\n    }\n\n    const size = sizeFromShape(logShape);\n    if (logShape.length <= 1 && size <= maxTextureSize) {\n      return [1, size];\n    } else if (logShape.length === 2 && logShape[0] <= maxTextureSize && logShape[1] <= maxTextureSize) {\n      return logShape as [number, number];\n    } else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTextureSize && logShape[2] <= maxTextureSize) {\n      return [logShape[0] * logShape[1], logShape[2]];\n    } else if (logShape.length === 3 && logShape[0] <= maxTextureSize && logShape[1] * logShape[2] <= maxTextureSize) {\n      return [logShape[0], logShape[1] * logShape[2]];\n    } else if (\n        logShape.length === 4 && logShape[0] * logShape[1] * logShape[2] <= maxTextureSize &&\n        logShape[3] <= maxTextureSize) {\n      return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n    } else if (\n        logShape.length === 4 && logShape[0] <= maxTextureSize &&\n        logShape[1] * logShape[2] * logShape[3] <= maxTextureSize) {\n      return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\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        return sizeToSquarishShape(size / 4).map(d => d * 2) as [number, number];\n      }\n      return sizeToSquarishShape(size);\n    }\n  }\n}\n\nexport function squeezeShape(shape: number[], axis?: number[]): {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) ? null : 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}\n\nexport function parseAxisParam(axis: number|number[], shape: number[]): number[] {\n  const rank = shape.length;\n\n  // Normalize input\n  axis = axis == null ? shape.map((_s, i) => i) : ([] as number[]).concat(axis);\n\n  // Check for valid range\n  assert(\n      axis.every(ax => ax >= -rank && ax < rank),\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(isInt),\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}\nexport function isInt(a: number): boolean {\n  return a % 1 === 0;\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}\nexport function getRowsCols(shape: number[]): [number, number] {\n  if (shape.length === 0) {\n    throw Error('Cannot get rows and columns of an empty shape array.');\n  }\n\n  return [shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]];\n}\nexport function sizeToSquarishShape(size: number): [number, number] {\n  const width = Math.ceil(Math.sqrt(size));\n  return [width, Math.ceil(size / width)];\n}\nexport function getBatchDim(shape: number[], dimsToSkip = 2): number {\n  return sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {ArrayUtil, BroadcastUtil, ShapeUtil} from '../../util';\n\nimport {GlslContext, GlslLib, GlslLibRoutine} from './glsl-definitions';\nimport {getGlsl} from './glsl-source';\nimport {squeezeShape} from './texture-layout-strategy';\nimport {TextureLayout} from './types';\nimport {generateShaderFuncNameFromInputSamplerName, generateShaderFuncNameFromInputSamplerNameAtOutCoords, getCoordsDataType, getGlChannels, getSqueezedParams, squeezeInputShape} from './utils';\n\n/**\n * GLSL Library responsible for data types and routines for manipulating\n * coordinates and mapping to/from tensor indices\n */\nexport class CoordsGlslLib extends GlslLib {\n  returnType: string;\n\n  constructor(context: GlslContext) {\n    super(context);\n  }\n  getFunctions(): {[name: string]: GlslLibRoutine} {\n    return {\n      ...this.offsetToCoords(),\n      ...this.coordsToOffset(),\n      ...this.toVec(),\n      ...this.valueFrom(),\n      // TODO return these only when packing is enabled.\n      ...this.getCommonUtilFuncs(),\n      ...this.getInputsSamplingSnippets(),\n      ...this.getOutputSamplingSnippet()\n    };\n  }\n  getCustomTypes() {\n    return {};\n  }\n  /**\n   * Produces a function that can map from\n   * 2D normalzied coordinates (s,t) to a flat offset\n   */\n  protected offsetToCoords(): {[name: string]: GlslLibRoutine} {\n    const funcName = 'offsetToCoords';\n    return {\n      offsetToCoords: new GlslLibRoutine(`\n      vec2 ${funcName}(int offset, int width, int height) {\n        int t = offset / width;\n        int s = offset - t*width;\n        vec2 coords = (vec2(s,t) + vec2(0.5,0.5)) / vec2(width, height);\n        return coords;\n      }\n      `)\n    };\n  }\n\n  /**\n   * Produces a function that can map from\n   * 2D normalzied coordinates (s,t) to a flat offset\n   */\n  protected coordsToOffset(): {[name: string]: GlslLibRoutine} {\n    const funcName = 'coordsToOffset';\n    return {\n      coordsToOffset: new GlslLibRoutine(`\n      int ${funcName}(vec2 coords, int width, int height) {\n        float s = coords.s * float(width);\n        float t = coords.t * float(height);\n        int offset = int(t) * width + int(s);\n        return offset;\n      }\n      `)\n    };\n  }\n\n  /**\n   * Generates code for output sampler.\n   */\n\n  protected getOutputSamplingSnippet(): {[name: string]: GlslLibRoutine} {\n    const outputLayout = this.context.outputTextureLayout;\n    if (outputLayout.isPacked) {\n      return this.getPackedOutputSamplingSnippet(outputLayout);\n    } else {\n      return this.getUnpackedOutputSamplingSnippet(outputLayout);\n    }\n  }\n\n  /**\n   * Generates code for packed output sampler.\n   */\n  protected getPackedOutputSamplingSnippet(outputLayout: TextureLayout): {[name: string]: GlslLibRoutine} {\n    const outShape = outputLayout.unpackedShape;\n    const outTexShape = [outputLayout.width, outputLayout.height];\n    const result: {[name: string]: GlslLibRoutine} = {};\n    const funcName = 'getOutputCoords';\n    switch (outShape.length) {\n      case 0:\n        result[funcName] = this.getOutputScalarCoords();\n        break;\n      case 1:\n        result[funcName] = this.getOutputPacked1DCoords(outShape as [number], outTexShape as [number, number]);\n        break;\n      case 2:\n        result[funcName] = this.getOutputPacked2DCoords(outShape as [number, number], outTexShape as [number, number]);\n        break;\n      case 3:\n        result[funcName] =\n            this.getOutputPacked3DCoords(outShape as [number, number, number], outTexShape as [number, number]);\n        break;\n      default:\n        result[funcName] = this.getOutputPackedNDCoords(outShape, outTexShape as [number, number]);\n    }\n    const glsl = getGlsl(this.context.glContext.version);\n    // TODO we need this to properly return a packed vec4 from kernels.\n    // Replace all '{glsl.output} = result' with 'setOutput(result)' in all kernels.\n    const floatTextureSetRGBASource = `\n      void setOutput(vec4 val) {\n        ${glsl.output} = val;\n      }\n    `;\n    const floatTextureSetRGBAFuncName = 'floatTextureSetRGBA';\n    result[floatTextureSetRGBAFuncName] = new GlslLibRoutine(floatTextureSetRGBASource);\n    return result;\n  }\n\n  /**\n   * Generates code for unpacked output sampler.\n   */\n  protected getUnpackedOutputSamplingSnippet(outputLayout: TextureLayout): {[name: string]: GlslLibRoutine} {\n    const outShape = outputLayout.unpackedShape;\n    const outTexShape = [outputLayout.width, outputLayout.height];\n    const result: {[name: string]: GlslLibRoutine} = {};\n    const funcName = 'getOutputCoords';\n    switch (outShape.length) {\n      case 0:\n        result[funcName] = this.getOutputScalarCoords();\n        break;\n      case 1:\n        result[funcName] = this.getOutputUnpacked1DCoords(outShape as [number], outTexShape as [number, number]);\n        break;\n      case 2:\n        result[funcName] =\n            this.getOutputUnpacked2DCoords(outShape as [number, number], outTexShape as [number, number]);\n        break;\n      case 3:\n        result[funcName] =\n            this.getOutputUnpacked3DCoords(outShape as [number, number, number], outTexShape as [number, number]);\n        break;\n      case 4:\n        result[funcName] = this.getOutputUnpacked4DCoords(\n            outShape as [number, number, number, number], outTexShape as [number, number]);\n        break;\n      case 5:\n        result[funcName] = this.getOutputUnpacked5DCoords(\n            outShape as [number, number, number, number, number], outTexShape as [number, number]);\n        break;\n      case 6:\n        result[funcName] = this.getOutputUnpacked6DCoords(\n            outShape as [number, number, number, number, number, number], outTexShape as [number, number]);\n        break;\n      default:\n        throw new Error(`Unsupported output dimensionality: ${outShape.length}`);\n    }\n    const glsl = getGlsl(this.context.glContext.version);\n    // TODO we need this to properly return a packed vec4 from kernels.\n    // Replace all '{glsl.output} = result' with 'setOutput(result)' in all kernels.\n    const floatTextureSetRSource = `\n        void setOutput(float val) {\n          ${glsl.output} = vec4(val, 0, 0, 0);\n        }\n    `;\n    const floatTextureSetRFuncName = 'floatTextureSetR';\n    result[floatTextureSetRFuncName] = new GlslLibRoutine(floatTextureSetRSource);\n    return result;\n  }\n\n  /**\n   * Scalar output coordinates.\n   */\n  protected getOutputScalarCoords(): GlslLibRoutine {\n    return new GlslLibRoutine(`\n      int getOutputCoords() {\n        return 0;\n      }\n    `);\n  }\n\n  /**\n   * 1D packed output coordinates.\n   */\n  protected getOutputPacked1DCoords(_shape: [number], texShape: [number, number]): GlslLibRoutine {\n    const packedTexShape = texShape;\n    let source = '';\n    if (packedTexShape[0] === 1) {\n      source = `\n          int getOutputCoords() {\n            return 2 * int(TexCoords.y * ${packedTexShape[1]}.0);\n          }\n        `;\n      return new GlslLibRoutine(source);\n    }\n\n    if (packedTexShape[1] === 1) {\n      source = `\n          int getOutputCoords() {\n            return 2 * int(TexCoords.x * ${packedTexShape[0]}.0);\n          }\n        `;\n      return new GlslLibRoutine(source);\n    }\n\n    source = `\n        int getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                 vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n          return 2 * (resTexRC.y * ${packedTexShape[0]} + resTexRC.x);\n        }\n      `;\n    return new GlslLibRoutine(source);\n  }\n\n  /**\n   * 2D packed output coordinates.\n   */\n  protected getOutputPacked2DCoords(shape: [number, number], texShape: [number, number]): GlslLibRoutine {\n    let source = '';\n    if (ArrayUtil.arraysEqual(shape, texShape)) {\n      source = `\n        ivec2 getOutputCoords() {\n          return 2 * ivec2(TexCoords.xy * vec2(${texShape[0]}, ${texShape[1]}));\n        }\n      `;\n      return new GlslLibRoutine(source);\n    }\n\n    const packedTexShape = texShape;\n    // texels needed to accommodate a logical row\n    const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n\n    /**\n     * getOutputCoords\n     *\n     * resTexRC: The rows and columns of the texels. If you move over one\n     * texel to the right in the packed texture, you are moving over one column\n     * (not two).\n     *\n     * index: The texel index\n     */\n    source = `\n        ivec2 getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n          int index = resTexRC.y * ${packedTexShape[0]} + resTexRC.x;\n\n          // reverse r and c order for packed texture\n          int r = imod(index, ${texelsInLogicalRow}) * 2;\n          int c = 2 * (index / ${texelsInLogicalRow});\n\n          return ivec2(r, c);\n        }\n      `;\n    return new GlslLibRoutine(source);\n  }\n\n  /**\n   * 3D packed output coordinates.\n   */\n  protected getOutputPacked3DCoords(shape: [number, number, number], texShape: [number, number]): GlslLibRoutine {\n    const packedTexShape = [texShape[0], texShape[1]];\n    const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n    const source = `\n        ivec3 getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n          int index = resTexRC.y * ${packedTexShape[0]} + resTexRC.x;\n\n          int b = index / ${texelsInBatch};\n          index -= b * ${texelsInBatch};\n\n          // reverse r and c order for packed texture\n          int r = imod(index, ${texelsInLogicalRow}) * 2;\n          int c = 2 * (index / ${texelsInLogicalRow});\n\n          return ivec3(b, r, c);\n        }\n      `;\n    return new GlslLibRoutine(source);\n  }\n\n  /**\n   * ND packed output coordinates.\n   */\n  protected getOutputPackedNDCoords(shape: readonly number[], texShape: [number, number]): GlslLibRoutine {\n    const packedTexShape = [texShape[0], texShape[1]];\n\n    const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n    let texelsInBatchN = texelsInBatch;\n    let batches = '';\n    let coords = 'b, r, c';\n\n    for (let b = 2; b < shape.length - 1; b++) {\n      texelsInBatchN *= shape[shape.length - b - 1];\n      batches = `\n      int b${b} = index / ${texelsInBatchN};\n      index -= b${b} * ${texelsInBatchN};\n    ` + batches;\n      coords = `b${b}, ` + coords;\n    }\n    const source = `\n      ivec${shape.length} getOutputCoords() {\n        ivec2 resTexRC = ivec2(TexCoords.xy *\n                              vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n        int index = resTexRC.y * ${packedTexShape[0]} + resTexRC.x;\n\n        ${batches}\n\n        int b = index / ${texelsInBatch};\n        index -= b * ${texelsInBatch};\n\n        // reverse r and c order for packed texture\n        int r = imod(index, ${texelsInLogicalRow}) * 2;\n        int c = 2 * (index / ${texelsInLogicalRow});\n\n        return ivec${shape.length}(${coords});\n      }\n    `;\n    return new GlslLibRoutine(source);\n  }\n\n  /**\n   * Unpacked 1D output coordinates.\n   */\n  protected getOutputUnpacked1DCoords(_shape: [number], texShape: [number, number]): GlslLibRoutine {\n    const source = `\n        int getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                vec2(${texShape[0]}, ${texShape[1]}));\n          return resTexRC.y * ${texShape[0]} + resTexRC.x;\n        }\n      `;\n    return new GlslLibRoutine(source);\n  }\n\n  /**\n   * Unpacked 2D output coordinates.\n   */\n  protected getOutputUnpacked2DCoords(shape: [number, number], texShape: [number, number]): GlslLibRoutine {\n    const source = `\n        ivec2 getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                vec2(${texShape[0]}, ${texShape[1]}));\n          int index = resTexRC.y * ${texShape[0]} + resTexRC.x;\n          int r = index / ${shape[1]};\n          int c = index - r * ${shape[1]};\n          return ivec2(r, c);\n        }\n      `;\n    return new GlslLibRoutine(source);\n  }\n\n  /**\n   * Unpacked 3D output coordinates.\n   */\n  protected getOutputUnpacked3DCoords(shape: [number, number, number], texShape: [number, number]): GlslLibRoutine {\n    let source = '';\n    const rank = shape.length;\n\n    let strides = null;\n    if (rank < 2) {\n      strides = [];\n    }\n\n    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    const coordsToCompute = ['r', 'c', 'd'];\n    const coordsFromIndexSnippet =\n        strides\n            .map((stride, i) => {\n              const line1 = `int ${coordsToCompute[i]} = index / ${stride}`;\n              const line2 = i === strides.length - 1 ?\n                  `int ${coordsToCompute[i + 1]} = index - ${coordsToCompute[i]} * ${stride}` :\n                  `index -= ${coordsToCompute[i]} * ${stride}`;\n              return `${line1}; ${line2};`;\n            })\n            .join('');\n\n    source = `\n        ivec3 getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                vec2(${texShape[0]}, ${texShape[1]}));\n          int index = resTexRC.y * ${texShape[0]} + resTexRC.x;\n          ${coordsFromIndexSnippet}\n          return ivec3(r, c, d);\n        }\n      `;\n    return new GlslLibRoutine(source);\n  }\n\n  /**\n   * Unpacked 4D output coordinates.\n   */\n  protected getOutputUnpacked4DCoords(shape: [number, number, number, number], texShape: [number, number]):\n      GlslLibRoutine {\n    let source = '';\n    const rank = shape.length;\n\n    let strides = null;\n    if (rank < 2) {\n      strides = [];\n    }\n\n    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    const coordsToCompute = ['r', 'c', 'd', 'd2'];\n    const coordsFromIndexSnippet =\n        strides\n            .map((stride, i) => {\n              const line1 = `int ${coordsToCompute[i]} = index / ${stride}`;\n              const line2 = i === strides.length - 1 ?\n                  `int ${coordsToCompute[i + 1]} = index - ${coordsToCompute[i]} * ${stride}` :\n                  `index -= ${coordsToCompute[i]} * ${stride}`;\n              return `${line1}; ${line2};`;\n            })\n            .join('');\n\n    source = `\n      ivec4 getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                vec2(${texShape[0]}, ${texShape[1]}));\n          int index = resTexRC.y * ${texShape[0]} + resTexRC.x;\n          ${coordsFromIndexSnippet}\n          return ivec4(r, c, d, d2);\n        }\n      `;\n    return new GlslLibRoutine(source);\n  }\n\n  /**\n   * Unpacked 5D output coordinates.\n   */\n  protected getOutputUnpacked5DCoords(shape: [number, number, number, number, number], texShape: [number, number]):\n      GlslLibRoutine {\n    let source = '';\n    const rank = shape.length;\n\n    let strides = null;\n    if (rank < 2) {\n      strides = [];\n    }\n\n    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    const coordsToCompute = ['r', 'c', 'd', 'd2', 'd3'];\n    const coordsFromIndexSnippet =\n        strides\n            .map((stride, i) => {\n              const line1 = `int ${coordsToCompute[i]} = index / ${stride}`;\n              const line2 = i === strides.length - 1 ?\n                  `int ${coordsToCompute[i + 1]} = index - ${coordsToCompute[i]} * ${stride}` :\n                  `index -= ${coordsToCompute[i]} * ${stride}`;\n              return `${line1}; ${line2};`;\n            })\n            .join('');\n\n    source = `\n      ivec5 getOutputCoords() {\n          ivec2 resTexRC = ivec2(TexCoords.xy *\n                                vec2(${texShape[0]}, ${texShape[1]}));\n          int index = resTexRC.y * ${texShape[0]} + resTexRC.x;\n          ${coordsFromIndexSnippet}\n          return ivec5(r, c, d, d2, d3);\n        }\n      `;\n    return new GlslLibRoutine(source);\n  }\n\n  /**\n   * Unpacked 6D output coordinates.\n   */\n  protected getOutputUnpacked6DCoords(shape: [number, number, number, number, number, number], texShape: [\n    number, number\n  ]): GlslLibRoutine {\n    let source = '';\n    const rank = shape.length;\n\n    let strides = null;\n    if (rank < 2) {\n      strides = [];\n    }\n\n    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    const coordsToCompute = ['r', 'c', 'd', 'd2', 'd3', 'd4'];\n    const coordsFromIndexSnippet =\n        strides\n            .map((stride, i) => {\n              const line1 = `int ${coordsToCompute[i]} = index / ${stride}`;\n              const line2 = i === strides.length - 1 ?\n                  `int ${coordsToCompute[i + 1]} = index - ${coordsToCompute[i]} * ${stride}` :\n                  `index -= ${coordsToCompute[i]} * ${stride}`;\n              return `${line1}; ${line2};`;\n            })\n            .join('');\n\n    source = `\n     ivec6 getOutputCoords() {\n         ivec2 resTexRC = ivec2(TexCoords.xy *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n         int index = resTexRC.y * ${texShape[0]} + resTexRC.x;\n         ${coordsFromIndexSnippet}\n         return ivec6(r, c, d, d2, d3, d4);\n       }\n     `;\n    return new GlslLibRoutine(source);\n  }\n\n  /**\n   * Generates code for common UV coords computation utility functions.\n   */\n  protected getCommonUtilFuncs(): {[name: string]: GlslLibRoutine} {\n    const result: {[name: string]: GlslLibRoutine} = {};\n    let funcName = 'uvFromFlat';\n    result[funcName] = new GlslLibRoutine(`\n    vec2 uvFromFlat(int texNumR, int texNumC, int index) {\n      int texC = index / texNumR;\n      int texR = index - texC * texNumR;\n      // TODO: swap texR, texC order in following function so row is corresponding to u and column is corresponding to\n      //       v.\n      return (vec2(texR, texC) + halfCR) / vec2(texNumR, texNumC);\n    }\n    `);\n    funcName = 'packedUVfrom1D';\n    result[funcName] = new GlslLibRoutine(`\n      vec2 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      `);\n    funcName = 'packedUVfrom2D';\n    result[funcName] = new GlslLibRoutine(`\n      vec2 packedUVfrom2D(int texNumR, int texNumC, int texelsInLogicalRow, 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      `);\n    funcName = 'packedUVfrom3D';\n    result[funcName] = new GlslLibRoutine(`\n      vec2 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      `);\n    funcName = 'sampleTexture';\n    const glsl = getGlsl(this.context.glContext.version);\n    result[funcName] = new GlslLibRoutine(`\n        float sampleTexture(sampler2D textureSampler, vec2 uv) {\n            return ${glsl.texture2D}(textureSampler, uv).r;\n        }`);\n    return result;\n  }\n\n  /**\n   * Constructing snippets for inputs\n   */\n  protected getInputsSamplingSnippets(): {[name: string]: GlslLibRoutine} {\n    const result: {[name: string]: GlslLibRoutine} = {};\n    const outputLayout = this.context.outputTextureLayout;\n    this.context.programInfo.inputNames.forEach((samplerName, i) => {\n      const inputLayout = this.context.inputTextureLayouts[i];\n      const funcName = generateShaderFuncNameFromInputSamplerName(samplerName);\n      if (inputLayout.isPacked) {\n        result[funcName] = this.getPackedSamplerFromInput(funcName, samplerName, inputLayout);\n      } else {\n        result[funcName] = this.getUnpackedSamplerFromInput(funcName, samplerName, inputLayout);\n      }\n\n      const outCoordFuncName = generateShaderFuncNameFromInputSamplerNameAtOutCoords(samplerName);\n      if (inputLayout.unpackedShape.length <= outputLayout.unpackedShape.length) {\n        if (inputLayout.isPacked) {\n          result[outCoordFuncName] =\n              this.getPackedSamplerAtOutputCoords(outCoordFuncName, inputLayout, outputLayout, samplerName);\n        } else {\n          result[outCoordFuncName] =\n              this.getUnpackedSamplerAtOutputCoords(outCoordFuncName, inputLayout, outputLayout, samplerName);\n        }\n      }\n    });\n\n    return result;\n  }\n\n  /**\n   * Constructing snippets for output coordinates of samplers\n   */\n  protected getPackedSamplerAtOutputCoords(\n      funcName: string, inputLayout: TextureLayout, outputLayout: TextureLayout, name: string): GlslLibRoutine {\n    const inShape = inputLayout.unpackedShape;\n    const outShape = outputLayout.unpackedShape;\n    const texName = name;\n    const texFuncSnippet = generateShaderFuncNameFromInputSamplerName(texName);\n\n    const inRank = inShape.length;\n    const outRank = outShape.length;\n\n    const broadcastDims = BroadcastUtil.getBroadcastDims(inShape, outShape);\n\n    const type = getCoordsDataType(outRank);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet: string;\n    const fields = getGlChannels();\n\n    if (inRank === 0) {\n      coordsSnippet = '';\n    } else if (outRank < 2 && broadcastDims.length >= 1) {\n      coordsSnippet = 'coords = 0;';\n    } else {\n      coordsSnippet = broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`).join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n      unpackedCoordsSnippet = 'coords';\n    } else {\n      unpackedCoordsSnippet = inShape.map((_s, i) => `coords.${fields[i + rankDiff]}`).join(', ');\n    }\n\n    let output = 'return outputValue;';\n    const inSize = ShapeUtil.size(inShape);\n    const isInputScalar = inSize === 1;\n    const outSize = ShapeUtil.size(outShape);\n    const isOutputScalar = outSize === 1;\n\n    if (inRank === 1 && !isInputScalar && !isOutputScalar) {\n      output = `\n        return vec4(outputValue.xy, outputValue.xy);\n      `;\n    } else if (isInputScalar && !isOutputScalar) {\n      if (outRank === 1) {\n        output = `\n          return vec4(outputValue.x, outputValue.x, 0., 0.);\n        `;\n      } else {\n        output = `\n          return vec4(outputValue.x);\n        `;\n      }\n    } else if (broadcastDims.length) {\n      const rows = inRank - 2;\n      const cols = inRank - 1;\n\n      if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n        output = 'return vec4(outputValue.x);';\n      } else if (broadcastDims.indexOf(rows) > -1) {\n        output = 'return vec4(outputValue.x, outputValue.y, ' +\n            'outputValue.x, outputValue.y);';\n      } else if (broadcastDims.indexOf(cols) > -1) {\n        output = 'return vec4(outputValue.xx, outputValue.zz);';\n      }\n    }\n\n    const swapLastDimsSnippet = `\n        int lastDim = coords.${fields[outRank - 1]};\n        coords.${fields[outRank - 1]} = coords.${fields[outRank - 2]};\n        coords.${fields[outRank - 2]} = lastDim;\n      `;\n    const source = `\n      vec4 ${funcName}() {\n        ${type} coords = getOutputCoords();\n        ${swapLastDimsSnippet}\n        ${coordsSnippet}\n        vec4 outputValue = ${texFuncSnippet}(${unpackedCoordsSnippet});\n        ${output}\n      }\n    `;\n    return new GlslLibRoutine(source, ['coordinates.getOutputCoords']);\n  }\n\n  /**\n   * Constructing snippets for unpacked output coordinates of samplers\n   */\n  protected getUnpackedSamplerAtOutputCoords(\n      funcName: string, inputLayout: TextureLayout, outputLayout: TextureLayout, name: string): GlslLibRoutine {\n    const outTexShape = [outputLayout.width, outputLayout.height];\n    const inTexShape = [inputLayout.width, inputLayout.height];\n    const inRank = inputLayout.unpackedShape.length;\n    const outRank = outputLayout.unpackedShape.length;\n    const inShape = inputLayout.unpackedShape;\n    const outShape = outputLayout.unpackedShape;\n    const texFuncSnippet = generateShaderFuncNameFromInputSamplerName(name);\n\n    if (inRank === outRank && ArrayUtil.arraysEqual(inTexShape, outTexShape)) {\n      const source = `\n          float ${funcName}() {\n            return sampleTexture(${name}, TexCoords);\n          }\n        `;\n      return new GlslLibRoutine(source, ['coordinates.sampleTexture']);\n    }\n\n    const type = getCoordsDataType(outRank);\n    const broadcastDims = BroadcastUtil.getBroadcastDims(inShape, outShape);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet: string;\n    const fields = getGlChannels();\n\n    if (inRank === 0) {\n      coordsSnippet = '';\n    } else if (outRank < 2 && broadcastDims.length >= 1) {\n      coordsSnippet = 'coords = 0;';\n    } else {\n      coordsSnippet = broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`).join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n      unpackedCoordsSnippet = 'coords';\n    } else {\n      unpackedCoordsSnippet = inputLayout.unpackedShape.map((_s, i) => `coords.${fields[i + rankDiff]}`).join(', ');\n    }\n    const source = `\n        float ${funcName}() {\n          ${type} coords = getOutputCoords();\n          ${coordsSnippet}\n          return ${texFuncSnippet}(${unpackedCoordsSnippet});\n        }\n      `;\n    return new GlslLibRoutine(source, ['coordinates.getOutputCoords']);\n  }\n\n  /**\n   * Constructing snippets for packed operations.\n   */\n  protected getPackedSamplerFromInput(funcName: string, name: string, inputLayout: TextureLayout): GlslLibRoutine {\n    switch (inputLayout.unpackedShape.length) {\n      case 0:\n        return this.getPackedSamplerScalar(funcName, name);\n      case 1:\n        return this.getPackedSampler1D(funcName, name, inputLayout);\n      case 2:\n        return this.getPackedSampler2D(funcName, name, inputLayout);\n      case 3:\n        return this.getPackedSampler3D(funcName, name, inputLayout);\n      default:\n        return this.getPackedSamplerND(funcName, name, inputLayout);\n    }\n  }\n\n  /**\n   * Constructing snippets for unpacked operations.\n   */\n  protected getUnpackedSamplerFromInput(funcName: string, name: string, inputLayout: TextureLayout): GlslLibRoutine {\n    const shape = inputLayout.unpackedShape;\n    switch (shape.length) {\n      case 0:\n        return this.getUnpackedSamplerScalar(funcName, name, inputLayout);\n      case 1:\n        return this.getUnpackedSampler1D(funcName, name, inputLayout);\n      case 2:\n        return this.getUnpackedSampler2D(funcName, name, inputLayout);\n      case 3:\n        return this.getUnpackedSampler3D(funcName, name, inputLayout);\n      case 4:\n        return this.getUnpackedSampler4D(funcName, name, inputLayout);\n      case 5:\n        return this.getUnpackedSampler5D(funcName, name, inputLayout);\n      case 6:\n        return this.getUnpackedSampler6D(funcName, name, inputLayout);\n      default:\n        // TODO support more dimensionalities\n        throw new Error(`Unsupported dimension ${shape.length}-D`);\n    }\n  }\n\n  /**\n   * Packed scalar snippet.\n   */\n  protected getPackedSamplerScalar(funcName: string, name: string): GlslLibRoutine {\n    const glsl = getGlsl(this.context.glContext.version);\n    const source = `\n          vec4 ${funcName}() {\n            return ${glsl.texture2D}(${name}, halfCR);\n          }\n        `;\n    return new GlslLibRoutine(source);\n  }\n\n  /**\n   * Packed 1D snippet.\n   */\n  protected getPackedSampler1D(funcName: string, name: string, inputLayout: TextureLayout): GlslLibRoutine {\n    const texShape = [inputLayout.width, inputLayout.height];\n    const packedTexShape = [texShape[1], texShape[0]];\n    const glsl = getGlsl(this.context.glContext.version);\n\n    const packedSampler = `vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n      ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return ${glsl.texture2D}(${name}, uv);\n    }`;\n    const source = packedSampler;\n    return new GlslLibRoutine(source, ['coordinates.packedUVfrom1D']);\n  }\n\n  /**\n   * Packed 2D snippet.\n   */\n  protected getPackedSampler2D(funcName: string, name: string, inputLayout: TextureLayout): GlslLibRoutine {\n    const shape = inputLayout.unpackedShape;\n    const texShape = [inputLayout.width, inputLayout.height];\n    const glsl = getGlsl(this.context.glContext.version);\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n\n    if (texShape != null && ArrayUtil.arraysEqual(shape, texShape)) {\n      const packedSampler = `vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n        return ${glsl.texture2D}(${name}, uv);\n      }`;\n\n      return new GlslLibRoutine(packedSampler);\n    }\n    const packedTexShape = texShape;\n    const valuesPerRow = Math.ceil(shape[1] / 2);\n    const packedSampler = `vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${packedTexShape[1]}, ${packedTexShape[0]}, ${valuesPerRow}, row, col);\n      return ${glsl.texture2D}(${name}, uv);\n    }`;\n    const source = packedSampler;\n    return new GlslLibRoutine(source, ['coordinates.packedUVfrom2D']);\n  }\n\n  /**\n   * Packed 3D snippet.\n   */\n  protected getPackedSampler3D(funcName: string, name: string, inputLayout: TextureLayout): GlslLibRoutine {\n    const shape = inputLayout.unpackedShape;\n    const texShape = [inputLayout.width, inputLayout.height];\n    const packedTexShape = [texShape[0], texShape[1]];\n    const glsl = getGlsl(this.context.glContext.version);\n\n    if (shape[0] === 1) {\n      const squeezedShape = shape.slice(1);\n      const keptDims = [1, 2];\n      const newInputShape = squeezeInputShape(shape, squeezedShape);\n      const params = ['b', 'row', 'col'];\n      // Deep copy of input texture layout.\n      const newInputLayout: TextureLayout = JSON.parse(JSON.stringify(inputLayout));\n      newInputLayout.unpackedShape = newInputShape;\n      const samplerRoutine = this.getPackedSamplerFromInput(funcName, name, newInputLayout);\n      const packedSampler = `${samplerRoutine.routineBody}\n      vec4 ${funcName}(int b, int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      } `;\n      const source = packedSampler;\n      return new GlslLibRoutine(source, samplerRoutine.dependencies);\n    }\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n\n    const valuesPerRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n\n    const packedSampler = `vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumC}, ${texNumR}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return ${glsl.texture2D}(${name}, uv);}`;\n    const source = packedSampler;\n    return new GlslLibRoutine(source, ['coordinates.packedUVfrom3D']);\n  }\n  /*\n   * Packed ND snippet.\n   */\n  protected getPackedSamplerND(funcName: string, name: string, inputLayout: TextureLayout): GlslLibRoutine {\n    const shape = inputLayout.unpackedShape;\n    const rank = shape.length;\n    const texShape = [inputLayout.width, inputLayout.height];\n    const glsl = getGlsl(this.context.glContext.version);\n\n    const packedTexShape = [texShape[0], texShape[1]];\n    const texNumR = packedTexShape[1];\n    const texNumC = packedTexShape[0];\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 packedSampler = `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 ${glsl.texture2D}(${name}, uv);\n    }`;\n    const source = packedSampler;\n    return new GlslLibRoutine(source);\n  }\n\n  /**\n   * Unpacked scalar snippet.\n   */\n  protected getUnpackedSamplerScalar(funcName: string, name: string, inputLayout: TextureLayout): GlslLibRoutine {\n    const [texNumR, texNumC] = [inputLayout.width, inputLayout.height];\n    if (texNumR === 1 && texNumC === 1) {\n      const source = `\n          float ${funcName}() {\n            return sampleTexture(${name}, halfCR);\n          }\n        `;\n      return new GlslLibRoutine(source, ['coordinates.sampleTexture']);\n    }\n\n    const source = `\n        float ${funcName}() {\n          int offset_${name} = coordsToOffset(TexCoords, ${texNumR}, ${texNumC});\n          vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, offset_${name});\n          return sampleTexture(${name}, uv);\n        }\n      `;\n    return new GlslLibRoutine(\n        source, ['coordinates.uvFromFlat', 'coordinates.sampleTexture', 'coordinates.coordsToOffset']);\n  }\n\n  /**\n   * Unpacked 1D snippet.