
  /*
  Face-API
  homepage: <https://github.com/vladmandic/face-api>
  author: <https://github.com/vladmandic>'
  */

var faceapi = (() => {
  var __defProp = Object.defineProperty;
  var __export = (target, all4) => {
    for (var name in all4)
      __defProp(target, name, {get: all4[name], enumerable: true});
  };

  // src/index.ts
  var src_exports = {};
  __export(src_exports, {
    AgeGenderNet: () => AgeGenderNet,
    BoundingBox: () => BoundingBox,
    Box: () => Box,
    ComposableTask: () => ComposableTask,
    ComputeAllFaceDescriptorsTask: () => ComputeAllFaceDescriptorsTask,
    ComputeFaceDescriptorsTaskBase: () => ComputeFaceDescriptorsTaskBase,
    ComputeSingleFaceDescriptorTask: () => ComputeSingleFaceDescriptorTask,
    DetectAllFaceLandmarksTask: () => DetectAllFaceLandmarksTask,
    DetectAllFacesTask: () => DetectAllFacesTask,
    DetectFaceLandmarksTaskBase: () => DetectFaceLandmarksTaskBase,
    DetectFacesTaskBase: () => DetectFacesTaskBase,
    DetectSingleFaceLandmarksTask: () => DetectSingleFaceLandmarksTask,
    DetectSingleFaceTask: () => DetectSingleFaceTask,
    Dimensions: () => Dimensions,
    FACE_EXPRESSION_LABELS: () => FACE_EXPRESSION_LABELS,
    FaceDetection: () => FaceDetection,
    FaceDetectionNet: () => FaceDetectionNet,
    FaceExpressionNet: () => FaceExpressionNet,
    FaceExpressions: () => FaceExpressions,
    FaceLandmark68Net: () => FaceLandmark68Net,
    FaceLandmark68TinyNet: () => FaceLandmark68TinyNet,
    FaceLandmarkNet: () => FaceLandmarkNet,
    FaceLandmarks: () => FaceLandmarks,
    FaceLandmarks5: () => FaceLandmarks5,
    FaceLandmarks68: () => FaceLandmarks68,
    FaceMatch: () => FaceMatch,
    FaceMatcher: () => FaceMatcher,
    FaceRecognitionNet: () => FaceRecognitionNet,
    Gender: () => Gender,
    LabeledBox: () => LabeledBox,
    LabeledFaceDescriptors: () => LabeledFaceDescriptors,
    NetInput: () => NetInput,
    NeuralNetwork: () => NeuralNetwork,
    ObjectDetection: () => ObjectDetection,
    Point: () => Point,
    PredictedBox: () => PredictedBox,
    Rect: () => Rect,
    SsdMobilenetv1: () => SsdMobilenetv1,
    SsdMobilenetv1Options: () => SsdMobilenetv1Options,
    TinyFaceDetector: () => TinyFaceDetector,
    TinyFaceDetectorOptions: () => TinyFaceDetectorOptions,
    TinyYolov2: () => TinyYolov2,
    TinyYolov2Options: () => TinyYolov2Options,
    allFaces: () => allFaces,
    allFacesSsdMobilenetv1: () => allFacesSsdMobilenetv1,
    allFacesTinyYolov2: () => allFacesTinyYolov2,
    awaitMediaLoaded: () => awaitMediaLoaded,
    bufferToImage: () => bufferToImage,
    computeFaceDescriptor: () => computeFaceDescriptor,
    createCanvas: () => createCanvas2,
    createCanvasFromMedia: () => createCanvasFromMedia,
    createFaceDetectionNet: () => createFaceDetectionNet,
    createFaceRecognitionNet: () => createFaceRecognitionNet,
    createSsdMobilenetv1: () => createSsdMobilenetv1,
    createTinyFaceDetector: () => createTinyFaceDetector,
    createTinyYolov2: () => createTinyYolov2,
    detectAllFaces: () => detectAllFaces,
    detectFaceLandmarks: () => detectFaceLandmarks,
    detectFaceLandmarksTiny: () => detectFaceLandmarksTiny,
    detectLandmarks: () => detectLandmarks,
    detectSingleFace: () => detectSingleFace,
    draw: () => draw_exports,
    env: () => env2,
    euclideanDistance: () => euclideanDistance,
    extendWithAge: () => extendWithAge,
    extendWithFaceDescriptor: () => extendWithFaceDescriptor,
    extendWithFaceDetection: () => extendWithFaceDetection,
    extendWithFaceExpressions: () => extendWithFaceExpressions,
    extendWithFaceLandmarks: () => extendWithFaceLandmarks,
    extendWithGender: () => extendWithGender,
    extractFaceTensors: () => extractFaceTensors,
    extractFaces: () => extractFaces,
    fetchImage: () => fetchImage,
    fetchJson: () => fetchJson,
    fetchNetWeights: () => fetchNetWeights,
    fetchOrThrow: () => fetchOrThrow,
    getContext2dOrThrow: () => getContext2dOrThrow,
    getMediaDimensions: () => getMediaDimensions,
    imageTensorToCanvas: () => imageTensorToCanvas,
    imageToSquare: () => imageToSquare,
    inverseSigmoid: () => inverseSigmoid,
    iou: () => iou,
    isMediaElement: () => isMediaElement,
    isMediaLoaded: () => isMediaLoaded,
    isWithAge: () => isWithAge,
    isWithFaceDetection: () => isWithFaceDetection,
    isWithFaceExpressions: () => isWithFaceExpressions,
    isWithFaceLandmarks: () => isWithFaceLandmarks,
    isWithGender: () => isWithGender,
    loadAgeGenderModel: () => loadAgeGenderModel,
    loadFaceDetectionModel: () => loadFaceDetectionModel,
    loadFaceExpressionModel: () => loadFaceExpressionModel,
    loadFaceLandmarkModel: () => loadFaceLandmarkModel,
    loadFaceLandmarkTinyModel: () => loadFaceLandmarkTinyModel,
    loadFaceRecognitionModel: () => loadFaceRecognitionModel,
    loadSsdMobilenetv1Model: () => loadSsdMobilenetv1Model,
    loadTinyFaceDetectorModel: () => loadTinyFaceDetectorModel,
    loadTinyYolov2Model: () => loadTinyYolov2Model,
    loadWeightMap: () => loadWeightMap,
    locateFaces: () => locateFaces,
    matchDimensions: () => matchDimensions,
    minBbox: () => minBbox,
    nets: () => nets,
    nonMaxSuppression: () => nonMaxSuppression2,
    normalize: () => normalize,
    padToSquare: () => padToSquare,
    predictAgeAndGender: () => predictAgeAndGender,
    recognizeFaceExpressions: () => recognizeFaceExpressions,
    resizeResults: () => resizeResults,
    resolveInput: () => resolveInput,
    shuffleArray: () => shuffleArray,
    sigmoid: () => sigmoid5,
    ssdMobilenetv1: () => ssdMobilenetv1,
    tf: () => tfjs_esm_exports,
    tinyFaceDetector: () => tinyFaceDetector,
    tinyYolov2: () => tinyYolov2,
    toNetInput: () => toNetInput,
    utils: () => utils_exports,
    validateConfig: () => validateConfig,
    version: () => version11
  });

  // dist/tfjs.esm.js
  var tfjs_esm_exports = {};
  __export(tfjs_esm_exports, {
    Abs: () => Abs,
    Acos: () => Acos,
    Acosh: () => Acosh,
    AdadeltaOptimizer: () => AdadeltaOptimizer,
    AdagradOptimizer: () => AdagradOptimizer,
    AdamOptimizer: () => AdamOptimizer,
    AdamaxOptimizer: () => AdamaxOptimizer,
    Add: () => Add,
    AddN: () => AddN,
    All: () => All,
    Any: () => Any,
    ArgMax: () => ArgMax,
    ArgMin: () => ArgMin,
    Asin: () => Asin,
    Asinh: () => Asinh,
    Atan: () => Atan,
    Atan2: () => Atan2,
    Atanh: () => Atanh,
    AvgPool: () => AvgPool,
    AvgPool3D: () => AvgPool3D,
    AvgPool3DGrad: () => AvgPool3DGrad,
    AvgPoolGrad: () => AvgPoolGrad,
    BackendWasm: () => BackendWasm,
    BatchMatMul: () => BatchMatMul,
    BatchToSpaceND: () => BatchToSpaceND,
    Bincount: () => Bincount,
    BroadcastTo: () => BroadcastTo,
    Callback: () => Callback,
    CallbackList: () => CallbackList,
    Cast: () => Cast,
    Ceil: () => Ceil,
    ClipByValue: () => ClipByValue,
    Complex: () => Complex,
    ComplexAbs: () => ComplexAbs,
    Concat: () => Concat,
    Conv2D: () => Conv2D,
    Conv2DBackpropFilter: () => Conv2DBackpropFilter,
    Conv2DBackpropInput: () => Conv2DBackpropInput,
    Conv3D: () => Conv3D,
    Conv3DBackpropFilterV2: () => Conv3DBackpropFilterV2,
    Conv3DBackpropInputV2: () => Conv3DBackpropInputV2,
    Cos: () => Cos,
    Cosh: () => Cosh,
    CropAndResize: () => CropAndResize,
    Cumsum: () => Cumsum,
    CustomCallback: () => CustomCallback,
    DataStorage: () => DataStorage,
    DenseBincount: () => DenseBincount,
    DepthToSpace: () => DepthToSpace,
    DepthwiseConv2dNative: () => DepthwiseConv2dNative,
    DepthwiseConv2dNativeBackpropFilter: () => DepthwiseConv2dNativeBackpropFilter,
    DepthwiseConv2dNativeBackpropInput: () => DepthwiseConv2dNativeBackpropInput,
    Diag: () => Diag,
    Dilation2D: () => Dilation2D,
    Dilation2DBackpropFilter: () => Dilation2DBackpropFilter,
    Dilation2DBackpropInput: () => Dilation2DBackpropInput,
    ENV: () => ENV,
    EarlyStopping: () => EarlyStopping,
    Elu: () => Elu,
    EluGrad: () => EluGrad,
    Environment: () => Environment,
    Equal: () => Equal,
    Erf: () => Erf,
    Exp: () => Exp,
    ExpandDims: () => ExpandDims,
    Expm1: () => Expm1,
    FFT: () => FFT,
    Fill: () => Fill,
    FlipLeftRight: () => FlipLeftRight,
    Floor: () => Floor,
    FloorDiv: () => FloorDiv,
    FromPixels: () => FromPixels,
    FusedBatchNorm: () => FusedBatchNorm,
    FusedConv2D: () => FusedConv2D,
    FusedDepthwiseConv2D: () => FusedDepthwiseConv2D,
    GatherNd: () => GatherNd,
    GatherV2: () => GatherV2,
    GraphModel: () => GraphModel,
    Greater: () => Greater,
    GreaterEqual: () => GreaterEqual,
    History: () => History,
    IFFT: () => IFFT,
    Identity: () => Identity,
    Imag: () => Imag,
    InputSpec: () => InputSpec,
    IsFinite: () => IsFinite,
    IsInf: () => IsInf,
    IsNan: () => IsNan,
    KernelBackend: () => KernelBackend,
    LRN: () => LRN,
    LRNGrad: () => LRNGrad,
    LayerVariable: () => LayerVariable,
    LayersModel: () => LayersModel,
    LeakyRelu: () => LeakyRelu,
    Less: () => Less,
    LessEqual: () => LessEqual,
    LinSpace: () => LinSpace,
    Log: () => Log,
    Log1p: () => Log1p,
    LogSoftmax: () => LogSoftmax,
    LogicalAnd: () => LogicalAnd,
    LogicalNot: () => LogicalNot,
    LogicalOr: () => LogicalOr,
    Max: () => Max,
    MaxPool: () => MaxPool,
    MaxPool3D: () => MaxPool3D,
    MaxPool3DGrad: () => MaxPool3DGrad,
    MaxPoolGrad: () => MaxPoolGrad,
    MaxPoolWithArgmax: () => MaxPoolWithArgmax,
    Maximum: () => Maximum,
    Mean: () => Mean,
    Min: () => Min,
    Minimum: () => Minimum,
    MirrorPad: () => MirrorPad,
    Mod: () => Mod,
    MomentumOptimizer: () => MomentumOptimizer,
    Multinomial: () => Multinomial,
    Multiply: () => Multiply,
    Neg: () => Neg,
    NonMaxSuppressionV3: () => NonMaxSuppressionV3,
    NonMaxSuppressionV4: () => NonMaxSuppressionV4,
    NonMaxSuppressionV5: () => NonMaxSuppressionV5,
    NotEqual: () => NotEqual,
    OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX,
    OneHot: () => OneHot,
    OnesLike: () => OnesLike,
    Optimizer: () => Optimizer,
    Pack: () => Pack,
    PadV2: () => PadV2,
    Pool: () => Pool,
    Pow: () => Pow,
    Prelu: () => Prelu,
    Prod: () => Prod,
    RMSPropOptimizer: () => RMSPropOptimizer,
    RNN: () => RNN,
    Range: () => Range,
    Rank: () => Rank,
    Real: () => Real,
    RealDiv: () => RealDiv,
    Reciprocal: () => Reciprocal,
    Reduction: () => Reduction,
    Relu: () => Relu,
    Relu6: () => Relu6,
    Reshape: () => Reshape,
    ResizeBilinear: () => ResizeBilinear,
    ResizeBilinearGrad: () => ResizeBilinearGrad,
    ResizeNearestNeighbor: () => ResizeNearestNeighbor,
    ResizeNearestNeighborGrad: () => ResizeNearestNeighborGrad,
    Reverse: () => Reverse,
    RotateWithOffset: () => RotateWithOffset,
    Round: () => Round,
    Rsqrt: () => Rsqrt,
    SGDOptimizer: () => SGDOptimizer,
    ScatterNd: () => ScatterNd,
    Select: () => Select,
    Selu: () => Selu,
    Sequential: () => Sequential,
    Sigmoid: () => Sigmoid,
    Sign: () => Sign,
    Sin: () => Sin,
    Sinh: () => Sinh,
    Slice: () => Slice,
    Softmax: () => Softmax,
    Softplus: () => Softplus,
    SpaceToBatchND: () => SpaceToBatchND,
    SparseToDense: () => SparseToDense,
    SplitV: () => SplitV,
    Sqrt: () => Sqrt,
    Square: () => Square,
    SquaredDifference: () => SquaredDifference,
    Step: () => Step,
    StridedSlice: () => StridedSlice,
    Sub: () => Sub,
    Sum: () => Sum,
    SymbolicTensor: () => SymbolicTensor,
    Tan: () => Tan,
    Tanh: () => Tanh,
    Tensor: () => Tensor,
    TensorBuffer: () => TensorBuffer,
    Tile: () => Tile,
    TopK: () => TopK,
    Transform: () => Transform,
    Transpose: () => Transpose,
    Unique: () => Unique,
    Unpack: () => Unpack,
    UnsortedSegmentSum: () => UnsortedSegmentSum,
    Variable: () => Variable,
    ZerosLike: () => ZerosLike,
    _FusedMatMul: () => _FusedMatMul,
    abs: () => abs,
    acos: () => acos,
    acosh: () => acosh,
    add: () => add2,
    addN: () => addN,
    all: () => all,
    any: () => any,
    argMax: () => argMax,
    argMin: () => argMin,
    asin: () => asin,
    asinh: () => asinh,
    atan: () => atan,
    atan2: () => atan2,
    atanh: () => atanh,
    avgPool: () => avgPool,
    avgPool3d: () => avgPool3d,
    backend: () => backend,
    backend_util: () => backend_util_exports,
    basicLSTMCell: () => basicLSTMCell,
    batchNorm: () => batchNorm,
    batchNorm2d: () => batchNorm2d,
    batchNorm3d: () => batchNorm3d,
    batchNorm4d: () => batchNorm4d,
    batchToSpaceND: () => batchToSpaceND,
    bincount: () => bincount,
    booleanMaskAsync: () => booleanMaskAsync,
    broadcastTo: () => broadcastTo,
    browser: () => browser_exports,
    buffer: () => buffer,
    callbacks: () => callbacks,
    cast: () => cast,
    ceil: () => ceil,
    clipByValue: () => clipByValue,
    clone: () => clone,
    complex: () => complex,
    concat: () => concat,
    concat1d: () => concat1d,
    concat2d: () => concat2d,
    concat3d: () => concat3d,
    concat4d: () => concat4d,
    constraints: () => exports_constraints_exports,
    conv1d: () => conv1d,
    conv2d: () => conv2d,
    conv2dTranspose: () => conv2dTranspose,
    conv3d: () => conv3d,
    conv3dTranspose: () => conv3dTranspose,
    copyRegisteredKernels: () => copyRegisteredKernels,
    cos: () => cos,
    cosh: () => cosh,
    cosineWindow: () => cosineWindow,
    cumsum: () => cumsum,
    customGrad: () => customGrad,
    data: () => dist_exports,
    denseBincount: () => denseBincount,
    deprecationWarn: () => deprecationWarn,
    depthToSpace: () => depthToSpace,
    depthwiseConv2d: () => depthwiseConv2d,
    deregisterOp: () => deregisterOp,
    device_util: () => device_util_exports,
    diag: () => diag,
    dilation2d: () => dilation2d,
    disableDeprecationWarnings: () => disableDeprecationWarnings,
    dispose: () => dispose,
    disposeVariables: () => disposeVariables,
    div: () => div,
    divNoNan: () => divNoNan,
    dot: () => dot,
    dropout: () => dropout,
    elu: () => elu,
    enableDebugMode: () => enableDebugMode,
    enableProdMode: () => enableProdMode,
    enclosingPowerOfTwo: () => enclosingPowerOfTwo,
    engine: () => engine,
    env: () => env,
    equal: () => equal,
    erf: () => erf,
    exp: () => exp,
    expandDims: () => expandDims,
    expm1: () => expm1,
    eye: () => eye,
    fft: () => fft,
    fill: () => fill,
    findBackend: () => findBackend,
    findBackendFactory: () => findBackendFactory,
    floor: () => floor,
    floorDiv: () => floorDiv,
    fused: () => fused_ops_exports,
    gather: () => gather,
    gatherND: () => gatherND,
    gather_util: () => gather_nd_util_exports,
    getBackend: () => getBackend,
    getGradient: () => getGradient,
    getKernel: () => getKernel,
    getKernelsForBackend: () => getKernelsForBackend,
    grad: () => grad,
    grads: () => grads,
    greater: () => greater,
    greaterEqual: () => greaterEqual,
    ifft: () => ifft,
    imag: () => imag,
    image: () => image,
    inTopKAsync: () => inTopKAsync,
    initializers: () => exports_initializers_exports,
    input: () => input,
    io: () => io_exports,
    irfft: () => irfft,
    isFinite: () => isFinite2,
    isInf: () => isInf,
    isNaN: () => isNaN2,
    keep: () => keep,
    kernel_impls: () => kernel_impls_exports,
    layers: () => exports_layers_exports,
    leakyRelu: () => leakyRelu,
    less: () => less,
    lessEqual: () => lessEqual,
    linalg: () => linalg,
    linspace: () => linspace,
    loadGraphModel: () => loadGraphModel,
    loadLayersModel: () => loadLayersModel,
    localResponseNormalization: () => localResponseNormalization,
    log: () => log,
    log1p: () => log1p,
    logSigmoid: () => logSigmoid,
    logSoftmax: () => logSoftmax,
    logSumExp: () => logSumExp,
    logicalAnd: () => logicalAnd,
    logicalNot: () => logicalNot,
    logicalOr: () => logicalOr,
    logicalXor: () => logicalXor,
    losses: () => losses,
    matMul: () => matMul,
    math: () => math_exports,
    max: () => max,
    maxPool: () => maxPool,
    maxPool3d: () => maxPool3d,
    maxPoolWithArgmax: () => maxPoolWithArgmax,
    maximum: () => maximum,
    mean: () => mean,
    memory: () => memory,
    metrics: () => exports_metrics_exports,
    min: () => min,
    minimum: () => minimum,
    mirrorPad: () => mirrorPad,
    mod: () => mod,
    model: () => model,
    models: () => exports_models_exports,
    moments: () => moments,
    movingAverage: () => movingAverage,
    mul: () => mul,
    multiRNNCell: () => multiRNNCell,
    multinomial: () => multinomial,
    neg: () => neg,
    nextFrame: () => nextFrame,
    norm: () => norm,
    notEqual: () => notEqual,
    oneHot: () => oneHot,
    ones: () => ones2,
    onesLike: () => onesLike,
    op: () => op,
    outerProduct: () => outerProduct,
    pad: () => pad,
    pad1d: () => pad1d,
    pad2d: () => pad2d,
    pad3d: () => pad3d,
    pad4d: () => pad4d,
    pool: () => pool,
    pow: () => pow,
    prelu: () => prelu,
    print: () => print2,
    prod: () => prod,
    profile: () => profile,
    rand: () => rand,
    randomGamma: () => randomGamma,
    randomNormal: () => randomNormal,
    randomUniform: () => randomUniform,
    range: () => range,
    ready: () => ready,
    real: () => real,
    reciprocal: () => reciprocal,
    registerBackend: () => registerBackend,
    registerCallbackConstructor: () => registerCallbackConstructor,
    registerGradient: () => registerGradient,
    registerKernel: () => registerKernel,
    registerOp: () => registerOp,
    regularizers: () => exports_regularizers_exports,
    relu: () => relu,
    relu6: () => relu6,
    removeBackend: () => removeBackend,
    reshape: () => reshape,
    reverse: () => reverse,
    reverse1d: () => reverse1d,
    reverse2d: () => reverse2d,
    reverse3d: () => reverse3d,
    reverse4d: () => reverse4d,
    rfft: () => rfft,
    round: () => round2,
    rsqrt: () => rsqrt,
    scalar: () => scalar,
    scatterND: () => scatterND,
    scatter_util: () => scatter_nd_util_exports,
    selu: () => selu,
    separableConv2d: () => separableConv2d,
    sequential: () => sequential,
    serialization: () => serialization_exports,
    setBackend: () => setBackend,
    setPlatform: () => setPlatform,
    setWasmPath: () => setWasmPath,
    setWasmPaths: () => setWasmPaths,
    setdiff1dAsync: () => setdiff1dAsync,
    sigmoid: () => sigmoid,
    sign: () => sign,
    signal: () => signal,
    sin: () => sin,
    sinh: () => sinh,
    slice: () => slice,
    slice1d: () => slice1d,
    slice2d: () => slice2d,
    slice3d: () => slice3d,
    slice4d: () => slice4d,
    slice_util: () => slice_util_exports,
    softmax: () => softmax,
    softplus: () => softplus,
    spaceToBatchND: () => spaceToBatchND,
    sparseToDense: () => sparseToDense,
    spectral: () => spectral,
    split: () => split,
    sqrt: () => sqrt,
    square: () => square,
    squaredDifference: () => squaredDifference,
    squeeze: () => squeeze,
    stack: () => stack,
    step: () => step,
    stridedSlice: () => stridedSlice,
    sub: () => sub,
    sum: () => sum2,
    sumOutType: () => sumOutType,
    tan: () => tan,
    tanh: () => tanh2,
    tensor: () => tensor,
    tensor1d: () => tensor1d,
    tensor2d: () => tensor2d,
    tensor3d: () => tensor3d,
    tensor4d: () => tensor4d,
    tensor5d: () => tensor5d,
    tensor6d: () => tensor6d,
    tensor_util: () => tensor_util_exports,
    test_util: () => test_util_exports,
    tidy: () => tidy,
    tile: () => tile,
    time: () => time,
    topk: () => topk,
    train: () => train,
    transpose: () => transpose,
    truncatedNormal: () => truncatedNormal,
    unique: () => unique,
    unregisterGradient: () => unregisterGradient,
    unregisterKernel: () => unregisterKernel,
    unsortedSegmentSum: () => unsortedSegmentSum,
    unstack: () => unstack,
    upcastType: () => upcastType,
    util: () => util_exports,
    valueAndGrad: () => valueAndGrad,
    valueAndGrads: () => valueAndGrads,
    variable: () => variable,
    variableGrads: () => variableGrads,
    version: () => version8,
    version_converter: () => version3,
    version_core: () => version,
    version_layers: () => version2,
    version_wasm: () => version9,
    where: () => where,
    whereAsync: () => whereAsync,
    zeros: () => zeros,
    zerosLike: () => zerosLike
  });
  var __create = Object.create;
  var __defProp2 = Object.defineProperty;
  var __getProtoOf = Object.getPrototypeOf;
  var __hasOwnProp = Object.prototype.hasOwnProperty;
  var __getOwnPropNames = Object.getOwnPropertyNames;
  var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
  var __markAsModule = (target) => __defProp2(target, "__esModule", {value: true});
  var __commonJS = (cb, mod4) => () => (mod4 || cb((mod4 = {exports: {}}).exports, mod4), mod4.exports);
  var __export2 = (target, all4) => {
    for (var name in all4)
      __defProp2(target, name, {get: all4[name], enumerable: true});
  };
  var __reExport = (target, module2, desc) => {
    if (module2 && typeof module2 === "object" || typeof module2 === "function") {
      for (let key of __getOwnPropNames(module2))
        if (!__hasOwnProp.call(target, key) && key !== "default")
          __defProp2(target, key, {get: () => module2[key], enumerable: !(desc = __getOwnPropDesc(module2, key)) || desc.enumerable});
    }
    return target;
  };
  var __toModule = (module2) => {
    return __reExport(__markAsModule(__defProp2(module2 != null ? __create(__getProtoOf(module2)) : {}, "default", module2 && module2.__esModule && "default" in module2 ? {get: () => module2.default, enumerable: true} : {value: module2, enumerable: true})), module2);
  };
  var require_browser = __commonJS(() => {
  });
  var require_alea = __commonJS((exports, module2) => {
    (function(global2, module22, define2) {
      function Alea(seed) {
        var me = this, mash = Mash();
        me.next = function() {
          var t = 2091639 * me.s0 + me.c * 23283064365386963e-26;
          me.s0 = me.s1;
          me.s1 = me.s2;
          return me.s2 = t - (me.c = t | 0);
        };
        me.c = 1;
        me.s0 = mash(" ");
        me.s1 = mash(" ");
        me.s2 = mash(" ");
        me.s0 -= mash(seed);
        if (me.s0 < 0) {
          me.s0 += 1;
        }
        me.s1 -= mash(seed);
        if (me.s1 < 0) {
          me.s1 += 1;
        }
        me.s2 -= mash(seed);
        if (me.s2 < 0) {
          me.s2 += 1;
        }
        mash = null;
      }
      function copy(f, t) {
        t.c = f.c;
        t.s0 = f.s0;
        t.s1 = f.s1;
        t.s2 = f.s2;
        return t;
      }
      function impl(seed, opts) {
        var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;
        prng.int32 = function() {
          return xg.next() * 4294967296 | 0;
        };
        prng.double = function() {
          return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;