\n   */\n  protected getUnpackedSampler1D(funcName: string, name: string, inputLayout: TextureLayout): GlslLibRoutine {\n    const tNumR = inputLayout.width;\n    const tNumC = inputLayout.height;\n\n    if (tNumC === 1 && tNumR === 1) {\n      const source = `\n        float ${funcName}(int index) {\n          return sampleTexture(${name}, halfCR);\n        }\n      `;\n      return new GlslLibRoutine(source, ['coordinates.sampleTexture']);\n    }\n\n    if (tNumC === 1) {\n      const source = `\n          float ${funcName}(int index) {\n            vec2 uv = vec2((float(index) + 0.5) / ${tNumR}.0, 0.5);\n            return sampleTexture(${name}, uv);\n          }\n        `;\n      return new GlslLibRoutine(source, ['coordinates.sampleTexture']);\n    }\n    if (tNumR === 1) {\n      const source = `\n          float ${funcName}(int index) {\n            vec2 uv = vec2(0.5, (float(index) + 0.5) / ${tNumC}.0);\n            return sampleTexture(${name}, uv);\n          }\n        `;\n      return new GlslLibRoutine(source, ['coordinates.sampleTexture']);\n    }\n    const source = `\n        float ${funcName}(int index) {\n          vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index);\n          return sampleTexture(${name}, uv);\n        }\n      `;\n    return new GlslLibRoutine(source, ['coordinates.uvFromFlat', 'coordinates.sampleTexture']);\n  }\n\n  /**\n   * Unpacked 2D snippet.\n   */\n\n  protected getUnpackedSampler2D(funcName: string, name: string, inputLayout: TextureLayout): GlslLibRoutine {\n    const shape = inputLayout.unpackedShape;\n\n    // TODO: modify row/col order for other dimensions.\n    const texShape = [inputLayout.height, inputLayout.width];\n\n    if (texShape != null && ArrayUtil.arraysEqual(shape, texShape)) {\n      const texNumR = texShape[1];\n      const texNumC = texShape[0];\n      const source = `\n          float ${funcName}(int row, int col) {\n            vec2 uv = (vec2(row, col) + halfCR) / vec2(${texNumR}.0, ${texNumC}.0);\n            return sampleTexture(${name}, uv);\n          }\n        `;\n      return new GlslLibRoutine(source, ['coordinates.sampleTexture']);\n    }\n\n    const {newShape, keptDims} = squeezeShape(shape as number[]);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n      const newInputShape = squeezeInputShape(shape, squeezedShape);\n      // Deep copy of input texture layout.\n      const newInputLayout: TextureLayout = JSON.parse(JSON.stringify(inputLayout));\n      newInputLayout.unpackedShape = newInputShape;\n\n      const params = ['col', 'row'];\n      const source = `\n          ${this.getUnpackedSamplerFromInput(funcName, name, newInputLayout).routineBody}\n          float ${funcName}(int row, int col) {\n            return ${funcName}(${getSqueezedParams(params, keptDims)});\n          }\n        `;\n      return new GlslLibRoutine(source, ['coordinates.sampleTexture']);\n    }\n\n    const texNumR = texShape[1];\n    const texNumC = texShape[0];\n    if (texNumC === 1) {\n      const source = `\n          float ${funcName}(int row, int col) {\n            int offset_${name} = coordsToOffset(TexCoords, ${texNumR}, ${texNumC});\n            float index = dot(vec3(row, col, offset_${name}), vec3(${shape[1]}, 1, 1));\n            vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n            return sampleTexture(${name}, uv);\n          }\n        `;\n      return new GlslLibRoutine(source, ['coordinates.sampleTexture', 'coordinates.coordsToOffset']);\n    }\n\n    if (texNumR === 1) {\n      const source = `\n          float ${funcName}(int row, int col) {\n            int offset_${name} = coordsToOffset(TexCoords, ${texNumR}, ${texNumC});\n            float index = dot(vec3(row, col, offset_${name}), vec3(${shape[1]}, 1, 1));\n            vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n            return sampleTexture(${name}, uv);\n          }\n        `;\n      return new GlslLibRoutine(source, ['coordinates.sampleTexture', 'coordinates.coordsToOffset']);\n    }\n\n    const source = `\n        float ${funcName}(int row, int col) {\n          int index = col * ${shape[1]} + row;\n          vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n          return sampleTexture(${name}, uv);\n        }\n      `;\n    return new GlslLibRoutine(\n        source, ['coordinates.uvFromFlat', 'coordinates.sampleTexture', 'coordinates.coordsToOffset']);\n  }\n\n  /**\n   * Unpacked 3D snippet.\n   */\n\n  protected getUnpackedSampler3D(funcName: string, name: string, inputLayout: TextureLayout): GlslLibRoutine {\n    const shape = inputLayout.unpackedShape;\n    const stride0 = shape[1] * shape[2];\n    const stride1 = shape[2];\n\n    const {newShape, keptDims} = squeezeShape(shape as number[]);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n      const newInputShape = squeezeInputShape(shape, squeezedShape);\n      const params = ['batch', 'col', 'row'];\n      // Deep copy of input texture layout.\n      const newInputLayout: TextureLayout = JSON.parse(JSON.stringify(inputLayout));\n      newInputLayout.unpackedShape = newInputShape;\n      const routine = this.getUnpackedSamplerFromInput(funcName, name, newInputLayout);\n      // TODO: revisit the logic here to make it simpler\n      const revDims = keptDims.reverse();\n      const source = `\n          ${routine.routineBody}\n          float ${funcName}(int batch, int row, int col) {\n            return ${funcName}(${getSqueezedParams(params, revDims)});\n          }\n        `;\n      return new GlslLibRoutine(source, routine.dependencies);\n    }\n\n    const texNumR = inputLayout.width;\n    const texNumC = inputLayout.height;\n    const source = `\n          float ${funcName}(int depth, int row, int col) {\n            // Explicitly use integer operations as dot() only works on floats.\n            int index = depth * ${stride0} + col * ${stride1} + row;\n            vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n            return sampleTexture(${name}, uv);\n          }\n      `;\n    return new GlslLibRoutine(\n        source, ['coordinates.uvFromFlat', 'coordinates.sampleTexture', 'coordinates.coordsToOffset']);\n  }\n\n  /**\n   * Unpacked 4D snippet.\n   */\n\n  protected getUnpackedSampler4D(funcName: string, name: string, inputLayout: TextureLayout): GlslLibRoutine {\n    const shape = inputLayout.unpackedShape;\n    const stride2 = shape[3];\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n\n    //\n    // TODO: re-enable this shortcut once the index calculation bug is fixed.\n    //\n    // const {newShape, keptDims} = squeezeShape(shape as number[]);\n    // if (newShape.length < shape.length) {\n    //   const newInputShape = squeezeInputShape(shape, newShape);\n    //   const params = ['row', 'col', 'depth', 'depth2'];\n    //   // Deep copy of input texture layout.\n    //   const newInputLayout: TextureLayout = JSON.parse(JSON.stringify(inputLayout));\n    //   newInputLayout.unpackedShape = newInputShape;\n    //   const source = `\n    //       ${this.getUnpackedSamplerFromInput(funcName, name, newInputLayout).routineBody}\n    //       float ${funcName}(int row, int col, int depth, int depth2) {\n    //         return ${funcName}(${getSqueezedParams(params, keptDims)});\n    //       }\n    //     `;\n    //   return new GlslLibRoutine(\n    //       source, ['coordinates.uvFromFlat', 'coordinates.sampleTexture', 'coordinates.coordsToOffset']);\n    // }\n\n    const texNumR = inputLayout.width;\n    const texNumC = inputLayout.height;\n    const source = `\n        float ${funcName}(int row, int col, int depth, int depth2) {\n          int index = row * ${stride0} + col * ${stride1} +\n              depth2 * ${stride2} + depth;\n          vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n          return sampleTexture(${name}, uv);\n        }\n      `;\n    return new GlslLibRoutine(source, ['coordinates.uvFromFlat', 'coordinates.sampleTexture']);\n  }\n\n  /**\n   * Unpacked 5D snippet.\n   */\n  protected getUnpackedSampler5D(funcName: string, name: string, inputLayout: TextureLayout): GlslLibRoutine {\n    const shape = inputLayout.unpackedShape;\n    const stride3 = shape[4];\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n\n    const {newShape, keptDims} = squeezeShape(shape as number[]);\n    if (newShape.length < shape.length) {\n      const newInputShape = squeezeInputShape(shape, newShape);\n      const params = ['row', 'col', 'depth', 'depth2', 'depth3'];\n      // Deep copy of input texture layout.\n      const newInputLayout: TextureLayout = JSON.parse(JSON.stringify(inputLayout));\n      newInputLayout.unpackedShape = newInputShape;\n\n      const source = `\n          ${this.getUnpackedSamplerFromInput(funcName, name, newInputLayout).routineBody}\n          float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n            return ${funcName}(${getSqueezedParams(params, keptDims)});\n          }\n        `;\n      return new GlslLibRoutine(source, ['coordinates.sampleTexture', 'coordinates.uvFromFlat']);\n    }\n\n    const texNumR = inputLayout.width;\n    const texNumC = inputLayout.height;\n    const source = `\n        float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n          int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth3 * ${stride3} + depth2;\n          vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n          return sampleTexture(${name}, uv);\n        }\n      `;\n    return new GlslLibRoutine(source, ['coordinates.sampleTexture', 'coordinates.uvFromFlat']);\n  }\n\n  /**\n   * Unpacked 6D snippet.\n   */\n  protected getUnpackedSampler6D(funcName: string, name: string, inputLayout: TextureLayout): GlslLibRoutine {\n    const shape = inputLayout.unpackedShape;\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\n    const {newShape, keptDims} = squeezeShape(shape as number[]);\n    if (newShape.length < shape.length) {\n      const newInputShape = squeezeInputShape(shape, newShape);\n      const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];\n      // Deep copy of input texture layout.\n      const newInputLayout: TextureLayout = JSON.parse(JSON.stringify(inputLayout));\n      newInputLayout.unpackedShape = newInputShape;\n\n      const source = `\n            ${this.getUnpackedSamplerFromInput(funcName, name, newInputLayout).routineBody}\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      return new GlslLibRoutine(source, ['coordinates.sampleTexture', 'coordinates.uvFromFlat']);\n    }\n\n    const texNumR = inputLayout.width;\n    const texNumC = inputLayout.height;\n    const source = `\n          float ${funcName}(int row, int col, int depth,\n            int depth2, int depth3, int depth4) {\n            int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n            depth2 * ${stride3} + depth3 * ${stride4} + depth4;\n            vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n            return sampleTexture(${name}, uv);\n          }\n        `;\n    return new GlslLibRoutine(\n        source, ['coordinates.uvFromFlat', 'coordinates.sampleTexture', 'coordinates.coordsToOffset']);\n  }\n\n  /**\n   * This is the main function to map from the given texture coordinates (s,t)\n   * to logical indices for the output\n   * There will only be one single variation of this\n   * Also see coordsToOffset and offsetToIndices for input-specific versions\n   */\n  protected toVec(): {[name: string]: GlslLibRoutine} {\n    const output = this.context.outputTextureLayout;\n    const rank = output.shape.length;\n    const strides = output.strides;\n    const xScale = output.width;\n    const yScale = output.height;\n\n    const stridesBlock = [];\n    for (let i = 0; i < rank - 1; ++i) {\n      stridesBlock.push(`\n        c[${i}] = offset / ${strides[i]};`);\n      stridesBlock.push(`\n        offset -= c[${i}] * ${strides[i]};`);\n    }\n    stridesBlock.push(`\n        c[${rank - 1}] = offset;`);\n    const body = `\n      void toVec(vec2 texCoords, out int c[${rank}]) {\n        int offset = coordsToOffset(texCoords, ${xScale}, ${yScale});\n        ${stridesBlock.join('')}\n      }\n      void toVec(int offset, out int c[${rank}]) {\n        ${stridesBlock.join('')}\n      }\n    `;\n    return {toVec: new GlslLibRoutine(body, ['coordinates.coordsToOffset'])};\n  }\n  /**\n   * These are value getter functions generated for each input\n   * Each function is hardwired to the name and dimensions of the input\n   * An '_T' variation is also produced which accesses values as if the\n   * input was transposed\n   */\n  protected valueFrom(): {[name: string]: GlslLibRoutine} {\n    const result: {[name: string]: GlslLibRoutine} = {};\n    this.context.programInfo.inputNames.forEach((name, i) => {\n      const layout = this.context.inputTextureLayouts[i];\n      const shape = layout.unpackedShape.length > 0 ? layout.unpackedShape : layout.shape;\n      const rank = shape.length;\n      let funcName = `_${name}`;\n      result[funcName] = new GlslLibRoutine(\n          this.getValueFromSingle(name, rank, layout.width, layout.height, false),\n          [`shapeUtils.indicesToOffset${funcName}`, 'coordinates.offsetToCoords', 'fragcolor.getColorAsFloat']);\n      funcName = funcName + '_T';\n      result[funcName] = new GlslLibRoutine(\n          this.getValueFromSingle(name, rank, layout.width, layout.height, true),\n          [`shapeUtils.indicesToOffset${funcName}`, 'coordinates.offsetToCoords', 'fragcolor.getColorAsFloat']);\n    });\n    return result;\n  }\n  /**\n   * Produces one value getter function for the name and rank given\n   * If a transpose is set proper offsetToCoords mapping will be used\n   * @param name name of the function\n   * @param rank rank of the input\n   * @param transpose whether or not should generate a transpose variation\n   */\n  protected getValueFromSingle(varName: string, rank: number, width: number, height: number, transpose: boolean):\n      string {\n    let name = `_${varName}`;\n    if (transpose) {\n      name = name + '_T';\n    }\n    const glsl = getGlsl(this.context.glContext.version);\n    return `\n        float ${name}(int m[${rank}]) {\n          int offset = indicesToOffset${name}(m);\n          vec2 coords = offsetToCoords(offset, ${width}, ${height});\n          float value = getColorAsFloat(${glsl.texture2D}(${varName}, coords));\n          return value;\n        }\n        `;\n  }\n\n  /**\n   * Produces a packed value getter function for the name and rank given\n   * If a transpose is set proper offsetToCoords mapping will be used\n   * @param name name of the function\n   * @param rank rank of the input\n   * @param transpose whether or not should generate a transpose variation\n   */\n  protected getPackedValueFrom(varName: string, rank: number, width: number, height: number, transpose: boolean):\n      string {\n    let name = `_${varName}_Pack`;\n    if (transpose) {\n      name = name + '_T';\n    }\n    const glsl = getGlsl(this.context.glContext.version);\n    return `\n        vec4 ${name}(int m[${rank}]) {\n          int offset = indicesToOffset_${varName}(m);\n          vec2 coords = offsetToCoords(offset, ${width}, ${height});\n          return ${glsl.texture2D}(${varName}, coords);\n        }\n        `;\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {GlslContext, GlslLib, GlslLibRoutine} from './glsl-definitions';\n\n/**\n * This GLSL library handles routines converting\n * float32 to/from Unsigned byte or float 16\n */\nexport class EncodingGlslLib extends GlslLib {\n  constructor(context: GlslContext) {\n    super(context);\n  }\n  getFunctions(): {[name: string]: GlslLibRoutine} {\n    return {...this.encodeFloat32(), ...this.decodeFloat32()};\n  }\n  getCustomTypes(): {[name: string]: string} {\n    return {};\n  }\n  protected encodeFloat32(): {[name: string]: GlslLibRoutine} {\n    return {\n      encode: new GlslLibRoutine(`highp vec4 encode(highp float f) {\n        return vec4(f, 0.0, 0.0, 0.0);\n      }\n        `)\n    };\n  }\n  protected decodeFloat32(): {[name: string]: GlslLibRoutine} {\n    return {\n      decode: new GlslLibRoutine(`highp float decode(highp vec4 rgba) {\n        return rgba.r;\n      }\n        `)\n    };\n  }\n  /**\n   * returns the routine to encode encode a 32bit float to a vec4 (of unsigned bytes)\n   * @credit: https://stackoverflow.com/questions/7059962/how-do-i-convert-a-vec4-rgba-value-to-a-float\n   */\n  protected encodeUint8(): {[name: string]: GlslLibRoutine} {\n    const endianness = EncodingGlslLib.isLittleEndian() ? 'rgba.rgba=rgba.abgr;' : '';\n    return {\n      encode: new GlslLibRoutine(`\n      highp vec4 encode(highp float f) {\n        highp float F = abs(f);\n        highp float Sign = step(0.0,-f);\n        highp float Exponent = floor(log2(F));\n        highp float Mantissa = (exp2(- Exponent) * F);\n        Exponent = floor(log2(F) + 127.0) + floor(log2(Mantissa));\n        highp vec4 rgba;\n        rgba[0] = 128.0 * Sign  + floor(Exponent*exp2(-1.0));\n        rgba[1] = 128.0 * mod(Exponent,2.0) + mod(floor(Mantissa*128.0),128.0);\n        rgba[2] = floor(mod(floor(Mantissa*exp2(23.0 -8.0)),exp2(8.0)));\n        rgba[3] = floor(exp2(23.0)*mod(Mantissa,exp2(-15.0)));\n        ${endianness}\n        rgba = rgba / 255.0; // values need to be normalized to [0,1]\n        return rgba;\n    }\n        `)\n    };\n  }\n  /**\n   * returns the routine to encode a vec4 of unsigned bytes to float32\n   * @credit: https://stackoverflow.com/questions/7059962/how-do-i-convert-a-vec4-rgba-value-to-a-float\n   */\n  protected decodeUint8(): {[name: string]: GlslLibRoutine} {\n    const endianness = EncodingGlslLib.isLittleEndian() ? 'rgba.rgba=rgba.abgr;' : '';\n    return {\n      decode: new GlslLibRoutine(`\n        highp float decode(highp vec4 rgba) {\n          rgba = rgba * 255.0; // values need to be de-normalized from [0,1] to [0,255]\n          ${endianness}\n          highp float Sign = 1.0 - step(128.0,rgba[0])*2.0;\n          highp float Exponent = 2.0 * mod(rgba[0],128.0) + step(128.0,rgba[1]) - 127.0;\n          highp float Mantissa = mod(rgba[1],128.0)*65536.0 + rgba[2]*256.0 +rgba[3] + float(0x800000);\n          highp float Result =  Sign * exp2(Exponent) * (Mantissa * exp2(-23.0 ));\n          return Result;\n      }\n        `)\n    };\n  }\n  /**\n   * Determines if the machine is little endian or not\n   * @credit: https://gist.github.com/TooTallNate/4750953\n   */\n  static isLittleEndian(): boolean {\n    const b = new ArrayBuffer(4);\n    const a = new Uint32Array(b);\n    const c = new Uint8Array(b);\n    a[0] = 0xdeadbeef;\n    if (c[0] === 0xef) {\n      return true;\n    }\n    if (c[0] === 0xde) {\n      return false;\n    }\n    throw new Error('unknown endianness');\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {GlslContext, GlslLib, GlslLibRoutine} from './glsl-definitions';\nimport {getGlsl} from './glsl-source';\n\n/**\n * This GLSL library handles routines around reading a texlet and writing to it\n * Reading and writing could be more than just dealing with one channel\n * It may require encoding/decoding to/from 4 channels into one\n */\nexport class FragColorGlslLib extends GlslLib {\n  constructor(context: GlslContext) {\n    super(context);\n  }\n  getFunctions(): {[name: string]: GlslLibRoutine} {\n    return {...this.setFragColor(), ...this.getColorAsFloat()};\n  }\n  getCustomTypes(): {[name: string]: string} {\n    return {};\n  }\n  protected setFragColor(): {[name: string]: GlslLibRoutine} {\n    const glsl = getGlsl(this.context.glContext.version);\n    return {\n      setFragColor: new GlslLibRoutine(\n          `\n        void setFragColor(float value) {\n            ${glsl.output} = encode(value);\n        }\n        `,\n          ['encoding.encode'])\n    };\n  }\n  protected getColorAsFloat(): {[name: string]: GlslLibRoutine} {\n    return {\n      getColorAsFloat: new GlslLibRoutine(\n          `\n        float getColorAsFloat(vec4 color) {\n            return decode(color);\n        }\n        `,\n          ['encoding.decode'])\n    };\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {GlslContext, GlslLib, GlslLibRoutine} from './glsl-definitions';\n\n/**\n * GLSL Library responsible for data types and routines for manipulating\n * coordinates and mapping to/from tensor indices\n */\nexport class ShapeUtilsGlslLib extends GlslLib {\n  constructor(context: GlslContext) {\n    super(context);\n  }\n  getFunctions(): {[name: string]: GlslLibRoutine} {\n    return {\n      ...this.bcastIndex(),\n      ...this.bcastMatmulIndex(),\n      ...this.offsetToIndices(),\n      ...this.indicesToOffset(),\n      ...this.incrementIndices()\n    };\n  }\n  getCustomTypes() {\n    return {};\n  }\n  protected bcastIndex(): {[name: string]: GlslLibRoutine} {\n    const outputRank = this.context.outputTextureLayout.shape.length;\n    const result: {[name: string]: GlslLibRoutine} = {};\n    this.context.programInfo.inputNames.forEach((name, i) => {\n      const shape = this.context.inputTextureLayouts[i].unpackedShape;\n      if (shape.length <= outputRank) {\n        const rank = shape.length;\n        const dimOffset = outputRank - rank;\n        const funcName = `bcastIndices_${name}`;\n        let block = '';\n        for (let i = 0; i < rank; ++i) {\n          block += `\n          realIndices[${i}] = int( mod(float(bcastedIndices[${dimOffset + i}]), ${shape[i]}.0) );\n          `;\n        }\n        const body = `\n        void ${funcName} (int bcastedIndices[${outputRank}], out int realIndices[${rank}]) {\n          ${block}\n        }\n        `;\n        result[funcName] = new GlslLibRoutine(body);\n      }\n    });\n    return result;\n  }\n  protected bcastMatmulIndex(): {[name: string]: GlslLibRoutine} {\n    const outputRank = this.context.outputTextureLayout.shape.length;\n    const result: {[name: string]: GlslLibRoutine} = {};\n    this.context.programInfo.inputNames.forEach((name, i) => {\n      const shape = this.context.inputTextureLayouts[i].shape;\n      if (!(shape.length < 2 || shape.length > outputRank)) {\n        const rank = shape.length;\n        const dimOffset = outputRank - rank;\n        const funcName = `bcastMatmulIndices_${name}`;\n        let block = '';\n        for (let i = 0; i < rank - 2; ++i) {\n          block += `\n          realIndices[${i}] = int( mod(float(bcastedIndices[${dimOffset + i}]), ${shape[i]}.0) );\n          `;\n        }\n        const body = `\n        void ${funcName}(int bcastedIndices[${outputRank}], out int realIndices[${rank}]) {\n          ${block}\n          realIndices[${rank - 1}] = bcastedIndices[${outputRank - 1}];\n          realIndices[${rank - 2}] = bcastedIndices[${outputRank - 2}];\n        }\n        `;\n        result[funcName] = new GlslLibRoutine(body);\n      }\n    });\n    return result;\n  }\n  protected indicesToOffset(): {[name: string]: GlslLibRoutine} {\n    const result: {[name: string]: GlslLibRoutine} = {};\n    this.context.programInfo.inputNames.forEach((name, i) => {\n      const shape = this.context.inputTextureLayouts[i].shape;\n      const strides = this.context.inputTextureLayouts[i].strides;\n      const rank = shape.length;\n      let funcName = `indicesToOffset_${name}`;\n      result[funcName] = new GlslLibRoutine(ShapeUtilsGlslLib.indexToOffsetSingle(funcName, rank, strides));\n      funcName = `indicesToOffset_${name}_T`;\n      result[funcName] =\n          new GlslLibRoutine(ShapeUtilsGlslLib.indexToOffsetSingle(funcName, rank, strides.slice().reverse()));\n    });\n    return result;\n  }\n  static indexToOffsetSingle(name: string, rank: number, strides: readonly number[]): string {\n    let block = '';\n    for (let i = rank - 1; i >= 0; --i) {\n      block += `\n        offset += indices[${i}] * ${strides[i]};\n        `;\n    }\n    return `\n      int ${name}(int indices[${rank}]) {\n        int offset = 0;\n        ${block}\n        return offset;\n      }\n      `;\n  }\n  protected offsetToIndices(): {[name: string]: GlslLibRoutine} {\n    const result: {[name: string]: GlslLibRoutine} = {};\n    this.context.programInfo.inputNames.forEach((name, i) => {\n      const shape = this.context.inputTextureLayouts[i].shape;\n      const strides = this.context.inputTextureLayouts[i].strides;\n      const rank = shape.length;\n      let funcName = `offsetToIndices_${name}`;\n      result[funcName] = new GlslLibRoutine(ShapeUtilsGlslLib.offsetToIndicesSingle(funcName, rank, strides));\n      funcName = `offsetToIndices_${name}_T`;\n      result[funcName] =\n          new GlslLibRoutine(ShapeUtilsGlslLib.offsetToIndicesSingle(funcName, rank, strides.slice().reverse()));\n    });\n    return result;\n  }\n  static offsetToIndicesSingle(name: string, rank: number, strides: readonly number[]): string {\n    const stridesBlock = [];\n    for (let i = 0; i < rank - 1; ++i) {\n      stridesBlock.push(`\n      indices[${i}] = offset / ${strides[i]};`);\n      stridesBlock.push(`\n        offset -= indices[${i}] * ${strides[i]};`);\n    }\n    stridesBlock.push(`\n      indices[${rank - 1}] = offset;`);\n    return `\n      void ${name}(int offset, out int indices[${rank}]) {\n        ${stridesBlock.join('')}\n      }\n      `;\n  }\n  protected incrementIndices(): {[name: string]: GlslLibRoutine} {\n    const result: {[name: string]: GlslLibRoutine} = {};\n    this.context.programInfo.inputNames.forEach((name, i) => {\n      const shape = this.context.inputTextureLayouts[i].shape;\n      const rank = shape.length;\n      const funcName = `incrementIndices_${name}`;\n      let shapeInit = '';\n      for (let i = 0; i < rank; ++i) {\n        shapeInit += `\n        shape[${i}] = ${shape[i]};`;\n      }\n      const body = `\n        void ${funcName}(int axis, out int indices[${rank}]) {\n          int shape[${rank}];\n          ${shapeInit};\n          for(int i = ${rank} -1 ; i >= 0; --i) {\n            if(i > axis) continue;\n            indices[i] += 1;\n            if(indices[i] < shape[i]) {\n              break;\n            }\n            indices[i] = 0;\n          }\n        }\n        `;\n      result[funcName] = new GlslLibRoutine(body);\n    });\n    return result;\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {GlslContext, GlslLib, GlslLibRoutine} from './glsl-definitions';\n\n/**\n * GLSL Library responsible for vec routines\n * Vec is an varible length int array. The length is fixed at the time of\n * generating the library functions from the dimensions of the output.\n */\nexport class VecGlslLib extends GlslLib {\n  constructor(context: GlslContext) {\n    super(context);\n  }\n  getCustomTypes(): {[name: string]: string} {\n    return {};\n  }\n  getFunctions(): {[name: string]: GlslLibRoutine} {\n    return {...this.binaryVecFunctions(), ...this.copyVec(), ...this.setVecItem(), ...this.getVecItem()};\n  }\n  protected binaryVecFunctions(): {[name: string]: GlslLibRoutine} {\n    const outputLayout = this.context.outputTextureLayout;\n    const rank = outputLayout.shape.length;\n    const nameOp: {[name: string]: string} = {add: '+=', sub: '-=', mul: '*=', div: '/='};\n    const result: {[name: string]: GlslLibRoutine} = {};\n    for (const name in nameOp) {\n      const fname = `${name}Vec`;\n      let assignmentBlock = '';\n      for (let i = 0; i < rank; ++i) {\n        assignmentBlock += `\n          dest[${i}] ${nameOp[name]} src[${i}];\n          `;\n      }\n      const body = `\n        void ${fname}(int src[${rank}], out int dest[${rank}]) {\n          ${assignmentBlock}\n        }\n        `;\n      result[fname] = new GlslLibRoutine(body);\n    }\n\n    return result;\n  }\n  protected copyVec(): {[name: string]: GlslLibRoutine} {\n    const outputLayout = this.context.outputTextureLayout;\n    const rank = outputLayout.shape.length;\n    let assignmentBlock = '';\n    for (let i = 0; i < rank; ++i) {\n      assignmentBlock += `\n        dest[${i}] = src[${i}];\n        `;\n    }\n    const body = `\n      void copyVec(int src[${rank}], out int dest[${rank}]) {\n        ${assignmentBlock}\n      }\n      `;\n    return {copyVec: new GlslLibRoutine(body)};\n  }\n\n  protected setVecItem(): {[name: string]: GlslLibRoutine} {\n    const outputLayout = this.context.outputTextureLayout;\n    const rank = outputLayout.shape.length;\n    let block = `\n        if(index < 0)\n            index =${rank} + index;\n        if (index == 0)\n            m[0] = value;\n        `;\n    for (let i = 1; i < rank - 1; ++i) {\n      block += `\n        else if (index == ${i})\n            m[${i}] = value;\n            `;\n    }\n    block += `\n        else\n            m[${rank - 1}] = value;\n        `;\n    const body = `\n      void setVecItem(out int m[${rank}], int index, int value) {\n        ${block}\n      }\n        `;\n    return {setVecItem: new GlslLibRoutine(body)};\n  }\n  protected getVecItem(): {[name: string]: GlslLibRoutine} {\n    const outputLayout = this.context.outputTextureLayout;\n    const rank = outputLayout.shape.length;\n    let block = `\n        if(index < 0)\n            index = ${rank} + index;\n        if (index == 0)\n            return m[0];\n      `;\n    for (let i = 1; i < rank - 1; ++i) {\n      block += `\n        else if (index == ${i})\n            return m[${i}];\n      `;\n    }\n    block += `\n        else\n            return m[${rank - 1}];\n        `;\n    const body = `\n      int getVecItem(int m[${rank}], int index) {\n        ${block}\n      }\n    `;\n    return {getVecItem: new GlslLibRoutine(body)};\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {CoordsGlslLib} from './glsl-coordinate-lib';\nimport {GlslContext, GlslLib} from './glsl-definitions';\nimport {EncodingGlslLib} from './glsl-encoding-lib';\nimport {FragColorGlslLib} from './glsl-fragcolor-lib';\nimport {ShapeUtilsGlslLib} from './glsl-shape-utils-lib';\nimport {VecGlslLib} from './glsl-vec-lib';\n\nexport const glslRegistry: {[name: string]: new (context: GlslContext) => GlslLib} = {\n  'encoding': EncodingGlslLib,\n  'fragcolor': FragColorGlslLib,\n  'vec': VecGlslLib,\n  'shapeUtils': ShapeUtilsGlslLib,\n  'coordinates': CoordsGlslLib,\n  //  'arrays': ArrayGlslSLib\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {GlslContext, GlslLib, GlslLibRoutineNode, TopologicalSortGlslRoutines} from './glsl-definitions';\nimport {replaceInlines} from './glsl-function-inliner';\nimport {glslRegistry} from './glsl-registered-libs';\nimport {getDefaultFragShaderMain, getFragShaderPreamble} from './glsl-source';\nimport {ProgramInfo, TextureLayout, VariableInfo} from './types';\nimport {WebGLContext} from './webgl-context';\n\n/**\n * Preprocessor for the additions to the GLSL language\n * It deals with:\n *  @include directives\n *  @inline\n *  Loop unrolling (not implemented)\n *  Macro resolution (not implemented)\n */\nexport class GlslPreprocessor {\n  readonly context: GlslContext;\n  readonly libs: {[name: string]: GlslLib} = {};\n  readonly glslLibRoutineDependencyGraph: {[routineName: string]: GlslLibRoutineNode} = {};\n\n  constructor(\n      glContext: WebGLContext, programInfo: ProgramInfo, inputTextureLayouts: TextureLayout[],\n      outputTextureLayout: TextureLayout) {\n    this.context = new GlslContext(glContext, programInfo, inputTextureLayouts, outputTextureLayout);\n\n    // construct GlslLibs\n    Object.keys(glslRegistry).forEach((name: string) => {\n      const lib = new glslRegistry[name](this.context);\n      this.libs[name] = lib;\n    });\n\n    // construct GlslRoutineDependencyGraph\n    const map = this.glslLibRoutineDependencyGraph;\n    for (const libName in this.libs) {\n      const lib = this.libs[libName];\n      const routinesInLib = lib.getFunctions();\n      for (const routine in routinesInLib) {\n        const key = libName + '.' + routine;\n        let currentNode: GlslLibRoutineNode;\n        if (map[key]) {\n          currentNode = map[key];\n          currentNode.routineBody = routinesInLib[routine].routineBody;\n        } else {\n          currentNode = new GlslLibRoutineNode(key, routinesInLib[routine].routineBody);\n          map[key] = currentNode;\n        }\n        const dependencies = routinesInLib[routine].dependencies;\n        if (dependencies) {\n          for (let i = 0; i < dependencies.length; ++i) {\n            if (!map[dependencies[i]]) {\n              const node = new GlslLibRoutineNode(dependencies[i]);\n              map[dependencies[i]] = node;\n              currentNode.addDependency(node);\n            } else {\n              currentNode.addDependency(map[dependencies[i]]);\n            }\n          }\n        }\n      }\n    }\n  }\n\n  preprocess(): string {\n    const programInfo = this.context.programInfo;\n    let source = programInfo.shaderSource;\n\n    // append main() function\n    if (!this.context.programInfo.hasMain) {\n      source = `${source}\n      ${getDefaultFragShaderMain(this.context.glContext.version, this.context.outputTextureLayout.shape.length)}`;\n    }\n    // replace inlines\n    source = replaceInlines(source);\n\n    // concat final source string\n    return `${getFragShaderPreamble(this.context.glContext.version)}\n    ${this.getUniforms(programInfo.inputNames, programInfo.variables)}\n    ${this.getImports(source)}\n    ${source}`;\n  }\n\n  protected getImports(script: string): string {\n    const routinesIncluded = this.selectGlslLibRoutinesToBeIncluded(script);\n\n    if (routinesIncluded.length === 0) {\n      return '';\n    }\n\n    let routines = '';\n    for (let i = 0; i < routinesIncluded.length; ++i) {\n      if (routinesIncluded[i].routineBody) {\n        routines += routinesIncluded[i].routineBody + '\\n';\n      } else {\n        throw new Error(`Missing body for the Glsl Library routine: ${routinesIncluded[i].name}`);\n      }\n    }\n\n    return routines;\n  }\n  private selectGlslLibRoutinesToBeIncluded(script: string): GlslLibRoutineNode[] {\n    const nodes: GlslLibRoutineNode[] = [];\n\n    Object.keys(this.glslLibRoutineDependencyGraph).forEach(classAndRoutine => {\n      const routine = classAndRoutine.split('.')[1];\n      if (script.indexOf(routine) !== -1) {\n        nodes.push(this.glslLibRoutineDependencyGraph[classAndRoutine]);\n      }\n    });\n\n    return TopologicalSortGlslRoutines.returnOrderedNodes(nodes);\n  }\n\n  protected getUniforms(samplers?: string[], variables?: VariableInfo[]): string {\n    const uniformLines: string[] = [];\n    if (samplers) {\n      for (const sampler of samplers) {\n        uniformLines.push(`uniform sampler2D ${sampler};`);\n      }\n    }\n    if (variables) {\n      for (const variable of variables) {\n        uniformLines.push(\n            `uniform ${variable.type} ${variable.name}${variable.arrayLength ? `[${variable.arrayLength}]` : ''};`);\n      }\n    }\n    return uniformLines.join('\\n');\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {env} from 'onnxruntime-common';\n\nimport {Logger, Profiler} from '../../instrument';\n\nimport {GlslPreprocessor} from './glsl-preprocessor';\nimport {getVertexShaderSource} from './glsl-source';\nimport {TextureLayoutStrategy} from './texture-layout-strategy';\nimport {Artifact, ProgramInfo, ProgramVariable, TextureData, TextureLayout, VariableInfo} from './types';\nimport {WebGLContext} from './webgl-context';\n\n/**\n * ProgramManager is the main class behind running computations\n * It builds ProgramInfo's into Artifacts\n * It compiles given ProgramInfo's into WebGL Prorams (cached as Artifacts)\n * Uses the artifact to run the computation by calling Draw on\n * the WebGL drawing buffer\n * ProgramManager automatically maps (binds) input variables to their\n * corresponding Location's in the binary program\n */\nexport class ProgramManager {\n  repo: Map<unknown, Artifact>;  // this should be per-session object\n  vertexShader: WebGLShader;\n  attributesBound: boolean;\n\n  constructor(\n      public profiler: Readonly<Profiler>, public glContext: WebGLContext,\n      public textureLayoutStrategy: TextureLayoutStrategy) {\n    this.repo = new Map();\n    this.attributesBound = false;\n  }\n  getArtifact(key: unknown): Artifact|undefined {\n    return this.repo.get(key);\n  }\n  setArtifact(key: unknown, artifact: Artifact): void {\n    this.repo.set(key, artifact);\n  }\n  run(buildArtifact: Artifact, inputs: TextureData[], output: TextureData): void {\n    this.profiler.event('op', `ProgramManager.run ${buildArtifact.programInfo.name ?? 'unknown kernel'}`, () => {\n      const gl = this.glContext.gl;\n      const program = buildArtifact.program;\n      gl.useProgram(program);\n      try {\n        this.bindOutput(output);\n        if (!this.attributesBound) {\n          this.bindAttributes(buildArtifact.attribLocations);\n        }\n        this.bindUniforms(buildArtifact.uniformLocations, buildArtifact.programInfo.variables ?? [], inputs);\n      } catch (err) {\n        Logger.error('ProgramManager', buildArtifact.programInfo.shaderSource);\n        throw err;\n      }\n      this.profiler.event('backend', 'GlContext.draw()', () => {\n        this.glContext.draw();\n      });\n    }, this.glContext);\n  }\n  dispose(): void {\n    if (this.vertexShader) {\n      this.glContext.deleteShader(this.vertexShader);\n    }\n    this.repo.forEach(a => this.glContext.deleteProgram(a.program));\n  }\n  build(programInfo: ProgramInfo, inputTextureLayouts: TextureLayout[], outputTextureLayout: TextureLayout): Artifact {\n    return this.profiler.event('backend', 'ProgramManager.build', () => {\n      const preprocessor = new GlslPreprocessor(this.glContext, programInfo, inputTextureLayouts, outputTextureLayout);\n      const fragScript = preprocessor.preprocess();\n      const program = this.compile(fragScript);\n      const artifact = {\n        programInfo,\n        program,\n        uniformLocations: this.getUniformLocations(\n            program, preprocessor.context.programInfo.inputNames, preprocessor.context.programInfo.variables),\n        attribLocations: this.getAttribLocations(program)\n      };\n      return artifact;\n    });\n  }\n  protected compile(fragShaderScript: string): WebGLProgram {\n    if (!this.vertexShader) {\n      Logger.verbose('ProrgramManager', 'Compiling and caching Vertex shader for the first time');\n      const vertexShaderScript = getVertexShaderSource(this.glContext.version);\n      this.vertexShader = this.glContext.compileShader(vertexShaderScript, this.glContext.gl.VERTEX_SHADER);\n    }\n    if (env.debug) {\n      Logger.verbose('ProrgramManager', `FragShader:\n${fragShaderScript}\n`);\n    }\n    const fragShader = this.glContext.compileShader(fragShaderScript, this.glContext.gl.FRAGMENT_SHADER);\n    const program = this.glContext.createProgram(this.vertexShader, fragShader);\n    this.glContext.deleteShader(fragShader);\n    return program;\n  }\n  bindOutput(td: TextureData): void {\n    const width = td.width;\n    const height = td.height;\n    Logger.verbose(\n        'ProrgramManager',\n        `Binding output texture to Framebuffer: w/h=${width}/${height}, shape=${td.shape}, type=${td.tensor.type}`);\n    this.glContext.attachFramebuffer(td.texture, width, height);\n  }\n  bindAttributes(attribLocations: Artifact.AttribLocations): void {\n    const positionHandle = attribLocations.position;\n    const textureCoordHandle = attribLocations.textureCoord;\n    this.glContext.setVertexAttributes(positionHandle, textureCoordHandle);\n    this.attributesBound = true;\n  }\n  bindUniforms(uniformLocations: Artifact.UniformLocations, variables: ProgramVariable[], textures: TextureData[]):\n      void {\n    const gl = this.glContext.gl;\n    let texturePosition = 0;\n    for (const {name, type, location, arrayLength} of uniformLocations) {\n      const value = variables.find(v => v.name === name)?.data;\n      if (type !== 'sampler2D' && !value) {\n        throw new Error(`variable '${name}' does not have data defined in program info`);\n      }\n      switch (type) {\n        case 'sampler2D':\n          this.bindTexture(textures[texturePosition], location, texturePosition);\n          texturePosition++;\n          break;\n        case 'float':\n          if (arrayLength) {\n            gl.uniform1fv(location, value as number[]);\n          } else {\n            gl.uniform1f(location, value as number);\n          }\n          break;\n        case 'int':\n          if (arrayLength) {\n            gl.uniform1iv(location, value as number[]);\n          } else {\n            gl.uniform1i(location, value as number);\n          }\n          break;\n        default:\n          throw new Error(`Uniform not implemented: ${type}`);\n      }\n    }\n  }\n  bindTexture(td: TextureData, uniformHandle: WebGLUniformLocation, position: number): void {\n    this.glContext.bindTextureToUniform(td.texture, position, uniformHandle);\n  }\n  getAttribLocations(program: WebGLProgram): Artifact.AttribLocations {\n    return {\n      position: this.getAttribLocation(program, 'position'),\n      textureCoord: this.getAttribLocation(program, 'textureCoord')\n    };\n  }\n  getUniformLocations(program: WebGLProgram, samplers?: string[], variables?: VariableInfo[]):\n      Artifact.UniformLocations {\n    const uniformLocations: Artifact.UniformLocations = [];\n    if (samplers) {\n      for (const sampler of samplers) {\n        uniformLocations.push({name: sampler, type: 'sampler2D', location: this.getUniformLocation(program, sampler)});\n      }\n    }\n    if (variables) {\n      for (const variable of variables) {\n        uniformLocations.push({...variable, location: this.getUniformLocation(program, variable.name)});\n      }\n    }\n    return uniformLocations;\n  }\n  getUniformLocation(program: WebGLProgram, name: string): WebGLUniformLocation {\n    const gl = this.glContext.gl;\n    const reference = gl.getUniformLocation(program, name);\n    if (reference === null) {\n      throw new Error(`Uniform ${name} not found.`);\n    }\n    return reference;\n  }\n  getAttribLocation(program: WebGLProgram, name: string): number {\n    const gl = this.glContext.gl;\n    const attributeLocation: number = gl.getAttribLocation(program, name);\n    return attributeLocation;\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Logger, Profiler} from '../../instrument';\nimport {Tensor} from '../../tensor';\n\nimport {Encoder, EncoderUsage} from './texture-data-encoder';\nimport {TextureLayoutStrategy} from './texture-layout-strategy';\nimport {TextureData, TextureLayout} from './types';\nimport {WebGLContext} from './webgl-context';\n\nexport interface TextureManagerConfig {\n  reuseTextures?: boolean;\n}\n\n/**\n * TextureManager is the mainly responsible for caching Textures\n * Textures are cached in 2 levels:\n *   1. the texures which are associated with a dataId (from Tensor)\n *    Caching these is crucial to performance. These are In-use Textures\n *   2. textures which are not in use by any current ProgramInfo/Tensor\n *     These are called Free Textures\n * TextureManager is also used to help creating textures. For this it\n * uses WebGLContext and TextureLayoutStrategy\n */\nexport class TextureManager {\n  private readonly inUseTextures: Map<string, WebGLTexture[]>;\n  private readonly idleTextures: Map<string, WebGLTexture[]>;\n  private readonly textureLookup: Map<WebGLTexture, string>;\n  private readonly pendingRead: Map<Tensor.Id, Array<(arr: Tensor.NumberType) => void>> = new Map();\n\n  constructor(\n      public glContext: WebGLContext, public layoutStrategy: TextureLayoutStrategy, public profiler: Readonly<Profiler>,\n      private config: TextureManagerConfig) {\n    if (config.reuseTextures) {\n      this.inUseTextures = new Map();\n      this.idleTextures = new Map();\n      this.textureLookup = new Map();\n    }\n  }\n  createTextureFromLayout(\n      dataType: Tensor.DataType, layout: TextureLayout, data?: Tensor.NumberType, usage?: EncoderUsage) {\n    const textureDataType = this.toEncoderType(dataType);\n\n    const encoder = this.glContext.getEncoder(textureDataType, layout.channels || 1, usage);\n    if (layout.isPacked && usage === EncoderUsage.UploadOnly) {\n      throw new Error('not implemented');\n    }\n    const width = layout.width;\n    const height = layout.height;\n\n    let key: string|undefined;\n    let inUseTextures: WebGLTexture[]|undefined;\n    if (this.config.reuseTextures) {\n      key = `${width}x${height}_${encoder.format}_${encoder.internalFormat}_${encoder.textureType}`;\n      inUseTextures = this.inUseTextures.get(key);\n      if (!inUseTextures) {\n        inUseTextures = [];\n        this.inUseTextures.set(key, inUseTextures);\n      }\n\n      const idleTextures = this.idleTextures.get(key);\n      if (idleTextures && idleTextures.length > 0) {\n        const texture = idleTextures.pop()!;\n        inUseTextures.push(texture);\n        if (usage === EncoderUsage.UploadOnly) {\n          this.glContext.updateTexture(texture, width, height, encoder, this.toTextureData(dataType, data)!);\n        }\n        return texture;\n      }\n    }\n\n    Logger.verbose('TextureManager', `Creating new texture of size ${layout.width}x${layout.height}`);\n    const texture = this.glContext.allocateTexture(width, height, encoder, this.toTextureData(dataType, data));\n\n    if (this.config.reuseTextures) {\n      inUseTextures!.push(texture);\n      this.textureLookup.set(texture, key!);\n    }\n    return texture;\n  }\n  readTexture(td: TextureData, dataType: Tensor.DataType, channels?: number): Tensor.NumberType {\n    if (!channels) {\n      channels = 1;\n    }\n    return this.profiler.event('backend', 'TextureManager.readTexture', () => {\n      const dataSize = td.shape.reduce((a, b) => a * b) * channels!;\n      const data = this.glContext.readTexture(\n          td.texture, td.width, td.height, dataSize, this.toEncoderType(dataType), channels!);\n      return this.toTensorData(dataType, data);\n    });\n  }\n  async readTextureAsync(td: TextureData, dataType: Tensor.DataType, channels?: number): Promise<Tensor.NumberType> {\n    const dataId = td.tensor.dataId;\n    if (!channels) {\n      channels = 1;\n    }\n    if (this.pendingRead.has(dataId)) {\n      const subscribers = this.pendingRead.get(dataId);\n      return new Promise<Tensor.NumberType>(resolve => subscribers?.push(resolve));\n    }\n    return this.profiler.event('backend', 'TextureManager.readTextureAsync', async () => {\n      this.pendingRead.set(dataId, []);\n      const dataSize = td.shape.reduce((a, b) => a * b) * channels!;\n      // add a fence waiting for the data to be ready\n      await this.glContext.createAndWaitForFence();\n      const data = this.glContext.readTexture(\n          td.texture, td.width, td.height, dataSize, this.toEncoderType(dataType), channels!);\n      const tensorData = this.toTensorData(dataType, data);\n      const subscribers = this.pendingRead.get(dataId);\n      this.pendingRead.delete(dataId);\n      subscribers?.forEach(resolve => resolve(tensorData));\n      return tensorData;\n    });\n  }\n  readUint8TextureAsFloat(td: TextureData): Float32Array {\n    return this.profiler.event('backend', 'TextureManager.readUint8TextureAsFloat', () => {\n      const dataSize = td.shape.reduce((a, b) => a * b);\n      const data = this.glContext.readTexture(td.texture, td.width, td.height, dataSize * 4, 'byte', 4);\n      return new Float32Array(data.buffer, data.byteOffset, dataSize);\n    });\n  }\n  releaseTexture(textureData: TextureData, deleteTexture?: boolean): void {\n    let key: string|undefined;\n    if (this.config.reuseTextures) {\n      key = this.textureLookup.get(textureData.texture);\n      if (key) {\n        if (deleteTexture) {\n          this.textureLookup.delete(key);\n        }\n        const inUseTextures = this.inUseTextures.get(key);\n        if (inUseTextures) {\n          const index = inUseTextures.indexOf(textureData.texture);\n          if (index !== -1) {\n            inUseTextures.splice(index, 1);\n            let idleTextures = this.idleTextures.get(key);\n            if (!idleTextures) {\n              idleTextures = [];\n              this.idleTextures.set(key, idleTextures);\n            }\n            idleTextures.push(textureData.texture);\n          }\n        }\n      }\n    }\n\n    if (!key || deleteTexture) {\n      Logger.verbose('TextureManager', `Deleting texture of size ${textureData.width}x${textureData.height}`);\n      this.glContext.deleteTexture(textureData.texture);\n    }\n  }\n  toTensorData(dataType: Tensor.DataType, data: Encoder.DataArrayType): Tensor.NumberType {\n    switch (dataType) {\n      case 'int16':\n        return data instanceof Int16Array ? data : Int16Array.from(data);\n      case 'int32':\n        return data instanceof Int32Array ? data : Int32Array.from(data);\n      case 'int8':\n        return data instanceof Int8Array ? data : Int8Array.from(data);\n      case 'uint16':\n        return data instanceof Uint16Array ? data : Uint16Array.from(data);\n      case 'uint32':\n        return data instanceof Uint32Array ? data : Uint32Array.from(data);\n      case 'uint8':\n      case 'bool':\n        return data instanceof Uint8Array ? data : Uint8Array.from(data);\n      case 'float32':\n        return data instanceof Float32Array ? data : Float32Array.from(data);\n      case 'float64':\n        return data instanceof Float64Array ? data : Float64Array.from(data);\n      default:\n        throw new Error(`TensorData type ${dataType} is not supported`);\n    }\n  }\n  toTextureData(_dataType: Tensor.DataType, data: Tensor.NumberType|undefined): Encoder.DataArrayType|undefined {\n    if (!data) {\n      return undefined;\n    }\n    return (data instanceof Float32Array) ? data : new Float32Array(data);\n    /*\n    switch (dataType) {\n      case 'int16':\n      case 'int32':\n      case 'uint16':\n      case 'uint32':\n        return (data.constructor === Uint32Array) ? data as Uint32Array : new Uint32Array(data);\n      case 'int8':\n      case 'uint8':\n      case 'bool':\n        return (data.constructor === Uint8Array) ? data as Uint8Array : new Uint8Array(data);\n      case 'float32':\n      case 'float64':\n        return (data.constructor === Float32Array) ? data as Float32Array : new Float32Array(data);\n      default:\n        throw new Error(`TensorData type ${dataType} is not supported`);\n    }\n    */\n  }\n  toEncoderType(_dataType: Tensor.DataType): Encoder.DataType {\n    return 'float';\n    // switch (dataType) {\n    //   case 'int16':\n    //   case 'int32':\n    //   case 'uint16':\n    //   case 'uint32':\n    //     return 'int';\n    //   case 'uint8':\n    //   case 'bool':\n    //     return 'byte';\n    //   case 'float32':\n    //   case 'float64':\n    //     return 'float';\n    //   default:\n    //     throw new Error(`TensorData type ${dataType} is not supported`);\n    // }\n  }\n  clearActiveTextures(): void {\n    this.glContext.clearActiveTextures();\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {SessionHandler} from '../../backend';\nimport {Graph} from '../../graph';\nimport {Logger} from '../../instrument';\nimport {Operator} from '../../operators';\nimport {OpSet, resolveOperator} from '../../opset';\nimport {Session} from '../../session';\nimport {Tensor} from '../../tensor';\nimport {WebGLBackend} from '../backend-webgl';\n\nimport {WebGLInferenceHandler} from './inference-handler';\nimport {WEBGL_OP_RESOLVE_RULES} from './op-resolve-rules';\nimport {ProgramManager} from './program-manager';\nimport {PreferLogicalStrategy, TextureLayoutStrategy} from './texture-layout-strategy';\nimport {TextureManager} from './texture-manager';\nimport {TextureData} from './types';\n\nexport class WebGLSessionHandler implements SessionHandler {\n  programManager: ProgramManager;\n  textureManager: TextureManager;\n  layoutStrategy: TextureLayoutStrategy;\n  packedTextureDataCache: Map<Tensor.Id, TextureData>;\n  unpackedTextureDataCache: Map<Tensor.Id, TextureData>;\n  pack2unpackMap: Map<Tensor.Id, Tensor.Id>;\n  unpack2packMap: Map<Tensor.Id, Tensor.Id>;\n  initializers: Set<Tensor.Id>;\n  pack?: boolean;\n\n  constructor(public readonly backend: WebGLBackend, public readonly context: Session.Context) {\n    this.layoutStrategy = new PreferLogicalStrategy(backend.glContext.maxTextureSize);\n    this.programManager = new ProgramManager(this.context.profiler, backend.glContext, this.layoutStrategy);\n    this.textureManager = new TextureManager(\n        backend.glContext, this.layoutStrategy, this.context.profiler,\n        {reuseTextures: backend.textureCacheMode === 'full'});\n    this.packedTextureDataCache = new Map();\n    this.unpackedTextureDataCache = new Map();\n    this.pack = backend.pack;\n    this.pack2unpackMap = new Map();\n    this.unpack2packMap = new Map();\n  }\n\n  createInferenceHandler() {\n    return new WebGLInferenceHandler(this);\n  }\n  onGraphInitialized(graph: Graph): void {\n    const initializers = graph.getValues().filter(v => v.from === -1 && v.tensor).map(v => v.tensor!.dataId);\n    this.initializers = new Set(initializers);\n  }\n  isInitializer(tensorId: Tensor.Id): boolean {\n    return this.initializers ? this.initializers.has(tensorId) : false;\n  }\n  addInitializer(tensorId: Tensor.Id): void {\n    this.initializers.add(tensorId);\n  }\n  getTextureData(tensorId: Tensor.Id, isPacked: boolean): TextureData|undefined {\n    if (isPacked) {\n      return this.packedTextureDataCache.get(tensorId);\n    } else {\n      return this.unpackedTextureDataCache.get(tensorId);\n    }\n  }\n  setTextureData(tensorId: Tensor.Id, textureData: TextureData, isPacked = false): void {\n    Logger.verbose('WebGLSessionHandler', 'Storing Texture data in cache');\n    if (isPacked) {\n      this.packedTextureDataCache.set(tensorId, textureData);\n    } else {\n      this.unpackedTextureDataCache.set(tensorId, textureData);\n    }\n  }\n  dispose(): void {\n    this.programManager.dispose();\n    this.textureManager.clearActiveTextures();\n    this.packedTextureDataCache.forEach(td => this.textureManager.releaseTexture(td, true));\n    this.packedTextureDataCache = new Map();\n    this.unpackedTextureDataCache.forEach(td => this.textureManager.releaseTexture(td, true));\n    this.unpackedTextureDataCache = new Map();\n  }\n  resolve(node: Graph.Node, opsets: readonly OpSet[], graph: Graph): Operator {\n    const op = resolveOperator(node, opsets, WEBGL_OP_RESOLVE_RULES);\n    return {impl: op.opImpl, context: op.opInit ? op.opInit(node, graph) : node};\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {env} from 'onnxruntime-common';\n\nimport * as DataEncoders from './texture-data-encoder';\nimport {DataEncoder, Encoder, EncoderUsage} from './texture-data-encoder';\nimport {repeatedTry} from './utils';\n\nexport interface FenceContext {\n  query: WebGLSync|null;\n  isFencePassed(): boolean;\n}\n\ntype PollItem = {\n  isDoneFn: () => boolean; resolveFn: () => void;\n};\n\nexport function linearSearchLastTrue(arr: Array<() => boolean>): number {\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\n/**\n * Abstraction and wrapper around WebGLRenderingContext and its operations\n */\nexport class WebGLContext {\n  gl: WebGLRenderingContext;\n  version: 1|2;\n\n  private vertexbuffer: WebGLBuffer;\n  private framebuffer: WebGLFramebuffer;\n\n  // WebGL flags and vital parameters\n  private isFloatTextureAttachableToFrameBuffer: boolean;\n  isFloat32DownloadSupported: boolean;\n  isRenderFloat32Supported: boolean;\n  isBlendSupported: boolean;\n  maxTextureSize: number;\n  // private maxCombinedTextureImageUnits: number;\n  private maxTextureImageUnits: number;\n  // private maxCubeMapTextureSize: number;\n  // private shadingLanguageVersion: string;\n  // private webglVendor: string;\n  // private webglVersion: string;\n\n  // WebGL2 flags and vital parameters\n  // private max3DTextureSize: number;\n  // private maxArrayTextureLayers: number;\n  // private maxColorAttachments: number;\n  // private maxDrawBuffers: number;\n\n  // WebGL extensions\n  // eslint-disable-next-line camelcase\n  textureFloatExtension: OES_texture_float|null;\n  // eslint-disable-next-line camelcase\n  textureHalfFloatExtension: OES_texture_half_float|null;\n\n  // WebGL2 extensions\n  colorBufferFloatExtension: unknown|null;\n  // eslint-disable-next-line @typescript-eslint/naming-convention\n  disjointTimerQueryWebgl2Extension: {TIME_ELAPSED_EXT: GLenum; GPU_DISJOINT_EXT: GLenum}|null;\n\n  private disposed: boolean;\n  private frameBufferBound = false;\n\n  constructor(gl: WebGLRenderingContext, version: 1|2) {\n    this.gl = gl;\n    this.version = version;\n\n    this.getExtensions();\n    this.vertexbuffer = this.createVertexbuffer();\n    this.framebuffer = this.createFramebuffer();\n    this.queryVitalParameters();\n  }\n\n  allocateTexture(width: number, height: number, encoder: DataEncoder, data?: Encoder.DataArrayType): WebGLTexture {\n    const gl = this.gl;\n    // create the texture\n    const texture = gl.createTexture();\n    // bind the texture so the following methods effect this texture.\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    const buffer = data ? encoder.encode(data, width * height) : null;\n    gl.texImage2D(\n        gl.TEXTURE_2D,\n        0,  // Level of detail.\n        encoder.internalFormat, width, height,\n        0,  // Always 0 in OpenGL ES.\n        encoder.format, encoder.textureType, buffer);\n    this.checkError();\n    return texture as WebGLTexture;\n  }\n  updateTexture(\n      texture: WebGLTexture, width: number, height: number, encoder: DataEncoder, data: Encoder.DataArrayType): void {\n    const gl = this.gl;\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const buffer = encoder.encode(data, width * height);\n    gl.texSubImage2D(\n        gl.TEXTURE_2D,\n        0,  // level\n        0,  // xoffset\n        0,  // yoffset\n        width, height, encoder.format, encoder.textureType, buffer);\n    this.checkError();\n  }\n  attachFramebuffer(texture: WebGLTexture, width: number, height: number): void {\n    const gl = this.gl;\n    // Make it the target for framebuffer operations - including rendering.\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, this.framebuffer);\n    gl.framebufferTexture2D(\n        gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture,\n        0);  // 0, we aren't using MIPMAPs\n    this.checkError();\n    gl.viewport(0, 0, width, height);\n    gl.scissor(0, 0, width, height);\n  }\n  readTexture(\n      texture: WebGLTexture, width: number, height: number, dataSize: number, dataType: Encoder.DataType,\n      channels: number): Encoder.DataArrayType {\n    const gl = this.gl;\n    if (!channels) {\n      channels = 1;\n    }\n    if (!this.frameBufferBound) {\n      this.attachFramebuffer(texture, width, height);\n    }\n    const encoder = this.getEncoder(dataType, channels);\n    const buffer = encoder.allocate(width * height);\n    // bind texture to framebuffer\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.framebufferTexture2D(\n        gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture,\n        0);  // 0, we aren't using MIPMAPs\n    // TODO: Check if framebuffer is ready\n    gl.readPixels(0, 0, width, height, gl.RGBA, encoder.textureType, buffer);\n    this.checkError();\n    // unbind FB\n    return encoder.decode(buffer, dataSize);\n  }\n\n  isFramebufferReady(): boolean {\n    // TODO: Implement logic to check if the framebuffer is ready\n    return true;\n  }\n  getActiveTexture(): string {\n    const gl = this.gl;\n    const n = gl.getParameter(this.gl.ACTIVE_TEXTURE);\n    return `TEXTURE${(n - gl.TEXTURE0)}`;\n  }\n  getTextureBinding(): WebGLTexture {\n    return this.gl.getParameter(this.gl.TEXTURE_BINDING_2D);\n  }\n  getFramebufferBinding(): WebGLFramebuffer {\n    return this.gl.getParameter(this.gl.FRAMEBUFFER_BINDING);\n  }\n  setVertexAttributes(positionHandle: number, textureCoordHandle: number): void {\n    const gl = this.gl;\n    gl.vertexAttribPointer(positionHandle, 3, gl.FLOAT, false, 20, 0);\n    gl.enableVertexAttribArray(positionHandle);\n    if (textureCoordHandle !== -1) {\n      gl.vertexAttribPointer(textureCoordHandle, 2, gl.FLOAT, false, 20, 12);\n      gl.enableVertexAttribArray(textureCoordHandle);\n    }\n    this.checkError();\n  }\n  createProgram(\n      vertexShader: WebGLShader,\n      fragShader: WebGLShader,\n      ): WebGLProgram {\n    const gl = this.gl;\n    const program = gl.createProgram()!;\n\n    // the program consists of our shaders\n    gl.attachShader(program, vertexShader);\n    gl.attachShader(program, fragShader);\n    gl.linkProgram(program);\n    return program;\n  }\n  compileShader(shaderSource: string, shaderType: number): WebGLShader {\n    const gl = this.gl;\n    const shader = gl.createShader(shaderType);\n    if (!shader) {\n      throw new Error(`createShader() returned null with type ${shaderType}`);\n    }\n\n    gl.shaderSource(shader, shaderSource);\n    gl.compileShader(shader);\n    if (gl.getShaderParameter(shader, gl.COMPILE_STATUS) === false) {\n      throw new Error(`Failed to compile shader: ${gl.getShaderInfoLog(shader)}\nShader source:\n${shaderSource}`);\n    }\n    return shader;\n  }\n  deleteShader(shader: WebGLShader): void {\n    this.gl.deleteShader(shader);\n  }\n  bindTextureToUniform(texture: WebGLTexture, position: number, uniformHandle: WebGLUniformLocation): void {\n    const gl = this.gl;\n    gl.activeTexture(gl.TEXTURE0 + position);\n    this.checkError();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    this.checkError();\n    gl.uniform1i(uniformHandle, position);\n    this.checkError();\n  }\n  draw(): void {\n    this.gl.drawArrays(this.gl.TRIANGLE_STRIP, 0, 4);\n    this.checkError();\n  }\n  checkError(): void {\n    if (env.debug) {\n      const gl = this.gl;\n      const error = gl.getError();\n      let label = '';\n      switch (error) {\n        case (gl.NO_ERROR):\n          return;\n        case (gl.INVALID_ENUM):\n          label = 'INVALID_ENUM';\n          break;\n        case (gl.INVALID_VALUE):\n          label = 'INVALID_VALUE';\n          break;\n        case (gl.INVALID_OPERATION):\n          label = 'INVALID_OPERATION';\n          break;\n        case (gl.INVALID_FRAMEBUFFER_OPERATION):\n          label = 'INVALID_FRAMEBUFFER_OPERATION';\n          break;\n        case (gl.OUT_OF_MEMORY):\n          label = 'OUT_OF_MEMORY';\n          break;\n        case (gl.CONTEXT_LOST_WEBGL):\n          label = 'CONTEXT_LOST_WEBGL';\n          break;\n        default:\n          label = `Unknown WebGL Error: ${error.toString(16)}`;\n      }\n      throw new Error(label);\n    }\n  }\n  deleteTexture(texture: WebGLTexture): void {\n    this.gl.deleteTexture(texture);\n  }\n  deleteProgram(program: WebGLProgram): void {\n    this.gl.deleteProgram(program);\n  }\n  getEncoder(dataType: Encoder.DataType, channels: number, usage: EncoderUsage = EncoderUsage.Default): DataEncoder {\n    if (this.version === 2) {\n      return new DataEncoders.RedFloat32DataEncoder(this.gl as WebGL2RenderingContext, channels);\n    }\n\n    switch (dataType) {\n      case 'float':\n        if (usage === EncoderUsage.UploadOnly || this.isRenderFloat32Supported) {\n          return new DataEncoders.RGBAFloatDataEncoder(this.gl, channels);\n        } else {\n          return new DataEncoders.RGBAFloatDataEncoder(\n              this.gl, channels, this.textureHalfFloatExtension!.HALF_FLOAT_OES);\n        }\n      case 'int':\n        throw new Error('not implemented');\n      case 'byte':\n        return new DataEncoders.Uint8DataEncoder(this.gl, channels);\n      default:\n        throw new Error(`Invalid dataType: ${dataType}`);\n    }\n  }\n  clearActiveTextures(): void {\n    const gl = this.gl;\n    for (let unit = 0; unit < this.maxTextureImageUnits; ++unit) {\n      gl.activeTexture(gl.TEXTURE0 + unit);\n      gl.bindTexture(gl.TEXTURE_2D, null);\n    }\n  }\n  dispose(): void {\n    if (this.disposed) {\n      return;\n    }\n    const gl = this.gl;\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteFramebuffer(this.framebuffer);\n    gl.bindBuffer(gl.ARRAY_BUFFER, null);\n    gl.deleteBuffer(this.vertexbuffer);\n    gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null);\n    gl.finish();\n    this.disposed = true;\n  }\n\n  private createDefaultGeometry(): Float32Array {\n    // Sets of x,y,z(=0),s,t coordinates.