        };
        prng.quick = prng;
        if (state) {
          if (typeof state == "object")
            copy(state, xg);
          prng.state = function() {
            return copy(xg, {});
          };
        }
        return prng;
      }
      function Mash() {
        var n = 4022871197;
        var mash = function(data) {
          data = data.toString();
          for (var i = 0; i < data.length; i++) {
            n += data.charCodeAt(i);
            var h = 0.02519603282416938 * n;
            n = h >>> 0;
            h -= n;
            h *= n;
            n = h >>> 0;
            h -= n;
            n += h * 4294967296;
          }
          return (n >>> 0) * 23283064365386963e-26;
        };
        return mash;
      }
      if (module22 && module22.exports) {
        module22.exports = impl;
      } else if (define2 && define2.amd) {
        define2(function() {
          return impl;
        });
      } else {
        this.alea = impl;
      }
    })(exports, typeof module2 == "object" && module2, typeof define == "function" && define);
  });
  var require_xor128 = __commonJS((exports, module2) => {
    (function(global2, module22, define2) {
      function XorGen(seed) {
        var me = this, strseed = "";
        me.x = 0;
        me.y = 0;
        me.z = 0;
        me.w = 0;
        me.next = function() {
          var t = me.x ^ me.x << 11;
          me.x = me.y;
          me.y = me.z;
          me.z = me.w;
          return me.w ^= me.w >>> 19 ^ t ^ t >>> 8;
        };
        if (seed === (seed | 0)) {
          me.x = seed;
        } else {
          strseed += seed;
        }
        for (var k = 0; k < strseed.length + 64; k++) {
          me.x ^= strseed.charCodeAt(k) | 0;
          me.next();
        }
      }
      function copy(f, t) {
        t.x = f.x;
        t.y = f.y;
        t.z = f.z;
        t.w = f.w;
        return t;
      }
      function impl(seed, opts) {
        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
          return (xg.next() >>> 0) / 4294967296;
        };
        prng.double = function() {
          do {
            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
          } while (result === 0);
          return result;
        };
        prng.int32 = xg.next;
        prng.quick = prng;
        if (state) {
          if (typeof state == "object")
            copy(state, xg);
          prng.state = function() {
            return copy(xg, {});
          };
        }
        return prng;
      }
      if (module22 && module22.exports) {
        module22.exports = impl;
      } else if (define2 && define2.amd) {
        define2(function() {
          return impl;
        });
      } else {
        this.xor128 = impl;
      }
    })(exports, typeof module2 == "object" && module2, typeof define == "function" && define);
  });
  var require_xorwow = __commonJS((exports, module2) => {
    (function(global2, module22, define2) {
      function XorGen(seed) {
        var me = this, strseed = "";
        me.next = function() {
          var t = me.x ^ me.x >>> 2;
          me.x = me.y;
          me.y = me.z;
          me.z = me.w;
          me.w = me.v;
          return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0;
        };
        me.x = 0;
        me.y = 0;
        me.z = 0;
        me.w = 0;
        me.v = 0;
        if (seed === (seed | 0)) {
          me.x = seed;
        } else {
          strseed += seed;
        }
        for (var k = 0; k < strseed.length + 64; k++) {
          me.x ^= strseed.charCodeAt(k) | 0;
          if (k == strseed.length) {
            me.d = me.x << 10 ^ me.x >>> 4;
          }
          me.next();
        }
      }
      function copy(f, t) {
        t.x = f.x;
        t.y = f.y;
        t.z = f.z;
        t.w = f.w;
        t.v = f.v;
        t.d = f.d;
        return t;
      }
      function impl(seed, opts) {
        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
          return (xg.next() >>> 0) / 4294967296;
        };
        prng.double = function() {
          do {
            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
          } while (result === 0);
          return result;
        };
        prng.int32 = xg.next;
        prng.quick = prng;
        if (state) {
          if (typeof state == "object")
            copy(state, xg);
          prng.state = function() {
            return copy(xg, {});
          };
        }
        return prng;
      }
      if (module22 && module22.exports) {
        module22.exports = impl;
      } else if (define2 && define2.amd) {
        define2(function() {
          return impl;
        });
      } else {
        this.xorwow = impl;
      }
    })(exports, typeof module2 == "object" && module2, typeof define == "function" && define);
  });
  var require_xorshift7 = __commonJS((exports, module2) => {
    (function(global2, module22, define2) {
      function XorGen(seed) {
        var me = this;
        me.next = function() {
          var X = me.x, i = me.i, t, v, w;
          t = X[i];
          t ^= t >>> 7;
          v = t ^ t << 24;
          t = X[i + 1 & 7];
          v ^= t ^ t >>> 10;
          t = X[i + 3 & 7];
          v ^= t ^ t >>> 3;
          t = X[i + 4 & 7];
          v ^= t ^ t << 7;
          t = X[i + 7 & 7];
          t = t ^ t << 13;
          v ^= t ^ t << 9;
          X[i] = v;
          me.i = i + 1 & 7;
          return v;
        };
        function init2(me2, seed2) {
          var j, w, X = [];
          if (seed2 === (seed2 | 0)) {
            w = X[0] = seed2;
          } else {
            seed2 = "" + seed2;
            for (j = 0; j < seed2.length; ++j) {
              X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;
            }
          }
          while (X.length < 8)
            X.push(0);
          for (j = 0; j < 8 && X[j] === 0; ++j)
            ;
          if (j == 8)
            w = X[7] = -1;
          else
            w = X[j];
          me2.x = X;
          me2.i = 0;
          for (j = 256; j > 0; --j) {
            me2.next();
          }
        }
        init2(me, seed);
      }
      function copy(f, t) {
        t.x = f.x.slice();
        t.i = f.i;
        return t;
      }
      function impl(seed, opts) {
        if (seed == null)
          seed = +new Date();
        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
          return (xg.next() >>> 0) / 4294967296;
        };
        prng.double = function() {
          do {
            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
          } while (result === 0);
          return result;
        };
        prng.int32 = xg.next;
        prng.quick = prng;
        if (state) {
          if (state.x)
            copy(state, xg);
          prng.state = function() {
            return copy(xg, {});
          };
        }
        return prng;
      }
      if (module22 && module22.exports) {
        module22.exports = impl;
      } else if (define2 && define2.amd) {
        define2(function() {
          return impl;
        });
      } else {
        this.xorshift7 = impl;
      }
    })(exports, typeof module2 == "object" && module2, typeof define == "function" && define);
  });
  var require_xor4096 = __commonJS((exports, module2) => {
    (function(global2, module22, define2) {
      function XorGen(seed) {
        var me = this;
        me.next = function() {
          var w = me.w, X = me.X, i = me.i, t, v;
          me.w = w = w + 1640531527 | 0;
          v = X[i + 34 & 127];
          t = X[i = i + 1 & 127];
          v ^= v << 13;
          t ^= t << 17;
          v ^= v >>> 15;
          t ^= t >>> 12;
          v = X[i] = v ^ t;
          me.i = i;
          return v + (w ^ w >>> 16) | 0;
        };
        function init2(me2, seed2) {
          var t, v, i, j, w, X = [], limit = 128;
          if (seed2 === (seed2 | 0)) {
            v = seed2;
            seed2 = null;
          } else {
            seed2 = seed2 + "\0";
            v = 0;
            limit = Math.max(limit, seed2.length);
          }
          for (i = 0, j = -32; j < limit; ++j) {
            if (seed2)
              v ^= seed2.charCodeAt((j + 32) % seed2.length);
            if (j === 0)
              w = v;
            v ^= v << 10;
            v ^= v >>> 15;
            v ^= v << 4;
            v ^= v >>> 13;
            if (j >= 0) {
              w = w + 1640531527 | 0;
              t = X[j & 127] ^= v + w;
              i = t == 0 ? i + 1 : 0;
            }
          }
          if (i >= 128) {
            X[(seed2 && seed2.length || 0) & 127] = -1;
          }
          i = 127;
          for (j = 4 * 128; j > 0; --j) {
            v = X[i + 34 & 127];
            t = X[i = i + 1 & 127];
            v ^= v << 13;
            t ^= t << 17;
            v ^= v >>> 15;
            t ^= t >>> 12;
            X[i] = v ^ t;
          }
          me2.w = w;
          me2.X = X;
          me2.i = i;
        }
        init2(me, seed);
      }
      function copy(f, t) {
        t.i = f.i;
        t.w = f.w;
        t.X = f.X.slice();
        return t;
      }
      ;
      function impl(seed, opts) {
        if (seed == null)
          seed = +new Date();
        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
          return (xg.next() >>> 0) / 4294967296;
        };
        prng.double = function() {
          do {
            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
          } while (result === 0);
          return result;
        };
        prng.int32 = xg.next;
        prng.quick = prng;
        if (state) {
          if (state.X)
            copy(state, xg);
          prng.state = function() {
            return copy(xg, {});
          };
        }
        return prng;
      }
      if (module22 && module22.exports) {
        module22.exports = impl;
      } else if (define2 && define2.amd) {
        define2(function() {
          return impl;
        });
      } else {
        this.xor4096 = impl;
      }
    })(exports, typeof module2 == "object" && module2, typeof define == "function" && define);
  });
  var require_tychei = __commonJS((exports, module2) => {
    (function(global2, module22, define2) {
      function XorGen(seed) {
        var me = this, strseed = "";
        me.next = function() {
          var b = me.b, c = me.c, d = me.d, a = me.a;
          b = b << 25 ^ b >>> 7 ^ c;
          c = c - d | 0;
          d = d << 24 ^ d >>> 8 ^ a;
          a = a - b | 0;
          me.b = b = b << 20 ^ b >>> 12 ^ c;
          me.c = c = c - d | 0;
          me.d = d << 16 ^ c >>> 16 ^ a;
          return me.a = a - b | 0;
        };
        me.a = 0;
        me.b = 0;
        me.c = 2654435769 | 0;
        me.d = 1367130551;
        if (seed === Math.floor(seed)) {
          me.a = seed / 4294967296 | 0;
          me.b = seed | 0;
        } else {
          strseed += seed;
        }
        for (var k = 0; k < strseed.length + 20; k++) {
          me.b ^= strseed.charCodeAt(k) | 0;
          me.next();
        }
      }
      function copy(f, t) {
        t.a = f.a;
        t.b = f.b;
        t.c = f.c;
        t.d = f.d;
        return t;
      }
      ;
      function impl(seed, opts) {
        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
          return (xg.next() >>> 0) / 4294967296;
        };
        prng.double = function() {
          do {
            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
          } while (result === 0);
          return result;
        };
        prng.int32 = xg.next;
        prng.quick = prng;
        if (state) {
          if (typeof state == "object")
            copy(state, xg);
          prng.state = function() {
            return copy(xg, {});
          };
        }
        return prng;
      }
      if (module22 && module22.exports) {
        module22.exports = impl;
      } else if (define2 && define2.amd) {
        define2(function() {
          return impl;
        });
      } else {
        this.tychei = impl;
      }
    })(exports, typeof module2 == "object" && module2, typeof define == "function" && define);
  });
  var require_crypto = __commonJS(() => {
  });
  var require_seedrandom = __commonJS((exports, module2) => {
    (function(pool3, math) {
      var global2 = this, width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto;
      function seedrandom5(seed, options, callback) {
        var key = [];
        options = options == true ? {entropy: true} : options || {};
        var shortseed = mixkey(flatten4(options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key);
        var arc4 = new ARC4(key);
        var prng = function() {
          var n = arc4.g(chunks), d = startdenom, x = 0;
          while (n < significance) {
            n = (n + x) * width;
            d *= width;
            x = arc4.g(1);
          }
          while (n >= overflow) {
            n /= 2;
            d /= 2;
            x >>>= 1;
          }
          return (n + x) / d;
        };
        prng.int32 = function() {
          return arc4.g(4) | 0;
        };
        prng.quick = function() {
          return arc4.g(4) / 4294967296;
        };
        prng.double = prng;
        mixkey(tostring(arc4.S), pool3);
        return (options.pass || callback || function(prng2, seed2, is_math_call, state) {
          if (state) {
            if (state.S) {
              copy(state, arc4);
            }
            prng2.state = function() {
              return copy(arc4, {});
            };
          }
          if (is_math_call) {
            math[rngname] = prng2;
            return seed2;
          } else
            return prng2;
        })(prng, shortseed, "global" in options ? options.global : this == math, options.state);
      }
      math["seed" + rngname] = seedrandom5;
      function ARC4(key) {
        var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];
        if (!keylen) {
          key = [keylen++];
        }
        while (i < width) {
          s[i] = i++;
        }
        for (i = 0; i < width; i++) {
          s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])];
          s[j] = t;
        }
        (me.g = function(count2) {
          var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S;
          while (count2--) {
            t2 = s2[i2 = mask & i2 + 1];
            r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)];
          }
          me.i = i2;
          me.j = j2;
          return r;
        })(width);
      }
      function copy(f, t) {
        t.i = f.i;
        t.j = f.j;
        t.S = f.S.slice();
        return t;
      }
      ;
      function flatten4(obj, depth) {
        var result = [], typ = typeof obj, prop;
        if (depth && typ == "object") {
          for (prop in obj) {
            try {
              result.push(flatten4(obj[prop], depth - 1));
            } catch (e) {
            }
          }
        }
        return result.length ? result : typ == "string" ? obj : obj + "\0";
      }
      function mixkey(seed, key) {
        var stringseed = seed + "", smear, j = 0;
        while (j < stringseed.length) {
          key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);
        }
        return tostring(key);
      }
      function autoseed() {
        try {
          var out;
          if (nodecrypto && (out = nodecrypto.randomBytes)) {
            out = out(width);
          } else {
            out = new Uint8Array(width);
            (global2.crypto || global2.msCrypto).getRandomValues(out);
          }
          return tostring(out);
        } catch (e) {
          var browser2 = global2.navigator, plugins = browser2 && browser2.plugins;
          return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];
        }
      }
      function tostring(a) {
        return String.fromCharCode.apply(0, a);
      }
      mixkey(math.random(), pool3);
      if (typeof module2 == "object" && module2.exports) {
        module2.exports = seedrandom5;
        try {
          nodecrypto = require_crypto();
        } catch (ex) {
        }
      } else if (typeof define == "function" && define.amd) {
        define(function() {
          return seedrandom5;
        });
      }
    })([], Math);
  });
  var require_seedrandom2 = __commonJS((exports, module2) => {
    var alea5 = require_alea();
    var xor128 = require_xor128();
    var xorwow = require_xorwow();
    var xorshift7 = require_xorshift7();
    var xor4096 = require_xor4096();
    var tychei = require_tychei();
    var sr = require_seedrandom();
    sr.alea = alea5;
    sr.xor128 = xor128;
    sr.xorwow = xorwow;
    sr.xorshift7 = xorshift7;
    sr.xor4096 = xor4096;
    sr.tychei = tychei;
    module2.exports = sr;
  });
  var require_alea2 = __commonJS((exports, module2) => {
    (function(global2, module22, define2) {
      function Alea(seed) {
        var me = this, mash = Mash();
        me.next = function() {
          var t = 2091639 * me.s0 + me.c * 23283064365386963e-26;
          me.s0 = me.s1;
          me.s1 = me.s2;
          return me.s2 = t - (me.c = t | 0);
        };
        me.c = 1;
        me.s0 = mash(" ");
        me.s1 = mash(" ");
        me.s2 = mash(" ");
        me.s0 -= mash(seed);
        if (me.s0 < 0) {
          me.s0 += 1;
        }
        me.s1 -= mash(seed);
        if (me.s1 < 0) {
          me.s1 += 1;
        }
        me.s2 -= mash(seed);
        if (me.s2 < 0) {
          me.s2 += 1;
        }
        mash = null;
      }
      function copy(f, t) {
        t.c = f.c;
        t.s0 = f.s0;
        t.s1 = f.s1;
        t.s2 = f.s2;
        return t;
      }
      function impl(seed, opts) {
        var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;
        prng.int32 = function() {
          return xg.next() * 4294967296 | 0;
        };
        prng.double = function() {
          return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;
        };
        prng.quick = prng;
        if (state) {
          if (typeof state == "object")
            copy(state, xg);
          prng.state = function() {
            return copy(xg, {});
          };
        }
        return prng;
      }
      function Mash() {
        var n = 4022871197;
        var mash = function(data) {
          data = String(data);
          for (var i = 0; i < data.length; i++) {
            n += data.charCodeAt(i);
            var h = 0.02519603282416938 * n;
            n = h >>> 0;
            h -= n;
            h *= n;
            n = h >>> 0;
            h -= n;
            n += h * 4294967296;
          }
          return (n >>> 0) * 23283064365386963e-26;
        };
        return mash;
      }
      if (module22 && module22.exports) {
        module22.exports = impl;
      } else if (define2 && define2.amd) {
        define2(function() {
          return impl;
        });
      } else {
        this.alea = impl;
      }
    })(exports, typeof module2 == "object" && module2, typeof define == "function" && define);
  });
  var require_xor1282 = __commonJS((exports, module2) => {
    (function(global2, module22, define2) {
      function XorGen(seed) {
        var me = this, strseed = "";
        me.x = 0;
        me.y = 0;
        me.z = 0;
        me.w = 0;
        me.next = function() {
          var t = me.x ^ me.x << 11;
          me.x = me.y;
          me.y = me.z;
          me.z = me.w;
          return me.w ^= me.w >>> 19 ^ t ^ t >>> 8;
        };
        if (seed === (seed | 0)) {
          me.x = seed;
        } else {
          strseed += seed;
        }
        for (var k = 0; k < strseed.length + 64; k++) {
          me.x ^= strseed.charCodeAt(k) | 0;
          me.next();
        }
      }
      function copy(f, t) {
        t.x = f.x;
        t.y = f.y;
        t.z = f.z;
        t.w = f.w;
        return t;
      }
      function impl(seed, opts) {
        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
          return (xg.next() >>> 0) / 4294967296;
        };
        prng.double = function() {
          do {
            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
          } while (result === 0);
          return result;
        };
        prng.int32 = xg.next;
        prng.quick = prng;
        if (state) {
          if (typeof state == "object")
            copy(state, xg);
          prng.state = function() {
            return copy(xg, {});
          };
        }
        return prng;
      }
      if (module22 && module22.exports) {
        module22.exports = impl;
      } else if (define2 && define2.amd) {
        define2(function() {
          return impl;
        });
      } else {
        this.xor128 = impl;
      }
    })(exports, typeof module2 == "object" && module2, typeof define == "function" && define);
  });
  var require_xorwow2 = __commonJS((exports, module2) => {
    (function(global2, module22, define2) {
      function XorGen(seed) {
        var me = this, strseed = "";
        me.next = function() {
          var t = me.x ^ me.x >>> 2;
          me.x = me.y;
          me.y = me.z;
          me.z = me.w;
          me.w = me.v;
          return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0;
        };
        me.x = 0;
        me.y = 0;
        me.z = 0;
        me.w = 0;
        me.v = 0;
        if (seed === (seed | 0)) {
          me.x = seed;
        } else {
          strseed += seed;
        }
        for (var k = 0; k < strseed.length + 64; k++) {
          me.x ^= strseed.charCodeAt(k) | 0;
          if (k == strseed.length) {
            me.d = me.x << 10 ^ me.x >>> 4;
          }
          me.next();
        }
      }
      function copy(f, t) {
        t.x = f.x;
        t.y = f.y;
        t.z = f.z;
        t.w = f.w;
        t.v = f.v;
        t.d = f.d;
        return t;
      }
      function impl(seed, opts) {
        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
          return (xg.next() >>> 0) / 4294967296;
        };
        prng.double = function() {
          do {
            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
          } while (result === 0);
          return result;
        };
        prng.int32 = xg.next;
        prng.quick = prng;
        if (state) {
          if (typeof state == "object")
            copy(state, xg);
          prng.state = function() {
            return copy(xg, {});
          };
        }
        return prng;
      }
      if (module22 && module22.exports) {
        module22.exports = impl;
      } else if (define2 && define2.amd) {
        define2(function() {
          return impl;
        });
      } else {
        this.xorwow = impl;
      }
    })(exports, typeof module2 == "object" && module2, typeof define == "function" && define);
  });
  var require_xorshift72 = __commonJS((exports, module2) => {
    (function(global2, module22, define2) {
      function XorGen(seed) {
        var me = this;
        me.next = function() {
          var X = me.x, i = me.i, t, v, w;
          t = X[i];
          t ^= t >>> 7;
          v = t ^ t << 24;
          t = X[i + 1 & 7];
          v ^= t ^ t >>> 10;