\n    return new Float32Array([\n      -1.0, 1.0,  0.0, 0.0, 1.0,  // upper left\n      -1.0, -1.0, 0.0, 0.0, 0.0,  // lower left\n      1.0,  1.0,  0.0, 1.0, 1.0,  // upper right\n      1.0,  -1.0, 0.0, 1.0, 0.0   // lower right\n    ]);\n  }\n  private createVertexbuffer(): WebGLBuffer {\n    const gl = this.gl;\n    const buffer = gl.createBuffer();\n    if (!buffer) {\n      throw new Error('createBuffer() returned null');\n    }\n    const geometry = this.createDefaultGeometry();\n    gl.bindBuffer(gl.ARRAY_BUFFER, buffer);\n    gl.bufferData(gl.ARRAY_BUFFER, geometry, gl.STATIC_DRAW);\n    this.checkError();\n    return buffer;\n  }\n  private createFramebuffer(): WebGLFramebuffer {\n    const fb = this.gl.createFramebuffer();\n    if (!fb) {\n      throw new Error('createFramebuffer returned null');\n    }\n    return fb;\n  }\n\n  private queryVitalParameters(): void {\n    const gl = this.gl;\n\n    this.isFloatTextureAttachableToFrameBuffer = this.checkFloatTextureAttachableToFrameBuffer();\n    this.isRenderFloat32Supported = this.checkRenderFloat32();\n    this.isFloat32DownloadSupported = this.checkFloat32Download();\n\n    if (this.version === 1 && !this.textureHalfFloatExtension && !this.isRenderFloat32Supported) {\n      throw new Error('both float32 and float16 TextureType are not supported');\n    }\n\n    this.isBlendSupported = !this.isRenderFloat32Supported || this.checkFloat32Blend();\n\n    // this.maxCombinedTextureImageUnits = gl.getParameter(gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS);\n    this.maxTextureSize = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n    this.maxTextureImageUnits = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n    // this.maxCubeMapTextureSize = gl.getParameter(gl.MAX_CUBE_MAP_TEXTURE_SIZE);\n    // this.shadingLanguageVersion = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);\n    // this.webglVendor = gl.getParameter(gl.VENDOR);\n    // this.webglVersion = gl.getParameter(gl.VERSION);\n\n    if (this.version === 2) {\n      // this.max3DTextureSize = gl.getParameter(WebGL2RenderingContext.MAX_3D_TEXTURE_SIZE);\n      // this.maxArrayTextureLayers = gl.getParameter(WebGL2RenderingContext.MAX_ARRAY_TEXTURE_LAYERS);\n      // this.maxColorAttachments = gl.getParameter(WebGL2RenderingContext.MAX_COLOR_ATTACHMENTS);\n      // this.maxDrawBuffers = gl.getParameter(WebGL2RenderingContext.MAX_DRAW_BUFFERS);\n    }\n  }\n  private getExtensions(): void {\n    if (this.version === 2) {\n      this.colorBufferFloatExtension = this.gl.getExtension('EXT_color_buffer_float');\n      this.disjointTimerQueryWebgl2Extension = this.gl.getExtension('EXT_disjoint_timer_query_webgl2');\n    } else {\n      this.textureFloatExtension = this.gl.getExtension('OES_texture_float');\n      this.textureHalfFloatExtension = this.gl.getExtension('OES_texture_half_float');\n    }\n  }\n\n  private checkFloatTextureAttachableToFrameBuffer(): boolean {\n    // test whether Float32 texture is supported:\n    // STEP.1 create a float texture\n    const gl = this.gl;\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    // eslint-disable-next-line @typescript-eslint/naming-convention\n    const internalFormat = this.version === 2 ? (gl as unknown as {RGBA32F: number}).RGBA32F : gl.RGBA;\n    gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, 1, 1, 0, gl.RGBA, gl.FLOAT, null);\n    // STEP.2 bind a frame buffer\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    // STEP.3 attach texture to framebuffer\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    // STEP.4 test whether framebuffer is complete\n    const isComplete = 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 isComplete;\n  }\n\n  private checkRenderFloat32(): boolean {\n    if (this.version === 2) {\n      if (!this.colorBufferFloatExtension) {\n        return false;\n      }\n    } else {\n      if (!this.textureFloatExtension) {\n        return false;\n      }\n    }\n    return this.isFloatTextureAttachableToFrameBuffer;\n  }\n\n  private checkFloat32Download(): boolean {\n    if (this.version === 2) {\n      if (!this.colorBufferFloatExtension) {\n        return false;\n      }\n    } else {\n      if (!this.textureFloatExtension) {\n        return false;\n      }\n      if (!this.gl.getExtension('WEBGL_color_buffer_float')) {\n        return false;\n      }\n    }\n    return this.isFloatTextureAttachableToFrameBuffer;\n  }\n\n  /**\n   * Check whether GL_BLEND is supported\n   */\n  private checkFloat32Blend(): boolean {\n    // it looks like currently (2019-05-08) there is no easy way to detect whether BLEND is supported\n    // https://github.com/microsoft/onnxjs/issues/145\n\n    const gl = this.gl;\n\n    let texture: WebGLTexture|null|undefined;\n    let frameBuffer: WebGLFramebuffer|null|undefined;\n    let vertexShader: WebGLShader|null|undefined;\n    let fragmentShader: WebGLShader|null|undefined;\n    let program: WebGLProgram|null|undefined;\n\n    try {\n      texture = gl.createTexture();\n      frameBuffer = gl.createFramebuffer();\n      gl.bindTexture(gl.TEXTURE_2D, texture);\n\n      // eslint-disable-next-line @typescript-eslint/naming-convention\n      const internalFormat = this.version === 2 ? (gl as unknown as {RGBA32F: number}).RGBA32F : gl.RGBA;\n      gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, 1, 1, 0, gl.RGBA, gl.FLOAT, null);\n\n      gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n      gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n      gl.enable(gl.BLEND);\n\n      vertexShader = gl.createShader(gl.VERTEX_SHADER);\n      if (!vertexShader) {\n        return false;\n      }\n      gl.shaderSource(vertexShader, 'void main(){}');\n      gl.compileShader(vertexShader);\n\n      fragmentShader = gl.createShader(gl.FRAGMENT_SHADER);\n      if (!fragmentShader) {\n        return false;\n      }\n      gl.shaderSource(fragmentShader, 'precision highp float;void main(){gl_FragColor=vec4(0.5);}');\n      gl.compileShader(fragmentShader);\n\n      program = gl.createProgram();\n      if (!program) {\n        return false;\n      }\n      gl.attachShader(program, vertexShader);\n      gl.attachShader(program, fragmentShader);\n      gl.linkProgram(program);\n      gl.useProgram(program);\n\n      gl.drawArrays(gl.POINTS, 0, 1);\n      return gl.getError() === gl.NO_ERROR;\n\n    } finally {\n      gl.disable(gl.BLEND);\n\n      if (program) {\n        gl.deleteProgram(program);\n      }\n      if (vertexShader) {\n        gl.deleteShader(vertexShader);\n      }\n      if (fragmentShader) {\n        gl.deleteShader(fragmentShader);\n      }\n      if (frameBuffer) {\n        gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n        gl.deleteFramebuffer(frameBuffer);\n      }\n      if (texture) {\n        gl.bindTexture(gl.TEXTURE_2D, null);\n        gl.deleteTexture(texture);\n      }\n    }\n  }\n\n  beginTimer(): WebGLQuery {\n    if (this.version === 2 && this.disjointTimerQueryWebgl2Extension) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      const ext = this.disjointTimerQueryWebgl2Extension;\n\n      const query = gl2.createQuery() as WebGLQuery;\n      gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);\n      return query;\n    } else {\n      // TODO: add webgl 1 handling.\n      throw new Error('WebGL1 profiling currently not supported.');\n    }\n  }\n\n  endTimer() {\n    if (this.version === 2 && this.disjointTimerQueryWebgl2Extension) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      const ext = this.disjointTimerQueryWebgl2Extension;\n      gl2.endQuery(ext.TIME_ELAPSED_EXT);\n      return;\n    } else {\n      // TODO: add webgl 1 handling.\n      throw new Error('WebGL1 profiling currently not supported');\n    }\n  }\n\n  isTimerResultAvailable(query: WebGLQuery): boolean {\n    let available = false, disjoint = false;\n    if (this.version === 2 && this.disjointTimerQueryWebgl2Extension) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      const ext = this.disjointTimerQueryWebgl2Extension;\n\n      available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n      disjoint = gl2.getParameter(ext.GPU_DISJOINT_EXT);\n    } else {\n      // TODO: add webgl 1 handling.\n      throw new Error('WebGL1 profiling currently not supported');\n    }\n\n    return available && !disjoint;\n  }\n\n  getTimerResult(query: WebGLQuery): number {\n    let timeElapsed = 0;\n    if (this.version === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      timeElapsed = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n      gl2.deleteQuery(query);\n    } else {\n      // TODO: add webgl 1 handling.\n      throw new Error('WebGL1 profiling currently not supported');\n    }\n    // return miliseconds\n    return timeElapsed / 1000000;\n  }\n\n  async waitForQueryAndGetTime(query: WebGLQuery): Promise<number> {\n    await repeatedTry(() => this.isTimerResultAvailable(query));\n    return this.getTimerResult(query);\n  }\n\n  public async createAndWaitForFence(): Promise<void> {\n    const fenceContext = this.createFence(this.gl);\n    return this.pollFence(fenceContext);\n  }\n\n  private createFence(gl: WebGLRenderingContext): FenceContext {\n    let isFencePassed: () => boolean;\n    const gl2 = gl as WebGL2RenderingContext;\n    const query = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n    gl.flush();\n    if (query === null) {\n      isFencePassed = () => true;\n    } else {\n      isFencePassed = () => {\n        const status = gl2.clientWaitSync(query, 0, 0);\n        return status === gl2.ALREADY_SIGNALED || status === gl2.CONDITION_SATISFIED;\n      };\n    }\n    return {query, isFencePassed};\n  }\n\n  async pollFence(fenceContext: FenceContext) {\n    return new Promise<void>(resolve => {\n      void this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n    });\n  }\n\n  private itemsToPoll: PollItem[] = [];\n\n  pollItems(): void {\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\n  private async addItemToPoll(isDoneFn: () => boolean, resolveFn: () => void) {\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    await repeatedTry(() => {\n      this.pollItems();\n      // End the loop if no more items to poll.\n      return this.itemsToPoll.length === 0;\n    });\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Logger} from '../../instrument';\n\nimport {WebGLContext} from './webgl-context';\n\nconst cache: {[contextId: string]: WebGLContext} = {};\n\n/**\n * This factory function creates proper WebGLRenderingContext based on\n * the current browsers capabilities\n * The order is from higher/most recent versions to most basic\n */\nexport function createWebGLContext(contextId?: 'webgl'|'webgl2'): WebGLContext {\n  let context: WebGLContext|undefined;\n  if ((!contextId || contextId === 'webgl2') && 'webgl2' in cache) {\n    context = cache.webgl2;\n  } else if ((!contextId || contextId === 'webgl') && 'webgl' in cache) {\n    context = cache.webgl;\n  }\n\n  if (!context) {\n    try {\n      // try to create webgl context from an offscreen canvas\n      const offscreenCanvas = createOffscreenCanvas();\n      context = createNewWebGLContext(offscreenCanvas, contextId);\n    } catch (e) {\n      // if failed, fallback to try to use a normal canvas element\n      const canvas = createCanvas();\n      context = createNewWebGLContext(canvas, contextId);\n    }\n  }\n\n  contextId = contextId || context.version === 1 ? 'webgl' : 'webgl2';\n  const gl = context.gl;\n\n  cache[contextId] = context;\n\n  if (gl.isContextLost()) {\n    delete cache[contextId];\n    return createWebGLContext(contextId);\n  }\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\n  return context;\n}\n\nexport function createNewWebGLContext(canvas: HTMLCanvasElement, contextId?: 'webgl'|'webgl2'): WebGLContext {\n  const contextAttributes: WebGLContextAttributes = {\n    alpha: false,\n    depth: false,\n    antialias: false,\n    stencil: false,\n    preserveDrawingBuffer: false,\n    premultipliedAlpha: false,\n    failIfMajorPerformanceCaveat: false\n  };\n  let gl: WebGLRenderingContext|null;\n  const ca = contextAttributes;\n  if (!contextId || contextId === 'webgl2') {\n    gl = canvas.getContext('webgl2', ca);\n    if (gl) {\n      try {\n        return new WebGLContext(gl, 2);\n      } catch (err) {\n        Logger.warning('GlContextFactory', `failed to create WebGLContext using contextId 'webgl2'. Error: ${err}`);\n      }\n    }\n  }\n  if (!contextId || contextId === 'webgl') {\n    gl = canvas.getContext('webgl', ca) || canvas.getContext('experimental-webgl', ca) as WebGLRenderingContext;\n    if (gl) {\n      try {\n        return new WebGLContext(gl, 1);\n      } catch (err) {\n        Logger.warning(\n            'GlContextFactory',\n            `failed to create WebGLContext using contextId 'webgl' or 'experimental-webgl'. Error: ${err}`);\n      }\n    }\n  }\n\n  throw new Error('WebGL is not supported');\n}\n\n// eslint-disable-next-line @typescript-eslint/naming-convention\ndeclare let OffscreenCanvas: {new (width: number, height: number): HTMLCanvasElement};\n\nfunction createCanvas(): HTMLCanvasElement {\n  if (typeof document === 'undefined') {\n    throw new TypeError('failed to create canvas: document is not supported');\n  }\n  const canvas: HTMLCanvasElement = document.createElement('canvas');\n  canvas.width = 1;\n  canvas.height = 1;\n  return canvas;\n}\n\nfunction createOffscreenCanvas(): HTMLCanvasElement {\n  if (typeof OffscreenCanvas === 'undefined') {\n    throw new TypeError('failed to create offscreen canvas: OffscreenCanvas is not supported');\n  }\n  return new OffscreenCanvas(1, 1);\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {env} from 'onnxruntime-common';\n\nimport {Backend, SessionHandler} from '../backend';\nimport {Logger} from '../instrument';\nimport {Session} from '../session';\n\nimport {WebGLSessionHandler} from './webgl/session-handler';\nimport {WebGLContext} from './webgl/webgl-context';\nimport {createWebGLContext} from './webgl/webgl-context-factory';\n\n/**\n * WebGLBackend is the entry point for all WebGL opeartions\n * When it starts it created the WebGLRenderingContext\n * and other main framework components such as Program and Texture Managers\n */\nexport class WebGLBackend implements Backend {\n  glContext: WebGLContext;\n\n  get contextId(): 'webgl'|'webgl2'|undefined {\n    return env.webgl.contextId;\n  }\n  set contextId(value: 'webgl'|'webgl2'|undefined) {\n    env.webgl.contextId = value;\n  }\n\n  get matmulMaxBatchSize(): number|undefined {\n    return env.webgl.matmulMaxBatchSize;\n  }\n  set matmulMaxBatchSize(value: number|undefined) {\n    env.webgl.matmulMaxBatchSize = value;\n  }\n\n  get textureCacheMode(): 'initializerOnly'|'full'|undefined {\n    return env.webgl.textureCacheMode;\n  }\n  set textureCacheMode(value: 'initializerOnly'|'full'|undefined) {\n    env.webgl.textureCacheMode = value;\n  }\n\n  get pack(): boolean|undefined {\n    return env.webgl.pack;\n  }\n  set pack(value: boolean|undefined) {\n    env.webgl.pack = value;\n  }\n\n  get async(): boolean|undefined {\n    return env.webgl.async;\n  }\n  set async(value: boolean|undefined) {\n    env.webgl.async = value;\n  }\n\n  initialize(): boolean {\n    try {\n      this.glContext = createWebGLContext(this.contextId);\n      if (typeof this.matmulMaxBatchSize !== 'number') {\n        this.matmulMaxBatchSize = 16;\n      }\n      if (typeof this.textureCacheMode !== 'string') {\n        this.textureCacheMode = 'full';\n      }\n      if (typeof this.pack !== 'boolean') {\n        this.pack = false;\n      }\n      if (typeof this.async !== 'boolean') {\n        this.async = false;\n      }\n\n      Logger.setWithEnv(env);\n\n      if (!env.webgl.context) {\n        Object.defineProperty(env.webgl, 'context', {value: this.glContext.gl});\n      }\n\n      Logger.verbose(\n          'WebGLBackend',\n          `Created WebGLContext: ${typeof this.glContext} with matmulMaxBatchSize: ${\n              this.matmulMaxBatchSize}; textureCacheMode: ${this.textureCacheMode}; pack: ${this.pack}; async: ${\n              this.async}.`);\n      return true;\n    } catch (e) {\n      Logger.warning('WebGLBackend', `Unable to initialize WebGLBackend. ${e}`);\n      return false;\n    }\n  }\n  createSessionHandler(context: Session.Context): SessionHandler {\n    return new WebGLSessionHandler(this, context);\n  }\n  dispose(): void {\n    this.glContext.dispose();\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {WebGLBackend} from './backends/backend-webgl';\nimport {Graph} from './graph';\nimport {Operator} from './operators';\nimport {OpSet} from './opset';\nimport {Session} from './session';\n\nexport interface InferenceHandler {\n  /**\n   * dispose the inference handler. it will be called as the last step in Session.run()\n   */\n  dispose(): void;\n}\n\nexport interface SessionHandler {\n  /**\n   * transform the graph at initialization time\n   * @param graphTransformer the graph transformer to manipulate the model graph\n   */\n  transformGraph?(graphTransformer: Graph.Transformer): void;\n\n  /**\n   * create an instance of InferenceHandler to use in a Session.run() call\n   */\n  createInferenceHandler(): InferenceHandler;\n\n  /**\n   * dispose the session handler. it will be called when a session is being disposed explicitly\n   */\n  dispose(): void;\n\n  /**\n   * Resolves the operator from the name and opset version; backend specific\n   * @param node the node to resolve\n   * @param opsets a list of opsets that exported from the model\n   * @param graph the completely initialized graph\n   */\n  resolve(node: Graph.Node, opsets: readonly OpSet[], graph: Graph): Operator;\n\n  /**\n   * This method let's the sessionHandler know that the graph initialization is complete\n   * @param graph the completely initialized graph\n   */\n  onGraphInitialized?(graph: Graph): void;\n\n  /**\n   * a reference to the corresponding backend\n   */\n  readonly backend: Backend;\n\n  /**\n   * a reference to the session context\n   */\n  readonly context: Session.Context;\n}\n\nexport interface Backend {\n  /**\n   * initialize the backend. will be called only once, when the first time the\n   * backend it to be used\n   */\n  initialize(): boolean|Promise<boolean>;\n\n  /**\n   * create an instance of SessionHandler to use in a Session object's lifecycle\n   */\n  createSessionHandler(context: Session.Context): SessionHandler;\n\n  /**\n   * dispose the backend. currently this will not be called\n   */\n  dispose(): void;\n}\n\n// caches all initialized backend instances\nconst backendsCache: Map<string, Backend> = new Map();\n\nexport const backend: {[name: string]: Backend} = {\n  webgl: new WebGLBackend()\n};\n\n/**\n * Resolve a reference to the backend. If a hint is specified, the corresponding\n * backend will be used.\n */\nexport async function resolveBackend(hint?: string|readonly string[]): Promise<Backend> {\n  if (!hint) {\n    return resolveBackend(['webgl']);\n  } else {\n    const hints = typeof hint === 'string' ? [hint] : hint;\n\n    for (const backendHint of hints) {\n      const cache = backendsCache.get(backendHint);\n      if (cache) {\n        return cache;\n      }\n\n      const backend = await tryLoadBackend(backendHint);\n      if (backend) {\n        return backend;\n      }\n    }\n  }\n\n  throw new Error('no available backend to use');\n}\n\nasync function tryLoadBackend(backendHint: string): Promise<Backend|undefined> {\n  const backendObj = backend;\n\n  if (typeof backendObj[backendHint] !== 'undefined' && isBackend(backendObj[backendHint])) {\n    const backend = backendObj[backendHint];\n    let init = backend.initialize();\n    if (typeof init === 'object' && 'then' in init) {\n      init = await init;\n    }\n    if (init) {\n      backendsCache.set(backendHint, backend);\n      return backend;\n    }\n  }\n\n  return undefined;\n}\n\nfunction isBackend(obj: unknown) {\n  // eslint-disable-next-line @typescript-eslint/no-explicit-any\n  const o = obj as any;\n\n  // check if an object is a Backend instance\n  if (\n      'initialize' in o && typeof o.initialize === 'function' &&                      // initialize()\n      'createSessionHandler' in o && typeof o.createSessionHandler === 'function' &&  // createSessionHandler()\n      'dispose' in o && typeof o.dispose === 'function'                               // dispose()\n  ) {\n    return true;\n  }\n\n  return false;\n}\n\nexport type BackendType = Backend;\nexport type SessionHandlerType = ReturnType<BackendType['createSessionHandler']>;\nexport type InferenceHandlerType = ReturnType<SessionHandlerType['createInferenceHandler']>;\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {SessionHandler} from './backend';\nimport {Graph} from './graph';\nimport {Logger, Profiler} from './instrument';\nimport {Operator} from './operators';\nimport {Tensor} from './tensor';\n\nclass KernelOp {\n  constructor(public op: Operator, public node: Graph.Node) {}\n}\n\nexport class ExecutionPlan {\n  constructor(private graph: Graph, ops: Operator[], private profiler: Readonly<Profiler>) {\n    this.initialize(ops);\n  }\n\n  initialize(ops: Operator[]) {\n    this.profiler.event('session', 'ExecutionPlan.initialize', () => {\n      const graphNodes = this.graph.getNodes();\n      if (graphNodes.length !== ops.length) {\n        throw new Error('The size of nodes and OPs do not match.');\n      }\n\n      this._ops = ops.map((op, i) => new KernelOp(op, graphNodes[i]));\n      this.reset();\n\n      // look for starter node(s)\n      this._starter = [];\n      this._ops.forEach((op, i) => {\n        let resolved = true;\n        for (const input of op.node.inputs) {\n          if (\n              !this._values[input]                                   // not an initialized input\n              && this.graph.getInputIndices().indexOf(input) === -1  // not model input\n          ) {\n            resolved = false;\n            break;\n          }\n        }\n        if (resolved) {\n          this._starter.push(i);\n        }\n      });\n    });\n  }\n\n  reset() {\n    this._values = this.graph.getValues().map(i => i.tensor);\n  }\n\n  async execute(sessionHandler: SessionHandler, modelInputs: Tensor[]): Promise<Tensor[]> {\n    return this.profiler.event('session', 'ExecutionPlan.execute', async () => {\n      // reset mediem result\n      this.reset();\n\n      // create inference handler\n      const inferenceHandler = sessionHandler.createInferenceHandler();\n\n      // populate inputs value\n      const graphInputs = this.graph.getInputIndices();\n      if (modelInputs.length !== graphInputs.length) {\n        throw new Error(`number of input tensors don't match the number of inputs to the model: actual: ${\n            modelInputs.length} expected: ${graphInputs.length}`);\n      }\n\n      modelInputs.forEach((input, i) => {\n        const index = graphInputs[i];\n        this._values[index] = input;\n      });\n\n      // prepare running sequence\n      const sequence: number[] = this._starter.slice(0);\n\n      // execution iterations\n      const graphValues = this.graph.getValues();\n      const graphNodes = this.graph.getNodes();\n\n      let rear = 0;\n      while (rear < sequence.length) {\n        const thisOpIndex = sequence[rear++];\n        const thisOp = this._ops[thisOpIndex];\n\n        // check input\n        const inputList = thisOp.node.inputs.map(i => this._values[i]);\n        if (inputList.indexOf(undefined) !== -1) {\n          throw new Error(`unresolved input detected: op: ${thisOp.node}`);\n        }\n\n        // run\n        const inputTensors = inputList as Tensor[];\n        Logger.verbose(\n            'ExecPlan',\n            `Running op:${thisOp.node.name} (${\n                inputTensors.map((t, i) => `'${thisOp.node.inputs[i]}': ${t.type}[${t.dims.join(',')}]`).join(', ')})`);\n\n        const outputList = await this.profiler.event(\n            'node', thisOp.node.name, async () => thisOp.op.impl(inferenceHandler, inputTensors, thisOp.op.context));\n\n        // check output\n        if (outputList.length !== thisOp.node.outputs.length) {\n          throw new Error('the size of output does not match model definition.');\n        }\n\n        // fill value\n        outputList.forEach((output, i) => {\n          const j = thisOp.node.outputs[i];\n          if (this._values[j]) {\n            throw new Error(`output [${j}] already has value: op:${thisOp.node.name}`);\n          }\n          this._values[j] = output;\n        });\n\n        // resolve downstream nodes\n        const downstreamNodes = new Set<number>();\n        outputList.forEach((_output, i) => {\n          const j = thisOp.node.outputs[i];\n          for (const currentDownstreamNodeIndex of graphValues[j].to) {\n            const currentDownstreamNode = graphNodes[currentDownstreamNodeIndex];\n            let resolved = true;\n            for (const k of currentDownstreamNode.inputs) {\n              if (!this._values[k]) {\n                resolved = false;\n                break;\n              }\n            }\n            if (resolved) {\n              downstreamNodes.add(currentDownstreamNodeIndex);\n            }\n          }\n        });\n        sequence.push(...downstreamNodes);\n      }\n\n      const output: Tensor[] = [];\n      for (let i = 0; i < this.graph.getOutputIndices().length; i++) {\n        const outputIndex = this.graph.getOutputIndices()[i];\n        const outputTensor = this._values[outputIndex];\n        if (outputTensor === undefined) {\n          throw new Error(`required output [${outputIndex}] does not have value`);\n        }\n        if (outputIndex === 0) {\n          await outputTensor.getData();\n        } else {\n          // eslint-disable-next-line no-unused-expressions\n          outputTensor.data;\n        }\n        output.push(outputTensor);\n      }\n      Logger.verbose('ExecPlan', 'disposing of inferenceHandler');\n      inferenceHandler.dispose();\n      return output;\n    });\n  }\n\n  _values: Array<Tensor|undefined>;\n  _ops: KernelOp[];\n  _starter: number[];\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport Long from 'long';\n\nimport {onnxruntime} from './ort-schema/flatbuffers/ort-generated';\nimport {onnx} from './ort-schema/protobuf/onnx';\nimport {Tensor} from './tensor';\nimport {decodeUtf8String, LongUtil} from './util';\n\nimport ortFbs = onnxruntime.experimental.fbs;\n\nexport declare namespace Attribute {\n  export interface DataTypeMap {\n    float: number;\n    int: number;\n    string: string;\n    tensor: Tensor;\n    floats: number[];\n    ints: number[];\n    strings: string[];\n    tensors: Tensor[];\n  }\n\n  export type DataType = keyof DataTypeMap;\n}\n\ntype ValueTypes = Attribute.DataTypeMap[Attribute.DataType];\n\ntype Value = [ValueTypes, Attribute.DataType];\n\nexport class Attribute {\n  constructor(attributes: onnx.IAttributeProto[]|ortFbs.Attribute[]|null|undefined) {\n    this._attributes = new Map();\n    if (attributes !== null && attributes !== undefined) {\n      for (const attr of attributes) {\n        if (attr instanceof onnx.AttributeProto) {\n          this._attributes.set(attr.name, [Attribute.getValue(attr), Attribute.getType(attr)]);\n        } else if (attr instanceof ortFbs.Attribute) {\n          this._attributes.set(attr.name()!, [Attribute.getValue(attr), Attribute.getType(attr)]);\n        }\n      }\n      if (this._attributes.size < attributes.length) {\n        throw new Error('duplicated attribute names');\n      }\n    }\n  }\n\n  set(key: string, type: Attribute.DataType, value: ValueTypes): void {\n    this._attributes.set(key, [value, type]);\n  }\n  delete(key: string): void {\n    this._attributes.delete(key);\n  }\n  getFloat(key: string, defaultValue?: Attribute.DataTypeMap['float']) {\n    return this.get(key, 'float', defaultValue);\n  }\n\n  getInt(key: string, defaultValue?: Attribute.DataTypeMap['int']) {\n    return this.get(key, 'int', defaultValue);\n  }\n\n  getString(key: string, defaultValue?: Attribute.DataTypeMap['string']) {\n    return this.get(key, 'string', defaultValue);\n  }\n\n  getTensor(key: string, defaultValue?: Attribute.DataTypeMap['tensor']) {\n    return this.get(key, 'tensor', defaultValue);\n  }\n\n  getFloats(key: string, defaultValue?: Attribute.DataTypeMap['floats']) {\n    return this.get(key, 'floats', defaultValue);\n  }\n\n  getInts(key: string, defaultValue?: Attribute.DataTypeMap['ints']) {\n    return this.get(key, 'ints', defaultValue);\n  }\n\n  getStrings(key: string, defaultValue?: Attribute.DataTypeMap['strings']) {\n    return this.get(key, 'strings', defaultValue);\n  }\n\n  getTensors(key: string, defaultValue?: Attribute.DataTypeMap['tensors']) {\n    return this.get(key, 'tensors', defaultValue);\n  }\n\n  private get<V extends Attribute.DataTypeMap[Attribute.DataType]>(\n      key: string, type: Attribute.DataType, defaultValue?: V): V {\n    const valueAndType = this._attributes.get(key);\n    if (valueAndType === undefined) {\n      if (defaultValue !== undefined) {\n        return defaultValue;\n      }\n      throw new Error(`required attribute not found: ${key}`);\n    }\n    if (valueAndType[1] !== type) {\n      throw new Error(`type mismatch: expected ${type} but got ${valueAndType[1]}`);\n    }\n    return valueAndType[0] as V;\n  }\n\n  private static getType(attr: onnx.IAttributeProto|ortFbs.Attribute): Attribute.DataType {\n    const type = attr instanceof onnx.AttributeProto ? (attr).type : (attr as ortFbs.Attribute).type();\n    switch (type) {\n      case onnx.AttributeProto.AttributeType.FLOAT:\n        return 'float';\n      case onnx.AttributeProto.AttributeType.INT:\n        return 'int';\n      case onnx.AttributeProto.AttributeType.STRING:\n        return 'string';\n      case onnx.AttributeProto.AttributeType.TENSOR:\n        return 'tensor';\n      case onnx.AttributeProto.AttributeType.FLOATS:\n        return 'floats';\n      case onnx.AttributeProto.AttributeType.INTS:\n        return 'ints';\n      case onnx.AttributeProto.AttributeType.STRINGS:\n        return 'strings';\n      case onnx.AttributeProto.AttributeType.TENSORS:\n        return 'tensors';\n      default:\n        throw new Error(`attribute type is not supported yet: ${onnx.AttributeProto.AttributeType[type]}`);\n    }\n  }\n\n  private static getValue(attr: onnx.IAttributeProto|ortFbs.Attribute) {\n    const attrType = attr instanceof onnx.AttributeProto ? attr.type : (attr as ortFbs.Attribute).type();\n    if (attrType === onnx.AttributeProto.AttributeType.GRAPH || attrType === onnx.AttributeProto.AttributeType.GRAPHS) {\n      throw new Error('graph attribute is not supported yet');\n    }\n\n    const value = this.getValueNoCheck(attr);\n\n    // cast LONG to number\n    if (attrType === onnx.AttributeProto.AttributeType.INT && LongUtil.isLong(value)) {\n      return LongUtil.longToNumber(value as Long | flatbuffers.Long);\n    }\n\n    // cast LONG[] to number[]\n    if (attrType === onnx.AttributeProto.AttributeType.INTS) {\n      const arr = (value as Array<number|Long|flatbuffers.Long>);\n      const numberValue: number[] = new Array<number>(arr.length);\n\n      for (let i = 0; i < arr.length; i++) {\n        const maybeLong = arr[i];\n        numberValue[i] = LongUtil.longToNumber(maybeLong);\n      }\n\n      return numberValue;\n    }\n\n    // cast onnx.TensorProto to onnxjs.Tensor\n    if (attrType === onnx.AttributeProto.AttributeType.TENSOR) {\n      return attr instanceof onnx.AttributeProto ? Tensor.fromProto(value as onnx.ITensorProto) :\n                                                   Tensor.fromOrtTensor(value as ortFbs.Tensor);\n    }\n\n    // cast onnx.TensorProto[] to onnxjs.Tensor[]\n    if (attrType === onnx.AttributeProto.AttributeType.TENSORS) {\n      if (attr instanceof onnx.AttributeProto) {\n        const tensorProtos = value as onnx.ITensorProto[];\n        return tensorProtos.map(value => Tensor.fromProto(value));\n      } else if (attr instanceof ortFbs.Attribute) {\n        const tensorProtos = value as ortFbs.Tensor[];\n        return tensorProtos.map(value => Tensor.fromOrtTensor(value));\n      }\n    }\n\n    // cast Uint8Array to string\n    if (attrType === onnx.AttributeProto.AttributeType.STRING) {\n      // string in onnx attribute is of uint8array type, so we need to convert it to string below. While in ort format,\n      // string attributes are returned as string, so no conversion is needed.\n      if (attr instanceof onnx.AttributeProto) {\n        const utf8String = value as Uint8Array;\n        return decodeUtf8String(utf8String);\n      }\n    }\n\n    // cast Uint8Array[] to string[]\n    if (attrType === onnx.AttributeProto.AttributeType.STRINGS) {\n      // strings in onnx attribute is returned as uint8array[], so we need to convert it to string[] below. While in ort\n      // format strings attributes are returned as string[], so no conversion is needed.\n      if (attr instanceof onnx.AttributeProto) {\n        const utf8Strings = value as Uint8Array[];\n        return utf8Strings.map(decodeUtf8String);\n      }\n    }\n\n    return value as ValueTypes;\n  }\n\n  private static getValueNoCheck(attr: onnx.IAttributeProto|ortFbs.Attribute) {\n    return attr instanceof (onnx.AttributeProto) ? this.getValueNoCheckFromOnnxFormat(attr) :\n                                                   this.getValueNoCheckFromOrtFormat(attr as ortFbs.Attribute);\n  }\n\n  private static getValueNoCheckFromOnnxFormat(attr: onnx.IAttributeProto) {\n    switch (attr.type!) {\n      case onnx.AttributeProto.AttributeType.FLOAT:\n        return attr.f;\n      case onnx.AttributeProto.AttributeType.INT:\n        return attr.i;\n      case onnx.AttributeProto.AttributeType.STRING:\n        return attr.s;\n      case onnx.AttributeProto.AttributeType.TENSOR:\n        return attr.t;\n      case onnx.AttributeProto.AttributeType.GRAPH:\n        return attr.g;\n      case onnx.AttributeProto.AttributeType.FLOATS:\n        return attr.floats;\n      case onnx.AttributeProto.AttributeType.INTS:\n        return attr.ints;\n      case onnx.AttributeProto.AttributeType.STRINGS:\n        return attr.strings;\n      case onnx.AttributeProto.AttributeType.TENSORS:\n        return attr.tensors;\n      case onnx.AttributeProto.AttributeType.GRAPHS:\n        return attr.graphs;\n      default:\n        throw new Error(`unsupported attribute type: ${onnx.AttributeProto.AttributeType[attr.type!]}`);\n    }\n  }\n\n  private static getValueNoCheckFromOrtFormat(attr: ortFbs.Attribute) {\n    switch (attr.type()) {\n      case ortFbs.AttributeType.FLOAT:\n        return attr.f();\n      case ortFbs.AttributeType.INT:\n        return attr.i();\n      case ortFbs.AttributeType.STRING:\n        return attr.s();\n      case ortFbs.AttributeType.TENSOR:\n        return attr.t();\n      case ortFbs.AttributeType.GRAPH:\n        return attr.g();\n      case ortFbs.AttributeType.FLOATS:\n        return attr.floatsArray();\n      case ortFbs.AttributeType.INTS: {\n        const ints = [];\n        for (let i = 0; i < attr.intsLength(); i++) {\n          ints.push(attr.ints(i)!);\n        }\n        return ints;\n      }\n      case ortFbs.AttributeType.STRINGS: {\n        const strings = [];\n        for (let i = 0; i < attr.stringsLength(); i++) {\n          strings.push(attr.strings(i));\n        }\n        return strings;\n      }\n      case ortFbs.AttributeType.TENSORS: {\n        const tensors = [];\n        for (let i = 0; i < attr.tensorsLength(); i++) {\n          tensors.push(attr.tensors(i)!);\n        }\n        return tensors;\n      }\n      // case ortFbs.AttributeType.GRAPHS:\n      // TODO: Subgraph not supported yet.\n      // const graphs = [];\n      // for (let i = 0; i < attr.graphsLength(); i++) {\n      //   graphs.push(attr.graphs(i)!);\n      // }\n      // return graphs;\n      default:\n        throw new Error(`unsupported attribute type: ${ortFbs.AttributeType[attr.type()]}`);\n    }\n  }\n\n  protected _attributes: Map<string, Value>;\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Attribute} from './attribute';\nimport {onnxruntime} from './ort-schema/flatbuffers/ort-generated';\nimport {onnx} from './ort-schema/protobuf/onnx';\nimport {Tensor} from './tensor';\nimport {LongUtil, MAX_CLIP, MIN_CLIP, ProtoUtil} from './util';\n\nimport ortFbs = onnxruntime.experimental.fbs;\n\nexport declare namespace Graph {\n  export interface Shape {\n    readonly dims: readonly number[];\n  }\n  export interface ValueType {\n    readonly tensorType: Tensor.DataType;\n    readonly shape: Shape;\n  }\n  export interface Value {\n    // the tensor data. empty for non-initialized inputs\n    readonly tensor?: Tensor;\n\n    // index to the Node where the value comes from. -1 for initializer.\n    readonly from: number;\n\n    // indices to the Nodes where the values go to.\n    readonly to: readonly number[];\n\n    // value type specification. empty for non-input values.\n    readonly type?: ValueType;\n  }\n  export interface Node {\n    // name of the node\n    readonly name: string;\n\n    // the operator type\n    readonly opType: string;\n\n    // indices to the Values where the inputs come from.\n    readonly inputs: readonly number[];\n\n    // indices to the Values where the outpus go to.\n    readonly outputs: readonly number[];\n\n    // the attributes that used by the operator\n    readonly attributes: Attribute;\n  }\n\n  /**\n   * a Transformer is an instance that allows all possible transformation operations that applied to a graph\n   */\n  export interface Transformer {\n    removeAllIdentityNodes(): void;\n    removeAllDropoutNodes(): void;\n    fuseConvActivationNodes(): void;\n    // TODO: add generic functions to manipulate the graph\n  }\n\n  // an initializer can use transformer to transform the graph\n  export interface Initializer {\n    transformGraph(transformer: Transformer): void;\n  }\n}\n\n// eslint-disable-next-line @typescript-eslint/no-redeclare\nexport interface Graph {\n  getInputIndices(): readonly number[];\n  getInputNames(): readonly string[];\n  getOutputIndices(): readonly number[];\n  getOutputNames(): readonly string[];\n  getValues(): readonly Graph.Value[];\n  getNodes(): readonly Graph.Node[];\n}\n\n// eslint-disable-next-line @typescript-eslint/naming-convention, @typescript-eslint/no-redeclare\nexport const Graph = {\n  /**\n   * construct a graph from a graph protobuf type\n   */\n  from: (graphProto: onnx.IGraphProto|ortFbs.Graph, initializer?: Graph.Initializer) =>\n      new GraphImpl(graphProto, initializer),\n};\n\nclass Value implements Graph.Value {\n  constructor(valueInfo?: onnx.IValueInfoProto) {\n    this._from = undefined;\n    this._to = [];\n    this.tensor = undefined;\n    this.type = undefined;\n\n    if (valueInfo) {\n      this.type = ProtoUtil.tensorValueTypeFromProto(valueInfo.type!.tensorType!);\n    }\n  }\n\n  _from?: number;  // -1 represent from initializer\n  get from() {\n    return this._from!;\n  }\n  _to: number[];\n  get to() {\n    return this._to;\n  }\n  type?: Graph.ValueType;\n  tensor?: Tensor;\n}\n\nclass Node implements Graph.Node {\n  constructor(_nodeProto: onnx.INodeProto|ortFbs.Node, name?: string) {\n    if (_nodeProto instanceof onnx.NodeProto) {\n      this.name = _nodeProto.name;\n      this.opType = _nodeProto.opType;\n      this.attributes = new Attribute(_nodeProto.attribute);\n    } else if (_nodeProto instanceof ortFbs.Node) {\n      this.name = name ?? _nodeProto.name()!;\n      this.opType = _nodeProto.opType()!;\n      this.attributes = new Attribute(ProtoUtil.tensorAttributesFromORTFormat(_nodeProto));\n    }\n\n    this.inputs = [];\n    this.outputs = [];\n    this.executeNode = true;\n  }\n\n  name: string;\n  opType: string;\n  inputs: number[];\n  outputs: number[];\n  attributes: Attribute;\n  executeNode: boolean;\n}\n\nclass GraphImpl implements Graph, Graph.Transformer {\n  private _allData: Value[];\n\n  private _allInputIndices: number[];\n  private _allInputNames: string[];\n\n  private _allOutputIndices: number[];\n  private _allOutputNames: string[];\n\n  private _nodes: Node[];\n\n  constructor(graph: onnx.IGraphProto|ortFbs.Graph, graphInitializer?: Graph.Initializer) {\n    if (!graph) {\n      throw new TypeError('graph is empty');\n    }\n\n    // build the graph - will throw exceptions if something fatal is detected\n    this.buildGraph(graph);\n\n    // execute any transformation logic for the graph (if applicable)\n    this.transformGraph(graphInitializer);\n\n    // check for cycles and other inconsistencies - will throw exceptions if something fatal is detected\n    this.checkIsAcyclic();\n  }\n\n  getInputIndices(): readonly number[] {\n    return this._allInputIndices;\n  }\n\n  getInputNames(): readonly string[] {\n    return this._allInputNames;\n  }\n\n  getOutputIndices(): readonly number[] {\n    return this._allOutputIndices;\n  }\n\n  getOutputNames(): readonly string[] {\n    return this._allOutputNames;\n  }\n\n  getValues(): readonly Graph.Value[] {\n    return this._allData;\n  }\n\n  getNodes(): readonly Graph.Node[] {\n    return this._nodes;\n  }\n\n  private buildGraph(graph: onnx.IGraphProto|ortFbs.Graph) {\n    // build the graph - will throw exceptions if something fatal is detected\n    if (graph instanceof onnx.GraphProto) {\n      this.buildGraphFromOnnxFormat(graph);\n    } else if (graph instanceof ortFbs.Graph) {\n      this.buildGraphFromOrtFormat(graph);\n    } else {\n      throw new TypeError('Graph type is not supported.');\n    }\n  }\n  private buildGraphFromOnnxFormat(graph: onnx.IGraphProto) {\n    const dataIndices = new Map<string, number>();\n    this._allData = [];\n\n    this._allInputIndices = [];\n    this._allInputNames = [];\n\n    this._allOutputIndices = [];\n    this._allOutputNames = [];\n\n    this._nodes = [];\n\n    const nodesIndices = new Map<string, number>();\n\n    // scan all inputs\n    if (!graph.input) {\n      throw new Error('missing information in graph: input');\n    }\n    const inputValueNames = [];\n    for (const i of graph.input) {\n      if (dataIndices.has(i.name!)) {\n        throw new Error(`duplicated input name: ${i.name}`);\n      }\n      const currentIndex = this._allData.push(new Value(i)) - 1;\n      dataIndices.set(i.name!, currentIndex);\n      inputValueNames.push(i.name!);\n    }\n\n    // scan all initializers\n    if (!graph.initializer) {\n      throw new Error('missing information in graph: initializer');\n    }\n    for (const i of graph.initializer) {\n      let index = dataIndices.get(i.name!);\n      if (index === undefined) {\n        const value = new Value();\n        value.type = {\n          shape: {dims: ProtoUtil.tensorDimsFromProto(i.dims!)},\n          tensorType: ProtoUtil.tensorDataTypeFromProto(i.dataType!)\n        };\n        index = this._allData.push(value) - 1;\n        dataIndices.set(i.name!, index);\n      }\n      this._allData[index]._from = -1;\n      this._allData[index].tensor = Tensor.fromProto(i);\n    }\n\n    // filter out input indices\n    for (let i = 0; i < this._allData.length; i++) {\n      if (!this._allData[i].tensor) {\n        this._allInputIndices.push(i);\n        this._allInputNames.push(inputValueNames[i]);\n      }\n    }\n\n    // scan all outputs\n    if (!graph.output) {\n      throw new Error('missing information in graph: output');\n    }\n    for (const i of graph.output) {\n      if (dataIndices.has(i.name!)) {\n        throw new Error(`duplicated output name: ${i.name}`);\n      }\n      const currentIndex = this._allData.push(new Value(i)) - 1;\n      dataIndices.set(i.name!, currentIndex);\n      this._allOutputIndices.push(currentIndex);\n      this._allOutputNames.push(i.name!);\n    }\n\n    // scan all nodes\n    if (!graph.node) {\n      throw new Error('missing information in graph: node');\n    }\n    for (const nodeProto of graph.node) {\n      if (!nodeProto.name) {\n        // assign a name to the node if it doesn't have one\n        for (let pick = 0;; pick++) {\n          const name = `unnamed_${nodeProto.opType}_${pick}`;\n          if (!nodesIndices.has(name)) {\n            nodeProto.name = name;\n            break;\n          }\n        }\n      }\n\n      if (nodesIndices.has(nodeProto.name)) {\n        throw new Error(`duplicated node name: ${nodeProto.name}`);\n      }\n      const currentIndex = this._nodes.push(new Node(nodeProto)) - 1;\n      nodesIndices.set(nodeProto.name, currentIndex);\n    }\n\n    // scan node's outputs\n    for (let i = 0; i < this._nodes.length; i++) {\n      const node = this._nodes[i];\n      const nodeProto = graph.node[i];\n      if (!nodeProto.output) {\n        throw new Error(`missing output for node: ${nodeProto.name}`);\n      }\n      for (const output of nodeProto.output) {\n        let dataIndex = dataIndices.get(output);\n        if (typeof dataIndex === 'undefined') {\n          dataIndex = this._allData.push(new Value()) - 1;\n          dataIndices.set(output, dataIndex);\n        }\n        node.outputs.push(dataIndex);\n\n        if (this._allData[dataIndex]._from !== undefined) {\n          throw new Error(`multiple nodes output to one data value: ${dataIndex}`);\n        }\n        this._allData[dataIndex]._from = i;\n\n        // for the 'Constant' operator, just create a new edge in the graph corresponding to the 'output' of the\n        // operator and ignore the node from the graph\n        if (nodeProto.opType === 'Constant') {\n          if (!nodeProto.attribute || nodeProto.attribute.length !== 1 || !nodeProto.attribute[0].t) {\n            throw new Error('missing attributes or missing tensor value in attributes for this Constant operator');\n          }\n          if (!nodeProto.output || nodeProto.output.length !== 1) {\n            throw new Error('missing output or incorrect number of outputs for this Constant operator');\n          }\n          node.outputs.pop();\n          node.executeNode = false;\n\n          this._allData[dataIndex]._from = -1;\n          this._allData[dataIndex].tensor = Tensor.fromProto(nodeProto.attribute[0].t);\n        }\n      }\n    }\n\n    // scan node's inputs\n    for (let i = 0; i < this._nodes.length; i++) {\n      const node = this._nodes[i];\n      const nodeProto = graph.node[i];\n\n      if (!nodeProto.input) {\n        throw new Error(`missing input for node: ${nodeProto.name}`);\n      }\n      for (const input of nodeProto.input) {\n        const dataIndex = dataIndices.get(input);\n        if (typeof dataIndex === 'undefined') {\n          // handle exception when opset > 9 and roi / scales not given\n          if (input === '' && (nodeProto.input.length === 3 || nodeProto.input.length === 4) &&\n              nodeProto.opType === 'Resize') {\n            continue;\n          }\n          throw new Error(`unrecognized input '${input}' for node: ${nodeProto.name}`);\n        }\n        node.inputs.push(dataIndex);\n\n        this._allData[dataIndex]._to.push(i);\n      }\n    }\n\n    return true;\n  }\n\n  private buildGraphFromOrtFormat(graph: ortFbs.Graph) {\n    const dataIndices = new Map<string, number>();\n    this._allData = [];\n\n    this._allInputIndices = [];\n    this._allInputNames = [];\n\n    this._allOutputIndices = [];\n    this._allOutputNames = [];\n\n    this._nodes = [];\n\n    const nodesIndices = new Map<string, number>();\n\n    // scan all inputs\n    const inputValueNames = [];\n    for (let i = 0; i < graph.inputsLength(); i++) {\n      const inputName = graph.inputs(i);\n      if (dataIndices.has(inputName)) {\n        throw new Error(`duplicated input name: ${inputName}`);\n      }\n      // Find the input typeInfo from nodeargs\n      for (let j = 0; j < graph.nodeArgsLength(); j++) {\n        if (graph.nodeArgs(j)?.name() === inputName) {\n          const value = new Value();\n          const valueType = graph.nodeArgs(j)?.type()?.valueType();\n          if (valueType !== ortFbs.TypeInfoValue.tensor_type) {\n            throw new Error('Unexpected value type for the nodeArg.');\n          }\n          const valueInfo = graph.nodeArgs(j)!.type()!.value(new ortFbs.TensorTypeAndShape())!;\n          const type = ProtoUtil.tensorDataTypeFromProto(valueInfo.elemType());\n          const shape = valueInfo.shape()!;\n          const dims = [];\n          for (let k = 0; k < shape.dimLength()!; k++) {\n            dims.push(LongUtil.longToNumber(shape.dim(k)!.value()!.dimValue()!));\n          }\n          value.type = {shape: {dims}, tensorType: type};\n          const currentIndex = this._allData.push(value) - 1;\n          dataIndices.set(inputName, currentIndex);\n          inputValueNames.push(inputName);\n        }\n      }\n    }\n    // check initializers\n    for (let i = 0; i < graph.initializersLength(); i++) {\n      const initializer = graph.initializers(i)!;\n      let index = dataIndices.get(initializer.name()!);\n      if (index === undefined) {\n        const value = new Value();\n        const dims = ProtoUtil.tensorDimsFromORTFormat(initializer);\n        const type = ProtoUtil.tensorDataTypeFromProto(initializer.dataType());\n        value.type = {shape: {dims}, tensorType: type};\n        index = this._allData.push(value) - 1;\n        dataIndices.set(initializer.name()!, index);\n      }\n      this._allData[index]._from = -1;\n      this._allData[index].tensor = Tensor.fromOrtTensor(initializer);\n    }\n\n    // filter out input indices\n    for (let i = 0; i < this._allData.length; i++) {\n      if (!this._allData[i].tensor) {\n        this._allInputIndices.push(i);\n        this._allInputNames.push(inputValueNames[i]);\n      }\n    }\n\n    // scan all outputs\n    for (let i = 0; i < graph.outputsLength(); i++) {\n      const outputName = graph.outputs(i);\n      if (dataIndices.has(outputName)) {\n        throw new Error(`duplicated output name: ${outputName}`);\n      }\n      const currentIndex = this._allData.push(new Value()) - 1;\n      dataIndices.set(outputName, currentIndex);\n      this._allOutputIndices.push(currentIndex);\n      this._allOutputNames.push(outputName);\n    }\n\n    // scan all nodes\n    if (!graph.nodes) {\n      throw new Error('missing information in graph: node');\n    }\n    for (let i = 0; i < graph.nodesLength(); i++) {\n      const nodeProto = graph.nodes(i);\n      let name = nodeProto!.name();\n      if (!name) {\n        // assign a name to the node if it doesn't have one\n        for (let pick = 0;; pick++) {\n          name = `unnamed_${nodeProto!.opType()}_${pick}`;\n          if (!nodesIndices.has(name)) {\n            // an unique name is found. break.\n            break;\n          }\n        }\n      }\n\n      if (nodesIndices.has(name)) {\n        throw new Error(`duplicated node name: ${name}`);\n      }\n      const currentIndex = this._nodes.push(new Node(nodeProto!, name)) - 1;\n      nodesIndices.set(name, currentIndex);\n    }\n\n    // scan node's outputs\n    for (let i = 0; i < this._nodes.length; i++) {\n      const node = this._nodes[i];\n      const nodeProto = graph.nodes(i);\n      if (nodeProto == null) {\n        throw new Error(`No node exists at index ${i}`);\n      }\n      if (nodeProto?.outputsLength() === 0) {\n        throw new Error(`missing output for node: ${nodeProto.name}`);\n      }\n      for (let j = 0; j < nodeProto?.outputsLength(); j++) {\n        const output = nodeProto?.outputs(j);\n        let dataIndex = dataIndices.get(output);\n        if (typeof dataIndex === 'undefined') {\n          dataIndex = this._allData.push(new Value()) - 1;\n          dataIndices.set(output, dataIndex);\n        }\n        node.outputs.push(dataIndex);\n\n        if (this._allData[dataIndex]._from !== undefined) {\n          throw new Error(`multiple nodes output to one data value: ${dataIndex}`);\n        }\n        this._allData[dataIndex]._from = i;\n\n        // for the 'Constant' operator, just create a new edge in the graph corresponding to the 'output' of the\n        // operator and ignore the node from the graph\n        if (nodeProto.opType() === 'Constant') {\n          if (nodeProto.attributesLength() !== 1 || !nodeProto.attributes(0)!.t()) {\n            throw new Error('missing attributes or missing tensor value in attributes for this Constant operator');\n          }\n          if (nodeProto.outputsLength() !== 1) {\n            throw new Error('missing output or incorrect number of outputs for this Constant operator');\n          }\n          node.outputs.pop();\n          node.executeNode = false;\n\n          this._allData[dataIndex]._from = -1;\n          this._allData[dataIndex].tensor = Tensor.fromOrtTensor(nodeProto.attributes(0)!.t()!);\n        }\n      }\n    }\n\n    // scan node's inputs\n    for (let i = 0; i < this._nodes.length; i++) {\n      const node = this._nodes[i];\n      const nodeProto = graph.nodes(i)!;\n\n      if (nodeProto.inputsLength() === 0) {\n        throw new Error(`missing input for node: ${nodeProto.name}`);\n      }\n      for (let j = 0; j < nodeProto.inputsLength()!; j++) {\n        const input = nodeProto.inputs(j)!;\n        const dataIndex = dataIndices.get(input);\n        if (typeof dataIndex === 'undefined') {\n          throw new Error(`unrecognized input '${input}' for node: ${nodeProto!.name()}`);\n        }\n        node.inputs.push(dataIndex);\n\n        this._allData[dataIndex]._to.push(i);\n      }\n    }\n  }\n\n  private checkIsAcyclic() {\n    // go through the graph and check for cycles or other fatal inconsistencies\n    const starters: Set<number> = new Set<number>();\n    this._allInputIndices.forEach(i => {\n      const data = this._allData[i];\n      data._to.forEach(j => {\n        starters.add(j);\n      });\n    });\n\n    // Iterative DFS to check for cycles\n    const nodesStack = Array.from(starters);\n    const nodesState = new Array<string>(this._nodes.length).fill('white');\n\n    while (nodesStack.length > 0) {\n      const nodeIndex = nodesStack.pop()!;\n      // this node has now been processed completely. Mark this node 'black' to denote this.\n      if (nodesState[nodeIndex] === 'gray') {\n        nodesState[nodeIndex] = 'black';\n      } else {\n        // this node is under processing stage. mark this node 'gray' to denote this.\n        nodesStack.push(nodeIndex);\n        nodesState[nodeIndex] = 'gray';\n\n        this._nodes[nodeIndex].outputs.forEach((outgoingEdgeIndex) => {\n          const data = this._allData[outgoingEdgeIndex];\n          if (typeof data.tensor !== 'undefined') {\n            throw new Error('node outputs should not be initialized');\n          }\n          if (data._from !== nodeIndex) {\n            throw new Error('from property of the Value object doesn\\'t match index of Node being processed');\n          }\n          data._to.forEach((downstreamNodeIndex) => {\n            // back edge found - cyclic\n            if (nodesState[downstreamNodeIndex] === 'gray') {\n              throw new Error('model graph is cyclic');\n            }\n            // tree edge found - continue processing by adding it to stack\n            else if (nodesState[downstreamNodeIndex] === 'white') {\n              nodesStack.push(downstreamNodeIndex);\n            }\n          });\n        });\n      }\n    }\n  }\n\n  private transformGraph(graphInitializer?: Graph.Initializer): void {\n    // apply common transform\n    this.removeAllIdentityNodes();\n    this.removeAllDropoutNodes();\n    this.fuseConvActivationNodes();\n    // apply initializer specific transform\n    if (graphInitializer) {\n      graphInitializer.transformGraph(this);\n    }\n\n    // finalize graph\n    this.finalizeGraph();\n  }\n\n  /**\n   * finalize the graph.\n   *\n   * this function should be called after all the transformation completed.\n   * this function removes all unnecessary nodes and values from the graph\n   */\n  finalizeGraph() {\n    let offset = 0;\n    // delete all nodes that are not being executed\n    // The graph is represented using these two arrays\n    // this._nodes - Array holding the kernels to execute - each entry is a kernel pointing to this._allData\n    // this._allData - hold 2 fields - to [] & from - these feileds hold the graph map for inputs and outputs per node\n    // newIndices - remapping the graph after reading the flag 'executeNode'\n    const newIndices = new Array<number>(this._nodes.length, 0);\n    let nodePossition = 0;\n\n    for (let i = 0; i < this._nodes.length; i++) {\n      // giving new indexes to the nodes based on execution flag\n      newIndices[i] = nodePossition;\n      if (this._nodes[i].executeNode) {\n        if (nodePossition !== i) {\n          this._nodes[nodePossition] = this._nodes[i];\n        }\n        nodePossition++;\n\n      } else {\n        // delete all output values\n        this._nodes[i].outputs.forEach(ind => {\n          this._allData[ind]._from = -2;\n        });\n      }\n    }\n\n    // removing the unused nodes\n    this._nodes.splice(nodePossition, this._nodes.length - nodePossition);\n\n    // Updating this._allData according to the new this._nodes\n    for (let i = 0; i < this._allData.length; i++) {\n      const currentData = this._allData[i];\n      if (currentData._from !== undefined && currentData._from !== -1 && currentData._from !== -2) {\n        currentData._from = newIndices[currentData._from];\n      }\n\n      for (let j = 0; j < currentData._to.length; j++) {\n        if (currentData._to[j] >= 0) {\n          currentData._to[j] = newIndices[currentData._to[j]];\n        } else {\n          throw new Error('Trying to update a removed node');\n        }\n      }\n    }\n\n    offset = 0;\n    // delete all values that are not being referenced\n    for (let i = 0; i < this._allData.length; i++) {\n      // if current value is neither linked to next node, nor an output value, remove it.\n      if (this._allData[i].from === -2 && this._allOutputIndices.indexOf(i + offset) === -1) {\n        offset++;\n        this._allData.splice(i, 1);\n        i--;\n        continue;\n      }\n      if (offset > 0) {\n        let ind = -1;\n        // if current value is neither an input value nor an initializer, find the node it's\n        // coming from and update the corresponding node output\n        if (this._allData[i].from !== undefined && this._allData[i].from !== -1) {\n          ind = this._nodes[this._allData[i].from].outputs.indexOf(i + offset);\n          if (ind !== -1) {\n            this._nodes[this._allData[i].from].outputs[ind] = i;\n          }\n        } else {\n          // if current value is an input value, update its reference in inputIndices\n          ind = this._allInputIndices.indexOf(i + offset);\n          if (ind !== -1) {\n            this._allInputIndices[ind] = i;\n          }\n        }\n\n        // find the node that the current value is linking to and update its input reference\n        this._allData[i].to.forEach(node => {\n          ind = this._nodes[node].inputs.indexOf(i + offset);\n          if (ind !== -1) {\n            this._nodes[node].inputs[ind] = i;\n          }\n        });\n        if (this._allData[i].to.length === 0) {\n          // if current value is a graph output, update its reference in outputIndices\n          ind = this._allOutputIndices.indexOf(i + offset);\n          if (ind !== -1) {\n            this._allOutputIndices[ind] = i;\n          }\n        }\n      }\n    }\n  }\n\n  /**\n   * Delete the specified node. Assume the node has one incoming input and the first output connected to other nodes.\n   * An input validation must be done before calling this function.\n   * @param nodeIndex The index of node to be deleted\n   */\n  private deleteNode(nodeIndex: number) {\n    const node = this._nodes[nodeIndex];\n    if (node.outputs.length > 1) {\n      for (let i = 1; i < node.outputs.length; i++) {\n        if (this._allData[node.outputs[i]].to.length > 0) {\n          throw new Error('Node deletion with more than one output connected to other nodes is not supported. ');\n        }\n      }\n    }\n\n    // this node wil not be executed\n    node.executeNode = false;\n    const inputValueIndex = node.inputs[0];\n    const outputValueIndex = node.outputs[0];\n    const nodesConsumingOutput = this._allData[outputValueIndex].to;\n\n    // remove this node from the to property of the input Value\n    for (let i = 0; i < node.inputs.length; i++) {\n      const delIndex = this._allData[node.inputs[i]].to.indexOf(nodeIndex);\n      // should not happen\n      if (delIndex === -1) {\n        throw new Error('The Value object doesn\\'t have the current Node in it\\'s \\'to\\' property ');\n      }\n      this._allData[node.inputs[i]].to.splice(delIndex, 1);\n    }\n\n    // clear node indices consuming this output Value\n    this._allData[outputValueIndex]._to = [];\n\n    // if the output of this node is a graph output, adjust the index appropriately\n    const index = this._allOutputIndices.indexOf(outputValueIndex);\n    if (index !== -1) {\n      this._allOutputIndices[index] = inputValueIndex;\n    }\n\n    // override the inputs for nodes consuming this node's output with the input to this node\n    if (nodesConsumingOutput && nodesConsumingOutput.length > 0) {\n      for (const nodeIndex of nodesConsumingOutput) {\n        const replaceIndex = this._nodes[nodeIndex].inputs.indexOf(outputValueIndex);\n        // should not happen\n        if (replaceIndex === -1) {\n          throw new Error('The Node object doesn\\'t have the output Value in it\\'s \\'inputs\\' property ');\n        }\n        this._nodes[nodeIndex].inputs[replaceIndex] = inputValueIndex;\n        this._allData[inputValueIndex].to.push(nodeIndex);\n      }\n    }\n  }\n\n  removeAllDropoutNodes() {\n    let nodeIndex = 0;\n    for (const node of this._nodes) {\n      // weed out 'Dropout' nodes so that no time is wasted in execution\n      if (node.opType === 'Dropout') {\n        // the node should have exactly 1 input and 1 or 2 outputs\n        if (node.inputs.length !== 1) {\n          throw new Error('Dropout nodes should only contain one input. ');\n        }\n        if (node.outputs.length !== 1 && node.outputs.length !== 2) {\n          throw new Error('Dropout nodes should contain either 1 or 2 output(s)');\n        }\n        // the second output should not be referenced by any other node\n        if (node.outputs.length === 2 && this._allData[node.outputs[1]]._to.length !== 0) {\n          throw new Error('Dropout nodes\\'s second output should not be referenced by other nodes');\n        }\n        this.deleteNode(nodeIndex);\n      }\n      nodeIndex++;\n    }\n  }\n\n  removeAllIdentityNodes() {\n    let nodeIndex = 0;\n    for (const node of this._nodes) {\n      // weed out 'Identity' nodes so that no time is wasted in execution\n      if (node.opType === 'Identity') {\n        this.deleteNode(nodeIndex);\n      }\n      nodeIndex++;\n    }\n  }\n\n  isActivation(n: Node): boolean {\n    switch (n.opType) {\n      // TODO: add other activation methods\n      case 'Relu':\n      case 'Sigmoid':\n      case 'Clip':\n        return true;\n      default:\n        return false;\n    }\n  }\n\n  fuseConvActivationNodes() {\n    for (const node of this._nodes) {\n      if (node.opType === 'Conv') {\n        const next = this._allData[node.outputs[0]]._to;\n        if (next.length === 1 && this.isActivation(this._nodes[next[0]])) {\n          const child = this._nodes[next[0]];\n          if (child.opType === 'Clip') {\n            if (child.inputs.length === 1) {\n              try {\n                node.attributes.set(\n                    'activation_params', 'floats',\n                    [child.attributes.getFloat('min'), child.attributes.getFloat('max')]);\n              } catch (e) {\n                node.attributes.set('activation_params', 'floats', [MIN_CLIP, MAX_CLIP]);\n              }\n            } else if (\n                child.inputs.length >= 3 && this._allData[child.inputs[1]].tensor !== undefined &&\n                this._allData[child.inputs[2]].tensor !== undefined) {\n              node.attributes.set('activation_params', 'floats', [\n                this._allData[child.inputs[1]].tensor!.floatData[0], this._allData[child.inputs[2]].tensor!.floatData[0]\n              ]);\n            } else {\n              // Skip fusion with clip node since clip min and clip max are not coming from initializer\n              continue;\n            }\n          }\n          node.attributes.set('activation', 'string', (child.opType));\n          this.deleteNode(next[0]);\n        }\n      }\n    }\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {flatbuffers} from 'flatbuffers';\n\nimport {Graph} from './graph';\nimport {OpSet} from './opset';\nimport {onnxruntime} from './ort-schema/flatbuffers/ort-generated';\nimport {onnx} from './ort-schema/protobuf/onnx';\nimport {LongUtil} from './util';\n\nimport ortFbs = onnxruntime.experimental.fbs;\n\nexport class Model {\n  // empty model\n  constructor() {}\n\n  load(buf: Uint8Array, graphInitializer?: Graph.Initializer, isOrtFormat?: boolean): void {\n    let onnxError: Error|undefined;\n    if (!isOrtFormat) {\n      // isOrtFormat === false || isOrtFormat === undefined\n      try {\n        this.loadFromOnnxFormat(buf, graphInitializer);\n        return;\n      } catch (e) {\n        if (isOrtFormat !== undefined) {\n          throw e;\n        }\n        onnxError = e;\n      }\n    }\n\n    try {\n      this.loadFromOrtFormat(buf, graphInitializer);\n    } catch (e) {\n      if (isOrtFormat !== undefined) {\n        throw e;\n      }\n      // Tried both formats and failed (when isOrtFormat === undefined)\n      throw new Error(`Failed to load model as ONNX format: ${onnxError}\\nas ORT format: ${e}`);\n    }\n  }\n\n  private loadFromOnnxFormat(buf: Uint8Array, graphInitializer?: Graph.Initializer): void {\n    const modelProto = onnx.ModelProto.decode(buf);\n    const irVersion = LongUtil.longToNumber(modelProto.irVersion);\n    if (irVersion < 3) {\n      throw new Error('only support ONNX model with IR_VERSION>=3');\n    }\n\n    this._opsets =\n        modelProto.opsetImport.map(i => ({domain: i.domain as string, version: LongUtil.longToNumber(i.version!)}));\n\n    this._graph = Graph.from(modelProto.graph!, graphInitializer);\n  }\n\n  private loadFromOrtFormat(buf: Uint8Array, graphInitializer?: Graph.Initializer): void {\n    const fb = new flatbuffers.ByteBuffer(buf);\n    const ortModel = ortFbs.InferenceSession.getRootAsInferenceSession(fb).model()!;\n    const irVersion = LongUtil.longToNumber(ortModel.irVersion());\n    if (irVersion < 3) {\n      throw new Error('only support ONNX model with IR_VERSION>=3');\n    }\n    this._opsets = [];\n    for (let i = 0; i < ortModel.opsetImportLength(); i++) {\n      const opsetId = ortModel.opsetImport(i)!;\n      this._opsets.push({domain: opsetId?.domain() as string, version: LongUtil.longToNumber(opsetId.version()!)});\n    }\n\n    this._graph = Graph.from(ortModel.graph()!, graphInitializer);\n  }\n\n  private _graph: Graph;\n  get graph(): Graph {\n    return this._graph;\n  }\n\n  private _opsets: OpSet[];\n  get opsets(): readonly OpSet[] {\n    return this._opsets;\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {resolveBackend, SessionHandlerType} from './backend';\nimport {ExecutionPlan} from './execution-plan';\nimport {Graph} from './graph';\nimport {Profiler} from './instrument';\nimport {Model} from './model';\nimport {Operator} from './operators';\nimport {Tensor} from './tensor';\n\nexport declare namespace Session {\n  export interface Config {\n    backendHint?: string;\n    profiler?: Profiler.Config;\n  }\n\n  export interface Context {\n    profiler: Readonly<Profiler>;\n    graphInputTypes?: Tensor.DataType[];\n    graphInputDims?: Array<readonly number[]>;\n  }\n}\n\nexport class Session {\n  constructor(config: Session.Config = {}) {\n    this._initialized = false;\n    this.backendHint = config.backendHint;\n    this.profiler = Profiler.create(config.profiler);\n    this.context = {profiler: this.profiler, graphInputTypes: [], graphInputDims: []};\n  }\n\n  get inputNames(): readonly string[] {\n    return this._model.graph.getInputNames();\n  }\n  get outputNames(): readonly string[] {\n    return this._model.graph.getOutputNames();\n  }\n\n  startProfiling() {\n    this.profiler.start();\n  }\n\n  endProfiling() {\n    this.profiler.stop();\n  }\n\n  async loadModel(uri: string): Promise<void>;\n  async loadModel(buffer: ArrayBuffer, byteOffset?: number, length?: number): Promise<void>;\n  async loadModel(buffer: Uint8Array): Promise<void>;\n  async loadModel(arg: string|ArrayBuffer|Uint8Array, byteOffset?: number, length?: number): Promise<void> {\n    await this.profiler.event('session', 'Session.loadModel', async () => {\n      // resolve backend and session handler\n      const backend = await resolveBackend(this.backendHint);\n      this.sessionHandler = backend.createSessionHandler(this.context);\n\n      this._model = new Model();\n      if (typeof arg === 'string') {\n        const isOrtFormat = arg.endsWith('.ort');\n        if (typeof process !