          t = X[i + 3 & 7];
          v ^= t ^ t >>> 3;
          t = X[i + 4 & 7];
          v ^= t ^ t << 7;
          t = X[i + 7 & 7];
          t = t ^ t << 13;
          v ^= t ^ t << 9;
          X[i] = v;
          me.i = i + 1 & 7;
          return v;
        };
        function init2(me2, seed2) {
          var j, w, X = [];
          if (seed2 === (seed2 | 0)) {
            w = X[0] = seed2;
          } else {
            seed2 = "" + seed2;
            for (j = 0; j < seed2.length; ++j) {
              X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;
            }
          }
          while (X.length < 8)
            X.push(0);
          for (j = 0; j < 8 && X[j] === 0; ++j)
            ;
          if (j == 8)
            w = X[7] = -1;
          else
            w = X[j];
          me2.x = X;
          me2.i = 0;
          for (j = 256; j > 0; --j) {
            me2.next();
          }
        }
        init2(me, seed);
      }
      function copy(f, t) {
        t.x = f.x.slice();
        t.i = f.i;
        return t;
      }
      function impl(seed, opts) {
        if (seed == null)
          seed = +new Date();
        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
          return (xg.next() >>> 0) / 4294967296;
        };
        prng.double = function() {
          do {
            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
          } while (result === 0);
          return result;
        };
        prng.int32 = xg.next;
        prng.quick = prng;
        if (state) {
          if (state.x)
            copy(state, xg);
          prng.state = function() {
            return copy(xg, {});
          };
        }
        return prng;
      }
      if (module22 && module22.exports) {
        module22.exports = impl;
      } else if (define2 && define2.amd) {
        define2(function() {
          return impl;
        });
      } else {
        this.xorshift7 = impl;
      }
    })(exports, typeof module2 == "object" && module2, typeof define == "function" && define);
  });
  var require_xor40962 = __commonJS((exports, module2) => {
    (function(global2, module22, define2) {
      function XorGen(seed) {
        var me = this;
        me.next = function() {
          var w = me.w, X = me.X, i = me.i, t, v;
          me.w = w = w + 1640531527 | 0;
          v = X[i + 34 & 127];
          t = X[i = i + 1 & 127];
          v ^= v << 13;
          t ^= t << 17;
          v ^= v >>> 15;
          t ^= t >>> 12;
          v = X[i] = v ^ t;
          me.i = i;
          return v + (w ^ w >>> 16) | 0;
        };
        function init2(me2, seed2) {
          var t, v, i, j, w, X = [], limit = 128;
          if (seed2 === (seed2 | 0)) {
            v = seed2;
            seed2 = null;
          } else {
            seed2 = seed2 + "\0";
            v = 0;
            limit = Math.max(limit, seed2.length);
          }
          for (i = 0, j = -32; j < limit; ++j) {
            if (seed2)
              v ^= seed2.charCodeAt((j + 32) % seed2.length);
            if (j === 0)
              w = v;
            v ^= v << 10;
            v ^= v >>> 15;
            v ^= v << 4;
            v ^= v >>> 13;
            if (j >= 0) {
              w = w + 1640531527 | 0;
              t = X[j & 127] ^= v + w;
              i = t == 0 ? i + 1 : 0;
            }
          }
          if (i >= 128) {
            X[(seed2 && seed2.length || 0) & 127] = -1;
          }
          i = 127;
          for (j = 4 * 128; j > 0; --j) {
            v = X[i + 34 & 127];
            t = X[i = i + 1 & 127];
            v ^= v << 13;
            t ^= t << 17;
            v ^= v >>> 15;
            t ^= t >>> 12;
            X[i] = v ^ t;
          }
          me2.w = w;
          me2.X = X;
          me2.i = i;
        }
        init2(me, seed);
      }
      function copy(f, t) {
        t.i = f.i;
        t.w = f.w;
        t.X = f.X.slice();
        return t;
      }
      ;
      function impl(seed, opts) {
        if (seed == null)
          seed = +new Date();
        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
          return (xg.next() >>> 0) / 4294967296;
        };
        prng.double = function() {
          do {
            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
          } while (result === 0);
          return result;
        };
        prng.int32 = xg.next;
        prng.quick = prng;
        if (state) {
          if (state.X)
            copy(state, xg);
          prng.state = function() {
            return copy(xg, {});
          };
        }
        return prng;
      }
      if (module22 && module22.exports) {
        module22.exports = impl;
      } else if (define2 && define2.amd) {
        define2(function() {
          return impl;
        });
      } else {
        this.xor4096 = impl;
      }
    })(exports, typeof module2 == "object" && module2, typeof define == "function" && define);
  });
  var require_tychei2 = __commonJS((exports, module2) => {
    (function(global2, module22, define2) {
      function XorGen(seed) {
        var me = this, strseed = "";
        me.next = function() {
          var b = me.b, c = me.c, d = me.d, a = me.a;
          b = b << 25 ^ b >>> 7 ^ c;
          c = c - d | 0;
          d = d << 24 ^ d >>> 8 ^ a;
          a = a - b | 0;
          me.b = b = b << 20 ^ b >>> 12 ^ c;
          me.c = c = c - d | 0;
          me.d = d << 16 ^ c >>> 16 ^ a;
          return me.a = a - b | 0;
        };
        me.a = 0;
        me.b = 0;
        me.c = 2654435769 | 0;
        me.d = 1367130551;
        if (seed === Math.floor(seed)) {
          me.a = seed / 4294967296 | 0;
          me.b = seed | 0;
        } else {
          strseed += seed;
        }
        for (var k = 0; k < strseed.length + 20; k++) {
          me.b ^= strseed.charCodeAt(k) | 0;
          me.next();
        }
      }
      function copy(f, t) {
        t.a = f.a;
        t.b = f.b;
        t.c = f.c;
        t.d = f.d;
        return t;
      }
      ;
      function impl(seed, opts) {
        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
          return (xg.next() >>> 0) / 4294967296;
        };
        prng.double = function() {
          do {
            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
          } while (result === 0);
          return result;
        };
        prng.int32 = xg.next;
        prng.quick = prng;
        if (state) {
          if (typeof state == "object")
            copy(state, xg);
          prng.state = function() {
            return copy(xg, {});
          };
        }
        return prng;
      }
      if (module22 && module22.exports) {
        module22.exports = impl;
      } else if (define2 && define2.amd) {
        define2(function() {
          return impl;
        });
      } else {
        this.tychei = impl;
      }
    })(exports, typeof module2 == "object" && module2, typeof define == "function" && define);
  });
  var require_seedrandom3 = __commonJS((exports, module2) => {
    (function(global2, pool3, math) {
      var width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto;
      function seedrandom5(seed, options, callback) {
        var key = [];
        options = options == true ? {entropy: true} : options || {};
        var shortseed = mixkey(flatten4(options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key);
        var arc4 = new ARC4(key);
        var prng = function() {
          var n = arc4.g(chunks), d = startdenom, x = 0;
          while (n < significance) {
            n = (n + x) * width;
            d *= width;
            x = arc4.g(1);
          }
          while (n >= overflow) {
            n /= 2;
            d /= 2;
            x >>>= 1;
          }
          return (n + x) / d;
        };
        prng.int32 = function() {
          return arc4.g(4) | 0;
        };
        prng.quick = function() {
          return arc4.g(4) / 4294967296;
        };
        prng.double = prng;
        mixkey(tostring(arc4.S), pool3);
        return (options.pass || callback || function(prng2, seed2, is_math_call, state) {
          if (state) {
            if (state.S) {
              copy(state, arc4);
            }
            prng2.state = function() {
              return copy(arc4, {});
            };
          }
          if (is_math_call) {
            math[rngname] = prng2;
            return seed2;
          } else
            return prng2;
        })(prng, shortseed, "global" in options ? options.global : this == math, options.state);
      }
      function ARC4(key) {
        var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];
        if (!keylen) {
          key = [keylen++];
        }
        while (i < width) {
          s[i] = i++;
        }
        for (i = 0; i < width; i++) {
          s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])];
          s[j] = t;
        }
        (me.g = function(count2) {
          var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S;
          while (count2--) {
            t2 = s2[i2 = mask & i2 + 1];
            r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)];
          }
          me.i = i2;
          me.j = j2;
          return r;
        })(width);
      }
      function copy(f, t) {
        t.i = f.i;
        t.j = f.j;
        t.S = f.S.slice();
        return t;
      }
      ;
      function flatten4(obj, depth) {
        var result = [], typ = typeof obj, prop;
        if (depth && typ == "object") {
          for (prop in obj) {
            try {
              result.push(flatten4(obj[prop], depth - 1));
            } catch (e) {
            }
          }
        }
        return result.length ? result : typ == "string" ? obj : obj + "\0";
      }
      function mixkey(seed, key) {
        var stringseed = seed + "", smear, j = 0;
        while (j < stringseed.length) {
          key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);
        }
        return tostring(key);
      }
      function autoseed() {
        try {
          var out;
          if (nodecrypto && (out = nodecrypto.randomBytes)) {
            out = out(width);
          } else {
            out = new Uint8Array(width);
            (global2.crypto || global2.msCrypto).getRandomValues(out);
          }
          return tostring(out);
        } catch (e) {
          var browser2 = global2.navigator, plugins = browser2 && browser2.plugins;
          return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];
        }
      }
      function tostring(a) {
        return String.fromCharCode.apply(0, a);
      }
      mixkey(math.random(), pool3);
      if (typeof module2 == "object" && module2.exports) {
        module2.exports = seedrandom5;
        try {
          nodecrypto = require_crypto();
        } catch (ex) {
        }
      } else if (typeof define == "function" && define.amd) {
        define(function() {
          return seedrandom5;
        });
      } else {
        math["seed" + rngname] = seedrandom5;
      }
    })(typeof self !== "undefined" ? self : exports, [], Math);
  });
  var require_seedrandom4 = __commonJS((exports, module2) => {
    var alea5 = require_alea2();
    var xor128 = require_xor1282();
    var xorwow = require_xorwow2();
    var xorshift7 = require_xorshift72();
    var xor4096 = require_xor40962();
    var tychei = require_tychei2();
    var sr = require_seedrandom3();
    sr.alea = alea5;
    sr.xor128 = xor128;
    sr.xorwow = xorwow;
    sr.xorshift7 = xorshift7;
    sr.xor4096 = xor4096;
    sr.tychei = tychei;
    module2.exports = sr;
  });
  var require_string_decoder = __commonJS(() => {
  });
  var require_path = __commonJS(() => {
  });
  var require_worker_threads = __commonJS(() => {
  });
  var require_perf_hooks = __commonJS(() => {
  });
  var require_tfjs_backend_wasm_threaded_simd = __commonJS((exports, module2) => {
    var WasmBackendModuleThreadedSimd = function() {
      var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0;
      if (typeof __filename !== "undefined")
        _scriptDir = _scriptDir || __filename;
      return function(WasmBackendModuleThreadedSimd2) {
        WasmBackendModuleThreadedSimd2 = WasmBackendModuleThreadedSimd2 || {};
        function GROWABLE_HEAP_I8() {
          if (wasmMemory.buffer != buffer2) {
            updateGlobalBufferAndViews(wasmMemory.buffer);
          }
          return HEAP8;
        }
        function GROWABLE_HEAP_U8() {
          if (wasmMemory.buffer != buffer2) {
            updateGlobalBufferAndViews(wasmMemory.buffer);
          }
          return HEAPU8;
        }
        function GROWABLE_HEAP_I32() {
          if (wasmMemory.buffer != buffer2) {
            updateGlobalBufferAndViews(wasmMemory.buffer);
          }
          return HEAP32;
        }
        function GROWABLE_HEAP_U32() {
          if (wasmMemory.buffer != buffer2) {
            updateGlobalBufferAndViews(wasmMemory.buffer);
          }
          return HEAPU32;
        }
        function GROWABLE_HEAP_F64() {
          if (wasmMemory.buffer != buffer2) {
            updateGlobalBufferAndViews(wasmMemory.buffer);
          }
          return HEAPF64;
        }
        var Module = typeof WasmBackendModuleThreadedSimd2 !== "undefined" ? WasmBackendModuleThreadedSimd2 : {};
        var readyPromiseResolve, readyPromiseReject;
        Module["ready"] = new Promise(function(resolve, reject) {
          readyPromiseResolve = resolve;
          readyPromiseReject = reject;
        });
        var moduleOverrides = {};
        var key;
        for (key in Module) {
          if (Module.hasOwnProperty(key)) {
            moduleOverrides[key] = Module[key];
          }
        }
        var arguments_ = [];
        var thisProgram = "./this.program";
        var quit_ = function(status, toThrow) {
          throw toThrow;
        };
        var ENVIRONMENT_IS_WEB = false;
        var ENVIRONMENT_IS_WORKER = false;
        var ENVIRONMENT_IS_NODE = false;
        var ENVIRONMENT_IS_SHELL = false;
        ENVIRONMENT_IS_WEB = typeof window === "object";
        ENVIRONMENT_IS_WORKER = typeof importScripts === "function";
        ENVIRONMENT_IS_NODE = typeof process === "object" && typeof process.versions === "object" && typeof process.versions.node === "string";
        ENVIRONMENT_IS_SHELL = !ENVIRONMENT_IS_WEB && !ENVIRONMENT_IS_NODE && !ENVIRONMENT_IS_WORKER;
        var ENVIRONMENT_IS_PTHREAD = Module["ENVIRONMENT_IS_PTHREAD"] || false;
        if (ENVIRONMENT_IS_PTHREAD) {
          buffer2 = Module["buffer"];
        }
        var scriptDirectory = "";
        function locateFile(path) {
          if (Module["locateFile"]) {
            return Module["locateFile"](path, scriptDirectory);
          }
          return scriptDirectory + path;
        }
        var read_, readAsync, readBinary, setWindowTitle;
        var nodeFS;
        var nodePath;
        if (ENVIRONMENT_IS_NODE) {
          if (ENVIRONMENT_IS_WORKER) {
            scriptDirectory = require_path().dirname(scriptDirectory) + "/";
          } else {
            scriptDirectory = __dirname + "/";
          }
          read_ = function shell_read(filename, binary) {
            if (!nodeFS)
              nodeFS = require("fs");
            if (!nodePath)
              nodePath = require_path();
            filename = nodePath["normalize"](filename);
            return nodeFS["readFileSync"](filename, binary ? null : "utf8");
          };
          readBinary = function readBinary2(filename) {
            var ret = read_(filename, true);
            if (!ret.buffer) {
              ret = new Uint8Array(ret);
            }
            assert3(ret.buffer);
            return ret;
          };
          if (process["argv"].length > 1) {
            thisProgram = process["argv"][1].replace(/\\/g, "/");
          }
          arguments_ = process["argv"].slice(2);
          process["on"]("uncaughtException", function(ex) {
            if (!(ex instanceof ExitStatus)) {
              throw ex;
            }
          });
          process["on"]("unhandledRejection", abort);
          quit_ = function(status) {
            process["exit"](status);
          };
          Module["inspect"] = function() {
            return "[Emscripten Module object]";
          };
          var nodeWorkerThreads;
          try {
            nodeWorkerThreads = require_worker_threads();
          } catch (e) {
            console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?');
            throw e;
          }
          global.Worker = nodeWorkerThreads.Worker;
        } else if (ENVIRONMENT_IS_SHELL) {
          if (typeof read != "undefined") {
            read_ = function shell_read(f) {
              return read(f);
            };
          }
          readBinary = function readBinary2(f) {
            var data;
            if (typeof readbuffer === "function") {
              return new Uint8Array(readbuffer(f));
            }
            data = read(f, "binary");
            assert3(typeof data === "object");
            return data;
          };
          if (typeof scriptArgs != "undefined") {
            arguments_ = scriptArgs;
          } else if (typeof arguments != "undefined") {
            arguments_ = arguments;
          }
          if (typeof quit === "function") {
            quit_ = function(status) {
              quit(status);
            };
          }
          if (typeof print !== "undefined") {
            if (typeof console === "undefined")
              console = {};
            console.log = print;
            console.warn = console.error = typeof printErr !== "undefined" ? printErr : print;
          }
        } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {
          if (ENVIRONMENT_IS_WORKER) {
            scriptDirectory = self.location.href;
          } else if (typeof document !== "undefined" && document.currentScript) {
            scriptDirectory = document.currentScript.src;
          }
          if (typeof _scriptDir !== "undefined" && _scriptDir) {
            scriptDirectory = _scriptDir;
          }
          if (scriptDirectory.indexOf("blob:") !== 0) {
            scriptDirectory = scriptDirectory.substr(0, scriptDirectory.lastIndexOf("/") + 1);
          } else {
            scriptDirectory = "";
          }
          if (ENVIRONMENT_IS_NODE) {
            read_ = function shell_read(filename, binary) {
              if (!nodeFS)
                nodeFS = require("fs");
              if (!nodePath)
                nodePath = require_path();
              filename = nodePath["normalize"](filename);
              return nodeFS["readFileSync"](filename, binary ? null : "utf8");
            };
            readBinary = function readBinary2(filename) {
              var ret = read_(filename, true);
              if (!ret.buffer) {
                ret = new Uint8Array(ret);
              }
              assert3(ret.buffer);
              return ret;
            };
          } else {
            read_ = function(url) {
              var xhr = new XMLHttpRequest();
              xhr.open("GET", url, false);
              xhr.send(null);
              return xhr.responseText;
            };
            if (ENVIRONMENT_IS_WORKER) {
              readBinary = function(url) {
                var xhr = new XMLHttpRequest();
                xhr.open("GET", url, false);
                xhr.responseType = "arraybuffer";
                xhr.send(null);
                return new Uint8Array(xhr.response);
              };
            }
            readAsync = function(url, onload, onerror) {
              var xhr = new XMLHttpRequest();
              xhr.open("GET", url, true);
              xhr.responseType = "arraybuffer";
              xhr.onload = function() {
                if (xhr.status == 200 || xhr.status == 0 && xhr.response) {
                  onload(xhr.response);
                  return;
                }
                onerror();
              };
              xhr.onerror = onerror;
              xhr.send(null);
            };
          }
          setWindowTitle = function(title) {
            document.title = title;
          };
        } else {
        }
        if (ENVIRONMENT_IS_NODE) {
          if (typeof performance === "undefined") {
            global.performance = require_perf_hooks().performance;
          }
        }
        var out = Module["print"] || console.log.bind(console);
        var err = Module["printErr"] || console.warn.bind(console);
        for (key in moduleOverrides) {
          if (moduleOverrides.hasOwnProperty(key)) {
            Module[key] = moduleOverrides[key];
          }
        }
        moduleOverrides = null;
        if (Module["arguments"])
          arguments_ = Module["arguments"];
        if (Module["thisProgram"])
          thisProgram = Module["thisProgram"];
        if (Module["quit"])
          quit_ = Module["quit"];
        var Atomics_load = Atomics.load;
        var Atomics_store = Atomics.store;
        var Atomics_compareExchange = Atomics.compareExchange;
        var wasmBinary;
        if (Module["wasmBinary"])
          wasmBinary = Module["wasmBinary"];
        var noExitRuntime = Module["noExitRuntime"] || true;
        if (typeof WebAssembly !== "object") {
          abort("no native wasm support detected");
        }
        var wasmMemory;
        var wasmModule;
        var ABORT = false;
        var EXITSTATUS;
        function assert3(condition, text) {
          if (!condition) {
            abort("Assertion failed: " + text);
          }
        }
        function getCFunc(ident) {
          var func2 = Module["_" + ident];
          assert3(func2, "Cannot call unknown function " + ident + ", make sure it is exported");
          return func2;
        }
        function ccall(ident, returnType, argTypes, args, opts) {
          var toC = {string: function(str) {
            var ret2 = 0;
            if (str !== null && str !== void 0 && str !== 0) {
              var len = (str.length << 2) + 1;
              ret2 = stackAlloc(len);
              stringToUTF8(str, ret2, len);
            }
            return ret2;
          }, array: function(arr) {
            var ret2 = stackAlloc(arr.length);
            writeArrayToMemory(arr, ret2);
            return ret2;
          }};
          function convertReturnValue(ret2) {
            if (returnType === "string")
              return UTF8ToString(ret2);
            if (returnType === "boolean")
              return Boolean(ret2);
            return ret2;
          }
          var func2 = getCFunc(ident);
          var cArgs = [];
          var stack2 = 0;
          if (args) {
            for (var i = 0; i < args.length; i++) {
              var converter = toC[argTypes[i]];
              if (converter) {
                if (stack2 === 0)
                  stack2 = stackSave();
                cArgs[i] = converter(args[i]);
              } else {
                cArgs[i] = args[i];
              }
            }
          }
          var ret = func2.apply(null, cArgs);
          ret = convertReturnValue(ret);
          if (stack2 !== 0)
            stackRestore(stack2);
          return ret;
        }
        function cwrap(ident, returnType, argTypes, opts) {
          argTypes = argTypes || [];
          var numericArgs = argTypes.every(function(type) {
            return type === "number";
          });
          var numericRet = returnType !== "string";
          if (numericRet && numericArgs && !opts) {
            return getCFunc(ident);
          }
          return function() {
            return ccall(ident, returnType, argTypes, arguments, opts);
          };
        }
        function UTF8ArrayToString(heap, idx, maxBytesToRead) {
          var endIdx = idx + maxBytesToRead;
          var str = "";
          while (!(idx >= endIdx)) {
            var u0 = heap[idx++];
            if (!u0)
              return str;
            if (!(u0 & 128)) {
              str += String.fromCharCode(u0);
              continue;
            }
            var u1 = heap[idx++] & 63;
            if ((u0 & 224) == 192) {
              str += String.fromCharCode((u0 & 31) << 6 | u1);
              continue;
            }
            var u2 = heap[idx++] & 63;
            if ((u0 & 240) == 224) {
              u0 = (u0 & 15) << 12 | u1 << 6 | u2;
            } else {
              u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63;
            }
            if (u0 < 65536) {
              str += String.fromCharCode(u0);
            } else {
              var ch = u0 - 65536;
              str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);
            }
          }
          return str;
        }
        function UTF8ToString(ptr, maxBytesToRead) {
          return ptr ? UTF8ArrayToString(GROWABLE_HEAP_U8(), ptr, maxBytesToRead) : "";
        }
        function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {
          if (!(maxBytesToWrite > 0))
            return 0;
          var startIdx = outIdx;
          var endIdx = outIdx + maxBytesToWrite - 1;
          for (var i = 0; i < str.length; ++i) {
            var u = str.charCodeAt(i);
            if (u >= 55296 && u <= 57343) {
              var u1 = str.charCodeAt(++i);
              u = 65536 + ((u & 1023) << 10) | u1 & 1023;
            }
            if (u <= 127) {
              if (outIdx >= endIdx)
                break;
              heap[outIdx++] = u;
            } else if (u <= 2047) {
              if (outIdx + 1 >= endIdx)
                break;
              heap[outIdx++] = 192 | u >> 6;
              heap[outIdx++] = 128 | u & 63;
            } else if (u <= 65535) {
              if (outIdx + 2 >= endIdx)
                break;
              heap[outIdx++] = 224 | u >> 12;
              heap[outIdx++] = 128 | u >> 6 & 63;
              heap[outIdx++] = 128 | u & 63;
            } else {
              if (outIdx + 3 >= endIdx)
                break;
              heap[outIdx++] = 240 | u >> 18;
              heap[outIdx++] = 128 | u >> 12 & 63;
              heap[outIdx++] = 128 | u >> 6 & 63;
              heap[outIdx++] = 128 | u & 63;
            }
          }
          heap[outIdx] = 0;
          return outIdx - startIdx;
        }
        function stringToUTF8(str, outPtr, maxBytesToWrite) {
          return stringToUTF8Array(str, GROWABLE_HEAP_U8(), outPtr, maxBytesToWrite);
        }
        function lengthBytesUTF8(str) {
          var len = 0;
          for (var i = 0; i < str.length; ++i) {
            var u = str.charCodeAt(i);
            if (u >= 55296 && u <= 57343)
              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i) & 1023;
            if (u <= 127)
              ++len;
            else if (u <= 2047)
              len += 2;
            else if (u <= 65535)
              len += 3;
            else
              len += 4;
          }
          return len;
        }
        function writeArrayToMemory(array2, buffer3) {
          GROWABLE_HEAP_I8().set(array2, buffer3);
        }
        function alignUp(x, multiple) {
          if (x % multiple > 0) {
            x += multiple - x % multiple;
          }
          return x;
        }
        var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;
        function updateGlobalBufferAndViews(buf) {
          buffer2 = buf;
          Module["HEAP8"] = HEAP8 = new Int8Array(buf);
          Module["HEAP16"] = HEAP16 = new Int16Array(buf);
          Module["HEAP32"] = HEAP32 = new Int32Array(buf);
          Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf);
          Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf);
          Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf);
          Module["HEAPF32"] = HEAPF32 = new Float32Array(buf);
          Module["HEAPF64"] = HEAPF64 = new Float64Array(buf);
        }
        var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216;
        if (ENVIRONMENT_IS_PTHREAD) {
          wasmMemory = Module["wasmMemory"];
          buffer2 = Module["buffer"];
        } else {
          if (Module["wasmMemory"]) {
            wasmMemory = Module["wasmMemory"];
          } else {
            wasmMemory = new WebAssembly.Memory({initial: INITIAL_MEMORY / 65536, maximum: 2147483648 / 65536, shared: true});
            if (!(wasmMemory.buffer instanceof SharedArrayBuffer)) {
              err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag");
              if (ENVIRONMENT_IS_NODE) {
                console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)");
              }
              throw Error("bad memory");
            }
          }
        }
        if (wasmMemory) {
          buffer2 = wasmMemory.buffer;
        }
        INITIAL_MEMORY = buffer2.byteLength;
        updateGlobalBufferAndViews(buffer2);
        var wasmTable;
        var __ATPRERUN__ = [];
        var __ATINIT__ = [];
        var __ATMAIN__ = [];
        var __ATEXIT__ = [];
        var __ATPOSTRUN__ = [];
        var runtimeInitialized = false;
        var runtimeExited = false;
        if (!ENVIRONMENT_IS_PTHREAD)
          __ATINIT__.push({func: function() {
            ___wasm_call_ctors();
          }});
        if (ENVIRONMENT_IS_PTHREAD)
          runtimeInitialized = true;
        function preRun() {
          if (ENVIRONMENT_IS_PTHREAD)
            return;
          if (Module["preRun"]) {
            if (typeof Module["preRun"] == "function")
              Module["preRun"] = [Module["preRun"]];
            while (Module["preRun"].length) {
              addOnPreRun(Module["preRun"].shift());
            }
          }
          callRuntimeCallbacks(__ATPRERUN__);
        }
        function initRuntime() {
          runtimeInitialized = true;
          callRuntimeCallbacks(__ATINIT__);
        }
        function preMain() {
          if (ENVIRONMENT_IS_PTHREAD)
            return;
          callRuntimeCallbacks(__ATMAIN__);
        }
        function exitRuntime() {
          if (ENVIRONMENT_IS_PTHREAD)
            return;
          runtimeExited = true;
        }
        function postRun() {
          if (ENVIRONMENT_IS_PTHREAD)
            return;
          if (Module["postRun"]) {
            if (typeof Module["postRun"] == "function")
              Module["postRun"] = [Module["postRun"]];
            while (Module["postRun"].length) {
              addOnPostRun(Module["postRun"].shift());
            }
          }
          callRuntimeCallbacks(__ATPOSTRUN__);
        }
        function addOnPreRun(cb) {
          __ATPRERUN__.unshift(cb);
        }
        function addOnPostRun(cb) {
          __ATPOSTRUN__.unshift(cb);
        }
        var runDependencies = 0;
        var runDependencyWatcher = null;
        var dependenciesFulfilled = null;
        function addRunDependency(id) {
          assert3(!ENVIRONMENT_IS_PTHREAD, "addRunDependency cannot be used in a pthread worker");
          runDependencies++;
          if (Module["monitorRunDependencies"]) {
            Module["monitorRunDependencies"](runDependencies);
          }
        }
        function removeRunDependency(id) {
          runDependencies--;
          if (Module["monitorRunDependencies"]) {
            Module["monitorRunDependencies"](runDependencies);
          }
          if (runDependencies == 0) {
            if (runDependencyWatcher !== null) {
              clearInterval(runDependencyWatcher);
              runDependencyWatcher = null;
            }
            if (dependenciesFulfilled) {
              var callback = dependenciesFulfilled;
              dependenciesFulfilled = null;
              callback();
            }
          }
        }
        Module["preloadedImages"] = {};
        Module["preloadedAudios"] = {};
        function abort(what) {
          if (Module["onAbort"]) {
            Module["onAbort"](what);
          }
          if (ENVIRONMENT_IS_PTHREAD)
            console.error("Pthread aborting at " + new Error().stack);
          what += "";
          err(what);
          ABORT = true;
          EXITSTATUS = 1;
          what = "abort(" + what + "). Build with -s ASSERTIONS=1 for more info.";
          var e = new WebAssembly.RuntimeError(what);
          readyPromiseReject(e);
          throw e;
        }
        function hasPrefix(str, prefix) {
          return String.prototype.startsWith ? str.startsWith(prefix) : str.indexOf(prefix) === 0;
        }
        var dataURIPrefix = "data:application/octet-stream;base64,";
        function isDataURI(filename) {
          return hasPrefix(filename, dataURIPrefix);
        }
        var fileURIPrefix = "file://";
        function isFileURI(filename) {
          return hasPrefix(filename, fileURIPrefix);
        }
        var wasmBinaryFile = "tfjs-backend-wasm-threaded-simd.wasm";
        if (!isDataURI(wasmBinaryFile)) {
          wasmBinaryFile = locateFile(wasmBinaryFile);
        }
        function getBinary(file) {
          try {
            if (file == wasmBinaryFile && wasmBinary) {
              return new Uint8Array(wasmBinary);
            }
            if (readBinary) {
              return readBinary(file);
            } else {
              throw "both async and sync fetching of the wasm failed";
            }
          } catch (err2) {
            abort(err2);
          }
        }
        function getBinaryPromise() {
          if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) {
            if (typeof fetch === "function" && !isFileURI(wasmBinaryFile)) {
              return fetch(wasmBinaryFile, {credentials: "same-origin"}).then(function(response) {
                if (!response["ok"]) {
                  throw "failed to load wasm binary file at '" + wasmBinaryFile + "'";
                }
                return response["arrayBuffer"]();
              }).catch(function() {
                return getBinary(wasmBinaryFile);
              });
            } else {
              if (readAsync) {
                return new Promise(function(resolve, reject) {
                  readAsync(wasmBinaryFile, function(response) {
                    resolve(new Uint8Array(response));
                  }, reject);
                });
              }
            }
          }
          return Promise.resolve().then(function() {
            return getBinary(wasmBinaryFile);
          });
        }
        function createWasm() {
          var info = {a: asmLibraryArg};
          function receiveInstance(instance, module22) {
            var exports3 = instance.exports;
            Module["asm"] = exports3;
            wasmTable = Module["asm"]["F"];
            wasmModule = module22;
            if (!ENVIRONMENT_IS_PTHREAD) {
              var numWorkersToLoad = PThread.unusedWorkers.length;
              PThread.unusedWorkers.forEach(function(w) {
                PThread.loadWasmModuleToWorker(w, function() {
                  if (!--numWorkersToLoad)
                    removeRunDependency("wasm-instantiate");
                });
              });
            }
          }
          if (!ENVIRONMENT_IS_PTHREAD) {
            addRunDependency("wasm-instantiate");
          }
          function receiveInstantiatedSource(output) {
            receiveInstance(output["instance"], output["module"]);
          }
          function instantiateArrayBuffer(receiver) {
            return getBinaryPromise().then(function(binary) {
              return WebAssembly.instantiate(binary, info);
            }).then(receiver, function(reason) {
              err("failed to asynchronously prepare wasm: " + reason);
              abort(reason);
            });
          }
          function instantiateAsync() {
            if (!wasmBinary && typeof WebAssembly.instantiateStreaming === "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === "function") {
              return fetch(wasmBinaryFile, {credentials: "same-origin"}).then(function(response) {
                var result = WebAssembly.instantiateStreaming(response, info);
                return result.then(receiveInstantiatedSource, function(reason) {
                  err("wasm streaming compile failed: " + reason);
                  err("falling back to ArrayBuffer instantiation");
                  return instantiateArrayBuffer(receiveInstantiatedSource);
                });
              });
            } else {
              return instantiateArrayBuffer(receiveInstantiatedSource);
            }
          }
          if (Module["instantiateWasm"]) {
            try {
              var exports2 = Module["instantiateWasm"](info, receiveInstance);
              return exports2;
            } catch (e) {
              err("Module.instantiateWasm callback failed with error: " + e);
              return false;
            }
          }
          instantiateAsync().catch(readyPromiseReject);
          return {};
        }
        var ASM_CONSTS = {8991: function($0, $1) {
          setTimeout(function() {
            __emscripten_do_dispatch_to_thread($0, $1);
          }, 0);
        }};
        function initPthreadsJS() {
          PThread.initRuntime();
        }
        function callRuntimeCallbacks(callbacks2) {
          while (callbacks2.length > 0) {
            var callback = callbacks2.shift();
            if (typeof callback == "function") {
              callback(Module);
              continue;
            }
            var func2 = callback.func;
            if (typeof func2 === "number") {
              if (callback.arg === void 0) {
                wasmTable.get(func2)();
              } else {
                wasmTable.get(func2)(callback.arg);
              }
            } else {
              func2(callback.arg === void 0 ? null : callback.arg);
            }
          }
        }
        function _emscripten_futex_wake(addr, count2) {
          if (addr <= 0 || addr > GROWABLE_HEAP_I8().length || addr & true || count2 < 0)
            return -28;
          if (count2 == 0)
            return 0;
          if (count2 >= 2147483647)
            count2 = Infinity;
          var mainThreadWaitAddress = Atomics.load(GROWABLE_HEAP_I32(), __emscripten_main_thread_futex >> 2);
          var mainThreadWoken = 0;
          if (mainThreadWaitAddress == addr) {
            var loadedAddr = Atomics.compareExchange(GROWABLE_HEAP_I32(), __emscripten_main_thread_futex >> 2, mainThreadWaitAddress, 0);
            if (loadedAddr == mainThreadWaitAddress) {
              --count2;
              mainThreadWoken = 1;
              if (count2 <= 0)
                return 1;
            }
          }
          var ret = Atomics.notify(GROWABLE_HEAP_I32(), addr >> 2, count2);
          if (ret >= 0)
            return ret + mainThreadWoken;
          throw "Atomics.notify returned an unexpected value " + ret;
        }
        Module["_emscripten_futex_wake"] = _emscripten_futex_wake;
        function killThread(pthread_ptr) {
          if (ENVIRONMENT_IS_PTHREAD)
            throw "Internal Error! killThread() can only ever be called from main application thread!";
          if (!pthread_ptr)
            throw "Internal Error! Null pthread_ptr in killThread!";
          GROWABLE_HEAP_I32()[pthread_ptr + 12 >> 2] = 0;
          var pthread = PThread.pthreads[pthread_ptr];
          pthread.worker.terminate();
          PThread.freeThreadData(pthread);
          PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker), 1);
          pthread.worker.pthread = void 0;
        }
        function cancelThread(pthread_ptr) {
          if (ENVIRONMENT_IS_PTHREAD)
            throw "Internal Error! cancelThread() can only ever be called from main application thread!";
          if (!pthread_ptr)
            throw "Internal Error! Null pthread_ptr in cancelThread!";
          var pthread = PThread.pthreads[pthread_ptr];
          pthread.worker.postMessage({cmd: "cancel"});
        }
        function cleanupThread(pthread_ptr) {
          if (ENVIRONMENT_IS_PTHREAD)
            throw "Internal Error! cleanupThread() can only ever be called from main application thread!";
          if (!pthread_ptr)
            throw "Internal Error! Null pthread_ptr in cleanupThread!";
          GROWABLE_HEAP_I32()[pthread_ptr + 12 >> 2] = 0;
          var pthread = PThread.pthreads[pthread_ptr];
          if (pthread) {
            var worker = pthread.worker;
            PThread.returnWorkerToPool(worker);
          }
        }
        var PThread = {unusedWorkers: [], runningWorkers: [], initMainThreadBlock: function() {
          var pthreadPoolSize = 8;
          for (var i = 0; i < pthreadPoolSize; ++i) {
            PThread.allocateUnusedWorker();
          }
        }, initRuntime: function() {
          var tb = _malloc(228);
          for (var i = 0; i < 228 / 4; ++i)
            GROWABLE_HEAP_U32()[tb / 4 + i] = 0;
          GROWABLE_HEAP_I32()[tb + 12 >> 2] = tb;
          var headPtr = tb + 152;
          GROWABLE_HEAP_I32()[headPtr >> 2] = headPtr;
          var tlsMemory = _malloc(512);
          for (var i = 0; i < 128; ++i)
            GROWABLE_HEAP_U32()[tlsMemory / 4 + i] = 0;
          Atomics.store(GROWABLE_HEAP_U32(), tb + 100 >> 2, tlsMemory);
          Atomics.store(GROWABLE_HEAP_U32(), tb + 40 >> 2, tb);
          __emscripten_thread_init(tb, !ENVIRONMENT_IS_WORKER, 1);
          _emscripten_register_main_browser_thread_id(tb);
        }, initWorker: function() {
        }, pthreads: {}, threadExitHandlers: [], setThreadStatus: function() {
        }, runExitHandlers: function() {
          while (PThread.threadExitHandlers.length > 0) {
            PThread.threadExitHandlers.pop()();
          }
          if (ENVIRONMENT_IS_PTHREAD && _pthread_self())
            ___pthread_tsd_run_dtors();
        }, threadExit: function(exitCode) {
          var tb = _pthread_self();
          if (tb) {
            Atomics.store(GROWABLE_HEAP_U32(), tb + 4 >> 2, exitCode);
            Atomics.store(GROWABLE_HEAP_U32(), tb + 0 >> 2, 1);
            Atomics.store(GROWABLE_HEAP_U32(), tb + 56 >> 2, 1);
            Atomics.store(GROWABLE_HEAP_U32(), tb + 60 >> 2, 0);
            PThread.runExitHandlers();
            _emscripten_futex_wake(tb + 0, 2147483647);
            __emscripten_thread_init(0, 0, 0);
            if (ENVIRONMENT_IS_PTHREAD) {
              postMessage({cmd: "exit"});
            }
          }
        }, threadCancel: function() {
          PThread.runExitHandlers();
          var tb = _pthread_self();
          Atomics.store(GROWABLE_HEAP_U32(), tb + 4 >> 2, -1);
          Atomics.store(GROWABLE_HEAP_U32(), tb + 0 >> 2, 1);
          _emscripten_futex_wake(tb + 0, 2147483647);
          __emscripten_thread_init(0, 0, 0);
          postMessage({cmd: "cancelDone"});
        }, terminateAllThreads: function() {
          for (var t in PThread.pthreads) {
            var pthread = PThread.pthreads[t];
            if (pthread && pthread.worker) {
              PThread.returnWorkerToPool(pthread.worker);
            }
          }
          PThread.pthreads = {};
          for (var i = 0; i < PThread.unusedWorkers.length; ++i) {
            var worker = PThread.unusedWorkers[i];
            worker.terminate();
          }
          PThread.unusedWorkers = [];
          for (var i = 0; i < PThread.runningWorkers.length; ++i) {
            var worker = PThread.runningWorkers[i];
            var pthread = worker.pthread;
            PThread.freeThreadData(pthread);
            worker.terminate();
          }
          PThread.runningWorkers = [];
        }, freeThreadData: function(pthread) {
          if (!pthread)
            return;
          if (pthread.threadInfoStruct) {
            var tlsMemory = GROWABLE_HEAP_I32()[pthread.threadInfoStruct + 100 >> 2];
            GROWABLE_HEAP_I32()[pthread.threadInfoStruct + 100 >> 2] = 0;
            _free(tlsMemory);
            _free(pthread.threadInfoStruct);
          }
          pthread.threadInfoStruct = 0;
          if (pthread.allocatedOwnStack && pthread.stackBase)
            _free(pthread.stackBase);
          pthread.stackBase = 0;
          if (pthread.worker)
            pthread.worker.pthread = null;
        }, returnWorkerToPool: function(worker) {
          PThread.runWithoutMainThreadQueuedCalls(function() {
            delete PThread.pthreads[worker.pthread.threadInfoStruct];
            PThread.unusedWorkers.push(worker);
            PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1);
            PThread.freeThreadData(worker.pthread);
            worker.pthread = void 0;
          });
        }, runWithoutMainThreadQueuedCalls: function(func2) {
          GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 0;
          try {
            func2();
          } finally {
            GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 1;
          }
        }, receiveObjectTransfer: function(data) {
        }, loadWasmModuleToWorker: function(worker, onFinishedLoading) {
          worker.onmessage = function(e) {
            var d = e["data"];
            var cmd = d["cmd"];
            if (worker.pthread)
              PThread.currentProxiedOperationCallerThread = worker.pthread.threadInfoStruct;
            if (d["targetThread"] && d["targetThread"] != _pthread_self()) {
              var thread = PThread.pthreads[d.targetThread];
              if (thread) {
                thread.worker.postMessage(e.data, d["transferList"]);
              } else {
                console.error('Internal error! Worker sent a message "' + cmd + '" to target pthread ' + d["targetThread"] + ", but that thread no longer exists!");
              }
              PThread.currentProxiedOperationCallerThread = void 0;
              return;
            }
            if (cmd === "processQueuedMainThreadWork") {
              _emscripten_main_thread_process_queued_calls();
            } else if (cmd === "spawnThread") {
              spawnThread(e.data);
            } else if (cmd === "cleanupThread") {
              cleanupThread(d["thread"]);
            } else if (cmd === "killThread") {
              killThread(d["thread"]);
            } else if (cmd === "cancelThread") {
              cancelThread(d["thread"]);