== 'undefined' && process.versions && process.versions.node) {\n          // node\n          const {readFile} = require('node:fs/promises');\n          const buf = await readFile(arg);\n          this.initialize(buf, isOrtFormat);\n        } else {\n          // browser\n          const response = await fetch(arg);\n          const buf = await response.arrayBuffer();\n          this.initialize(new Uint8Array(buf), isOrtFormat);\n        }\n      } else if (!ArrayBuffer.isView(arg)) {\n        // load model from ArrayBuffer\n        const arr = new Uint8Array(arg, byteOffset || 0, length || arg.byteLength);\n        this.initialize(arr);\n      } else {\n        // load model from Uint8array\n        this.initialize(arg);\n      }\n    });\n  }\n\n  private initialize(modelProtoBlob: Uint8Array, isOrtFormat?: boolean): void {\n    if (this._initialized) {\n      throw new Error('already initialized');\n    }\n\n    this.profiler.event('session', 'Session.initialize', () => {\n      // load graph\n      const graphInitializer =\n          this.sessionHandler.transformGraph ? this.sessionHandler as Graph.Initializer : undefined;\n      this._model.load(modelProtoBlob, graphInitializer, isOrtFormat);\n\n      // graph is completely initialzied at this stage , let the interested handlers know\n      if (this.sessionHandler.onGraphInitialized) {\n        this.sessionHandler.onGraphInitialized(this._model.graph);\n      }\n      // initialize each operator in the graph\n      this.initializeOps(this._model.graph);\n\n      // instantiate an ExecutionPlan object to be used by the Session object\n      this._executionPlan = new ExecutionPlan(this._model.graph, this._ops, this.profiler);\n    });\n\n    this._initialized = true;\n  }\n\n  async run(inputs: Map<string, Tensor>|Tensor[]): Promise<Map<string, Tensor>> {\n    if (!this._initialized) {\n      throw new Error('session not initialized yet');\n    }\n\n    return this.profiler.event('session', 'Session.run', async () => {\n      const inputTensors = this.normalizeAndValidateInputs(inputs);\n\n      const outputTensors = await this._executionPlan.execute(this.sessionHandler, inputTensors);\n\n      return this.createOutput(outputTensors);\n    });\n  }\n\n  private normalizeAndValidateInputs(inputs: Map<string, Tensor>|Tensor[]): Tensor[] {\n    const modelInputNames = this._model.graph.getInputNames();\n\n    // normalize inputs\n    // inputs: Tensor[]\n    if (Array.isArray(inputs)) {\n      if (inputs.length !== modelInputNames.length) {\n        throw new Error(`incorrect input array length: expected ${modelInputNames.length} but got ${inputs.length}`);\n      }\n    }\n    // convert map to array\n    // inputs: Map<string, Tensor>\n    else {\n      if (inputs.size !== modelInputNames.length) {\n        throw new Error(`incorrect input map size: expected ${modelInputNames.length} but got ${inputs.size}`);\n      }\n\n      const sortedInputs = new Array<Tensor>(inputs.size);\n      let sortedInputsIndex = 0;\n      for (let i = 0; i < modelInputNames.length; ++i) {\n        const tensor = inputs.get(modelInputNames[i]);\n        if (!tensor) {\n          throw new Error(`missing input tensor for: '${name}'`);\n        }\n        sortedInputs[sortedInputsIndex++] = tensor;\n      }\n\n      inputs = sortedInputs;\n    }\n\n    // validate dims requirements\n    // First session run - graph input data is not cached for the session\n    if (!this.context.graphInputTypes || this.context.graphInputTypes.length === 0 || !this.context.graphInputDims ||\n        this.context.graphInputDims.length === 0) {\n      const modelInputIndices = this._model.graph.getInputIndices();\n      const modelValues = this._model.graph.getValues();\n\n      const graphInputDims = new Array<readonly number[]>(modelInputIndices.length);\n\n      for (let i = 0; i < modelInputIndices.length; ++i) {\n        const graphInput = modelValues[modelInputIndices[i]];\n        graphInputDims[i] = graphInput.type!.shape.dims;\n\n        // cached for second and subsequent runs.\n        // Some parts of the framework works on the assumption that the graph and types and shapes are static\n        this.context.graphInputTypes!.push(graphInput.type!.tensorType);\n        this.context.graphInputDims!.push(inputs[i].dims);\n      }\n\n      this.validateInputTensorDims(graphInputDims, inputs, true);\n    }\n\n    // Second and subsequent session runs - graph input data is cached for the session\n    else {\n      this.validateInputTensorDims(this.context.graphInputDims, inputs, false);\n    }\n\n    // validate types requirement\n    this.validateInputTensorTypes(this.context.graphInputTypes!, inputs);\n\n    return inputs;\n  }\n\n  private validateInputTensorTypes(graphInputTypes: Tensor.DataType[], givenInputs: Tensor[]) {\n    for (let i = 0; i < givenInputs.length; i++) {\n      const expectedType = graphInputTypes[i];\n      const actualType = givenInputs[i].type;\n      if (expectedType !== actualType) {\n        throw new Error(`input tensor[${i}] check failed: expected type '${expectedType}' but got ${actualType}`);\n      }\n    }\n  }\n\n  private validateInputTensorDims(\n      graphInputDims: Array<readonly number[]>, givenInputs: Tensor[], noneDimSupported: boolean) {\n    for (let i = 0; i < givenInputs.length; i++) {\n      const expectedDims = graphInputDims[i];\n      const actualDims = givenInputs[i].dims;\n      if (!this.compareTensorDims(expectedDims, actualDims, noneDimSupported)) {\n        throw new Error(`input tensor[${i}] check failed: expected shape '[${expectedDims.join(',')}]' but got [${\n            actualDims.join(',')}]`);\n      }\n    }\n  }\n\n  private compareTensorDims(expectedDims: readonly number[], actualDims: readonly number[], noneDimSupported: boolean):\n      boolean {\n    if (expectedDims.length !== actualDims.length) {\n      return false;\n    }\n\n    for (let i = 0; i < expectedDims.length; ++i) {\n      if (expectedDims[i] !== actualDims[i] && (!noneDimSupported || expectedDims[i] !== 0)) {\n        // data shape mis-match AND not a 'None' dimension.\n        return false;\n      }\n    }\n\n    return true;\n  }\n\n  private createOutput(outputTensors: Tensor[]): Map<string, Tensor> {\n    const modelOutputNames = this._model.graph.getOutputNames();\n    if (outputTensors.length !== modelOutputNames.length) {\n      throw new Error('expected number of outputs do not match number of generated outputs');\n    }\n\n    const output = new Map<string, Tensor>();\n    for (let i = 0; i < modelOutputNames.length; ++i) {\n      output.set(modelOutputNames[i], outputTensors[i]);\n    }\n\n    return output;\n  }\n\n  private initializeOps(graph: Graph): void {\n    const nodes = graph.getNodes();\n    this._ops = new Array(nodes.length);\n\n    for (let i = 0; i < nodes.length; i++) {\n      this._ops[i] = this.sessionHandler.resolve(nodes[i], this._model.opsets, graph);\n    }\n  }\n\n  private _model: Model;\n  private _initialized: boolean;\n\n  private _ops: Operator[];\n  private _executionPlan: ExecutionPlan;\n\n  private backendHint?: string;\n\n  private sessionHandler: SessionHandlerType;\n  private context: Session.Context;\n  private profiler: Readonly<Profiler>;\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {InferenceSession, InferenceSessionHandler, SessionHandler, Tensor} from 'onnxruntime-common';\n\nimport {Session} from './session';\nimport {Tensor as OnnxjsTensor} from './tensor';\n\nexport class OnnxjsSessionHandler implements InferenceSessionHandler {\n  constructor(private session: Session) {\n    this.inputNames = this.session.inputNames;\n    this.outputNames = this.session.outputNames;\n  }\n\n  async dispose(): Promise<void> {}\n  inputNames: readonly string[];\n  outputNames: readonly string[];\n  async run(\n      feeds: SessionHandler.FeedsType, _fetches: SessionHandler.FetchesType,\n      _options: InferenceSession.RunOptions): Promise<SessionHandler.ReturnType> {\n    const inputMap = new Map<string, OnnxjsTensor>();\n    for (const name in feeds) {\n      if (Object.hasOwnProperty.call(feeds, name)) {\n        const feed = feeds[name];\n        inputMap.set(\n            name,\n            new OnnxjsTensor(\n                feed.dims, feed.type as OnnxjsTensor.DataType, undefined, undefined,\n                feed.data as OnnxjsTensor.NumberType));\n      }\n    }\n    const outputMap = await this.session.run(inputMap);\n    const output: SessionHandler.ReturnType = {};\n    outputMap.forEach((tensor, name) => {\n      output[name] = new Tensor(tensor.type, tensor.data, tensor.dims);\n    });\n    return output;\n  }\n  startProfiling(): void {\n    this.session.startProfiling();\n  }\n  endProfiling(): void {\n    this.session.endProfiling();\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\n/* eslint-disable import/no-internal-modules */\nimport {Backend, InferenceSession, InferenceSessionHandler} from 'onnxruntime-common';\n\nimport {Session} from './onnxjs/session';\nimport {OnnxjsSessionHandler} from './onnxjs/session-handler-inference';\n\nclass OnnxjsBackend implements Backend {\n  // eslint-disable-next-line @typescript-eslint/no-empty-function\n  async init(): Promise<void> {}\n\n  async createInferenceSessionHandler(pathOrBuffer: string|Uint8Array, options?: InferenceSession.SessionOptions):\n      Promise<InferenceSessionHandler> {\n    // NOTE: Session.Config(from onnx.js) is not compatible with InferenceSession.SessionOptions(from\n    // onnxruntime-common).\n    //       In future we should remove Session.Config and use InferenceSession.SessionOptions.\n    //       Currently we allow this to happen to make test runner work.\n    const session = new Session(options as unknown as Session.Config);\n\n    // typescript cannot merge method override correctly (so far in 4.2.3). need if-else to call the method.\n    if (typeof pathOrBuffer === 'string') {\n      await session.loadModel(pathOrBuffer);\n    } else {\n      await session.loadModel(pathOrBuffer);\n    }\n\n    return new OnnxjsSessionHandler(session);\n  }\n}\n\nexport const onnxjsBackend = new OnnxjsBackend();\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nexport const isNode = !!(typeof process !== 'undefined' && process.versions && process.versions.node);\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\n/// <reference lib=\"webworker\" />\n\n//\n// * type hack for \"HTMLImageElement\"\n//\n// in typescript, the type of \"HTMLImageElement\" is defined in lib.dom.d.ts, which is conflict with lib.webworker.d.ts.\n// when we use webworker, the lib.webworker.d.ts will be used, which does not have HTMLImageElement defined.\n//\n// we will get the following errors complaining that HTMLImageElement is not defined:\n//\n// ====================================================================================================================\n//\n// ../common/dist/cjs/tensor-factory.d.ts:187:29 - error TS2552: Cannot find name 'HTMLImageElement'. Did you mean\n// 'HTMLLIElement'?\n//\n// 187     fromImage(imageElement: HTMLImageElement, options?: TensorFromImageElementOptions):\n// Promise<TypedTensor<'float32'> | TypedTensor<'uint8'>>;\n//                                 ~~~~~~~~~~~~~~~~\n//\n// node_modules/@webgpu/types/dist/index.d.ts:83:7 - error TS2552: Cannot find name 'HTMLImageElement'. Did you mean\n// 'HTMLLIElement'?\n//\n// 83     | HTMLImageElement\n//          ~~~~~~~~~~~~~~~~\n//\n// ====================================================================================================================\n//\n// `HTMLImageElement` is only used in type declaration and not in real code. So we define it as `unknown` here to\n// bypass the type check.\n\n//\n// * type hack for \"document\"\n//\n// in typescript, the type of \"document\" is defined in lib.dom.d.ts, so it's not available in webworker.\n//\n// we will get the following errors complaining that document is not defined:\n//\n// ====================================================================================================================\n//\n// lib/wasm/wasm-utils-import.ts:7:33 - error TS2584: Cannot find name 'document'. Do you need to change your target\n// library? Try changing the 'lib' compiler option to include 'dom'.\n//\n// 7 export const scriptSrc = typeof document !== 'undefined' ? (document?.currentScript as HTMLScriptElement)?.src :\n//                                   ~~~~~~~~\n//\n// lib/wasm/wasm-utils-import.ts:7:61 - error TS2584: Cannot find name 'document'. Do you need to change your target\n// library? Try changing the 'lib' compiler option to include 'dom'.\n//\n// 7 export const scriptSrc = typeof document !== 'undefined' ? (document?.currentScript as HTMLScriptElement)?.src :\n//                                                               ~~~~~~~~\n//\n// lib/wasm/wasm-utils-import.ts:7:88 - error TS2552: Cannot find name 'HTMLScriptElement'. Did you mean\n// 'HTMLLIElement'?\n//\n// 7 export const scriptSrc = typeof document !== 'undefined' ? (document?.currentScript as HTMLScriptElement)?.src :\n//                                                                                          ~~~~~~~~~~~~~~~~~\n// ====================================================================================================================\n//\n// `document` is used to get the current script URL, which is not available in webworker. This file is served as a\n// \"dual\" file for entries of both webworker and the esm module.\n//\ndeclare global {\n  type HTMLImageElement = unknown;\n  type HTMLScriptElement = {src?: string};\n  const document: undefined|{currentScript?: HTMLScriptElement};\n}\n\n/**\n * @summary\n *\n * This file is served as a \"dual\" file for both entries of the following:\n * - The proxy worker itself.\n *   - When used as a worker, it listens to the messages from the main thread and performs the corresponding operations.\n *   - Should be imported directly using `new Worker()` in the main thread.\n *\n * - The ESM module that creates the proxy worker (as a worker launcher).\n *   - When used as a worker launcher, it creates the proxy worker and returns it.\n *   - Should be imported using `import()` in the main thread, with the query parameter `import=1`.\n *\n * This file will be always compiling into ESM format.\n */\n\nimport type {OrtWasmMessage, SerializableTensorMetadata} from '../proxy-messages.js';\nimport {createSession, copyFromExternalBuffer, endProfiling, extractTransferableBuffers, initEp, initRuntime, releaseSession, run} from '../wasm-core-impl.js';\nimport {initializeWebAssembly} from '../wasm-factory.js';\nimport {scriptSrc} from '../wasm-utils-import.js';\n\nconst WORKER_NAME = 'ort-wasm-proxy-worker';\nconst isProxyWorker = globalThis.self?.name === WORKER_NAME;\n\nif (isProxyWorker) {\n  // Worker thread\n  self.onmessage = (ev: MessageEvent<OrtWasmMessage>): void => {\n    const {type, in : message} = ev.data;\n    try {\n      switch (type) {\n        case 'init-wasm':\n          initializeWebAssembly(message!.wasm)\n              .then(\n                  () => {\n                    initRuntime(message!).then(\n                        () => {\n                          postMessage({type});\n                        },\n                        err => {\n                          postMessage({type, err});\n                        });\n                  },\n                  err => {\n                    postMessage({type, err});\n                  });\n          break;\n        case 'init-ep': {\n          const {epName, env} = message!;\n          initEp(env, epName)\n              .then(\n                  () => {\n                    postMessage({type});\n                  },\n                  err => {\n                    postMessage({type, err});\n                  });\n          break;\n        }\n        case 'copy-from': {\n          const {buffer} = message!;\n          const bufferData = copyFromExternalBuffer(buffer);\n          postMessage({type, out: bufferData} as OrtWasmMessage);\n          break;\n        }\n        case 'create': {\n          const {model, options} = message!;\n          createSession(model, options)\n              .then(\n                  sessionMetadata => {\n                    postMessage({type, out: sessionMetadata} as OrtWasmMessage);\n                  },\n                  err => {\n                    postMessage({type, err});\n                  });\n          break;\n        }\n        case 'release':\n          releaseSession(message!);\n          postMessage({type});\n          break;\n        case 'run': {\n          const {sessionId, inputIndices, inputs, outputIndices, options} = message!;\n          run(sessionId, inputIndices, inputs, outputIndices, new Array(outputIndices.length).fill(null), options)\n              .then(\n                  outputs => {\n                    if (outputs.some(o => o[3] !== 'cpu')) {\n                      postMessage({type, err: 'Proxy does not support non-cpu tensor location.'});\n                    } else {\n                      postMessage(\n                          {type, out: outputs} as OrtWasmMessage,\n                          extractTransferableBuffers([...inputs, ...outputs] as SerializableTensorMetadata[]));\n                    }\n                  },\n                  err => {\n                    postMessage({type, err});\n                  });\n          break;\n        }\n        case 'end-profiling':\n          endProfiling(message!);\n          postMessage({type});\n          break;\n        default:\n      }\n    } catch (err) {\n      postMessage({type, err} as OrtWasmMessage);\n    }\n  };\n}\n\nexport default isProxyWorker ?\n    null :\n    (urlOverride?: string) =>\n        new Worker(urlOverride ?? scriptSrc!, {type: BUILD_DEFS.IS_ESM ? 'module' : 'classic', name: WORKER_NAME});\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport type {OrtWasmModule} from './wasm-types';\nimport {isNode} from './wasm-utils-env';\n\n/**\n * The classic script source URL. This is not always available in non ESModule environments.\n *\n * In Node.js, this is undefined.\n */\nexport const scriptSrc =\n    // if Nodejs, return undefined\n    isNode ? undefined :\n             // if It's ESM, use import.meta.url\n             BUILD_DEFS.ESM_IMPORT_META_URL ??\n        // use `document.currentScript.src` if available\n        (typeof document !== 'undefined' ? (document.currentScript as HTMLScriptElement)?.src :\n                                           // use `self.location.href` if available\n                                           (typeof self !== 'undefined' ? self.location?.href : undefined));\n\n/**\n * The origin of the current location.\n *\n * In Node.js, this is undefined.\n */\nconst origin = isNode || typeof location === 'undefined' ? undefined : location.origin;\n\n/**\n * Check if the given filename with prefix is from the same origin.\n */\nconst isSameOrigin = (filename: string, prefixOverride?: string) => {\n  try {\n    const baseUrl = prefixOverride ?? scriptSrc;\n    const url = baseUrl ? new URL(filename, baseUrl) : new URL(filename);\n    return url.origin === origin;\n  } catch {\n    return false;\n  }\n};\n\n/**\n * Normalize the inputs to an absolute URL with the given prefix override. If failed, return undefined.\n */\nconst normalizeUrl = (filename: string, prefixOverride?: string) => {\n  const baseUrl = prefixOverride ?? scriptSrc;\n  try {\n    const url = baseUrl ? new URL(filename, baseUrl) : new URL(filename);\n    return url.href;\n  } catch {\n    return undefined;\n  }\n};\n\n/**\n * Create a fallback URL if an absolute URL cannot be created by the normalizeUrl function.\n */\nconst fallbackUrl = (filename: string, prefixOverride?: string) => `${prefixOverride ?? './'}${filename}`;\n\n/**\n * This helper function is used to preload a module from a URL.\n *\n * If the origin of the worker URL is different from the current origin, the worker cannot be loaded directly.\n * See discussions in https://github.com/webpack-contrib/worker-loader/issues/154\n *\n * In this case, we will fetch the worker URL and create a new Blob URL with the same origin as a workaround.\n *\n * @param absoluteUrl - The absolute URL to preload.\n *\n * @returns - A promise that resolves to a new Blob URL\n */\nconst preload = async(absoluteUrl: string): Promise<string> => {\n  const response = await fetch(absoluteUrl, {credentials: 'same-origin'});\n  const blob = await response.blob();\n  return URL.createObjectURL(blob);\n};\n\n/**\n * This helper function is used to dynamically import a module from a URL.\n *\n * The build script has special handling for this function to ensure that the URL is not bundled into the final output.\n *\n * @param url - The URL to import.\n *\n * @returns - A promise that resolves to the default export of the module.\n */\nconst dynamicImportDefault = async<T>(url: string): Promise<T> => (await import(/* webpackIgnore: true */ url)).default;\n\n/**\n * The proxy worker factory imported from the proxy worker module.\n *\n * This is only available when the WebAssembly proxy is not disabled.\n */\nconst createProxyWorker: ((urlOverride?: string) => Worker)|undefined =\n    // eslint-disable-next-line @typescript-eslint/no-require-imports, @typescript-eslint/no-var-requires\n    BUILD_DEFS.DISABLE_WASM_PROXY ? undefined : require('./proxy-worker/main').default;\n\n/**\n * Import the proxy worker.\n *\n * This function will perform the following steps:\n * 1. If a preload is needed, it will preload the module and return the object URL.\n * 2. Use the proxy worker factory to create the proxy worker.\n *\n * @returns - A promise that resolves to a tuple of 2 elements:\n *            - The object URL of the preloaded module, or undefined if no preload is needed.\n *            - The proxy worker.\n */\nexport const importProxyWorker = async(): Promise<[undefined | string, Worker]> => {\n  if (!scriptSrc) {\n    throw new Error('Failed to load proxy worker: cannot determine the script source URL.');\n  }\n\n  // If the script source is from the same origin, we can use the embedded proxy module directly.\n  if (isSameOrigin(scriptSrc)) {\n    return [undefined, createProxyWorker!()];\n  }\n\n  // Otherwise, need to preload\n  const url = await preload(scriptSrc);\n  return [url, createProxyWorker!(url)];\n};\n\n/**\n * The embedded WebAssembly module.\n *\n * This is only available in ESM and when embedding is not disabled.\n */\nconst embeddedWasmModule: EmscriptenModuleFactory<OrtWasmModule>|undefined =\n    BUILD_DEFS.IS_ESM && BUILD_DEFS.DISABLE_DYNAMIC_IMPORT ?\n    // eslint-disable-next-line @typescript-eslint/no-require-imports, @typescript-eslint/no-var-requires\n    require(\n        !BUILD_DEFS.DISABLE_TRAINING ? '../../dist/ort-training-wasm-simd-threaded.mjs' :\n            !BUILD_DEFS.DISABLE_JSEP ? '../../dist/ort-wasm-simd-threaded.jsep.mjs' :\n                                       '../../dist/ort-wasm-simd-threaded.mjs')\n        .default :\n    undefined;\n\n/**\n * Import the WebAssembly module.\n *\n * This function will perform the following steps:\n * 1. If BUILD_DEFS.DISABLE_DYNAMIC_IMPORT is true, use the embedded module.\n * 2. If a preload is needed, it will preload the module and return the object URL.\n * 3. Otherwise, it will perform a dynamic import of the module.\n *\n * @returns - A promise that resolves to a tuple of 2 elements:\n *            - The object URL of the preloaded module, or undefined if no preload is needed.\n *            - The default export of the module, which is a factory function to create the WebAssembly module.\n */\nexport const importWasmModule = async(\n    urlOverride: string|undefined, prefixOverride: string|undefined,\n    isMultiThreaded: boolean): Promise<[undefined | string, EmscriptenModuleFactory<OrtWasmModule>]> => {\n  if (BUILD_DEFS.DISABLE_DYNAMIC_IMPORT) {\n    return [undefined, embeddedWasmModule!];\n  } else {\n    const wasmModuleFilename = !BUILD_DEFS.DISABLE_TRAINING ? 'ort-training-wasm-simd-threaded.mjs' :\n        !BUILD_DEFS.DISABLE_JSEP                            ? 'ort-wasm-simd-threaded.jsep.mjs' :\n                                                              'ort-wasm-simd-threaded.mjs';\n    const wasmModuleUrl = urlOverride ?? normalizeUrl(wasmModuleFilename, prefixOverride);\n    // need to preload if all of the following conditions are met:\n    // 1. not in Node.js.\n    //    - Node.js does not have the same origin policy for creating workers.\n    // 2. multi-threaded is enabled.\n    //    - If multi-threaded is disabled, no worker will be created. So we don't need to preload the module.\n    // 3. the absolute URL is available.\n    //    - If the absolute URL is failed to be created, the origin cannot be determined. In this case, we will not\n    //    preload the module.\n    // 4. the worker URL is not from the same origin.\n    //    - If the worker URL is from the same origin, we can create the worker directly.\n    const needPreload = !isNode && isMultiThreaded && wasmModuleUrl && !isSameOrigin(wasmModuleUrl, prefixOverride);\n    const url = needPreload ? (await preload(wasmModuleUrl)) :\n                              (wasmModuleUrl ?? fallbackUrl(wasmModuleFilename, prefixOverride));\n    return [needPreload ? url : undefined, await dynamicImportDefault<EmscriptenModuleFactory<OrtWasmModule>>(url)];\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Env} from 'onnxruntime-common';\n\nimport type {OrtWasmModule} from './wasm-types';\nimport {importWasmModule} from './wasm-utils-import';\n\nlet wasm: OrtWasmModule|undefined;\nlet initialized = false;\nlet initializing = false;\nlet aborted = false;\n\nconst isMultiThreadSupported = (): boolean => {\n  // If 'SharedArrayBuffer' is not available, WebAssembly threads will not work.\n  if (typeof SharedArrayBuffer === 'undefined') {\n    return false;\n  }\n\n  try {\n    // Test for transferability of SABs (for browsers. needed for Firefox)\n    // https://groups.google.com/forum/#!msg/mozilla.dev.platform/IHkBZlHETpA/dwsMNchWEQAJ\n    if (typeof MessageChannel !== 'undefined') {\n      new MessageChannel().port1.postMessage(new SharedArrayBuffer(1));\n    }\n\n    // Test for WebAssembly threads capability (for both browsers and Node.js)\n    // This typed array is a WebAssembly program containing threaded instructions.\n    return WebAssembly.validate(new Uint8Array([\n      0, 97, 115, 109, 1, 0,  0,  0, 1, 4, 1,  96, 0,   0,  3, 2, 1,  0, 5,\n      4, 1,  3,   1,   1, 10, 11, 1, 9, 0, 65, 0,  254, 16, 2, 0, 26, 11\n    ]));\n  } catch (e) {\n    return false;\n  }\n};\n\nconst isSimdSupported = (): boolean => {\n  try {\n    // Test for WebAssembly SIMD capability (for both browsers and Node.js)\n    // This typed array is a WebAssembly program containing SIMD instructions.\n\n    // The binary data is generated from the following code by wat2wasm:\n    //\n    // (module\n    //   (type $t0 (func))\n    //   (func $f0 (type $t0)\n    //     (drop\n    //       (i32x4.dot_i16x8_s\n    //         (i8x16.splat\n    //           (i32.const 0))\n    //         (v128.const i32x4 0x00000000 0x00000000 0x00000000 0x00000000)))))\n\n    return WebAssembly.validate(new Uint8Array([\n      0,   97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 10, 30, 1,   28,  0, 65, 0,\n      253, 15, 253, 12,  0, 0, 0, 0, 0, 0, 0, 0,  0, 0, 0, 0, 0, 0, 0,  0,  253, 186, 1, 26, 11\n    ]));\n  } catch (e) {\n    return false;\n  }\n};\n\nexport const initializeWebAssembly = async(flags: Env.WebAssemblyFlags): Promise<void> => {\n  if (initialized) {\n    return Promise.resolve();\n  }\n  if (initializing) {\n    throw new Error('multiple calls to \\'initializeWebAssembly()\\' detected.');\n  }\n  if (aborted) {\n    throw new Error('previous call to \\'initializeWebAssembly()\\' failed.');\n  }\n\n  initializing = true;\n\n  // wasm flags are already initialized\n  const timeout = flags.initTimeout!;\n  let numThreads = flags.numThreads!;\n\n  // ensure SIMD is supported\n  if (!isSimdSupported()) {\n    throw new Error('WebAssembly SIMD is not supported in the current environment.');\n  }\n\n  // check if multi-threading is supported\n  const multiThreadSupported = isMultiThreadSupported();\n  if (numThreads > 1 && !multiThreadSupported) {\n    if (typeof self !== 'undefined' && !self.crossOriginIsolated) {\n      // eslint-disable-next-line no-console\n      console.warn(\n          'env.wasm.numThreads is set to ' + numThreads +\n          ', but this will not work unless you enable crossOriginIsolated mode. ' +\n          'See https://web.dev/cross-origin-isolation-guide/ for more info.');\n    }\n\n    // eslint-disable-next-line no-console\n    console.warn(\n        'WebAssembly multi-threading is not supported in the current environment. ' +\n        'Falling back to single-threading.');\n\n    // set flags.numThreads to 1 so that OrtInit() will not create a global thread pool.\n    flags.numThreads = numThreads = 1;\n  }\n\n  const wasmPaths = flags.wasmPaths;\n  const wasmPrefixOverride = typeof wasmPaths === 'string' ? wasmPaths : undefined;\n  const mjsPathOverrideFlag = (wasmPaths as Env.WasmFilePaths)?.mjs;\n  const mjsPathOverride = (mjsPathOverrideFlag as URL)?.href ?? mjsPathOverrideFlag;\n  const wasmPathOverrideFlag = (wasmPaths as Env.WasmFilePaths)?.wasm;\n  const wasmPathOverride = (wasmPathOverrideFlag as URL)?.href ?? wasmPathOverrideFlag;\n  const wasmBinaryOverride = flags.wasmBinary;\n\n  const [objectUrl, ortWasmFactory] = (await importWasmModule(mjsPathOverride, wasmPrefixOverride, numThreads > 1));\n\n  let isTimeout = false;\n\n  const tasks: Array<Promise<void>> = [];\n\n  // promise for timeout\n  if (timeout > 0) {\n    tasks.push(new Promise((resolve) => {\n      setTimeout(() => {\n        isTimeout = true;\n        resolve();\n      }, timeout);\n    }));\n  }\n\n  // promise for module initialization\n  tasks.push(new Promise((resolve, reject) => {\n    const config: Partial<OrtWasmModule> = {\n      /**\n       * The number of threads. WebAssembly will create (Module.numThreads - 1) workers. If it is 1, no worker will be\n       * created.\n       */\n      numThreads,\n    };\n\n    if (wasmBinaryOverride) {\n      /**\n       * Set a custom buffer which contains the WebAssembly binary. This will skip the wasm file fetching.\n       */\n      config.wasmBinary = wasmBinaryOverride;\n    } else if (wasmPathOverride || wasmPrefixOverride) {\n      /**\n       * A callback function to locate the WebAssembly file. The function should return the full path of the file.\n       *\n       * Since Emscripten 3.1.58, this function is only called for the .wasm file.\n       */\n      config.locateFile = (fileName, scriptDirectory) =>\n          wasmPathOverride ?? (wasmPrefixOverride ?? scriptDirectory) + fileName;\n    }\n\n    ortWasmFactory(config).then(\n        // wasm module initialized successfully\n        module => {\n          initializing = false;\n          initialized = true;\n          wasm = module;\n          resolve();\n          if (objectUrl) {\n            URL.revokeObjectURL(objectUrl);\n          }\n        },\n        // wasm module failed to initialize\n        (what) => {\n          initializing = false;\n          aborted = true;\n          reject(what);\n        });\n  }));\n\n  await Promise.race(tasks);\n\n  if (isTimeout) {\n    throw new Error(`WebAssembly backend initializing failed due to timeout: ${timeout}ms`);\n  }\n};\n\nexport const getInstance = (): OrtWasmModule => {\n  if (initialized && wasm) {\n    return wasm;\n  }\n\n  throw new Error('WebAssembly is not initialized yet.');\n};\n\nexport const dispose = (): void => {\n  if (initialized && !initializing && !aborted) {\n    // TODO: currently \"PThread.terminateAllThreads()\" is not exposed in the wasm module.\n    //       And this function is not yet called by any code.\n    //       If it is needed in the future, we should expose it in the wasm module and uncomment the following line.\n\n    // wasm?.PThread?.terminateAllThreads();\n    wasm = undefined;\n\n    initializing = false;\n    initialized = false;\n    aborted = true;\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {getInstance} from './wasm-factory';\n\nexport const allocWasmString = (data: string, allocs: number[]): number => {\n  const wasm = getInstance();\n\n  const dataLength = wasm.lengthBytesUTF8(data) + 1;\n  const dataOffset = wasm._malloc(dataLength);\n  wasm.stringToUTF8(data, dataOffset, dataLength);\n  allocs.push(dataOffset);\n\n  return dataOffset;\n};\n\ninterface ExtraOptionsHandler {\n  (name: string, value: string): void;\n}\n\nexport const iterateExtraOptions =\n    (options: Record<string, unknown>, prefix: string, seen: WeakSet<Record<string, unknown>>,\n     handler: ExtraOptionsHandler): void => {\n      if (typeof options == 'object' && options !== null) {\n        if (seen.has(options)) {\n          throw new Error('Circular reference in options');\n        } else {\n          seen.add(options);\n        }\n      }\n\n      Object.entries(options).forEach(([key, value]) => {\n        const name = (prefix) ? prefix + key : key;\n        if (typeof value === 'object') {\n          iterateExtraOptions(value as Record<string, unknown>, name + '.', seen, handler);\n        } else if (typeof value === 'string' || typeof value === 'number') {\n          handler(name, value.toString());\n        } else if (typeof value === 'boolean') {\n          handler(name, (value) ? '1' : '0');\n        } else {\n          throw new Error(`Can't handle extra config type: ${typeof value}`);\n        }\n      });\n    };\n\n/**\n * check web assembly API's last error and throw error if any error occurred.\n * @param message a message used when an error occurred.\n */\nexport const checkLastError = (message: string): void => {\n  const wasm = getInstance();\n\n  const stack = wasm.stackSave();\n  try {\n    const paramsOffset = wasm.stackAlloc(8);\n    wasm._OrtGetLastError(paramsOffset, paramsOffset + 4);\n    const errorCode = wasm.HEAP32[paramsOffset / 4];\n    const errorMessagePointer = wasm.HEAPU32[paramsOffset / 4 + 1];\n    const errorMessage = errorMessagePointer ? wasm.UTF8ToString(errorMessagePointer) : '';\n    throw new Error(`${message} ERROR_CODE: ${errorCode}, ERROR_MESSAGE: ${errorMessage}`);\n  } finally {\n    wasm.stackRestore(stack);\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {InferenceSession} from 'onnxruntime-common';\n\nimport {getInstance} from './wasm-factory';\nimport {allocWasmString, checkLastError, iterateExtraOptions} from './wasm-utils';\n\nexport const setRunOptions = (options: InferenceSession.RunOptions): [number, number[]] => {\n  const wasm = getInstance();\n  let runOptionsHandle = 0;\n  const allocs: number[] = [];\n\n  const runOptions: InferenceSession.RunOptions = options || {};\n\n  try {\n    if (options?.logSeverityLevel === undefined) {\n      runOptions.logSeverityLevel = 2;  // Default to warning\n    } else if (\n        typeof options.logSeverityLevel !