            } else if (cmd === "loaded") {
              worker.loaded = true;
              if (onFinishedLoading)
                onFinishedLoading(worker);
              if (worker.runPthread) {
                worker.runPthread();
                delete worker.runPthread;
              }
            } else if (cmd === "print") {
              out("Thread " + d["threadId"] + ": " + d["text"]);
            } else if (cmd === "printErr") {
              err("Thread " + d["threadId"] + ": " + d["text"]);
            } else if (cmd === "alert") {
              alert("Thread " + d["threadId"] + ": " + d["text"]);
            } else if (cmd === "exit") {
              var detached = worker.pthread && Atomics.load(GROWABLE_HEAP_U32(), worker.pthread.threadInfoStruct + 64 >> 2);
              if (detached) {
                PThread.returnWorkerToPool(worker);
              }
            } else if (cmd === "exitProcess") {
              try {
                exit(d["returnCode"]);
              } catch (e2) {
                if (e2 instanceof ExitStatus)
                  return;
                throw e2;
              }
            } else if (cmd === "cancelDone") {
              PThread.returnWorkerToPool(worker);
            } else if (cmd === "objectTransfer") {
              PThread.receiveObjectTransfer(e.data);
            } else if (e.data.target === "setimmediate") {
              worker.postMessage(e.data);
            } else {
              err("worker sent an unknown command " + cmd);
            }
            PThread.currentProxiedOperationCallerThread = void 0;
          };
          worker.onerror = function(e) {
            err("pthread sent an error! " + e.filename + ":" + e.lineno + ": " + e.message);
          };
          if (ENVIRONMENT_IS_NODE) {
            worker.on("message", function(data) {
              worker.onmessage({data});
            });
            worker.on("error", function(data) {
              worker.onerror(data);
            });
            worker.on("exit", function(data) {
            });
          }
          worker.postMessage({cmd: "load", urlOrBlob: Module["mainScriptUrlOrBlob"] || _scriptDir, wasmMemory, wasmModule});
        }, allocateUnusedWorker: function() {
          var pthreadMainJs = locateFile("tfjs-backend-wasm-threaded-simd.worker.js");
          PThread.unusedWorkers.push(new Worker(pthreadMainJs));
        }, getNewWorker: function() {
          if (PThread.unusedWorkers.length == 0) {
            PThread.allocateUnusedWorker();
            PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0]);
          }
          if (PThread.unusedWorkers.length > 0)
            return PThread.unusedWorkers.pop();
          else
            return null;
        }, busySpinWait: function(msecs) {
          var t = performance.now() + msecs;
          while (performance.now() < t) {
          }
        }};
        function establishStackSpace(stackTop, stackMax) {
          _emscripten_stack_set_limits(stackTop, stackMax);
          stackRestore(stackTop);
        }
        Module["establishStackSpace"] = establishStackSpace;
        function getNoExitRuntime() {
          return noExitRuntime;
        }
        Module["getNoExitRuntime"] = getNoExitRuntime;
        function invokeEntryPoint(ptr, arg) {
          return wasmTable.get(ptr)(arg);
        }
        Module["invokeEntryPoint"] = invokeEntryPoint;
        function ___assert_fail(condition, filename, line, func2) {
          abort("Assertion failed: " + UTF8ToString(condition) + ", at: " + [filename ? UTF8ToString(filename) : "unknown filename", line, func2 ? UTF8ToString(func2) : "unknown function"]);
        }
        function ___call_main(argc, argv) {
          var returnCode = _main(argc, argv);
        }
        var _emscripten_get_now;
        if (ENVIRONMENT_IS_NODE) {
          _emscripten_get_now = function() {
            var t = process["hrtime"]();
            return t[0] * 1e3 + t[1] / 1e6;
          };
        } else if (ENVIRONMENT_IS_PTHREAD) {
          _emscripten_get_now = function() {
            return performance.now() - Module["__performance_now_clock_drift"];
          };
        } else if (typeof dateNow !== "undefined") {
          _emscripten_get_now = dateNow;
        } else
          _emscripten_get_now = function() {
            return performance.now();
          };
        function setErrNo(value) {
          GROWABLE_HEAP_I32()[___errno_location() >> 2] = value;
          return value;
        }
        function _atexit(func2, arg) {
          if (ENVIRONMENT_IS_PTHREAD)
            return _emscripten_proxy_to_main_thread_js(1, 1, func2, arg);
        }
        function __emscripten_notify_thread_queue(targetThreadId, mainThreadId) {
          if (targetThreadId == mainThreadId) {
            postMessage({cmd: "processQueuedMainThreadWork"});
          } else if (ENVIRONMENT_IS_PTHREAD) {
            postMessage({targetThread: targetThreadId, cmd: "processThreadQueue"});
          } else {
            var pthread = PThread.pthreads[targetThreadId];
            var worker = pthread && pthread.worker;
            if (!worker) {
              return;
            }
            worker.postMessage({cmd: "processThreadQueue"});
          }
          return 1;
        }
        function _abort() {
          abort();
        }
        function _emscripten_asm_const_int(code, sigPtr, argbuf) {
          var args = readAsmConstArgs(sigPtr, argbuf);
          return ASM_CONSTS[code].apply(null, args);
        }
        function _emscripten_conditional_set_current_thread_status(expectedStatus, newStatus) {
        }
        function _emscripten_futex_wait(addr, val, timeout) {
          if (addr <= 0 || addr > GROWABLE_HEAP_I8().length || addr & true)
            return -28;
          if (!ENVIRONMENT_IS_WEB) {
            var ret = Atomics.wait(GROWABLE_HEAP_I32(), addr >> 2, val, timeout);
            if (ret === "timed-out")
              return -73;
            if (ret === "not-equal")
              return -6;
            if (ret === "ok")
              return 0;
            throw "Atomics.wait returned an unexpected value " + ret;
          } else {
            if (Atomics.load(GROWABLE_HEAP_I32(), addr >> 2) != val) {
              return -6;
            }
            var tNow = performance.now();
            var tEnd = tNow + timeout;
            var lastAddr = Atomics.exchange(GROWABLE_HEAP_I32(), __emscripten_main_thread_futex >> 2, addr);
            while (1) {
              tNow = performance.now();
              if (tNow > tEnd) {
                lastAddr = Atomics.exchange(GROWABLE_HEAP_I32(), __emscripten_main_thread_futex >> 2, 0);
                return -73;
              }
              lastAddr = Atomics.exchange(GROWABLE_HEAP_I32(), __emscripten_main_thread_futex >> 2, 0);
              if (lastAddr == 0) {
                break;
              }
              _emscripten_main_thread_process_queued_calls();
              if (Atomics.load(GROWABLE_HEAP_I32(), addr >> 2) != val) {
                return -6;
              }
              lastAddr = Atomics.exchange(GROWABLE_HEAP_I32(), __emscripten_main_thread_futex >> 2, addr);
            }
            return 0;
          }
        }
        function _emscripten_memcpy_big(dest, src, num) {
          GROWABLE_HEAP_U8().copyWithin(dest, src, src + num);
        }
        function _emscripten_num_logical_cores() {
          if (ENVIRONMENT_IS_NODE)
            return require("os").cpus().length;
          return navigator["hardwareConcurrency"];
        }
        function _emscripten_proxy_to_main_thread_js(index, sync) {
          var numCallArgs = arguments.length - 2;
          var stack2 = stackSave();
          var serializedNumCallArgs = numCallArgs;
          var args = stackAlloc(serializedNumCallArgs * 8);
          var b = args >> 3;
          for (var i = 0; i < numCallArgs; i++) {
            var arg = arguments[2 + i];
            GROWABLE_HEAP_F64()[b + i] = arg;
          }
          var ret = _emscripten_run_in_main_runtime_thread_js(index, serializedNumCallArgs, args, sync);
          stackRestore(stack2);
          return ret;
        }
        var _emscripten_receive_on_main_thread_js_callArgs = [];
        var readAsmConstArgsArray = [];
        function readAsmConstArgs(sigPtr, buf) {
          readAsmConstArgsArray.length = 0;
          var ch;
          buf >>= 2;
          while (ch = GROWABLE_HEAP_U8()[sigPtr++]) {
            var double = ch < 105;
            if (double && buf & 1)
              buf++;
            readAsmConstArgsArray.push(double ? GROWABLE_HEAP_F64()[buf++ >> 1] : GROWABLE_HEAP_I32()[buf]);
            ++buf;
          }
          return readAsmConstArgsArray;
        }
        function _emscripten_receive_on_main_thread_js(index, numCallArgs, args) {
          _emscripten_receive_on_main_thread_js_callArgs.length = numCallArgs;
          var b = args >> 3;
          for (var i = 0; i < numCallArgs; i++) {
            _emscripten_receive_on_main_thread_js_callArgs[i] = GROWABLE_HEAP_F64()[b + i];
          }
          var isEmAsmConst = index < 0;
          var func2 = !isEmAsmConst ? proxiedFunctionTable[index] : ASM_CONSTS[-index - 1];
          return func2.apply(null, _emscripten_receive_on_main_thread_js_callArgs);
        }
        function _emscripten_get_heap_size() {
          return GROWABLE_HEAP_U8().length;
        }
        function emscripten_realloc_buffer(size) {
          try {
            wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16);
            updateGlobalBufferAndViews(wasmMemory.buffer);
            return 1;
          } catch (e) {
          }
        }
        function _emscripten_resize_heap(requestedSize) {
          var oldSize = _emscripten_get_heap_size();
          if (requestedSize <= oldSize) {
            return false;
          }
          var maxHeapSize = 2147483648;
          if (requestedSize > maxHeapSize) {
            return false;
          }
          for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {
            var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);
            overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);
            var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536));
            var replacement = emscripten_realloc_buffer(newSize);
            if (replacement) {
              return true;
            }
          }
          return false;
        }
        var JSEvents = {inEventHandler: 0, removeAllEventListeners: function() {
          for (var i = JSEvents.eventHandlers.length - 1; i >= 0; --i) {
            JSEvents._removeHandler(i);
          }
          JSEvents.eventHandlers = [];
          JSEvents.deferredCalls = [];
        }, registerRemoveEventListeners: function() {
          if (!JSEvents.removeEventListenersRegistered) {
            __ATEXIT__.push(JSEvents.removeAllEventListeners);
            JSEvents.removeEventListenersRegistered = true;
          }
        }, deferredCalls: [], deferCall: function(targetFunction, precedence, argsList) {
          function arraysHaveEqualContent(arrA, arrB) {
            if (arrA.length != arrB.length)
              return false;
            for (var i2 in arrA) {
              if (arrA[i2] != arrB[i2])
                return false;
            }
            return true;
          }
          for (var i in JSEvents.deferredCalls) {
            var call = JSEvents.deferredCalls[i];
            if (call.targetFunction == targetFunction && arraysHaveEqualContent(call.argsList, argsList)) {
              return;
            }
          }
          JSEvents.deferredCalls.push({targetFunction, precedence, argsList});
          JSEvents.deferredCalls.sort(function(x, y) {
            return x.precedence < y.precedence;
          });
        }, removeDeferredCalls: function(targetFunction) {
          for (var i = 0; i < JSEvents.deferredCalls.length; ++i) {
            if (JSEvents.deferredCalls[i].targetFunction == targetFunction) {
              JSEvents.deferredCalls.splice(i, 1);
              --i;
            }
          }
        }, canPerformEventHandlerRequests: function() {
          return JSEvents.inEventHandler && JSEvents.currentEventHandler.allowsDeferredCalls;
        }, runDeferredCalls: function() {
          if (!JSEvents.canPerformEventHandlerRequests()) {
            return;
          }
          for (var i = 0; i < JSEvents.deferredCalls.length; ++i) {
            var call = JSEvents.deferredCalls[i];
            JSEvents.deferredCalls.splice(i, 1);
            --i;
            call.targetFunction.apply(null, call.argsList);
          }
        }, eventHandlers: [], removeAllHandlersOnTarget: function(target, eventTypeString) {
          for (var i = 0; i < JSEvents.eventHandlers.length; ++i) {
            if (JSEvents.eventHandlers[i].target == target && (!eventTypeString || eventTypeString == JSEvents.eventHandlers[i].eventTypeString)) {
              JSEvents._removeHandler(i--);
            }
          }
        }, _removeHandler: function(i) {
          var h = JSEvents.eventHandlers[i];
          h.target.removeEventListener(h.eventTypeString, h.eventListenerFunc, h.useCapture);
          JSEvents.eventHandlers.splice(i, 1);
        }, registerOrRemoveHandler: function(eventHandler) {
          var jsEventHandler = function jsEventHandler2(event) {
            ++JSEvents.inEventHandler;
            JSEvents.currentEventHandler = eventHandler;
            JSEvents.runDeferredCalls();
            eventHandler.handlerFunc(event);
            JSEvents.runDeferredCalls();
            --JSEvents.inEventHandler;
          };
          if (eventHandler.callbackfunc) {
            eventHandler.eventListenerFunc = jsEventHandler;
            eventHandler.target.addEventListener(eventHandler.eventTypeString, jsEventHandler, eventHandler.useCapture);
            JSEvents.eventHandlers.push(eventHandler);
            JSEvents.registerRemoveEventListeners();
          } else {
            for (var i = 0; i < JSEvents.eventHandlers.length; ++i) {
              if (JSEvents.eventHandlers[i].target == eventHandler.target && JSEvents.eventHandlers[i].eventTypeString == eventHandler.eventTypeString) {
                JSEvents._removeHandler(i--);
              }
            }
          }
        }, queueEventHandlerOnThread_iiii: function(targetThread, eventHandlerFunc, eventTypeId, eventData, userData) {
          var stackTop = stackSave();
          var varargs = stackAlloc(12);
          GROWABLE_HEAP_I32()[varargs >> 2] = eventTypeId;
          GROWABLE_HEAP_I32()[varargs + 4 >> 2] = eventData;
          GROWABLE_HEAP_I32()[varargs + 8 >> 2] = userData;
          __emscripten_call_on_thread(0, targetThread, 637534208, eventHandlerFunc, eventData, varargs);
          stackRestore(stackTop);
        }, getTargetThreadForEventCallback: function(targetThread) {
          switch (targetThread) {
            case 1:
              return 0;
            case 2:
              return PThread.currentProxiedOperationCallerThread;
            default:
              return targetThread;
          }
        }, getNodeNameForTarget: function(target) {
          if (!target)
            return "";
          if (target == window)
            return "#window";
          if (target == screen)
            return "#screen";
          return target && target.nodeName ? target.nodeName : "";
        }, fullscreenEnabled: function() {
          return document.fullscreenEnabled || document.webkitFullscreenEnabled;
        }};
        function stringToNewUTF8(jsString) {
          var length = lengthBytesUTF8(jsString) + 1;
          var cString = _malloc(length);
          stringToUTF8(jsString, cString, length);
          return cString;
        }
        function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height) {
          var stackTop = stackSave();
          var varargs = stackAlloc(12);
          var targetCanvasPtr = 0;
          if (targetCanvas) {
            targetCanvasPtr = stringToNewUTF8(targetCanvas);
          }
          GROWABLE_HEAP_I32()[varargs >> 2] = targetCanvasPtr;
          GROWABLE_HEAP_I32()[varargs + 4 >> 2] = width;
          GROWABLE_HEAP_I32()[varargs + 8 >> 2] = height;
          __emscripten_call_on_thread(0, targetThread, 657457152, 0, targetCanvasPtr, varargs);
          stackRestore(stackTop);
        }
        function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, targetCanvas, width, height) {
          targetCanvas = targetCanvas ? UTF8ToString(targetCanvas) : "";
          _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height);
        }
        function maybeCStringToJsString(cString) {
          return cString > 2 ? UTF8ToString(cString) : cString;
        }
        var specialHTMLTargets = [0, typeof document !== "undefined" ? document : 0, typeof window !== "undefined" ? window : 0];
        function findEventTarget(target) {
          target = maybeCStringToJsString(target);
          var domElement = specialHTMLTargets[target] || (typeof document !== "undefined" ? document.querySelector(target) : void 0);
          return domElement;
        }
        function findCanvasEventTarget(target) {
          return findEventTarget(target);
        }
        function _emscripten_set_canvas_element_size_calling_thread(target, width, height) {
          var canvas = findCanvasEventTarget(target);
          if (!canvas)
            return -4;
          if (canvas.canvasSharedPtr) {
            GROWABLE_HEAP_I32()[canvas.canvasSharedPtr >> 2] = width;
            GROWABLE_HEAP_I32()[canvas.canvasSharedPtr + 4 >> 2] = height;
          }
          if (canvas.offscreenCanvas || !canvas.controlTransferredOffscreen) {
            if (canvas.offscreenCanvas)
              canvas = canvas.offscreenCanvas;
            var autoResizeViewport = false;
            if (canvas.GLctxObject && canvas.GLctxObject.GLctx) {
              var prevViewport = canvas.GLctxObject.GLctx.getParameter(2978);
              autoResizeViewport = prevViewport[0] === 0 && prevViewport[1] === 0 && prevViewport[2] === canvas.width && prevViewport[3] === canvas.height;
            }
            canvas.width = width;
            canvas.height = height;
            if (autoResizeViewport) {
              canvas.GLctxObject.GLctx.viewport(0, 0, width, height);
            }
          } else if (canvas.canvasSharedPtr) {
            var targetThread = GROWABLE_HEAP_I32()[canvas.canvasSharedPtr + 8 >> 2];
            _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, target, width, height);
            return 1;
          } else {
            return -4;
          }
          return 0;
        }
        function _emscripten_set_canvas_element_size_main_thread(target, width, height) {
          if (ENVIRONMENT_IS_PTHREAD)
            return _emscripten_proxy_to_main_thread_js(2, 1, target, width, height);
          return _emscripten_set_canvas_element_size_calling_thread(target, width, height);
        }
        function _emscripten_set_canvas_element_size(target, width, height) {
          var canvas = findCanvasEventTarget(target);
          if (canvas) {
            return _emscripten_set_canvas_element_size_calling_thread(target, width, height);
          } else {
            return _emscripten_set_canvas_element_size_main_thread(target, width, height);
          }
        }
        function _emscripten_set_current_thread_status(newStatus) {
        }
        function _emscripten_set_thread_name(threadId, name) {
        }
        function __webgl_enable_ANGLE_instanced_arrays(ctx) {
          var ext = ctx.getExtension("ANGLE_instanced_arrays");
          if (ext) {
            ctx["vertexAttribDivisor"] = function(index, divisor) {
              ext["vertexAttribDivisorANGLE"](index, divisor);
            };
            ctx["drawArraysInstanced"] = function(mode, first, count2, primcount) {
              ext["drawArraysInstancedANGLE"](mode, first, count2, primcount);
            };
            ctx["drawElementsInstanced"] = function(mode, count2, type, indices, primcount) {
              ext["drawElementsInstancedANGLE"](mode, count2, type, indices, primcount);
            };
            return 1;
          }
        }
        function __webgl_enable_OES_vertex_array_object(ctx) {
          var ext = ctx.getExtension("OES_vertex_array_object");
          if (ext) {
            ctx["createVertexArray"] = function() {
              return ext["createVertexArrayOES"]();
            };
            ctx["deleteVertexArray"] = function(vao) {
              ext["deleteVertexArrayOES"](vao);
            };
            ctx["bindVertexArray"] = function(vao) {
              ext["bindVertexArrayOES"](vao);
            };
            ctx["isVertexArray"] = function(vao) {
              return ext["isVertexArrayOES"](vao);
            };
            return 1;
          }
        }
        function __webgl_enable_WEBGL_draw_buffers(ctx) {
          var ext = ctx.getExtension("WEBGL_draw_buffers");
          if (ext) {
            ctx["drawBuffers"] = function(n, bufs) {
              ext["drawBuffersWEBGL"](n, bufs);
            };
            return 1;
          }
        }
        function __webgl_enable_WEBGL_multi_draw(ctx) {
          return !!(ctx.multiDrawWebgl = ctx.getExtension("WEBGL_multi_draw"));
        }
        var GL = {counter: 1, buffers: [], programs: [], framebuffers: [], renderbuffers: [], textures: [], uniforms: [], shaders: [], vaos: [], contexts: {}, offscreenCanvases: {}, timerQueriesEXT: [], programInfos: {}, stringCache: {}, unpackAlignment: 4, recordError: function recordError(errorCode) {
          if (!GL.lastError) {
            GL.lastError = errorCode;
          }
        }, getNewId: function(table) {
          var ret = GL.counter++;
          for (var i = table.length; i < ret; i++) {
            table[i] = null;
          }
          return ret;
        }, getSource: function(shader, count2, string, length) {
          var source = "";
          for (var i = 0; i < count2; ++i) {
            var len = length ? GROWABLE_HEAP_I32()[length + i * 4 >> 2] : -1;
            source += UTF8ToString(GROWABLE_HEAP_I32()[string + i * 4 >> 2], len < 0 ? void 0 : len);
          }
          return source;
        }, createContext: function(canvas, webGLContextAttributes) {
          var ctx = canvas.getContext("webgl", webGLContextAttributes);
          if (!ctx)
            return 0;
          var handle = GL.registerContext(ctx, webGLContextAttributes);
          return handle;
        }, registerContext: function(ctx, webGLContextAttributes) {
          var handle = _malloc(8);
          GROWABLE_HEAP_I32()[handle + 4 >> 2] = _pthread_self();
          var context = {handle, attributes: webGLContextAttributes, version: webGLContextAttributes.majorVersion, GLctx: ctx};
          if (ctx.canvas)
            ctx.canvas.GLctxObject = context;
          GL.contexts[handle] = context;
          if (typeof webGLContextAttributes.enableExtensionsByDefault === "undefined" || webGLContextAttributes.enableExtensionsByDefault) {
            GL.initExtensions(context);
          }
          return handle;
        }, makeContextCurrent: function(contextHandle) {
          GL.currentContext = GL.contexts[contextHandle];
          Module.ctx = GLctx = GL.currentContext && GL.currentContext.GLctx;
          return !(contextHandle && !GLctx);
        }, getContext: function(contextHandle) {
          return GL.contexts[contextHandle];
        }, deleteContext: function(contextHandle) {
          if (GL.currentContext === GL.contexts[contextHandle])
            GL.currentContext = null;
          if (typeof JSEvents === "object")
            JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);
          if (GL.contexts[contextHandle] && GL.contexts[contextHandle].GLctx.canvas)
            GL.contexts[contextHandle].GLctx.canvas.GLctxObject = void 0;
          _free(GL.contexts[contextHandle].handle);
          GL.contexts[contextHandle] = null;
        }, initExtensions: function(context) {
          if (!context)
            context = GL.currentContext;
          if (context.initExtensionsDone)
            return;
          context.initExtensionsDone = true;
          var GLctx2 = context.GLctx;
          __webgl_enable_ANGLE_instanced_arrays(GLctx2);
          __webgl_enable_OES_vertex_array_object(GLctx2);
          __webgl_enable_WEBGL_draw_buffers(GLctx2);
          GLctx2.disjointTimerQueryExt = GLctx2.getExtension("EXT_disjoint_timer_query");
          __webgl_enable_WEBGL_multi_draw(GLctx2);
          var exts = GLctx2.getSupportedExtensions() || [];
          exts.forEach(function(ext) {
            if (ext.indexOf("lose_context") < 0 && ext.indexOf("debug") < 0) {
              GLctx2.getExtension(ext);
            }
          });
        }, populateUniformTable: function(program) {
          var p2 = GL.programs[program];
          var ptable = GL.programInfos[program] = {uniforms: {}, maxUniformLength: 0, maxAttributeLength: -1, maxUniformBlockNameLength: -1};
          var utable = ptable.uniforms;
          var numUniforms = GLctx.getProgramParameter(p2, 35718);
          for (var i = 0; i < numUniforms; ++i) {
            var u = GLctx.getActiveUniform(p2, i);
            var name = u.name;
            ptable.maxUniformLength = Math.max(ptable.maxUniformLength, name.length + 1);
            if (name.slice(-1) == "]") {
              name = name.slice(0, name.lastIndexOf("["));
            }
            var loc = GLctx.getUniformLocation(p2, name);
            if (loc) {
              var id = GL.getNewId(GL.uniforms);
              utable[name] = [u.size, id];
              GL.uniforms[id] = loc;
              for (var j = 1; j < u.size; ++j) {
                var n = name + "[" + j + "]";
                loc = GLctx.getUniformLocation(p2, n);
                id = GL.getNewId(GL.uniforms);
                GL.uniforms[id] = loc;
              }
            }
          }
        }};
        var __emscripten_webgl_power_preferences = ["default", "low-power", "high-performance"];
        function _emscripten_webgl_do_create_context(target, attributes) {
          var a = attributes >> 2;
          var powerPreference = GROWABLE_HEAP_I32()[a + (24 >> 2)];
          var contextAttributes = {alpha: !!GROWABLE_HEAP_I32()[a + (0 >> 2)], depth: !!GROWABLE_HEAP_I32()[a + (4 >> 2)], stencil: !!GROWABLE_HEAP_I32()[a + (8 >> 2)], antialias: !!GROWABLE_HEAP_I32()[a + (12 >> 2)], premultipliedAlpha: !!GROWABLE_HEAP_I32()[a + (16 >> 2)], preserveDrawingBuffer: !!GROWABLE_HEAP_I32()[a + (20 >> 2)], powerPreference: __emscripten_webgl_power_preferences[powerPreference], failIfMajorPerformanceCaveat: !!GROWABLE_HEAP_I32()[a + (28 >> 2)], majorVersion: GROWABLE_HEAP_I32()[a + (32 >> 2)], minorVersion: GROWABLE_HEAP_I32()[a + (36 >> 2)], enableExtensionsByDefault: GROWABLE_HEAP_I32()[a + (40 >> 2)], explicitSwapControl: GROWABLE_HEAP_I32()[a + (44 >> 2)], proxyContextToMainThread: GROWABLE_HEAP_I32()[a + (48 >> 2)], renderViaOffscreenBackBuffer: GROWABLE_HEAP_I32()[a + (52 >> 2)]};
          var canvas = findCanvasEventTarget(target);
          if (!canvas) {
            return 0;
          }
          if (contextAttributes.explicitSwapControl) {
            return 0;
          }
          var contextHandle = GL.createContext(canvas, contextAttributes);
          return contextHandle;
        }
        function _emscripten_webgl_create_context(a0, a12) {
          return _emscripten_webgl_do_create_context(a0, a12);
        }
        var SYSCALLS = {mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) {
          var buffer3 = SYSCALLS.buffers[stream];
          if (curr === 0 || curr === 10) {
            (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0));
            buffer3.length = 0;
          } else {
            buffer3.push(curr);
          }
        }, varargs: void 0, get: function() {
          SYSCALLS.varargs += 4;
          var ret = GROWABLE_HEAP_I32()[SYSCALLS.varargs - 4 >> 2];
          return ret;
        }, getStr: function(ptr) {
          var ret = UTF8ToString(ptr);
          return ret;
        }, get64: function(low, high) {
          return low;
        }};
        function _fd_close(fd) {
          if (ENVIRONMENT_IS_PTHREAD)
            return _emscripten_proxy_to_main_thread_js(3, 1, fd);
          return 0;
        }
        function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {
          if (ENVIRONMENT_IS_PTHREAD)
            return _emscripten_proxy_to_main_thread_js(4, 1, fd, offset_low, offset_high, whence, newOffset);
        }
        function _fd_write(fd, iov, iovcnt, pnum) {
          if (ENVIRONMENT_IS_PTHREAD)
            return _emscripten_proxy_to_main_thread_js(5, 1, fd, iov, iovcnt, pnum);
          var num = 0;
          for (var i = 0; i < iovcnt; i++) {
            var ptr = GROWABLE_HEAP_I32()[iov + i * 8 >> 2];
            var len = GROWABLE_HEAP_I32()[iov + (i * 8 + 4) >> 2];
            for (var j = 0; j < len; j++) {
              SYSCALLS.printChar(fd, GROWABLE_HEAP_U8()[ptr + j]);
            }
            num += len;
          }
          GROWABLE_HEAP_I32()[pnum >> 2] = num;
          return 0;
        }
        function _pthread_cleanup_pop(execute2) {
          var routine = PThread.threadExitHandlers.pop();
          if (execute2)
            routine();
        }
        function _pthread_cleanup_push(routine, arg) {
          PThread.threadExitHandlers.push(function() {
            wasmTable.get(routine)(arg);
          });
        }
        function spawnThread(threadParams) {
          if (ENVIRONMENT_IS_PTHREAD)
            throw "Internal Error! spawnThread() can only ever be called from main application thread!";
          var worker = PThread.getNewWorker();
          if (worker.pthread !== void 0)
            throw "Internal error!";
          if (!threadParams.pthread_ptr)
            throw "Internal error, no pthread ptr!";
          PThread.runningWorkers.push(worker);
          var tlsMemory = _malloc(128 * 4);
          for (var i = 0; i < 128; ++i) {
            GROWABLE_HEAP_I32()[tlsMemory + i * 4 >> 2] = 0;
          }
          var stackHigh = threadParams.stackBase + threadParams.stackSize;
          var pthread = PThread.pthreads[threadParams.pthread_ptr] = {worker, stackBase: threadParams.stackBase, stackSize: threadParams.stackSize, allocatedOwnStack: threadParams.allocatedOwnStack, threadInfoStruct: threadParams.pthread_ptr};
          var tis = pthread.threadInfoStruct >> 2;
          Atomics.store(GROWABLE_HEAP_U32(), tis + (64 >> 2), threadParams.detached);
          Atomics.store(GROWABLE_HEAP_U32(), tis + (100 >> 2), tlsMemory);
          Atomics.store(GROWABLE_HEAP_U32(), tis + (40 >> 2), pthread.threadInfoStruct);
          Atomics.store(GROWABLE_HEAP_U32(), tis + (80 >> 2), threadParams.stackSize);
          Atomics.store(GROWABLE_HEAP_U32(), tis + (76 >> 2), stackHigh);
          Atomics.store(GROWABLE_HEAP_U32(), tis + (104 >> 2), threadParams.stackSize);
          Atomics.store(GROWABLE_HEAP_U32(), tis + (104 + 8 >> 2), stackHigh);
          Atomics.store(GROWABLE_HEAP_U32(), tis + (104 + 12 >> 2), threadParams.detached);
          var global_libc = _emscripten_get_global_libc();
          var global_locale = global_libc + 40;
          Atomics.store(GROWABLE_HEAP_U32(), tis + (172 >> 2), global_locale);
          worker.pthread = pthread;
          var msg = {cmd: "run", start_routine: threadParams.startRoutine, arg: threadParams.arg, threadInfoStruct: threadParams.pthread_ptr, stackBase: threadParams.stackBase, stackSize: threadParams.stackSize};
          worker.runPthread = function() {
            msg.time = performance.now();
            worker.postMessage(msg, threadParams.transferList);
          };
          if (worker.loaded) {
            worker.runPthread();
            delete worker.runPthread;
          }
        }
        function _pthread_create(pthread_ptr, attr, start_routine, arg) {
          if (typeof SharedArrayBuffer === "undefined") {
            err("Current environment does not support SharedArrayBuffer, pthreads are not available!");
            return 6;
          }
          if (!pthread_ptr) {
            err("pthread_create called with a null thread pointer!");
            return 28;
          }
          var transferList = [];
          var error = 0;
          if (ENVIRONMENT_IS_PTHREAD && (transferList.length === 0 || error)) {
            return _emscripten_sync_run_in_main_thread_4(687865856, pthread_ptr, attr, start_routine, arg);
          }
          if (error)
            return error;
          var stackSize = 0;
          var stackBase = 0;
          var detached = 0;
          if (attr && attr != -1) {
            stackSize = GROWABLE_HEAP_I32()[attr >> 2];
            stackSize += 81920;
            stackBase = GROWABLE_HEAP_I32()[attr + 8 >> 2];
            detached = GROWABLE_HEAP_I32()[attr + 12 >> 2] !== 0;
          } else {
            stackSize = 2097152;
          }
          var allocatedOwnStack = stackBase == 0;
          if (allocatedOwnStack) {
            stackBase = _memalign(16, stackSize);
          } else {
            stackBase -= stackSize;
            assert3(stackBase > 0);
          }
          var threadInfoStruct = _malloc(228);
          for (var i = 0; i < 228 >> 2; ++i)
            GROWABLE_HEAP_U32()[(threadInfoStruct >> 2) + i] = 0;
          GROWABLE_HEAP_I32()[pthread_ptr >> 2] = threadInfoStruct;
          GROWABLE_HEAP_I32()[threadInfoStruct + 12 >> 2] = threadInfoStruct;
          var headPtr = threadInfoStruct + 152;
          GROWABLE_HEAP_I32()[headPtr >> 2] = headPtr;
          var threadParams = {stackBase, stackSize, allocatedOwnStack, detached, startRoutine: start_routine, pthread_ptr: threadInfoStruct, arg, transferList};
          if (ENVIRONMENT_IS_PTHREAD) {
            threadParams.cmd = "spawnThread";
            postMessage(threadParams, transferList);
          } else {
            spawnThread(threadParams);
          }
          return 0;
        }
        function _sysconf(name) {
          if (ENVIRONMENT_IS_PTHREAD)
            return _emscripten_proxy_to_main_thread_js(6, 1, name);
          switch (name) {
            case 30:
              return 16384;
            case 85:
              var maxHeapSize = 2147483648;
              return maxHeapSize / 16384;
            case 132:
            case 133:
            case 12:
            case 137:
            case 138:
            case 15:
            case 235:
            case 16:
            case 17:
            case 18:
            case 19:
            case 20:
            case 149:
            case 13:
            case 10:
            case 236:
            case 153:
            case 9:
            case 21:
            case 22:
            case 159:
            case 154:
            case 14:
            case 77:
            case 78:
            case 139:
            case 82:
            case 68:
            case 67:
            case 164:
            case 11:
            case 29:
            case 47:
            case 48:
            case 95:
            case 52:
            case 51:
            case 46:
              return 200809;
            case 27:
            case 246:
            case 127:
            case 128:
            case 23:
            case 24:
            case 160:
            case 161:
            case 181:
            case 182:
            case 242:
            case 183:
            case 184:
            case 243:
            case 244:
            case 245:
            case 165:
            case 178:
            case 179:
            case 49:
            case 50:
            case 168:
            case 169:
            case 175:
            case 170:
            case 171:
            case 172:
            case 97:
            case 76:
            case 32:
            case 173:
            case 35:
            case 80:
            case 81:
            case 79:
              return -1;
            case 176:
            case 177:
            case 7:
            case 155:
            case 8:
            case 157:
            case 125:
            case 126:
            case 92:
            case 93:
            case 129:
            case 130:
            case 131:
            case 94:
            case 91:
              return 1;
            case 74:
            case 60:
            case 69:
            case 70:
            case 4:
              return 1024;
            case 31:
            case 42:
            case 72:
              return 32;
            case 87:
            case 26:
            case 33:
              return 2147483647;
            case 34:
            case 1:
              return 47839;
            case 38:
            case 36:
              return 99;
            case 43:
            case 37:
              return 2048;
            case 0:
              return 2097152;
            case 3:
              return 65536;
            case 28:
              return 32768;
            case 44:
              return 32767;
            case 75:
              return 16384;
            case 39:
              return 1e3;
            case 89:
              return 700;
            case 71:
              return 256;
            case 40:
              return 255;
            case 2:
              return 100;
            case 180:
              return 64;
            case 25:
              return 20;
            case 5:
              return 16;
            case 6:
              return 6;
            case 73:
              return 4;
            case 84: {
              if (typeof navigator === "object")
                return navigator["hardwareConcurrency"] || 1;
              return 1;
            }
          }
          setErrNo(28);
          return -1;
        }
        if (!ENVIRONMENT_IS_PTHREAD)
          PThread.initMainThreadBlock();
        var GLctx;
        var proxiedFunctionTable = [null, _atexit, _emscripten_set_canvas_element_size_main_thread, _fd_close, _fd_seek, _fd_write, _sysconf];
        var asmLibraryArg = {e: ___assert_fail, r: ___call_main, x: __emscripten_notify_thread_queue, b: _abort, y: _emscripten_asm_const_int, j: _emscripten_conditional_set_current_thread_status, c: _emscripten_futex_wait, d: _emscripten_futex_wake, f: _emscripten_get_now, p: _emscripten_memcpy_big, z: _emscripten_num_logical_cores, u: _emscripten_receive_on_main_thread_js, q: _emscripten_resize_heap, v: _emscripten_set_canvas_element_size, i: _emscripten_set_current_thread_status, t: _emscripten_set_thread_name, w: _emscripten_webgl_create_context, m: _fd_close, n: _fd_seek, g: _fd_write, o: initPthreadsJS, a: wasmMemory || Module["wasmMemory"], k: _pthread_cleanup_pop, l: _pthread_cleanup_push, h: _pthread_create, s: _sysconf};
        var asm = createWasm();
        var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() {
          return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["A"]).apply(null, arguments);
        };
        var _init = Module["_init"] = function() {
          return (_init = Module["_init"] = Module["asm"]["B"]).apply(null, arguments);
        };
        var _register_tensor = Module["_register_tensor"] = function() {
          return (_register_tensor = Module["_register_tensor"] = Module["asm"]["C"]).apply(null, arguments);
        };
        var _dispose_data = Module["_dispose_data"] = function() {
          return (_dispose_data = Module["_dispose_data"] = Module["asm"]["D"]).apply(null, arguments);
        };
        var _dispose = Module["_dispose"] = function() {
          return (_dispose = Module["_dispose"] = Module["asm"]["E"]).apply(null, arguments);
        };
        var _Abs = Module["_Abs"] = function() {
          return (_Abs = Module["_Abs"] = Module["asm"]["G"]).apply(null, arguments);
        };
        var _Add = Module["_Add"] = function() {
          return (_Add = Module["_Add"] = Module["asm"]["H"]).apply(null, arguments);
        };
        var _AddN = Module["_AddN"] = function() {
          return (_AddN = Module["_AddN"] = Module["asm"]["I"]).apply(null, arguments);
        };
        var _ArgMax = Module["_ArgMax"] = function() {
          return (_ArgMax = Module["_ArgMax"] = Module["asm"]["J"]).apply(null, arguments);
        };
        var _AvgPool = Module["_AvgPool"] = function() {
          return (_AvgPool = Module["_AvgPool"] = Module["asm"]["K"]).apply(null, arguments);
        };
        var _BatchMatMul = Module["_BatchMatMul"] = function() {
          return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["L"]).apply(null, arguments);
        };
        var _Ceil = Module["_Ceil"] = function() {
          return (_Ceil = Module["_Ceil"] = Module["asm"]["M"]).apply(null, arguments);
        };
        var _ClipByValue = Module["_ClipByValue"] = function() {
          return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["N"]).apply(null, arguments);
        };
        var _Conv2D = Module["_Conv2D"] = function() {
          return (_Conv2D = Module["_Conv2D"] = Module["asm"]["O"]).apply(null, arguments);
        };
        var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() {
          return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["P"]).apply(null, arguments);
        };
        var _Cos = Module["_Cos"] = function() {
          return (_Cos = Module["_Cos"] = Module["asm"]["Q"]).apply(null, arguments);
        };
        var _CropAndResize = Module["_CropAndResize"] = function() {
          return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["R"]).apply(null, arguments);
        };
        var _Cumsum = Module["_Cumsum"] = function() {
          return (_Cumsum = Module["_Cumsum"] = Module["asm"]["S"]).apply(null, arguments);
        };
        var _DepthToSpace = Module["_DepthToSpace"] = function() {
          return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["T"]).apply(null, arguments);
        };
        var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() {
          return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["U"]).apply(null, arguments);
        };
        var _Equal = Module["_Equal"] = function() {
          return (_Equal = Module["_Equal"] = Module["asm"]["V"]).apply(null, arguments);
        };
        var _Exp = Module["_Exp"] = function() {
          return (_Exp = Module["_Exp"] = Module["asm"]["W"]).apply(null, arguments);
        };
        var _FlipLeftRight = Module["_FlipLeftRight"] = function() {
          return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["X"]).apply(null, arguments);
        };
        var _Floor = Module["_Floor"] = function() {
          return (_Floor = Module["_Floor"] = Module["asm"]["Y"]).apply(null, arguments);
        };
        var _FloorDiv = Module["_FloorDiv"] = function() {
          return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["Z"]).apply(null, arguments);
        };
        var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() {
          return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["_"]).apply(null, arguments);
        };
        var _FusedConv2D = Module["_FusedConv2D"] = function() {
          return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["$"]).apply(null, arguments);
        };
        var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() {
          return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["aa"]).apply(null, arguments);
        };
        var _Gather = Module["_Gather"] = function() {
          return (_Gather = Module["_Gather"] = Module["asm"]["ba"]).apply(null, arguments);
        };
        var _GatherNd = Module["_GatherNd"] = function() {
          return (_GatherNd = Module["_GatherNd"] = Module["asm"]["ca"]).apply(null, arguments);
        };
        var _Greater = Module["_Greater"] = function() {
          return (_Greater = Module["_Greater"] = Module["asm"]["da"]).apply(null, arguments);
        };
        var _GreaterEqual = Module["_GreaterEqual"] = function() {
          return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["ea"]).apply(null, arguments);
        };
        var _LeakyRelu = Module["_LeakyRelu"] = function() {
          return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["fa"]).apply(null, arguments);
        };
        var _Less = Module["_Less"] = function() {
          return (_Less = Module["_Less"] = Module["asm"]["ga"]).apply(null, arguments);
        };
        var _LessEqual = Module["_LessEqual"] = function() {
          return (_LessEqual = Module["_LessEqual"] = Module["asm"]["ha"]).apply(null, arguments);
        };
        var _Log = Module["_Log"] = function() {
          return (_Log = Module["_Log"] = Module["asm"]["ia"]).apply(null, arguments);
        };
        var _LogicalAnd = Module["_LogicalAnd"] = function() {
          return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["ja"]).apply(null, arguments);
        };
        var _Max = Module["_Max"] = function() {
          return (_Max = Module["_Max"] = Module["asm"]["ka"]).apply(null, arguments);
        };
        var _MaxPool = Module["_MaxPool"] = function() {
          return (_MaxPool = Module["_MaxPool"] = Module["asm"]["la"]).apply(null, arguments);
        };
        var _Maximum = Module["_Maximum"] = function() {
          return (_Maximum = Module["_Maximum"] = Module["asm"]["ma"]).apply(null, arguments);
        };
        var _Mean = Module["_Mean"] = function() {
          return (_Mean = Module["_Mean"] = Module["asm"]["na"]).apply(null, arguments);
        };
        var _Min = Module["_Min"] = function() {
          return (_Min = Module["_Min"] = Module["asm"]["oa"]).apply(null, arguments);
        };
        var _Minimum = Module["_Minimum"] = function() {
          return (_Minimum = Module["_Minimum"] = Module["asm"]["pa"]).apply(null, arguments);
        };
        var _Multiply = Module["_Multiply"] = function() {
          return (_Multiply = Module["_Multiply"] = Module["asm"]["qa"]).apply(null, arguments);
        };