== 'number' || !Number.isInteger(options.logSeverityLevel) ||\n        options.logSeverityLevel < 0 || options.logSeverityLevel > 4) {\n      throw new Error(`log serverity level is not valid: ${options.logSeverityLevel}`);\n    }\n\n    if (options?.logVerbosityLevel === undefined) {\n      runOptions.logVerbosityLevel = 0;  // Default to 0\n    } else if (typeof options.logVerbosityLevel !== 'number' || !Number.isInteger(options.logVerbosityLevel)) {\n      throw new Error(`log verbosity level is not valid: ${options.logVerbosityLevel}`);\n    }\n\n    if (options?.terminate === undefined) {\n      runOptions.terminate = false;\n    }\n\n    let tagDataOffset = 0;\n    if (options?.tag !== undefined) {\n      tagDataOffset = allocWasmString(options.tag, allocs);\n    }\n\n    runOptionsHandle = wasm._OrtCreateRunOptions(\n        runOptions.logSeverityLevel!, runOptions.logVerbosityLevel!, !!runOptions.terminate!, tagDataOffset);\n    if (runOptionsHandle === 0) {\n      checkLastError('Can\\'t create run options.');\n    }\n\n    if (options?.extra !== undefined) {\n      iterateExtraOptions(options.extra, '', new WeakSet<Record<string, unknown>>(), (key, value) => {\n        const keyDataOffset = allocWasmString(key, allocs);\n        const valueDataOffset = allocWasmString(value, allocs);\n\n        if (wasm._OrtAddRunConfigEntry(runOptionsHandle, keyDataOffset, valueDataOffset) !== 0) {\n          checkLastError(`Can't set a run config entry: ${key} - ${value}.`);\n        }\n      });\n    }\n\n    return [runOptionsHandle, allocs];\n  } catch (e) {\n    if (runOptionsHandle !== 0) {\n      wasm._OrtReleaseRunOptions(runOptionsHandle);\n    }\n    allocs.forEach(alloc => wasm._free(alloc));\n    throw e;\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {InferenceSession} from 'onnxruntime-common';\n\nimport {getInstance} from './wasm-factory';\nimport {allocWasmString, checkLastError, iterateExtraOptions} from './wasm-utils';\n\nconst getGraphOptimzationLevel = (graphOptimizationLevel: string|unknown): number => {\n  switch (graphOptimizationLevel) {\n    case 'disabled':\n      return 0;\n    case 'basic':\n      return 1;\n    case 'extended':\n      return 2;\n    case 'all':\n      return 99;\n    default:\n      throw new Error(`unsupported graph optimization level: ${graphOptimizationLevel}`);\n  }\n};\n\nconst getExecutionMode = (executionMode: 'sequential'|'parallel'): number => {\n  switch (executionMode) {\n    case 'sequential':\n      return 0;\n    case 'parallel':\n      return 1;\n    default:\n      throw new Error(`unsupported execution mode: ${executionMode}`);\n  }\n};\n\nconst appendDefaultOptions = (options: InferenceSession.SessionOptions): void => {\n  if (!options.extra) {\n    options.extra = {};\n  }\n  if (!options.extra.session) {\n    options.extra.session = {};\n  }\n  const session = options.extra.session as Record<string, string>;\n  if (!session.use_ort_model_bytes_directly) {\n    // eslint-disable-next-line camelcase\n    session.use_ort_model_bytes_directly = '1';\n  }\n\n  // if using JSEP with WebGPU, always disable memory pattern\n  if (options.executionProviders &&\n      options.executionProviders.some(ep => (typeof ep === 'string' ? ep : ep.name) === 'webgpu')) {\n    options.enableMemPattern = false;\n  }\n};\n\nconst setExecutionProviders =\n    (sessionOptionsHandle: number, executionProviders: readonly InferenceSession.ExecutionProviderConfig[],\n     allocs: number[]): void => {\n      for (const ep of executionProviders) {\n        let epName = typeof ep === 'string' ? ep : ep.name;\n\n        // check EP name\n        switch (epName) {\n          case 'webnn':\n            epName = 'WEBNN';\n            if (typeof ep !== 'string') {\n              const webnnOptions = ep as InferenceSession.WebNNExecutionProviderOption;\n              // const context = (webnnOptions as InferenceSession.WebNNOptionsWithMLContext)?.context;\n              const deviceType = (webnnOptions as InferenceSession.WebNNContextOptions)?.deviceType;\n              if (deviceType) {\n                const keyDataOffset = allocWasmString('deviceType', allocs);\n                const valueDataOffset = allocWasmString(deviceType, allocs);\n                if (getInstance()._OrtAddSessionConfigEntry(sessionOptionsHandle, keyDataOffset, valueDataOffset) !==\n                    0) {\n                  checkLastError(`Can't set a session config entry: 'deviceType' - ${deviceType}.`);\n                }\n              }\n            }\n            break;\n          case 'webgpu':\n            epName = 'JS';\n            if (typeof ep !== 'string') {\n              const webgpuOptions = ep as InferenceSession.WebGpuExecutionProviderOption;\n              if (webgpuOptions?.preferredLayout) {\n                if (webgpuOptions.preferredLayout !== 'NCHW' && webgpuOptions.preferredLayout !== 'NHWC') {\n                  throw new Error(`preferredLayout must be either 'NCHW' or 'NHWC': ${webgpuOptions.preferredLayout}`);\n                }\n                const keyDataOffset = allocWasmString('preferredLayout', allocs);\n                const valueDataOffset = allocWasmString(webgpuOptions.preferredLayout, allocs);\n                if (getInstance()._OrtAddSessionConfigEntry(sessionOptionsHandle, keyDataOffset, valueDataOffset) !==\n                    0) {\n                  checkLastError(\n                      `Can't set a session config entry: 'preferredLayout' - ${webgpuOptions.preferredLayout}.`);\n                }\n              }\n            }\n            break;\n          case 'wasm':\n          case 'cpu':\n            continue;\n          default:\n            throw new Error(`not supported execution provider: ${epName}`);\n        }\n\n        const epNameDataOffset = allocWasmString(epName, allocs);\n        if (getInstance()._OrtAppendExecutionProvider(sessionOptionsHandle, epNameDataOffset) !== 0) {\n          checkLastError(`Can't append execution provider: ${epName}.`);\n        }\n      }\n    };\n\nexport const setSessionOptions = (options?: InferenceSession.SessionOptions): [number, number[]] => {\n  const wasm = getInstance();\n  let sessionOptionsHandle = 0;\n  const allocs: number[] = [];\n\n  const sessionOptions: InferenceSession.SessionOptions = options || {};\n  appendDefaultOptions(sessionOptions);\n\n  try {\n    const graphOptimizationLevel = getGraphOptimzationLevel(sessionOptions.graphOptimizationLevel ?? 'all');\n    const executionMode = getExecutionMode(sessionOptions.executionMode ?? 'sequential');\n    const logIdDataOffset =\n        typeof sessionOptions.logId === 'string' ? allocWasmString(sessionOptions.logId, allocs) : 0;\n\n    const logSeverityLevel = sessionOptions.logSeverityLevel ?? 2;  // Default to 2 - warning\n    if (!Number.isInteger(logSeverityLevel) || logSeverityLevel < 0 || logSeverityLevel > 4) {\n      throw new Error(`log serverity level is not valid: ${logSeverityLevel}`);\n    }\n\n    const logVerbosityLevel = sessionOptions.logVerbosityLevel ?? 0;  // Default to 0 - verbose\n    if (!Number.isInteger(logVerbosityLevel) || logVerbosityLevel < 0 || logVerbosityLevel > 4) {\n      throw new Error(`log verbosity level is not valid: ${logVerbosityLevel}`);\n    }\n\n    const optimizedModelFilePathOffset = typeof sessionOptions.optimizedModelFilePath === 'string' ?\n        allocWasmString(sessionOptions.optimizedModelFilePath, allocs) :\n        0;\n\n    sessionOptionsHandle = wasm._OrtCreateSessionOptions(\n        graphOptimizationLevel, !!sessionOptions.enableCpuMemArena, !!sessionOptions.enableMemPattern, executionMode,\n        !!sessionOptions.enableProfiling, 0, logIdDataOffset, logSeverityLevel, logVerbosityLevel,\n        optimizedModelFilePathOffset);\n    if (sessionOptionsHandle === 0) {\n      checkLastError('Can\\'t create session options.');\n    }\n\n    if (sessionOptions.executionProviders) {\n      setExecutionProviders(sessionOptionsHandle, sessionOptions.executionProviders, allocs);\n    }\n\n    if (sessionOptions.enableGraphCapture !== undefined) {\n      if (typeof sessionOptions.enableGraphCapture !== 'boolean') {\n        throw new Error(`enableGraphCapture must be a boolean value: ${sessionOptions.enableGraphCapture}`);\n      }\n      const keyDataOffset = allocWasmString('enableGraphCapture', allocs);\n      const valueDataOffset = allocWasmString(sessionOptions.enableGraphCapture.toString(), allocs);\n      if (wasm._OrtAddSessionConfigEntry(sessionOptionsHandle, keyDataOffset, valueDataOffset) !== 0) {\n        checkLastError(\n            `Can't set a session config entry: 'enableGraphCapture' - ${sessionOptions.enableGraphCapture}.`);\n      }\n    }\n\n    if (sessionOptions.freeDimensionOverrides) {\n      for (const [name, value] of Object.entries(sessionOptions.freeDimensionOverrides)) {\n        if (typeof name !== 'string') {\n          throw new Error(`free dimension override name must be a string: ${name}`);\n        }\n        if (typeof value !== 'number' || !Number.isInteger(value) || value < 0) {\n          throw new Error(`free dimension override value must be a non-negative integer: ${value}`);\n        }\n        const nameOffset = allocWasmString(name, allocs);\n        if (wasm._OrtAddFreeDimensionOverride(sessionOptionsHandle, nameOffset, value) !== 0) {\n          checkLastError(`Can't set a free dimension override: ${name} - ${value}.`);\n        }\n      }\n    }\n\n    if (sessionOptions.extra !== undefined) {\n      iterateExtraOptions(sessionOptions.extra, '', new WeakSet<Record<string, unknown>>(), (key, value) => {\n        const keyDataOffset = allocWasmString(key, allocs);\n        const valueDataOffset = allocWasmString(value, allocs);\n\n        if (wasm._OrtAddSessionConfigEntry(sessionOptionsHandle, keyDataOffset, valueDataOffset) !== 0) {\n          checkLastError(`Can't set a session config entry: ${key} - ${value}.`);\n        }\n      });\n    }\n\n    return [sessionOptionsHandle, allocs];\n  } catch (e) {\n    if (sessionOptionsHandle !== 0) {\n      wasm._OrtReleaseSessionOptions(sessionOptionsHandle);\n    }\n    allocs.forEach(alloc => wasm._free(alloc));\n    throw e;\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Tensor} from 'onnxruntime-common';\n\n// a dummy type declaration for Float16Array in case any polyfill is available.\ndeclare global {\n  // eslint-disable-next-line @typescript-eslint/naming-convention, @typescript-eslint/no-explicit-any\n  const Float16Array: any;\n}\n\n// This file includes common definitions. They do NOT have dependency on the WebAssembly instance.\n\n/**\n * Copied from ONNX definition. Use this to drop dependency 'onnx_proto' to decrease compiled .js file size.\n */\nexport const enum DataType {\n  undefined = 0,\n  float = 1,\n  uint8 = 2,\n  int8 = 3,\n  uint16 = 4,\n  int16 = 5,\n  int32 = 6,\n  int64 = 7,\n  string = 8,\n  bool = 9,\n  float16 = 10,\n  double = 11,\n  uint32 = 12,\n  uint64 = 13,\n  complex64 = 14,\n  complex128 = 15,\n  bfloat16 = 16\n}\n\n/**\n * Map string tensor data to enum value\n */\nexport const tensorDataTypeStringToEnum = (type: string): DataType => {\n  switch (type) {\n    case 'int8':\n      return DataType.int8;\n    case 'uint8':\n      return DataType.uint8;\n    case 'bool':\n      return DataType.bool;\n    case 'int16':\n      return DataType.int16;\n    case 'uint16':\n      return DataType.uint16;\n    case 'int32':\n      return DataType.int32;\n    case 'uint32':\n      return DataType.uint32;\n    case 'float16':\n      return DataType.float16;\n    case 'float32':\n      return DataType.float;\n    case 'float64':\n      return DataType.double;\n    case 'string':\n      return DataType.string;\n    case 'int64':\n      return DataType.int64;\n    case 'uint64':\n      return DataType.uint64;\n\n    default:\n      throw new Error(`unsupported data type: ${type}`);\n  }\n};\n\n/**\n * Map enum value to string tensor data\n */\nexport const tensorDataTypeEnumToString = (typeProto: DataType): Tensor.Type => {\n  switch (typeProto) {\n    case DataType.int8:\n      return 'int8';\n    case DataType.uint8:\n      return 'uint8';\n    case DataType.bool:\n      return 'bool';\n    case DataType.int16:\n      return 'int16';\n    case DataType.uint16:\n      return 'uint16';\n    case DataType.int32:\n      return 'int32';\n    case DataType.uint32:\n      return 'uint32';\n    case DataType.float16:\n      return 'float16';\n    case DataType.float:\n      return 'float32';\n    case DataType.double:\n      return 'float64';\n    case DataType.string:\n      return 'string';\n    case DataType.int64:\n      return 'int64';\n    case DataType.uint64:\n      return 'uint64';\n\n    default:\n      throw new Error(`unsupported data type: ${typeProto}`);\n  }\n};\n\n/**\n * get tensor element size in bytes by the given data type\n * @returns size in integer or undefined if the data type is not supported\n */\nexport const getTensorElementSize = (dateType: number): number|\n    undefined => [undefined, 4, 1, 1, 2, 2, 4, 8, undefined, 1, 2, 8, 4, 8, undefined, undefined, undefined][dateType];\n\n/**\n * get typed array constructor by the given tensor type\n */\nexport const tensorTypeToTypedArrayConstructor = (type: Tensor.Type): Float32ArrayConstructor|Uint8ArrayConstructor|\n    Int8ArrayConstructor|Uint16ArrayConstructor|Int16ArrayConstructor|Int32ArrayConstructor|BigInt64ArrayConstructor|\n    Uint8ArrayConstructor|Float64ArrayConstructor|Uint32ArrayConstructor|BigUint64ArrayConstructor => {\n      switch (type) {\n        case 'float16':\n          // allow Float16Array polyfill.\n          return typeof Float16Array !== 'undefined' && Float16Array.from ? Float16Array : Uint16Array;\n        case 'float32':\n          return Float32Array;\n        case 'uint8':\n          return Uint8Array;\n        case 'int8':\n          return Int8Array;\n        case 'uint16':\n          return Uint16Array;\n        case 'int16':\n          return Int16Array;\n        case 'int32':\n          return Int32Array;\n        case 'bool':\n          return Uint8Array;\n        case 'float64':\n          return Float64Array;\n        case 'uint32':\n          return Uint32Array;\n        case 'int64':\n          return BigInt64Array;\n        case 'uint64':\n          return BigUint64Array;\n        default:\n          throw new Error(`unsupported type: ${type}`);\n      }\n    };\n\n/**\n * Map string log level to integer value\n */\nexport const logLevelStringToEnum = (logLevel?: 'verbose'|'info'|'warning'|'error'|'fatal'): number => {\n  switch (logLevel) {\n    case 'verbose':\n      return 0;\n    case 'info':\n      return 1;\n    case 'warning':\n      return 2;\n    case 'error':\n      return 3;\n    case 'fatal':\n      return 4;\n    default:\n      throw new Error(`unsupported logging level: ${logLevel}`);\n  }\n};\n\n/**\n * Check whether the given tensor type is supported by GPU buffer\n */\nexport const isGpuBufferSupportedType = (type: Tensor.Type): type is Tensor.GpuBufferDataTypes => type === 'float32' ||\n    type === 'float16' || type === 'int32' || type === 'int64' || type === 'uint32' || type === 'uint8' ||\n    type === 'bool';\n\n/**\n * Map string data location to integer value\n */\nexport const dataLocationStringToEnum = (location: Tensor.DataLocation): number => {\n  switch (location) {\n    case 'none':\n      return 0;\n    case 'cpu':\n      return 1;\n    case 'cpu-pinned':\n      return 2;\n    case 'texture':\n      return 3;\n    case 'gpu-buffer':\n      return 4;\n    default:\n      throw new Error(`unsupported data location: ${location}`);\n  }\n};\n\n/**\n * Map integer data location to string value\n */\nexport const dataLocationEnumToString = (location: number): Tensor.DataLocation|undefined =>\n    (['none', 'cpu', 'cpu-pinned', 'texture', 'gpu-buffer'] as const)[location];\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {isNode} from './wasm-utils-env';\n\n/**\n * Load a file into a Uint8Array.\n *\n * @param file - the file to load. Can be a URL/path, a Blob, an ArrayBuffer, or a Uint8Array.\n * @returns a Uint8Array containing the file data.\n */\nexport const loadFile = async(file: string|Blob|ArrayBufferLike|Uint8Array): Promise<Uint8Array> => {\n  if (typeof file === 'string') {\n    if (isNode) {\n      // load file into ArrayBuffer in Node.js\n      try {\n        const {readFile} = require('node:fs/promises');\n        return new Uint8Array(await readFile(file));\n      } catch (e) {\n        if (e.code === 'ERR_FS_FILE_TOO_LARGE') {\n          // file is too large, use fs.createReadStream instead\n          const {createReadStream} = require('node:fs');\n          const stream = createReadStream(file);\n          const chunks: Uint8Array[] = [];\n          for await (const chunk of stream) {\n            chunks.push(chunk);\n          }\n          return new Uint8Array(Buffer.concat(chunks));\n        }\n        throw e;\n      }\n    } else {\n      // load file into ArrayBuffer in browsers\n      const response = await fetch(file);\n      if (!response.ok) {\n        throw new Error(`failed to load external data file: ${file}`);\n      }\n      const contentLengthHeader = response.headers.get('Content-Length');\n      const fileSize = contentLengthHeader ? parseInt(contentLengthHeader, 10) : 0;\n      if (fileSize < 1073741824 /* 1GB */) {\n        // when Content-Length header is not set, we cannot determine the file size. We assume it is small enough to\n        // load into memory.\n        return new Uint8Array(await response.arrayBuffer());\n      } else {\n        // file is too large, use stream instead\n        if (!response.body) {\n          throw new Error(`failed to load external data file: ${file}, no response body.`);\n        }\n        const reader = response.body.getReader();\n\n        let buffer;\n        try {\n          // try to create ArrayBuffer directly\n          buffer = new ArrayBuffer(fileSize);\n        } catch (e) {\n          if (e instanceof RangeError) {\n            // use WebAssembly Memory to allocate larger ArrayBuffer\n            const pages = Math.ceil(fileSize / 65536);\n            buffer = new WebAssembly.Memory({initial: pages, maximum: pages}).buffer;\n          } else {\n            throw e;\n          }\n        }\n\n        let offset = 0;\n        // eslint-disable-next-line no-constant-condition\n        while (true) {\n          const {done, value} = await reader.read();\n          if (done) {\n            break;\n          }\n          const chunkSize = value.byteLength;\n          const chunk = new Uint8Array(buffer, offset, chunkSize);\n          chunk.set(value);\n          offset += chunkSize;\n        }\n        return new Uint8Array(buffer, 0, fileSize);\n      }\n    }\n\n  } else if (file instanceof Blob) {\n    return new Uint8Array(await file.arrayBuffer());\n  } else if (file instanceof Uint8Array) {\n    return file;\n  } else {\n    return new Uint8Array(file);\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\n// WebNN API currently does not have a TypeScript definition file. This file is a workaround with types generated from\n// WebNN API specification.\n// https://github.com/webmachinelearning/webnn/issues/677\n/// <reference path=\"jsep/webnn/webnn.d.ts\" />\n\nimport {Env, InferenceSession, Tensor} from 'onnxruntime-common';\n\nimport {SerializableInternalBuffer, SerializableSessionMetadata, SerializableTensorMetadata, TensorMetadata} from './proxy-messages';\nimport {setRunOptions} from './run-options';\nimport {setSessionOptions} from './session-options';\nimport {dataLocationStringToEnum, getTensorElementSize, isGpuBufferSupportedType, logLevelStringToEnum, tensorDataTypeEnumToString, tensorDataTypeStringToEnum, tensorTypeToTypedArrayConstructor} from './wasm-common';\nimport {getInstance} from './wasm-factory';\nimport {allocWasmString, checkLastError} from './wasm-utils';\nimport {loadFile} from './wasm-utils-load-file';\n\n// #region Initializations\n\n/**\n * There are 4 different \"initialization\" steps for ORT. They happen in different places and different time.\n *\n * 1. JavaScript initialization for onnxruntime-common and onnxruntime-web.\n *    This is the first initialization step. In this step, onnxruntime-web calls onnxruntime-common's registerBackend()\n * function multiple times to register all the available backends. The backend registration is very fast. It only\n * registers the backend name with the uninitialized backend object. No heavy initialization is done in this step.\n *    Refer to web/lib/index.ts for the backend registration.\n *\n * 2. WebAssembly artifact initialization.\n *    This happens when any registered wasm backend is used for the first time (ie. `ort.InferenceSession.create()` or\n * `ort.TrainingSession.create()` is called). In this step, onnxruntime-web does the followings:\n *     - create a proxy worker and make sure the proxy worker is ready to receive messages, if proxy is enabled.\n *     - perform feature detection, locate correct WebAssembly artifact path and call the Emscripten generated\n * JavaScript code to initialize the WebAssembly runtime.\n *         - if proxy is enabled, this step happens in the proxy worker using message 'init-wasm'.\n *         - downloading the 'ort-wasm{...}.wasm' file is done in this step.\n *         - if multi-thread is enabled, one or more webworker will be created to initialize the PThread threadpool.\n *\n * 3. ORT environment initialization.\n *    This happens after step 2. In this step, onnxruntime-web performs ONNX Runtime environment initialization.\n * Function `_OrtInit()` is called in this step.\n *     - if proxy is enabled, this step happens in the proxy worker using message 'init-ort'.\n *     - logging level (ort.env.logLevel) and thread number (ort.env.wasm.numThreads) are set in this step.\n *\n * 4. Session initialization.\n *    This happens when `ort.InferenceSession.create()` or `ort.TrainingSession.create()` is called. Unlike the first 3\n * steps (they only called once), this step will be done for each session. In this step, onnxruntime-web does the\n * followings:\n *    If the parameter is a URL:\n *    - download the model data from the URL.\n *    - copy the model data to the WASM heap. (proxy: 'copy-from')\n *    - dereference the model buffer. This step allows the original ArrayBuffer to be garbage collected.\n *    - call `_OrtCreateSession()` to create the session. (proxy: 'create')\n *\n *    If the parameter is a Uint8Array object:\n *    - copy the model data to the WASM heap. (proxy: 'copy-from')\n *    - call `_OrtCreateSession()` to create the session. (proxy: 'create')\n *\n *\n */\n\n/**\n * initialize ORT environment.\n *\n * @param numThreads SetGlobalIntraOpNumThreads(numThreads)\n * @param loggingLevel CreateEnv(static_cast<OrtLoggingLevel>(logging_level))\n */\nconst initOrt = (numThreads: number, loggingLevel: number): void => {\n  const errorCode = getInstance()._OrtInit(numThreads, loggingLevel);\n  if (errorCode !== 0) {\n    checkLastError('Can\\'t initialize onnxruntime.');\n  }\n};\n\n/**\n * initialize runtime environment.\n * @param env passed in the environment config object.\n */\nexport const initRuntime = async(env: Env): Promise<void> => {\n  // init ORT\n  initOrt(env.wasm.numThreads!, logLevelStringToEnum(env.logLevel));\n};\n\n/**\n * perform EP specific initialization.\n *\n * @param env\n * @param epName\n */\nexport const initEp = async(env: Env, epName: string): Promise<void> => {\n  if (!BUILD_DEFS.DISABLE_JSEP) {\n    // eslint-disable-next-line @typescript-eslint/no-require-imports, @typescript-eslint/no-var-requires\n    const initJsep = require('./jsep/init').init;\n\n    if (epName === 'webgpu') {\n      // perform WebGPU availability check\n      if (typeof navigator === 'undefined' || !navigator.gpu) {\n        throw new Error('WebGPU is not supported in current environment');\n      }\n\n      let adapter = env.webgpu.adapter as GPUAdapter | null;\n      if (!adapter) {\n        // if adapter is not set, request a new adapter.\n        const powerPreference = env.webgpu.powerPreference;\n        if (powerPreference !== undefined && powerPreference !== 'low-power' &&\n            powerPreference !== 'high-performance') {\n          throw new Error(`Invalid powerPreference setting: \"${powerPreference}\"`);\n        }\n        const forceFallbackAdapter = env.webgpu.forceFallbackAdapter;\n        if (forceFallbackAdapter !== undefined && typeof forceFallbackAdapter !== 'boolean') {\n          throw new Error(`Invalid forceFallbackAdapter setting: \"${forceFallbackAdapter}\"`);\n        }\n        adapter = await navigator.gpu.requestAdapter({powerPreference, forceFallbackAdapter});\n        if (!adapter) {\n          throw new Error(\n              'Failed to get GPU adapter. ' +\n              'You may need to enable flag \"--enable-unsafe-webgpu\" if you are using Chrome.');\n        }\n      } else {\n        // if adapter is set, validate it.\n        if (typeof adapter.limits !== 'object' || typeof adapter.features !== 'object' ||\n            typeof adapter.requestDevice !== 'function') {\n          throw new Error('Invalid GPU adapter set in `env.webgpu.adapter`. It must be a GPUAdapter object.');\n        }\n      }\n\n      await initJsep('webgpu', getInstance(), env, adapter);\n    }\n    if (epName === 'webnn') {\n      // perform WebNN availability check\n      if (typeof navigator === 'undefined' || !(navigator as unknown as {ml: unknown}).ml) {\n        throw new Error('WebNN is not supported in current environment');\n      }\n\n      await initJsep('webnn', getInstance(), env);\n    }\n  }\n};\n\n// #endregion Initializations\n\n/**\n * valid data locations for input/output tensors.\n */\ntype SupportedTensorDataLocationForInputOutput = 'cpu'|'cpu-pinned'|'gpu-buffer';\n\ntype IOBindingState = {\n  /**\n   * the handle of IO binding.\n   */\n  readonly handle: number;\n\n  /**\n   * the preferred location for each output tensor.\n   *\n   * value is one of 'cpu', 'cpu-pinned', 'gpu-buffer'.\n   */\n  readonly outputPreferredLocations: readonly SupportedTensorDataLocationForInputOutput[];\n\n  /**\n   * enum value of the preferred location for each output tensor.\n   */\n  readonly outputPreferredLocationsEncoded: readonly number[];\n};\n\n/**\n *  tuple elements are: InferenceSession ID; inputNamesUTF8Encoded; outputNamesUTF8Encoded; bindingState\n */\ntype SessionMetadata = [\n  inferenceSessionId: number, inputNamesUTF8Encoded: number[], outputNamesUTF8Encoded: number[],\n  bindingState: IOBindingState|null, enableGraphCapture: boolean, inputOutputBound: boolean\n];\n\nconst activeSessions = new Map<number, SessionMetadata>();\n\n/**\n * get the input/output count of the session.\n * @param sessionHandle the handle representing the session. should be non-zero.\n * @returns a tuple including 2 numbers, representing the input count and output count.\n */\nconst getSessionInputOutputCount = (sessionHandle: number): [number, number] => {\n  const wasm = getInstance();\n  const stack = wasm.stackSave();\n  try {\n    const dataOffset = wasm.stackAlloc(8);\n    const errorCode = wasm._OrtGetInputOutputCount(sessionHandle, dataOffset, dataOffset + 4);\n    if (errorCode !== 0) {\n      checkLastError('Can\\'t get session input/output count.');\n    }\n    return [wasm.HEAP32[dataOffset / 4], wasm.HEAP32[dataOffset / 4 + 1]];\n  } finally {\n    wasm.stackRestore(stack);\n  }\n};\n\n/**\n * allocate the memory and memcpy the external buffer.\n *\n * @param model - the external buffer containing the model data. Must not be the same buffer as the WASM heap.\n * @returns a 2-elements tuple - the pointer and size of the allocated buffer\n */\nexport const copyFromExternalBuffer = (model: Uint8Array): [number, number] => {\n  const wasm = getInstance();\n  const modelDataOffset = wasm._malloc(model.byteLength);\n  if (modelDataOffset === 0) {\n    throw new Error(`Can't create a session. failed to allocate a buffer of size ${model.byteLength}.`);\n  }\n  wasm.HEAPU8.set(model, modelDataOffset);\n  return [modelDataOffset, model.byteLength];\n};\n\n/**\n * create an inference session from a model data buffer.\n *\n * @param modelData - either a Uint8Array object representing the model data, or a 2-elements tuple containing the\n *     pointer and size of the model data buffer.\n * @param options an optional session options object.\n * @returns a 3-elements tuple containing [session handle, input names, output names]\n */\nexport const createSession = async(\n    modelData: Uint8Array|SerializableInternalBuffer,\n    options?: InferenceSession.SessionOptions): Promise<SerializableSessionMetadata> => {\n  let modelDataOffset: number, modelDataLength: number;\n  const wasm = getInstance();\n\n  if (Array.isArray(modelData)) {\n    // if model data is an array, it must be a 2-elements tuple containing the pointer and size of the model data\n    [modelDataOffset, modelDataLength] = modelData;\n  } else if (modelData.buffer === wasm.HEAPU8.buffer) {\n    // if model data uses the same buffer as the WASM heap, we don't need to copy it.\n    [modelDataOffset, modelDataLength] = [modelData.byteOffset, modelData.byteLength];\n  } else {\n    // otherwise, copy the model data to the WASM heap.\n    [modelDataOffset, modelDataLength] = copyFromExternalBuffer(modelData);\n  }\n\n  let sessionHandle = 0;\n  let sessionOptionsHandle = 0;\n  let ioBindingHandle = 0;\n  let allocs: number[] = [];\n  const inputNamesUTF8Encoded = [];\n  const outputNamesUTF8Encoded = [];\n\n  try {\n    [sessionOptionsHandle, allocs] = setSessionOptions(options);\n\n    if (options?.externalData && wasm.mountExternalData) {\n      const loadingPromises = [];\n      for (const file of options.externalData) {\n        const path = typeof file === 'string' ? file : file.path;\n        loadingPromises.push(loadFile(typeof file === 'string' ? file : file.data).then(data => {\n          wasm.mountExternalData!(path, data);\n        }));\n      }\n\n      // wait for all external data files to be loaded\n      await Promise.all(loadingPromises);\n    }\n\n    for (const provider of options?.executionProviders ?? []) {\n      const providerName = typeof provider === 'string' ? provider : provider.name;\n      if (providerName === 'webnn') {\n        if (wasm.currentContext) {\n          throw new Error('WebNN execution provider is already set.');\n        }\n        if (typeof provider !== 'string') {\n          const webnnOptions = provider as InferenceSession.WebNNExecutionProviderOption;\n          const context = (webnnOptions as InferenceSession.WebNNOptionsWithMLContext)?.context;\n          const gpuDevice = (webnnOptions as InferenceSession.WebNNOptionsWebGpu)?.gpuDevice;\n          const deviceType = (webnnOptions as InferenceSession.WebNNContextOptions)?.deviceType;\n          const numThreads = (webnnOptions as InferenceSession.WebNNContextOptions)?.numThreads;\n          const powerPreference = (webnnOptions as InferenceSession.WebNNContextOptions)?.powerPreference;\n          if (context) {\n            wasm.currentContext = context as MLContext;\n          } else if (gpuDevice) {\n            wasm.currentContext = await navigator.ml.createContext(gpuDevice);\n          } else {\n            wasm.currentContext = await navigator.ml.createContext({deviceType, numThreads, powerPreference});\n          }\n        } else {\n          wasm.currentContext = await navigator.ml.createContext();\n        }\n        break;\n      }\n    }\n\n    sessionHandle = await wasm._OrtCreateSession(modelDataOffset, modelDataLength, sessionOptionsHandle);\n    if (sessionHandle === 0) {\n      checkLastError('Can\\'t create a session.');\n    }\n\n    // clear current MLContext after session creation\n    if (wasm.currentContext) {\n      wasm.currentContext = undefined;\n    }\n\n    const [inputCount, outputCount] = getSessionInputOutputCount(sessionHandle);\n\n    const enableGraphCapture = !!options?.enableGraphCapture;\n\n    const inputNames = [];\n    const outputNames = [];\n    const outputPreferredLocations: SupportedTensorDataLocationForInputOutput[] = [];\n    for (let i = 0; i < inputCount; i++) {\n      const name = wasm._OrtGetInputName(sessionHandle, i);\n      if (name === 0) {\n        checkLastError('Can\\'t get an input name.');\n      }\n      inputNamesUTF8Encoded.push(name);\n      inputNames.push(wasm.UTF8ToString(name));\n    }\n    for (let i = 0; i < outputCount; i++) {\n      const name = wasm._OrtGetOutputName(sessionHandle, i);\n      if (name === 0) {\n        checkLastError('Can\\'t get an output name.');\n      }\n      outputNamesUTF8Encoded.push(name);\n      const nameString = wasm.UTF8ToString(name);\n      outputNames.push(nameString);\n\n      if (!BUILD_DEFS.DISABLE_JSEP) {\n        if (enableGraphCapture && options?.preferredOutputLocation === undefined) {\n          outputPreferredLocations.push('gpu-buffer');\n          continue;\n        }\n        const location = typeof options?.preferredOutputLocation === 'string' ?\n            options.preferredOutputLocation :\n            options?.preferredOutputLocation?.[nameString] ?? 'cpu';\n        if (location !== 'cpu' && location !== 'cpu-pinned' && location !== 'gpu-buffer') {\n          throw new Error(`Not supported preferred output location: ${location}.`);\n        }\n        if (enableGraphCapture && location !== 'gpu-buffer') {\n          throw new Error(`Not supported preferred output location: ${\n              location}. Only 'gpu-buffer' location is supported when enableGraphCapture is true.`);\n        }\n        outputPreferredLocations.push(location);\n      }\n    }\n\n    // use IO binding only when at least one output is preffered to be on GPU.\n    let bindingState: IOBindingState|null = null;\n    if (!BUILD_DEFS.DISABLE_JSEP && outputPreferredLocations.some(l => l === 'gpu-buffer')) {\n      ioBindingHandle = wasm._OrtCreateBinding(sessionHandle);\n      if (ioBindingHandle === 0) {\n        checkLastError('Can\\'t create IO binding.');\n      }\n\n      bindingState = {\n        handle: ioBindingHandle,\n        outputPreferredLocations,\n        outputPreferredLocationsEncoded: outputPreferredLocations.map(l => dataLocationStringToEnum(l)),\n      };\n    }\n\n    activeSessions.set(\n        sessionHandle,\n        [sessionHandle, inputNamesUTF8Encoded, outputNamesUTF8Encoded, bindingState, enableGraphCapture, false]);\n    return [sessionHandle, inputNames, outputNames];\n  } catch (e) {\n    inputNamesUTF8Encoded.forEach(buf => wasm._OrtFree(buf));\n    outputNamesUTF8Encoded.forEach(buf => wasm._OrtFree(buf));\n\n    if (ioBindingHandle !== 0) {\n      wasm._OrtReleaseBinding(ioBindingHandle);\n    }\n\n    if (sessionHandle !== 0) {\n      wasm._OrtReleaseSession(sessionHandle);\n    }\n    throw e;\n  } finally {\n    wasm._free(modelDataOffset);\n    if (sessionOptionsHandle !== 0) {\n      wasm._OrtReleaseSessionOptions(sessionOptionsHandle);\n    }\n    allocs.forEach(alloc => wasm._free(alloc));\n\n    // unmount external data if necessary\n    wasm.unmountExternalData?.();\n  }\n};\n\nexport const releaseSession = (sessionId: number): void => {\n  const wasm = getInstance();\n  const session = activeSessions.get(sessionId);\n  if (!session) {\n    throw new Error(`cannot release session. invalid session id: ${sessionId}`);\n  }\n  const [sessionHandle, inputNamesUTF8Encoded, outputNamesUTF8Encoded, ioBindingState, enableGraphCapture] = session;\n\n  if (ioBindingState) {\n    if (enableGraphCapture) {\n      wasm._OrtClearBoundOutputs(ioBindingState.handle);\n    }\n    wasm._OrtReleaseBinding(ioBindingState.handle);\n  }\n\n  wasm.jsepOnReleaseSession?.(sessionId);\n\n  inputNamesUTF8Encoded.forEach(buf => wasm._OrtFree(buf));\n  outputNamesUTF8Encoded.forEach(buf => wasm._OrtFree(buf));\n  wasm._OrtReleaseSession(sessionHandle);\n  activeSessions.delete(sessionId);\n};\n\nexport const prepareInputOutputTensor =\n    (tensor: TensorMetadata|null, tensorHandles: number[], allocs: number[], sessionId: number, index: number,\n     enableGraphCapture = false): void => {\n      if (!tensor) {\n        tensorHandles.push(0);\n        return;\n      }\n\n      const wasm = getInstance();\n\n      const dataType = tensor[0];\n      const dims = tensor[1];\n      const location = tensor[3];\n\n      let rawData: number;\n      let dataByteLength: number;\n\n      if (dataType === 'string' && location === 'gpu-buffer') {\n        throw new Error('String tensor is not supported on GPU.');\n      }\n\n      if (enableGraphCapture && location !== 'gpu-buffer') {\n        throw new Error(\n            `External buffer must be provided for input/output index ${index} when enableGraphCapture is true.`);\n      }\n\n      if (location === 'gpu-buffer') {\n        const gpuBuffer = tensor[2].gpuBuffer as GPUBuffer;\n        const elementSizeInBytes = getTensorElementSize(tensorDataTypeStringToEnum(dataType))!;\n        dataByteLength = dims.reduce((a, b) => a * b, 1) * elementSizeInBytes;\n\n        const registerBuffer = wasm.jsepRegisterBuffer;\n        if (!registerBuffer) {\n          throw new Error('Tensor location \"gpu-buffer\" is not supported without using WebGPU.');\n        }\n        rawData = registerBuffer(sessionId, index, gpuBuffer, dataByteLength);\n      } else {\n        const data = tensor[2];\n\n        if (Array.isArray(data)) {\n          // string tensor\n          dataByteLength = 4 * data.length;\n          rawData = wasm._malloc(dataByteLength);\n          allocs.push(rawData);\n          let dataIndex = rawData / 4;\n          for (let i = 0; i < data.length; i++) {\n            if (typeof data[i] !== 'string') {\n              throw new TypeError(`tensor data at index ${i} is not a string`);\n            }\n            wasm.HEAPU32[dataIndex++] = allocWasmString(data[i], allocs);\n          }\n        } else {\n          dataByteLength = data.byteLength;\n          rawData = wasm._malloc(dataByteLength);\n          allocs.push(rawData);\n          wasm.HEAPU8.set(new Uint8Array(data.buffer, data.byteOffset, dataByteLength), rawData);\n        }\n      }\n\n      const stack = wasm.stackSave();\n      const dimsOffset = wasm.stackAlloc(4 * dims.length);\n      try {\n        let dimIndex = dimsOffset / 4;\n        dims.forEach(d => wasm.HEAP32[dimIndex++] = d);\n        const tensor = wasm._OrtCreateTensor(\n            tensorDataTypeStringToEnum(dataType), rawData, dataByteLength, dimsOffset, dims.length,\n            dataLocationStringToEnum(location));\n        if (tensor === 0) {\n          checkLastError(`Can't create tensor for input/output. session=${sessionId}, index=${index}.`);\n        }\n        tensorHandles.push(tensor);\n      } finally {\n        wasm.stackRestore(stack);\n      }\n    };\n\n/**\n * perform inference run\n */\nexport const run = async(\n    sessionId: number, inputIndices: number[], inputTensors: TensorMetadata[], outputIndices: number[],\n    outputTensors: Array<TensorMetadata|null>, options: InferenceSession.RunOptions): Promise<TensorMetadata[]> => {\n  const wasm = getInstance();\n  const session = activeSessions.get(sessionId);\n  if (!session) {\n    throw new Error(`cannot run inference. invalid session id: ${sessionId}`);\n  }\n  const sessionHandle = session[0];\n  const inputNamesUTF8Encoded = session[1];\n  const outputNamesUTF8Encoded = session[2];\n  const ioBindingState = session[3];\n  const enableGraphCapture = session[4];\n  const inputOutputBound = session[5];\n\n  const inputCount = inputIndices.length;\n  const outputCount = outputIndices.length;\n\n  let runOptionsHandle = 0;\n  let runOptionsAllocs: number[] = [];\n\n  const inputTensorHandles: number[] = [];\n  const outputTensorHandles: number[] = [];\n  const inputOutputAllocs: number[] = [];\n\n  const beforeRunStack = wasm.stackSave();\n  const inputValuesOffset = wasm.stackAlloc(inputCount * 4);\n  const inputNamesOffset = wasm.stackAlloc(inputCount * 4);\n  const outputValuesOffset = wasm.stackAlloc(outputCount * 4);\n  const outputNamesOffset = wasm.stackAlloc(outputCount * 4);\n\n  try {\n    [runOptionsHandle, runOptionsAllocs] = setRunOptions(options);\n\n    // create input tensors\n    for (let i = 0; i < inputCount; i++) {\n      prepareInputOutputTensor(\n          inputTensors[i], inputTensorHandles, inputOutputAllocs, sessionId, inputIndices[i], enableGraphCapture);\n    }\n\n    // create output tensors\n    for (let i = 0; i < outputCount; i++) {\n      prepareInputOutputTensor(\n          outputTensors[i], outputTensorHandles, inputOutputAllocs, sessionId, inputCount + outputIndices[i],\n          enableGraphCapture);\n    }\n\n    let inputValuesIndex = inputValuesOffset / 4;\n    let inputNamesIndex = inputNamesOffset / 4;\n    let outputValuesIndex = outputValuesOffset / 4;\n    let outputNamesIndex = outputNamesOffset / 4;\n    for (let i = 0; i < inputCount; i++) {\n      wasm.HEAPU32[inputValuesIndex++] = inputTensorHandles[i];\n      wasm.HEAPU32[inputNamesIndex++] = inputNamesUTF8Encoded[inputIndices[i]];\n    }\n    for (let i = 0; i < outputCount; i++) {\n      wasm.HEAPU32[outputValuesIndex++] = outputTensorHandles[i];\n      wasm.HEAPU32[outputNamesIndex++] = outputNamesUTF8Encoded[outputIndices[i]];\n    }\n\n    if (!BUILD_DEFS.DISABLE_JSEP && ioBindingState && !inputOutputBound) {\n      const {handle, outputPreferredLocations, outputPreferredLocationsEncoded} = ioBindingState;\n\n      if (inputNamesUTF8Encoded.length !== inputCount) {\n        throw new Error(`input count from feeds (${\n            inputCount}) is expected to be always equal to model's input count (${inputNamesUTF8Encoded.length}).`);\n      }\n\n      // process inputs\n      for (let i = 0; i < inputCount; i++) {\n        const index = inputIndices[i];\n        const errorCode = await wasm._OrtBindInput(handle, inputNamesUTF8Encoded[index], inputTensorHandles[i]);\n        if (errorCode !== 0) {\n          checkLastError(`Can't bind input[${i}] for session=${sessionId}.`);\n        }\n      }\n\n      // process pre-allocated outputs\n      for (let i = 0; i < outputCount; i++) {\n        const index = outputIndices[i];\n        const location = outputTensors[i]?.[3];  // undefined means output is not pre-allocated.\n\n        if (location) {\n          // output is pre-allocated. bind the tensor.\n          const errorCode = wasm._OrtBindOutput(handle, outputNamesUTF8Encoded[index], outputTensorHandles[i], 0);\n          if (errorCode !== 0) {\n            checkLastError(`Can't bind pre-allocated output[${i}] for session=${sessionId}.`);\n          }\n        } else {\n          // output is not pre-allocated. reset preferred location.\n          const errorCode =\n              wasm._OrtBindOutput(handle, outputNamesUTF8Encoded[index], 0, outputPreferredLocationsEncoded[index]);\n          if (errorCode !== 0) {\n            checkLastError(`Can't bind output[${i}] to ${outputPreferredLocations[i]} for session=${sessionId}.`);\n          }\n        }\n      }\n      activeSessions.set(\n          sessionId,\n          [sessionHandle, inputNamesUTF8Encoded, outputNamesUTF8Encoded, ioBindingState, enableGraphCapture, true]);\n    }\n\n    wasm.jsepOnRunStart?.(sessionHandle);\n    let errorCode: number;\n    if (!BUILD_DEFS.DISABLE_JSEP && ioBindingState) {\n      errorCode = await wasm._OrtRunWithBinding(\n          sessionHandle, ioBindingState.handle, outputCount, outputValuesOffset, runOptionsHandle);\n    } else {\n      errorCode = await wasm._OrtRun(\n          sessionHandle, inputNamesOffset, inputValuesOffset, inputCount, outputNamesOffset, outputCount,\n          outputValuesOffset, runOptionsHandle);\n    }\n\n    if (errorCode !== 0) {\n      checkLastError('failed to call OrtRun().');\n    }\n\n    const output: TensorMetadata[] = [];\n\n    for (let i = 0; i < outputCount; i++) {\n      const tensor = wasm.HEAPU32[outputValuesOffset / 4 + i];\n      if (tensor === outputTensorHandles[i]) {\n        // output tensor is pre-allocated. no need to copy data.\n        output.push(outputTensors[i]!);\n        continue;\n      }\n\n      const beforeGetTensorDataStack = wasm.stackSave();\n      // stack allocate 4 pointer value\n      const tensorDataOffset = wasm.stackAlloc(4 * 4);\n\n      let keepOutputTensor = false;\n      let type: Tensor.Type|undefined, dataOffset = 0;\n      try {\n        const errorCode = wasm._OrtGetTensorData(\n            tensor, tensorDataOffset, tensorDataOffset + 4, tensorDataOffset + 8, tensorDataOffset + 12);\n        if (errorCode !== 0) {\n          checkLastError(`Can't access output tensor data on index ${i}.`);\n        }\n        let tensorDataIndex = tensorDataOffset / 4;\n        const dataType = wasm.HEAPU32[tensorDataIndex++];\n        dataOffset = wasm.HEAPU32[tensorDataIndex++];\n        const dimsOffset = wasm.HEAPU32[tensorDataIndex++];\n        const dimsLength = wasm.HEAPU32[tensorDataIndex++];\n        const dims = [];\n        for (let i = 0; i < dimsLength; i++) {\n          dims.push(wasm.HEAPU32[dimsOffset / 4 + i]);\n        }\n        wasm._OrtFree(dimsOffset);\n\n        const size = dims.reduce((a, b) => a * b, 1);\n        type = tensorDataTypeEnumToString(dataType);\n\n        const preferredLocation = ioBindingState?.outputPreferredLocations[outputIndices[i]];\n\n        if (type === 'string') {\n          if (preferredLocation === 'gpu-buffer') {\n            throw new Error('String tensor is not supported on GPU.');\n          }\n          const stringData: string[] = [];\n          let dataIndex = dataOffset / 4;\n          for (let i = 0; i < size; i++) {\n            const offset = wasm.HEAPU32[dataIndex++];\n            const maxBytesToRead = i === size - 1 ? undefined : wasm.HEAPU32[dataIndex] - offset;\n            stringData.push(wasm.UTF8ToString(offset, maxBytesToRead));\n          }\n          output.push([type, dims, stringData, 'cpu']);\n        } else {\n          // If a certain output's preferred location is GPU but the tensor is empty, we still need to create a CPU\n          // tensor for it. There is no mapping GPU buffer for an empty tensor.\n          if (preferredLocation === 'gpu-buffer' && size > 0) {\n            const getBuffer = wasm.jsepGetBuffer;\n            if (!getBuffer) {\n              throw new Error('preferredLocation \"gpu-buffer\" is not supported without using WebGPU.');\n            }\n            const gpuBuffer = getBuffer(dataOffset);\n            const elementSize = getTensorElementSize(dataType);\n            if (elementSize === undefined || !isGpuBufferSupportedType(type)) {\n              throw new Error(`Unsupported data type: ${type}`);\n            }\n\n            // do not release the tensor right now. it will be released when user calls tensor.dispose().\n            keepOutputTensor = true;\n\n            output.push([\n              type, dims, {\n                gpuBuffer,\n                download: wasm.jsepCreateDownloader!(gpuBuffer, size * elementSize, type),\n                dispose: () => {\n                  wasm._OrtReleaseTensor(tensor);\n                }\n              },\n              'gpu-buffer'\n            ]);\n          } else {\n            const typedArrayConstructor = tensorTypeToTypedArrayConstructor(type);\n            const data = new typedArrayConstructor(size);\n            new Uint8Array(data.buffer, data.byteOffset, data.byteLength)\n                .set(wasm.HEAPU8.subarray(dataOffset, dataOffset + data.byteLength));\n            output.push([type, dims, data, 'cpu']);\n          }\n        }\n      } finally {\n        wasm.stackRestore(beforeGetTensorDataStack);\n        if (type === 'string' && dataOffset) {\n          wasm._free(dataOffset);\n        }\n        if (!keepOutputTensor) {\n          wasm._OrtReleaseTensor(tensor);\n        }\n      }\n    }\n\n    if (ioBindingState && !enableGraphCapture) {\n      wasm._OrtClearBoundOutputs(ioBindingState.handle);\n      activeSessions.set(\n          sessionId,\n          [sessionHandle, inputNamesUTF8Encoded, outputNamesUTF8Encoded, ioBindingState, enableGraphCapture, false]);\n    }\n    return output;\n  } finally {\n    wasm.stackRestore(beforeRunStack);\n\n    inputTensorHandles.forEach(v => wasm._OrtReleaseTensor(v));\n    outputTensorHandles.forEach(v => wasm._OrtReleaseTensor(v));\n    inputOutputAllocs.forEach(p => wasm._free(p));\n\n    if (runOptionsHandle !== 0) {\n      wasm._OrtReleaseRunOptions(runOptionsHandle);\n    }\n    runOptionsAllocs.forEach(p => wasm._free(p));\n  }\n};\n\n/**\n * end profiling\n */\nexport const endProfiling = (sessionId: number): void => {\n  const wasm = getInstance();\n  const session = activeSessions.get(sessionId);\n  if (!session) {\n    throw new Error('invalid session id');\n  }\n  const sessionHandle = session[0];\n\n  // profile file name is not used yet, but it must be freed.\n  const profileFileName = wasm._OrtEndProfiling(sessionHandle);\n  if (profileFileName === 0) {\n    checkLastError('Can\\'t get an profile file name.');\n  }\n  wasm._OrtFree(profileFileName);\n};\n\nexport const extractTransferableBuffers = (tensors: readonly SerializableTensorMetadata[]): ArrayBufferLike[] => {\n  const buffers: ArrayBufferLike[] = [];\n  for (const tensor of tensors) {\n    const data = tensor[2];\n    if (!Array.isArray(data) && 'buffer' in data) {\n      buffers.push(data.buffer);\n    }\n  }\n  return buffers;\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {env, InferenceSession} from 'onnxruntime-common';\n\nimport {OrtWasmMessage, SerializableInternalBuffer, SerializableSessionMetadata, SerializableTensorMetadata, TensorMetadata} from './proxy-messages';\nimport * as core from './wasm-core-impl';\nimport {initializeWebAssembly} from './wasm-factory';\nimport {importProxyWorker} from './wasm-utils-import';\n\nconst isProxy = (): boolean => !!env.wasm.proxy && typeof document !== 'undefined';\nlet proxyWorker: Worker|undefined;\nlet initializing = false;\nlet initialized = false;\nlet aborted = false;\nlet temporaryObjectUrl: string|undefined;\n\ntype PromiseCallbacks<T = void> = [resolve: (result: T) => void, reject: (reason: unknown) => void];\nlet initWasmCallbacks: PromiseCallbacks;\nconst queuedCallbacks: Map<OrtWasmMessage['type'], Array<PromiseCallbacks<unknown>>> = new Map();\n\nconst enqueueCallbacks = (type: OrtWasmMessage['type'], callbacks: PromiseCallbacks<unknown>): void => {\n  const queue = queuedCallbacks.get(type);\n  if (queue) {\n    queue.push(callbacks);\n  } else {\n    queuedCallbacks.set(type, [callbacks]);\n  }\n};\n\nconst ensureWorker = (): void => {\n  if (initializing || !initialized || aborted || !proxyWorker) {\n    throw new Error('worker not ready');\n  }\n};\n\nconst onProxyWorkerMessage = (ev: MessageEvent<OrtWasmMessage>): void => {\n  switch (ev.data.type) {\n    case 'init-wasm':\n      initializing = false;\n      if (ev.data.err) {\n        aborted = true;\n        initWasmCallbacks[1](ev.data.err);\n      } else {\n        initialized = true;\n        initWasmCallbacks[0]();\n      }\n      if (temporaryObjectUrl) {\n        URL.revokeObjectURL(temporaryObjectUrl);\n        temporaryObjectUrl = undefined;\n      }\n      break;\n    case 'init-ep':\n    case 'copy-from':\n    case 'create':\n    case 'release':\n    case 'run':\n    case 'end-profiling': {\n      const callbacks = queuedCallbacks.get(ev.data.type)!;\n      if (ev.data.err) {\n        callbacks.shift()![1](ev.data.err);\n      } else {\n        callbacks.shift()![0](ev.data.out!);\n      }\n      break;\n    }\n    default:\n  }\n};\n\n\nexport const initializeWebAssemblyAndOrtRuntime = async(): Promise<void> => {\n  if (initialized) {\n    return;\n  }\n  if (initializing) {\n    throw new Error('multiple calls to \\'initWasm()\\' detected.');\n  }\n  if (aborted) {\n    throw new Error('previous call to \\'initWasm()\\' failed.');\n  }\n\n  initializing = true;\n\n  if (!BUILD_DEFS.DISABLE_WASM_PROXY && isProxy()) {\n    return new Promise<void>((resolve, reject) => {\n      proxyWorker?.terminate();\n\n      void importProxyWorker().then(([objectUrl, worker]) => {\n        try {\n          proxyWorker = worker;\n          proxyWorker.onerror = (ev: ErrorEvent) => reject(ev);\n          proxyWorker.onmessage = onProxyWorkerMessage;\n          initWasmCallbacks = [resolve, reject];\n          const message: OrtWasmMessage = {type: 'init-wasm', in : env};\n          proxyWorker.postMessage(message);\n          temporaryObjectUrl = objectUrl;\n        } catch (e) {\n          reject(e);\n        }\n      }, reject);\n    });\n\n  } else {\n    try {\n      await initializeWebAssembly(env.wasm);\n      await core.initRuntime(env);\n      initialized = true;\n    } catch (e) {\n      aborted = true;\n      throw e;\n    } finally {\n      initializing = false;\n    }\n  }\n};\n\nexport const initializeOrtEp = async(epName: string): Promise<void> => {\n  if (!BUILD_DEFS.DISABLE_WASM_PROXY && isProxy()) {\n    ensureWorker();\n    return new Promise<void>((resolve, reject) => {\n      enqueueCallbacks('init-ep', [resolve, reject]);\n      const message: OrtWasmMessage = {type: 'init-ep', in : {epName, env}};\n      proxyWorker!.postMessage(message);\n    });\n  } else {\n    await core.initEp(env, epName);\n  }\n};\n\nexport const copyFromExternalBuffer = async(buffer: Uint8Array): Promise<SerializableInternalBuffer> => {\n  if (!BUILD_DEFS.DISABLE_WASM_PROXY && isProxy()) {\n    ensureWorker();\n    return new Promise<SerializableInternalBuffer>((resolve, reject) => {\n      enqueueCallbacks('copy-from', [resolve, reject]);\n      const message: OrtWasmMessage = {type: 'copy-from', in : {buffer}};\n      proxyWorker!.postMessage(message, [buffer.buffer]);\n    });\n  } else {\n    return core.copyFromExternalBuffer(buffer);\n  }\n};\n\nexport const createSession =\n    async(model: SerializableInternalBuffer|Uint8Array, options?: InferenceSession.SessionOptions):\n        Promise<SerializableSessionMetadata> => {\n          if (!BUILD_DEFS.DISABLE_WASM_PROXY && isProxy()) {\n            // check unsupported options\n            if (options?.preferredOutputLocation) {\n              throw new Error('session option \"preferredOutputLocation\" is not supported for proxy.');\n            }\n            ensureWorker();\n            return new Promise<SerializableSessionMetadata>((resolve, reject) => {\n              enqueueCallbacks('create', [resolve, reject]);\n              const message: OrtWasmMessage = {type: 'create', in : {model, options: {...options}}};\n              const transferable: Transferable[] = [];\n              if (model instanceof Uint8Array) {\n                transferable.push(model.buffer);\n              }\n              proxyWorker!.postMessage(message, transferable);\n            });\n          } else {\n            return core.createSession(model, options);\n          }\n        };\n\nexport const releaseSession = async(sessionId: number): Promise<void> => {\n  if (!BUILD_DEFS.DISABLE_WASM_PROXY && isProxy()) {\n    ensureWorker();\n    return new Promise<void>((resolve, reject) => {\n      enqueueCallbacks('release', [resolve, reject]);\n      const message: OrtWasmMessage = {type: 'release', in : sessionId};\n      proxyWorker!.postMessage(message);\n    });\n  } else {\n    core.releaseSession(sessionId);\n  }\n};\n\nexport const run = async(\n    sessionId: number, inputIndices: number[], inputs: TensorMetadata[], outputIndices: number[],\n    outputs: Array<TensorMetadata|null>, options: InferenceSession.RunOptions): Promise<TensorMetadata[]> => {\n  if (!BUILD_DEFS.DISABLE_WASM_PROXY && isProxy()) {\n    // check inputs location\n    if (inputs.some(t => t[3] !== 'cpu')) {\n      throw new Error('input tensor on GPU is not supported for proxy.');\n    }\n    // check outputs location\n    if (outputs.some(t => t)) {\n      throw new Error('pre-allocated output tensor is not supported for proxy.');\n    }\n    ensureWorker();\n    return new Promise<SerializableTensorMetadata[]>((resolve, reject) => {\n      enqueueCallbacks('run', [resolve, reject]);\n      const serializableInputs = inputs as SerializableTensorMetadata[];  // every input is on CPU.\n      const message: OrtWasmMessage =\n          {type: 'run', in : {sessionId, inputIndices, inputs: serializableInputs, outputIndices, options}};\n      proxyWorker!.postMessage(message, core.extractTransferableBuffers(serializableInputs));\n    });\n  } else {\n    return core.run(sessionId, inputIndices, inputs, outputIndices, outputs, options);\n  }\n};\n\nexport const endProfiling = async(sessionId: number): Promise<void> => {\n  if (!BUILD_DEFS.DISABLE_WASM_PROXY && isProxy()) {\n    ensureWorker();\n    return new Promise<void>((resolve, reject) => {\n      enqueueCallbacks('end-profiling', [resolve, reject]);\n      const message: OrtWasmMessage = {type: 'end-profiling', in : sessionId};\n      proxyWorker!.postMessage(message);\n    });\n  } else {\n    core.endProfiling(sessionId);\n  }\n};\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {InferenceSession, InferenceSessionHandler, SessionHandler, Tensor, TRACE_FUNC_BEGIN, TRACE_FUNC_END} from 'onnxruntime-common';\n\nimport {SerializableInternalBuffer, TensorMetadata} from './proxy-messages';\nimport {copyFromExternalBuffer, createSession, endProfiling, releaseSession, run} from './proxy-wrapper';\nimport {isGpuBufferSupportedType} from './wasm-common';\nimport {isNode} from './wasm-utils-env';\nimport {loadFile} from './wasm-utils-load-file';\n\nexport const encodeTensorMetadata = (tensor: Tensor, getName: () => string): TensorMetadata => {\n  switch (tensor.location) {\n    case 'cpu':\n      return [tensor.type, tensor.dims, tensor.data, 'cpu'];\n    case 'gpu-buffer':\n      return [tensor.type, tensor.dims, {gpuBuffer: tensor.gpuBuffer}, 'gpu-buffer'];\n    default:\n      throw new Error(`invalid data location: ${tensor.location} for ${getName()}`);\n  }\n};\n\nexport const decodeTensorMetadata = (tensor: TensorMetadata): Tensor => {\n  switch (tensor[3]) {\n    case 'cpu':\n      return new Tensor(tensor[0], tensor[2], tensor[1]);\n    case 'gpu-buffer': {\n      const dataType = tensor[0];\n      if (!isGpuBufferSupportedType(dataType)) {\n        throw new Error(`not supported data type: ${dataType} for deserializing GPU tensor`);\n      }\n      const {gpuBuffer, download, dispose} = tensor[2];\n      return Tensor.fromGpuBuffer(gpuBuffer, {dataType, dims: tensor[1], download, dispose});\n    }\n    default:\n      throw new Error(`invalid data location: ${tensor[3]}`);\n  }\n};\n\nexport class OnnxruntimeWebAssemblySessionHandler implements InferenceSessionHandler {\n  private sessionId: number;\n\n  inputNames: string[];\n  outputNames: string[];\n\n  async fetchModelAndCopyToWasmMemory(path: string): Promise<SerializableInternalBuffer> {\n    // fetch model from url and move to wasm heap.\n    return copyFromExternalBuffer(await loadFile(path));\n  }\n\n  async loadModel(pathOrBuffer: string|Uint8Array, options?: InferenceSession.SessionOptions): Promise<void> {\n    TRACE_FUNC_BEGIN();\n    let model: Parameters<typeof createSession>[0];\n\n    if (typeof pathOrBuffer === 'string') {\n      if (isNode) {\n        // node\n        model = await loadFile(pathOrBuffer);\n      } else {\n        // browser\n        // fetch model and copy to wasm heap.\n        model = await this.fetchModelAndCopyToWasmMemory(pathOrBuffer);\n      }\n    } else {\n      model = pathOrBuffer;\n    }\n\n    [this.sessionId, this.inputNames, this.outputNames] = await createSession(model, options);\n    TRACE_FUNC_END();\n  }\n\n  async dispose(): Promise<void> {\n    return releaseSession(this.sessionId);\n  }\n\n  async run(feeds: SessionHandler.FeedsType, fetches: SessionHandler.FetchesType, options: InferenceSession.RunOptions):\n      Promise<SessionHandler.ReturnType> {\n    TRACE_FUNC_BEGIN();\n    const inputArray: Tensor[] = [];\n    const inputIndices: number[] = [];\n    Object.entries(feeds).forEach(kvp => {\n      const name = kvp[0];\n      const tensor = kvp[1];\n      const index = this.inputNames.indexOf(name);\n      if (index === -1) {\n        throw new Error(`invalid input '${name}'`);\n      }\n      inputArray.push(tensor);\n      inputIndices.push(index);\n    });\n\n    const outputArray: Array<Tensor|null> = [];\n    const outputIndices: number[] = [];\n    Object.entries(fetches).forEach(kvp => {\n      const name = kvp[0];\n      const tensor = kvp[1];\n      const index = this.outputNames.indexOf(name);\n      if (index === -1) {\n        throw new Error(`invalid output '${name}'`);\n      }\n      outputArray.push(tensor);\n      outputIndices.push(index);\n    });\n\n    const inputs =\n        inputArray.map((t, i) => encodeTensorMetadata(t, () => `input \"${this.inputNames[inputIndices[i]]}\"`));\n    const outputs = outputArray.map(\n        (t, i) => t ? encodeTensorMetadata(t, () => `output \"${this.outputNames[outputIndices[i]]}\"`) : null);\n\n    const results = await run(this.sessionId, inputIndices, inputs, outputIndices, outputs, options);\n\n    const resultMap: SessionHandler.ReturnType = {};\n    for (let i = 0; i < results.length; i++) {\n      resultMap[this.outputNames[outputIndices[i]]] = outputArray[i] ?? decodeTensorMetadata(results[i]);\n    }\n    TRACE_FUNC_END();\n    return resultMap;\n  }\n\n  startProfiling(): void {\n    // TODO: implement profiling\n  }\n\n  endProfiling(): void {\n    void endProfiling(this.sessionId);\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {Backend, env, InferenceSession, InferenceSessionHandler} from 'onnxruntime-common';\n\nimport {initializeOrtEp, initializeWebAssemblyAndOrtRuntime} from './wasm/proxy-wrapper';\nimport {OnnxruntimeWebAssemblySessionHandler} from './wasm/session-handler-inference';\nimport {scriptSrc} from './wasm/wasm-utils-import';\n\n/**\n * This function initializes all flags for WebAssembly.\n *\n * Those flags are accessible from `ort.env.wasm`. Users are allow to set those flags before the first inference session\n * being created, to override default value.\n */\nexport const initializeFlags = (): void => {\n  if (typeof env.wasm.initTimeout !== 'number' || env.wasm.initTimeout < 0) {\n    env.wasm.initTimeout = 0;\n  }\n\n  if (env.wasm.simd === false) {\n    // eslint-disable-next-line no-console\n    console.warn(\n        'Deprecated property \"env.wasm.simd\" is set to false. ' +\n        'non-SIMD build is no longer provided, and this setting will be ignored.');\n  }\n\n  if (typeof env.wasm.proxy !== 'boolean') {\n    env.wasm.proxy = false;\n  }\n\n  if (typeof env.wasm.trace !== 'boolean') {\n    env.wasm.trace = false;\n  }\n\n  if (typeof env.wasm.numThreads !== 'number' || !Number.isInteger(env.wasm.numThreads) || env.wasm.numThreads <= 0) {\n    // The following logic only applies when `ort.env.wasm.numThreads` is not set by user. We will always honor user's\n    // setting if it is provided.\n\n    // Browser: when crossOriginIsolated is false, SharedArrayBuffer is not available so WebAssembly threads will not\n    // work. In this case, we will set numThreads to 1.\n    //\n    // There is an exception: when the browser is configured to force-enable SharedArrayBuffer (e.g. Chromuim with\n    // --enable-features=SharedArrayBuffer), it is possible that `self.crossOriginIsolated` is false and\n    // SharedArrayBuffer is available at the same time. This is usually for testing. In this case,  we will still set\n    // numThreads to 1 here. If we want to enable multi-threading in test, we should set `ort.env.wasm.numThreads` to a\n    // value greater than 1.\n    if (typeof self !== 'undefined' && !self.crossOriginIsolated) {\n      env.wasm.numThreads = 1;\n    } else {\n      const numCpuLogicalCores =\n          typeof navigator === 'undefined' ? require('node:os').cpus().length : navigator.hardwareConcurrency;\n      env.wasm.numThreads = Math.min(4, Math.ceil((numCpuLogicalCores || 1) / 2));\n    }\n  }\n\n  if (!BUILD_DEFS.DISABLE_DYNAMIC_IMPORT) {\n    // overwrite wasm paths override if not set\n    if (env.wasm.wasmPaths === undefined && scriptSrc && scriptSrc.indexOf('blob:') !== 0) {\n      env.wasm.wasmPaths = scriptSrc.substring(0, scriptSrc.lastIndexOf('/') + 1);\n    }\n  }\n};\n\nexport class OnnxruntimeWebAssemblyBackend implements Backend {\n  /**\n   * This function initializes the WebAssembly backend.\n   *\n   * This function will be called only once for each backend name. It will be called the first time when\n   * `ort.InferenceSession.create()` is called with a registered backend name.\n   *\n   * @param backendName - the registered backend name.\n   */\n  async init(backendName: string): Promise<void> {\n    // populate wasm flags\n    initializeFlags();\n\n    // init wasm\n    await initializeWebAssemblyAndOrtRuntime();\n\n    // performe EP specific initialization\n    await initializeOrtEp(backendName);\n  }\n  createInferenceSessionHandler(path: string, options?: InferenceSession.SessionOptions):\n      Promise<InferenceSessionHandler>;\n  createInferenceSessionHandler(buffer: Uint8Array, options?: InferenceSession.SessionOptions):\n      Promise<InferenceSessionHandler>;\n  async createInferenceSessionHandler(pathOrBuffer: string|Uint8Array, options?: InferenceSession.SessionOptions):\n      Promise<InferenceSessionHandler> {\n    const handler = new OnnxruntimeWebAssemblySessionHandler();\n    await handler.loadModel(pathOrBuffer, options);\n    return Promise.resolve(handler);\n  }\n}\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\nimport {OnnxruntimeWebAssemblyBackend} from './backend-wasm';\nexport const wasmBackend = new OnnxruntimeWebAssemblyBackend();\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\n/* eslint-disable @typescript-eslint/no-var-requires, @typescript-eslint/no-require-imports */\n\n// We use \"require\" instead of \"import\" here because import statement must be put in top level. Our current code does\n// not allow bundler to tree-shaking code as expected because some codes are treated as having side effects.\n// So we import code inside the if-clause to allow bundler remove the code safely.\n\nexport * from 'onnxruntime-common';\nimport * as ort from 'onnxruntime-common';\nexport default ort;\n\nimport {registerBackend, env} from 'onnxruntime-common';\nimport {version} from './version';\n\nif (!BUILD_DEFS.DISABLE_WEBGL) {\n  const onnxjsBackend = require('./backend-onnxjs').onnxjsBackend;\n  registerBackend('webgl', onnxjsBackend, -10);\n}\n\nif (!BUILD_DEFS.DISABLE_WASM) {\n  const wasmBackend = BUILD_DEFS.DISABLE_TRAINING ? require('./backend-wasm-inference').wasmBackend :\n                                                    require('./backend-wasm-training').wasmBackend;\n  if (!BUILD_DEFS.DISABLE_JSEP) {\n    registerBackend('webgpu', wasmBackend, 5);\n    registerBackend('webnn', wasmBackend, 5);\n  }\n  registerBackend('cpu', wasmBackend, 10);\n  registerBackend('wasm', wasmBackend, 10);\n}\n\nObject.defineProperty(env.versions, 'web', {value: version, enumerable: true});\n","// Copyright (c) Microsoft Corporation. All rights reserved.\n// Licensed under the MIT License.\n\n// This file is generated by /js/scripts/update-version.ts\n// Do not modify file content manually.\n\nexport const version = '1.19.0';\n"]}