        var _Neg = Module["_Neg"] = function() {
          return (_Neg = Module["_Neg"] = Module["asm"]["ra"]).apply(null, arguments);
        };
        var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() {
          return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["sa"]).apply(null, arguments);
        };
        var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() {
          return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["ta"]).apply(null, arguments);
        };
        var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() {
          return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["ua"]).apply(null, arguments);
        };
        var _NotEqual = Module["_NotEqual"] = function() {
          return (_NotEqual = Module["_NotEqual"] = Module["asm"]["va"]).apply(null, arguments);
        };
        var _OneHot = Module["_OneHot"] = function() {
          return (_OneHot = Module["_OneHot"] = Module["asm"]["wa"]).apply(null, arguments);
        };
        var _PadV2 = Module["_PadV2"] = function() {
          return (_PadV2 = Module["_PadV2"] = Module["asm"]["xa"]).apply(null, arguments);
        };
        var _Pow = Module["_Pow"] = function() {
          return (_Pow = Module["_Pow"] = Module["asm"]["ya"]).apply(null, arguments);
        };
        var _Prelu = Module["_Prelu"] = function() {
          return (_Prelu = Module["_Prelu"] = Module["asm"]["za"]).apply(null, arguments);
        };
        var _Prod = Module["_Prod"] = function() {
          return (_Prod = Module["_Prod"] = Module["asm"]["Aa"]).apply(null, arguments);
        };
        var _RealDiv = Module["_RealDiv"] = function() {
          return (_RealDiv = Module["_RealDiv"] = Module["asm"]["Ba"]).apply(null, arguments);
        };
        var _Relu = Module["_Relu"] = function() {
          return (_Relu = Module["_Relu"] = Module["asm"]["Ca"]).apply(null, arguments);
        };
        var _Relu6 = Module["_Relu6"] = function() {
          return (_Relu6 = Module["_Relu6"] = Module["asm"]["Da"]).apply(null, arguments);
        };
        var _ResizeBilinear = Module["_ResizeBilinear"] = function() {
          return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["Ea"]).apply(null, arguments);
        };
        var _Reverse = Module["_Reverse"] = function() {
          return (_Reverse = Module["_Reverse"] = Module["asm"]["Fa"]).apply(null, arguments);
        };
        var _RotateWithOffset = Module["_RotateWithOffset"] = function() {
          return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["Ga"]).apply(null, arguments);
        };
        var _Round = Module["_Round"] = function() {
          return (_Round = Module["_Round"] = Module["asm"]["Ha"]).apply(null, arguments);
        };
        var _Rsqrt = Module["_Rsqrt"] = function() {
          return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Ia"]).apply(null, arguments);
        };
        var _ScatterNd = Module["_ScatterNd"] = function() {
          return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["Ja"]).apply(null, arguments);
        };
        var _SelectV2 = Module["_SelectV2"] = function() {
          return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["Ka"]).apply(null, arguments);
        };
        var _Sigmoid = Module["_Sigmoid"] = function() {
          return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["La"]).apply(null, arguments);
        };
        var _Sin = Module["_Sin"] = function() {
          return (_Sin = Module["_Sin"] = Module["asm"]["Ma"]).apply(null, arguments);
        };
        var _Softmax = Module["_Softmax"] = function() {
          return (_Softmax = Module["_Softmax"] = Module["asm"]["Na"]).apply(null, arguments);
        };
        var _Sqrt = Module["_Sqrt"] = function() {
          return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Oa"]).apply(null, arguments);
        };
        var _Square = Module["_Square"] = function() {
          return (_Square = Module["_Square"] = Module["asm"]["Pa"]).apply(null, arguments);
        };
        var _SquaredDifference = Module["_SquaredDifference"] = function() {
          return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["Qa"]).apply(null, arguments);
        };
        var _Step = Module["_Step"] = function() {
          return (_Step = Module["_Step"] = Module["asm"]["Ra"]).apply(null, arguments);
        };
        var _StridedSlice = Module["_StridedSlice"] = function() {
          return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["Sa"]).apply(null, arguments);
        };
        var _Sub = Module["_Sub"] = function() {
          return (_Sub = Module["_Sub"] = Module["asm"]["Ta"]).apply(null, arguments);
        };
        var _Sum = Module["_Sum"] = function() {
          return (_Sum = Module["_Sum"] = Module["asm"]["Ua"]).apply(null, arguments);
        };
        var _Tanh = Module["_Tanh"] = function() {
          return (_Tanh = Module["_Tanh"] = Module["asm"]["Va"]).apply(null, arguments);
        };
        var _Tile = Module["_Tile"] = function() {
          return (_Tile = Module["_Tile"] = Module["asm"]["Wa"]).apply(null, arguments);
        };
        var _TopK = Module["_TopK"] = function() {
          return (_TopK = Module["_TopK"] = Module["asm"]["Xa"]).apply(null, arguments);
        };
        var _Transpose = Module["_Transpose"] = function() {
          return (_Transpose = Module["_Transpose"] = Module["asm"]["Ya"]).apply(null, arguments);
        };
        var __FusedMatMul = Module["__FusedMatMul"] = function() {
          return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["Za"]).apply(null, arguments);
        };
        var _malloc = Module["_malloc"] = function() {
          return (_malloc = Module["_malloc"] = Module["asm"]["_a"]).apply(null, arguments);
        };
        var _free = Module["_free"] = function() {
          return (_free = Module["_free"] = Module["asm"]["$a"]).apply(null, arguments);
        };
        var ___errno_location = Module["___errno_location"] = function() {
          return (___errno_location = Module["___errno_location"] = Module["asm"]["ab"]).apply(null, arguments);
        };
        var _emscripten_get_global_libc = Module["_emscripten_get_global_libc"] = function() {
          return (_emscripten_get_global_libc = Module["_emscripten_get_global_libc"] = Module["asm"]["bb"]).apply(null, arguments);
        };
        var _pthread_self = Module["_pthread_self"] = function() {
          return (_pthread_self = Module["_pthread_self"] = Module["asm"]["cb"]).apply(null, arguments);
        };
        var ___pthread_tsd_run_dtors = Module["___pthread_tsd_run_dtors"] = function() {
          return (___pthread_tsd_run_dtors = Module["___pthread_tsd_run_dtors"] = Module["asm"]["db"]).apply(null, arguments);
        };
        var _emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = function() {
          return (_emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = Module["asm"]["eb"]).apply(null, arguments);
        };
        var _emscripten_current_thread_process_queued_calls = Module["_emscripten_current_thread_process_queued_calls"] = function() {
          return (_emscripten_current_thread_process_queued_calls = Module["_emscripten_current_thread_process_queued_calls"] = Module["asm"]["fb"]).apply(null, arguments);
        };
        var _emscripten_register_main_browser_thread_id = Module["_emscripten_register_main_browser_thread_id"] = function() {
          return (_emscripten_register_main_browser_thread_id = Module["_emscripten_register_main_browser_thread_id"] = Module["asm"]["gb"]).apply(null, arguments);
        };
        var __emscripten_do_dispatch_to_thread = Module["__emscripten_do_dispatch_to_thread"] = function() {
          return (__emscripten_do_dispatch_to_thread = Module["__emscripten_do_dispatch_to_thread"] = Module["asm"]["hb"]).apply(null, arguments);
        };
        var _emscripten_sync_run_in_main_thread_4 = Module["_emscripten_sync_run_in_main_thread_4"] = function() {
          return (_emscripten_sync_run_in_main_thread_4 = Module["_emscripten_sync_run_in_main_thread_4"] = Module["asm"]["ib"]).apply(null, arguments);
        };
        var _emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = function() {
          return (_emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = Module["asm"]["jb"]).apply(null, arguments);
        };
        var __emscripten_call_on_thread = Module["__emscripten_call_on_thread"] = function() {
          return (__emscripten_call_on_thread = Module["__emscripten_call_on_thread"] = Module["asm"]["kb"]).apply(null, arguments);
        };
        var _emscripten_tls_init = Module["_emscripten_tls_init"] = function() {
          return (_emscripten_tls_init = Module["_emscripten_tls_init"] = Module["asm"]["lb"]).apply(null, arguments);
        };
        var __emscripten_thread_init = Module["__emscripten_thread_init"] = function() {
          return (__emscripten_thread_init = Module["__emscripten_thread_init"] = Module["asm"]["mb"]).apply(null, arguments);
        };
        var stackSave = Module["stackSave"] = function() {
          return (stackSave = Module["stackSave"] = Module["asm"]["nb"]).apply(null, arguments);
        };
        var stackRestore = Module["stackRestore"] = function() {
          return (stackRestore = Module["stackRestore"] = Module["asm"]["ob"]).apply(null, arguments);
        };
        var stackAlloc = Module["stackAlloc"] = function() {
          return (stackAlloc = Module["stackAlloc"] = Module["asm"]["pb"]).apply(null, arguments);
        };
        var _emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = function() {
          return (_emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = Module["asm"]["qb"]).apply(null, arguments);
        };
        var _memalign = Module["_memalign"] = function() {
          return (_memalign = Module["_memalign"] = Module["asm"]["rb"]).apply(null, arguments);
        };
        var __emscripten_allow_main_runtime_queued_calls = Module["__emscripten_allow_main_runtime_queued_calls"] = 9880;
        var __emscripten_main_thread_futex = Module["__emscripten_main_thread_futex"] = 11368;
        Module["cwrap"] = cwrap;
        Module["PThread"] = PThread;
        Module["PThread"] = PThread;
        Module["wasmMemory"] = wasmMemory;
        Module["ExitStatus"] = ExitStatus;
        var calledRun;
        function ExitStatus(status) {
          this.name = "ExitStatus";
          this.message = "Program terminated with exit(" + status + ")";
          this.status = status;
        }
        dependenciesFulfilled = function runCaller() {
          if (!calledRun)
            run();
          if (!calledRun)
            dependenciesFulfilled = runCaller;
        };
        function run(args) {
          args = args || arguments_;
          if (runDependencies > 0) {
            return;
          }
          if (ENVIRONMENT_IS_PTHREAD) {
            readyPromiseResolve(Module);
            postMessage({cmd: "loaded"});
            return;
          }
          preRun();
          if (runDependencies > 0) {
            return;
          }
          function doRun() {
            if (calledRun)
              return;
            calledRun = true;
            Module["calledRun"] = true;
            if (ABORT)
              return;
            initRuntime();
            preMain();
            readyPromiseResolve(Module);
            if (Module["onRuntimeInitialized"])
              Module["onRuntimeInitialized"]();
            postRun();
          }
          if (Module["setStatus"]) {
            Module["setStatus"]("Running...");
            setTimeout(function() {
              setTimeout(function() {
                Module["setStatus"]("");
              }, 1);
              doRun();
            }, 1);
          } else {
            doRun();
          }
        }
        Module["run"] = run;
        function exit(status, implicit) {
          if (implicit && noExitRuntime && status === 0) {
            return;
          }
          if (!implicit) {
            if (ENVIRONMENT_IS_PTHREAD) {
              postMessage({cmd: "exitProcess", returnCode: status});
              throw new ExitStatus(status);
            } else {
            }
          }
          if (noExitRuntime) {
          } else {
            PThread.terminateAllThreads();
            EXITSTATUS = status;
            exitRuntime();
            if (Module["onExit"])
              Module["onExit"](status);
            ABORT = true;
          }
          quit_(status, new ExitStatus(status));
        }
        if (Module["preInit"]) {
          if (typeof Module["preInit"] == "function")
            Module["preInit"] = [Module["preInit"]];
          while (Module["preInit"].length > 0) {
            Module["preInit"].pop()();
          }
        }
        if (ENVIRONMENT_IS_PTHREAD) {
          noExitRuntime = false;
          PThread.initWorker();
        }
        run();
        return WasmBackendModuleThreadedSimd2.ready;
      };
    }();
    if (typeof exports === "object" && typeof module2 === "object")
      module2.exports = WasmBackendModuleThreadedSimd;
    else if (typeof define === "function" && define["amd"])
      define([], function() {
        return WasmBackendModuleThreadedSimd;
      });
    else if (typeof exports === "object")
      exports["WasmBackendModuleThreadedSimd"] = WasmBackendModuleThreadedSimd;
  });
  var require_tfjs_backend_wasm = __commonJS((exports, module2) => {
    var WasmBackendModule = function() {
      var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0;
      if (typeof __filename !== "undefined")
        _scriptDir = _scriptDir || __filename;
      return function(WasmBackendModule2) {
        WasmBackendModule2 = WasmBackendModule2 || {};
        var Module = typeof WasmBackendModule2 !== "undefined" ? WasmBackendModule2 : {};
        var readyPromiseResolve, readyPromiseReject;
        Module["ready"] = new Promise(function(resolve, reject) {
          readyPromiseResolve = resolve;
          readyPromiseReject = reject;
        });
        var moduleOverrides = {};
        var key;
        for (key in Module) {
          if (Module.hasOwnProperty(key)) {
            moduleOverrides[key] = Module[key];
          }
        }
        var arguments_ = [];
        var thisProgram = "./this.program";
        var quit_ = function(status, toThrow) {
          throw toThrow;
        };
        var ENVIRONMENT_IS_WEB = false;
        var ENVIRONMENT_IS_WORKER = false;
        var ENVIRONMENT_IS_NODE = false;
        var ENVIRONMENT_IS_SHELL = false;
        ENVIRONMENT_IS_WEB = typeof window === "object";
        ENVIRONMENT_IS_WORKER = typeof importScripts === "function";
        ENVIRONMENT_IS_NODE = typeof process === "object" && typeof process.versions === "object" && typeof process.versions.node === "string";
        ENVIRONMENT_IS_SHELL = !ENVIRONMENT_IS_WEB && !ENVIRONMENT_IS_NODE && !ENVIRONMENT_IS_WORKER;
        var scriptDirectory = "";
        function locateFile(path) {
          if (Module["locateFile"]) {
            return Module["locateFile"](path, scriptDirectory);
          }
          return scriptDirectory + path;
        }
        var read_, readAsync, readBinary, setWindowTitle;
        var nodeFS;
        var nodePath;
        if (ENVIRONMENT_IS_NODE) {
          if (ENVIRONMENT_IS_WORKER) {
            scriptDirectory = require_path().dirname(scriptDirectory) + "/";
          } else {
            scriptDirectory = __dirname + "/";
          }
          read_ = function shell_read(filename, binary) {
            if (!nodeFS)
              nodeFS = require("fs");
            if (!nodePath)
              nodePath = require_path();
            filename = nodePath["normalize"](filename);
            return nodeFS["readFileSync"](filename, binary ? null : "utf8");
          };
          readBinary = function readBinary2(filename) {
            var ret = read_(filename, true);
            if (!ret.buffer) {
              ret = new Uint8Array(ret);
            }
            assert3(ret.buffer);
            return ret;
          };
          if (process["argv"].length > 1) {
            thisProgram = process["argv"][1].replace(/\\/g, "/");
          }
          arguments_ = process["argv"].slice(2);
          process["on"]("uncaughtException", function(ex) {
            if (!(ex instanceof ExitStatus)) {
              throw ex;
            }
          });
          process["on"]("unhandledRejection", abort);
          quit_ = function(status) {
            process["exit"](status);
          };
          Module["inspect"] = function() {
            return "[Emscripten Module object]";
          };
        } else if (ENVIRONMENT_IS_SHELL) {
          if (typeof read != "undefined") {
            read_ = function shell_read(f) {
              return read(f);
            };
          }
          readBinary = function readBinary2(f) {
            var data;
            if (typeof readbuffer === "function") {
              return new Uint8Array(readbuffer(f));
            }
            data = read(f, "binary");
            assert3(typeof data === "object");
            return data;
          };
          if (typeof scriptArgs != "undefined") {
            arguments_ = scriptArgs;
          } else if (typeof arguments != "undefined") {
            arguments_ = arguments;
          }
          if (typeof quit === "function") {
            quit_ = function(status) {
              quit(status);
            };
          }
          if (typeof print !== "undefined") {
            if (typeof console === "undefined")
              console = {};
            console.log = print;
            console.warn = console.error = typeof printErr !== "undefined" ? printErr : print;
          }
        } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {
          if (ENVIRONMENT_IS_WORKER) {
            scriptDirectory = self.location.href;
          } else if (typeof document !== "undefined" && document.currentScript) {
            scriptDirectory = document.currentScript.src;
          }
          if (_scriptDir) {
            scriptDirectory = _scriptDir;
          }
          if (scriptDirectory.indexOf("blob:") !== 0) {
            scriptDirectory = scriptDirectory.substr(0, scriptDirectory.lastIndexOf("/") + 1);
          } else {
            scriptDirectory = "";
          }
          {
            read_ = function(url) {
              var xhr = new XMLHttpRequest();
              xhr.open("GET", url, false);
              xhr.send(null);
              return xhr.responseText;
            };
            if (ENVIRONMENT_IS_WORKER) {
              readBinary = function(url) {
                var xhr = new XMLHttpRequest();
                xhr.open("GET", url, false);
                xhr.responseType = "arraybuffer";
                xhr.send(null);
                return new Uint8Array(xhr.response);
              };
            }
            readAsync = function(url, onload, onerror) {
              var xhr = new XMLHttpRequest();
              xhr.open("GET", url, true);
              xhr.responseType = "arraybuffer";
              xhr.onload = function() {
                if (xhr.status == 200 || xhr.status == 0 && xhr.response) {
                  onload(xhr.response);
                  return;
                }
                onerror();
              };
              xhr.onerror = onerror;
              xhr.send(null);
            };
          }
          setWindowTitle = function(title) {
            document.title = title;
          };
        } else {
        }
        var out = Module["print"] || console.log.bind(console);
        var err = Module["printErr"] || console.warn.bind(console);
        for (key in moduleOverrides) {
          if (moduleOverrides.hasOwnProperty(key)) {
            Module[key] = moduleOverrides[key];
          }
        }
        moduleOverrides = null;
        if (Module["arguments"])
          arguments_ = Module["arguments"];
        if (Module["thisProgram"])
          thisProgram = Module["thisProgram"];
        if (Module["quit"])
          quit_ = Module["quit"];
        var wasmBinary;
        if (Module["wasmBinary"])
          wasmBinary = Module["wasmBinary"];
        var noExitRuntime = Module["noExitRuntime"] || true;
        if (typeof WebAssembly !== "object") {
          abort("no native wasm support detected");
        }
        var wasmMemory;
        var ABORT = false;
        var EXITSTATUS;
        function assert3(condition, text) {
          if (!condition) {
            abort("Assertion failed: " + text);
          }
        }
        function getCFunc(ident) {
          var func2 = Module["_" + ident];
          assert3(func2, "Cannot call unknown function " + ident + ", make sure it is exported");
          return func2;
        }
        function ccall(ident, returnType, argTypes, args, opts) {
          var toC = {string: function(str) {
            var ret2 = 0;
            if (str !== null && str !== void 0 && str !== 0) {
              var len = (str.length << 2) + 1;
              ret2 = stackAlloc(len);
              stringToUTF8(str, ret2, len);
            }
            return ret2;
          }, array: function(arr) {
            var ret2 = stackAlloc(arr.length);
            writeArrayToMemory(arr, ret2);
            return ret2;
          }};
          function convertReturnValue(ret2) {
            if (returnType === "string")
              return UTF8ToString(ret2);
            if (returnType === "boolean")
              return Boolean(ret2);
            return ret2;
          }
          var func2 = getCFunc(ident);
          var cArgs = [];
          var stack2 = 0;
          if (args) {
            for (var i = 0; i < args.length; i++) {
              var converter = toC[argTypes[i]];
              if (converter) {
                if (stack2 === 0)
                  stack2 = stackSave();
                cArgs[i] = converter(args[i]);
              } else {
                cArgs[i] = args[i];
              }
            }
          }
          var ret = func2.apply(null, cArgs);
          ret = convertReturnValue(ret);
          if (stack2 !== 0)
            stackRestore(stack2);
          return ret;
        }
        function cwrap(ident, returnType, argTypes, opts) {
          argTypes = argTypes || [];
          var numericArgs = argTypes.every(function(type) {
            return type === "number";
          });
          var numericRet = returnType !== "string";
          if (numericRet && numericArgs && !opts) {
            return getCFunc(ident);
          }
          return function() {
            return ccall(ident, returnType, argTypes, arguments, opts);
          };
        }
        var UTF8Decoder = typeof TextDecoder !== "undefined" ? new TextDecoder("utf8") : void 0;
        function UTF8ArrayToString(heap, idx, maxBytesToRead) {
          var endIdx = idx + maxBytesToRead;
          var endPtr = idx;
          while (heap[endPtr] && !(endPtr >= endIdx))
            ++endPtr;
          if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) {
            return UTF8Decoder.decode(heap.subarray(idx, endPtr));
          } else {
            var str = "";
            while (idx < endPtr) {
              var u0 = heap[idx++];
              if (!(u0 & 128)) {
                str += String.fromCharCode(u0);
                continue;
              }
              var u1 = heap[idx++] & 63;
              if ((u0 & 224) == 192) {
                str += String.fromCharCode((u0 & 31) << 6 | u1);
                continue;
              }
              var u2 = heap[idx++] & 63;
              if ((u0 & 240) == 224) {
                u0 = (u0 & 15) << 12 | u1 << 6 | u2;
              } else {
                u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63;
              }
              if (u0 < 65536) {
                str += String.fromCharCode(u0);
              } else {
                var ch = u0 - 65536;
                str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);
              }
            }
          }
          return str;
        }
        function UTF8ToString(ptr, maxBytesToRead) {
          return ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : "";
        }
        function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {
          if (!(maxBytesToWrite > 0))
            return 0;
          var startIdx = outIdx;
          var endIdx = outIdx + maxBytesToWrite - 1;
          for (var i = 0; i < str.length; ++i) {
            var u = str.charCodeAt(i);
            if (u >= 55296 && u <= 57343) {
              var u1 = str.charCodeAt(++i);
              u = 65536 + ((u & 1023) << 10) | u1 & 1023;
            }
            if (u <= 127) {
              if (outIdx >= endIdx)
                break;
              heap[outIdx++] = u;
            } else if (u <= 2047) {
              if (outIdx + 1 >= endIdx)
                break;
              heap[outIdx++] = 192 | u >> 6;
              heap[outIdx++] = 128 | u & 63;
            } else if (u <= 65535) {
              if (outIdx + 2 >= endIdx)
                break;
              heap[outIdx++] = 224 | u >> 12;
              heap[outIdx++] = 128 | u >> 6 & 63;
              heap[outIdx++] = 128 | u & 63;
            } else {
              if (outIdx + 3 >= endIdx)
                break;
              heap[outIdx++] = 240 | u >> 18;
              heap[outIdx++] = 128 | u >> 12 & 63;
              heap[outIdx++] = 128 | u >> 6 & 63;
              heap[outIdx++] = 128 | u & 63;
            }
          }
          heap[outIdx] = 0;
          return outIdx - startIdx;
        }
        function stringToUTF8(str, outPtr, maxBytesToWrite) {
          return stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite);
        }
        function writeArrayToMemory(array2, buffer3) {
          HEAP8.set(array2, buffer3);
        }
        function alignUp(x, multiple) {
          if (x % multiple > 0) {
            x += multiple - x % multiple;
          }
          return x;
        }
        var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;
        function updateGlobalBufferAndViews(buf) {
          buffer2 = buf;
          Module["HEAP8"] = HEAP8 = new Int8Array(buf);
          Module["HEAP16"] = HEAP16 = new Int16Array(buf);
          Module["HEAP32"] = HEAP32 = new Int32Array(buf);
          Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf);
          Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf);
          Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf);
          Module["HEAPF32"] = HEAPF32 = new Float32Array(buf);
          Module["HEAPF64"] = HEAPF64 = new Float64Array(buf);
        }
        var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216;
        var wasmTable;
        var __ATPRERUN__ = [];
        var __ATINIT__ = [];
        var __ATMAIN__ = [];
        var __ATPOSTRUN__ = [];
        var runtimeInitialized = false;
        __ATINIT__.push({func: function() {
          ___wasm_call_ctors();
        }});
        function preRun() {
          if (Module["preRun"]) {
            if (typeof Module["preRun"] == "function")
              Module["preRun"] = [Module["preRun"]];
            while (Module["preRun"].length) {
              addOnPreRun(Module["preRun"].shift());
            }
          }
          callRuntimeCallbacks(__ATPRERUN__);
        }
        function initRuntime() {
          runtimeInitialized = true;
          callRuntimeCallbacks(__ATINIT__);
        }
        function preMain() {
          callRuntimeCallbacks(__ATMAIN__);
        }
        function postRun() {
          if (Module["postRun"]) {
            if (typeof Module["postRun"] == "function")
              Module["postRun"] = [Module["postRun"]];
            while (Module["postRun"].length) {
              addOnPostRun(Module["postRun"].shift());
            }
          }
          callRuntimeCallbacks(__ATPOSTRUN__);
        }
        function addOnPreRun(cb) {
          __ATPRERUN__.unshift(cb);
        }
        function addOnPostRun(cb) {
          __ATPOSTRUN__.unshift(cb);
        }
        var runDependencies = 0;
        var runDependencyWatcher = null;
        var dependenciesFulfilled = null;
        function addRunDependency(id) {
          runDependencies++;
          if (Module["monitorRunDependencies"]) {
            Module["monitorRunDependencies"](runDependencies);
          }
        }
        function removeRunDependency(id) {
          runDependencies--;
          if (Module["monitorRunDependencies"]) {
            Module["monitorRunDependencies"](runDependencies);
          }
          if (runDependencies == 0) {
            if (runDependencyWatcher !== null) {
              clearInterval(runDependencyWatcher);
              runDependencyWatcher = null;
            }
            if (dependenciesFulfilled) {
              var callback = dependenciesFulfilled;
              dependenciesFulfilled = null;
              callback();
            }
          }
        }
        Module["preloadedImages"] = {};
        Module["preloadedAudios"] = {};
        function abort(what) {
          if (Module["onAbort"]) {
            Module["onAbort"](what);
          }
          what += "";
          err(what);
          ABORT = true;
          EXITSTATUS = 1;
          what = "abort(" + what + "). Build with -s ASSERTIONS=1 for more info.";
          var e = new WebAssembly.RuntimeError(what);
          readyPromiseReject(e);
          throw e;
        }
        function hasPrefix(str, prefix) {
          return String.prototype.startsWith ? str.startsWith(prefix) : str.indexOf(prefix) === 0;
        }
        var dataURIPrefix = "data:application/octet-stream;base64,";
        function isDataURI(filename) {
          return hasPrefix(filename, dataURIPrefix);
        }
        var fileURIPrefix = "file://";
        function isFileURI(filename) {
          return hasPrefix(filename, fileURIPrefix);
        }
        var wasmBinaryFile = "tfjs-backend-wasm.wasm";
        if (!isDataURI(wasmBinaryFile)) {
          wasmBinaryFile = locateFile(wasmBinaryFile);
        }
        function getBinary(file) {
          try {
            if (file == wasmBinaryFile && wasmBinary) {
              return new Uint8Array(wasmBinary);
            }
            if (readBinary) {
              return readBinary(file);
            } else {
              throw "both async and sync fetching of the wasm failed";
            }
          } catch (err2) {
            abort(err2);
          }
        }
        function getBinaryPromise() {
          if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) {
            if (typeof fetch === "function" && !isFileURI(wasmBinaryFile)) {
              return fetch(wasmBinaryFile, {credentials: "same-origin"}).then(function(response) {
                if (!response["ok"]) {
                  throw "failed to load wasm binary file at '" + wasmBinaryFile + "'";
                }
                return response["arrayBuffer"]();
              }).catch(function() {
                return getBinary(wasmBinaryFile);
              });
            } else {
              if (readAsync) {
                return new Promise(function(resolve, reject) {
                  readAsync(wasmBinaryFile, function(response) {
                    resolve(new Uint8Array(response));
                  }, reject);
                });
              }
            }
          }
          return Promise.resolve().then(function() {
            return getBinary(wasmBinaryFile);
          });
        }
        function createWasm() {
          var info = {a: asmLibraryArg};
          function receiveInstance(instance, module22) {
            var exports3 = instance.exports;
            Module["asm"] = exports3;
            wasmMemory = Module["asm"]["g"];
            updateGlobalBufferAndViews(wasmMemory.buffer);
            wasmTable = Module["asm"]["m"];
            removeRunDependency("wasm-instantiate");
          }
          addRunDependency("wasm-instantiate");
          function receiveInstantiatedSource(output) {
            receiveInstance(output["instance"]);
          }
          function instantiateArrayBuffer(receiver) {
            return getBinaryPromise().then(function(binary) {
              return WebAssembly.instantiate(binary, info);
            }).then(receiver, function(reason) {
              err("failed to asynchronously prepare wasm: " + reason);
              abort(reason);
            });
          }
          function instantiateAsync() {
            if (!wasmBinary && typeof WebAssembly.instantiateStreaming === "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === "function") {
              return fetch(wasmBinaryFile, {credentials: "same-origin"}).then(function(response) {
                var result = WebAssembly.instantiateStreaming(response, info);
                return result.then(receiveInstantiatedSource, function(reason) {
                  err("wasm streaming compile failed: " + reason);
                  err("falling back to ArrayBuffer instantiation");
                  return instantiateArrayBuffer(receiveInstantiatedSource);
                });
              });
            } else {
              return instantiateArrayBuffer(receiveInstantiatedSource);
            }
          }
          if (Module["instantiateWasm"]) {
            try {
              var exports2 = Module["instantiateWasm"](info, receiveInstance);
              return exports2;
            } catch (e) {
              err("Module.instantiateWasm callback failed with error: " + e);
              return false;
            }
          }
          instantiateAsync().catch(readyPromiseReject);
          return {};
        }
        function callRuntimeCallbacks(callbacks2) {
          while (callbacks2.length > 0) {
            var callback = callbacks2.shift();
            if (typeof callback == "function") {
              callback(Module);
              continue;
            }
            var func2 = callback.func;
            if (typeof func2 === "number") {
              if (callback.arg === void 0) {
                wasmTable.get(func2)();
              } else {
                wasmTable.get(func2)(callback.arg);
              }
            } else {
              func2(callback.arg === void 0 ? null : callback.arg);
            }
          }
        }
        function _abort() {
          abort();
        }
        function _emscripten_memcpy_big(dest, src, num) {
          HEAPU8.copyWithin(dest, src, src + num);
        }
        function _emscripten_get_heap_size() {
          return HEAPU8.length;
        }
        function emscripten_realloc_buffer(size) {
          try {
            wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16);
            updateGlobalBufferAndViews(wasmMemory.buffer);
            return 1;
          } catch (e) {
          }
        }
        function _emscripten_resize_heap(requestedSize) {
          var oldSize = _emscripten_get_heap_size();
          var maxHeapSize = 2147483648;
          if (requestedSize > maxHeapSize) {
            return false;
          }
          for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {
            var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);
            overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);
            var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536));
            var replacement = emscripten_realloc_buffer(newSize);
            if (replacement) {
              return true;
            }
          }
          return false;
        }
        var SYSCALLS = {mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) {
          var buffer3 = SYSCALLS.buffers[stream];
          if (curr === 0 || curr === 10) {
            (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0));
            buffer3.length = 0;
          } else {
            buffer3.push(curr);
          }
        }, varargs: void 0, get: function() {
          SYSCALLS.varargs += 4;
          var ret = HEAP32[SYSCALLS.varargs - 4 >> 2];
          return ret;
        }, getStr: function(ptr) {
          var ret = UTF8ToString(ptr);
          return ret;
        }, get64: function(low, high) {
          return low;
        }};
        function _fd_close(fd) {
          return 0;
        }
        function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {
        }
        function _fd_write(fd, iov, iovcnt, pnum) {
          var num = 0;
          for (var i = 0; i < iovcnt; i++) {
            var ptr = HEAP32[iov + i * 8 >> 2];
            var len = HEAP32[iov + (i * 8 + 4) >> 2];
            for (var j = 0; j < len; j++) {
              SYSCALLS.printChar(fd, HEAPU8[ptr + j]);
            }
            num += len;
          }
          HEAP32[pnum >> 2] = num;
          return 0;
        }
        var asmLibraryArg = {a: _abort, d: _emscripten_memcpy_big, e: _emscripten_resize_heap, f: _fd_close, c: _fd_seek, b: _fd_write};
        var asm = createWasm();
        var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() {
          return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["h"]).apply(null, arguments);
        };
        var _init = Module["_init"] = function() {
          return (_init = Module["_init"] = Module["asm"]["i"]).apply(null, arguments);
        };
        var _register_tensor = Module["_register_tensor"] = function() {
          return (_register_tensor = Module["_register_tensor"] = Module["asm"]["j"]).apply(null, arguments);
        };
        var _dispose_data = Module["_dispose_data"] = function() {
          return (_dispose_data = Module["_dispose_data"] = Module["asm"]["k"]).apply(null, arguments);
        };
        var _dispose = Module["_dispose"] = function() {
          return (_dispose = Module["_dispose"] = Module["asm"]["l"]).apply(null, arguments);
        };
        var _Abs = Module["_Abs"] = function() {
          return (_Abs = Module["_Abs"] = Module["asm"]["n"]).apply(null, arguments);
        };
        var _Add = Module["_Add"] = function() {
          return (_Add = Module["_Add"] = Module["asm"]["o"]).apply(null, arguments);
        };
        var _AddN = Module["_AddN"] = function() {
          return (_AddN = Module["_AddN"] = Module["asm"]["p"]).apply(null, arguments);
        };
        var _ArgMax = Module["_ArgMax"] = function() {
          return (_ArgMax = Module["_ArgMax"] = Module["asm"]["q"]).apply(null, arguments);
        };
        var _AvgPool = Module["_AvgPool"] = function() {
          return (_AvgPool = Module["_AvgPool"] = Module["asm"]["r"]).apply(null, arguments);
        };
        var _BatchMatMul = Module["_BatchMatMul"] = function() {
          return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["s"]).apply(null, arguments);
        };
        var _Ceil = Module["_Ceil"] = function() {
          return (_Ceil = Module["_Ceil"] = Module["asm"]["t"]).apply(null, arguments);
        };
        var _ClipByValue = Module["_ClipByValue"] = function() {
          return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["u"]).apply(null, arguments);
        };
        var _Conv2D = Module["_Conv2D"] = function() {
          return (_Conv2D = Module["_Conv2D"] = Module["asm"]["v"]).apply(null, arguments);
        };
        var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() {
          return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["w"]).apply(null, arguments);
        };
        var _Cos = Module["_Cos"] = function() {
          return (_Cos = Module["_Cos"] = Module["asm"]["x"]).apply(null, arguments);
        };
        var _CropAndResize = Module["_CropAndResize"] = function() {
          return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["y"]).apply(null, arguments);
        };
        var _Cumsum = Module["_Cumsum"] = function() {
          return (_Cumsum = Module["_Cumsum"] = Module["asm"]["z"]).apply(null, arguments);
        };
        var _DepthToSpace = Module["_DepthToSpace"] = function() {
          return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["A"]).apply(null, arguments);
        };
        var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() {
          return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["B"]).apply(null, arguments);
        };
        var _Equal = Module["_Equal"] = function() {
          return (_Equal = Module["_Equal"] = Module["asm"]["C"]).apply(null, arguments);
        };
        var _Exp = Module["_Exp"] = function() {
          return (_Exp = Module["_Exp"] = Module["asm"]["D"]).apply(null, arguments);
        };
        var _FlipLeftRight = Module["_FlipLeftRight"] = function() {
          return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["E"]).apply(null, arguments);
        };
        var _Floor = Module["_Floor"] = function() {
          return (_Floor = Module["_Floor"] = Module["asm"]["F"]).apply(null, arguments);
        };
        var _FloorDiv = Module["_FloorDiv"] = function() {
          return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["G"]).apply(null, arguments);
        };
        var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() {
          return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["H"]).apply(null, arguments);
        };
        var _FusedConv2D = Module["_FusedConv2D"] = function() {
          return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["I"]).apply(null, arguments);
        };
        var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() {
          return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["J"]).apply(null, arguments);
        };
        var _Gather = Module["_Gather"] = function() {
          return (_Gather = Module["_Gather"] = Module["asm"]["K"]).apply(null, arguments);
        };
        var _GatherNd = Module["_GatherNd"] = function() {
          return (_GatherNd = Module["_GatherNd"] = Module["asm"]["L"]).apply(null, arguments);
        };
        var _Greater = Module["_Greater"] = function() {
          return (_Greater = Module["_Greater"] = Module["asm"]["M"]).apply(null, arguments);
        };
        var _GreaterEqual = Module["_GreaterEqual"] = function() {
          return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["N"]).apply(null, arguments);
        };
        var _LeakyRelu = Module["_LeakyRelu"] = function() {
          return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["O"]).apply(null, arguments);
        };
        var _Less = Module["_Less"] = function() {
          return (_Less = Module["_Less"] = Module["asm"]["P"]).apply(null, arguments);
        };
        var _LessEqual = Module["_LessEqual"] = function() {
          return (_LessEqual = Module["_LessEqual"] = Module["asm"]["Q"]).apply(null, arguments);
        };
        var _Log = Module["_Log"] = function() {
          return (_Log = Module["_Log"] = Module["asm"]["R"]).apply(null, arguments);
        };
        var _LogicalAnd = Module["_LogicalAnd"] = function() {
          return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["S"]).apply(null, arguments);
        };
        var _Max = Module["_Max"] = function() {
          return (_Max = Module["_Max"] = Module["asm"]["T"]).apply(null, arguments);
        };
        var _MaxPool = Module["_MaxPool"] = function() {
          return (_MaxPool = Module["_MaxPool"] = Module["asm"]["U"]).apply(null, arguments);
        };
        var _Maximum = Module["_Maximum"] = function() {
          return (_Maximum = Module["_Maximum"] = Module["asm"]["V"]).apply(null, arguments);
        };
        var _Mean = Module["_Mean"] = function() {
          return (_Mean = Module["_Mean"] = Module["asm"]["W"]).apply(null, arguments);
        };
        var _Min = Module["_Min"] = function() {
          return (_Min = Module["_Min"] = Module["asm"]["X"]).apply(null, arguments);
        };
        var _Minimum = Module["_Minimum"] = function() {
          return (_Minimum = Module["_Minimum"] = Module["asm"]["Y"]).apply(null, arguments);
        };
        var _Multiply = Module["_Multiply"] = function() {
          return (_Multiply = Module["_Multiply"] = Module["asm"]["Z"]).apply(null, arguments);
        };
        var _Neg = Module["_Neg"] = function() {
          return (_Neg = Module["_Neg"] = Module["asm"]["_"]).apply(null, arguments);
        };
        var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() {
          return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["$"]).apply(null, arguments);
        };
        var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() {
          return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["aa"]).apply(null, arguments);
        };
        var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() {
          return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["ba"]).apply(null, arguments);
        };
        var _NotEqual = Module["_NotEqual"] = function() {
          return (_NotEqual = Module["_NotEqual"] = Module["asm"]["ca"]).apply(null, arguments);
        };
        var _OneHot = Module["_OneHot"] = function() {
          return (_OneHot = Module["_OneHot"] = Module["asm"]["da"]).apply(null, arguments);
        };
        var _PadV2 = Module["_PadV2"] = function() {
          return (_PadV2 = Module["_PadV2"] = Module["asm"]["ea"]).apply(null, arguments);
        };
        var _Pow = Module["_Pow"] = function() {
          return (_Pow = Module["_Pow"] = Module["asm"]["fa"]).apply(null, arguments);
        };
        var _Prelu = Module["_Prelu"] = function() {
          return (_Prelu = Module["_Prelu"] = Module["asm"]["ga"]).apply(null, arguments);
        };
        var _Prod = Module["_Prod"] = function() {
          return (_Prod = Module["_Prod"] = Module["asm"]["ha"]).apply(null, arguments);
        };
        var _RealDiv = Module["_RealDiv"] = function() {
          return (_RealDiv = Module["_RealDiv"] = Module["asm"]["ia"]).apply(null, arguments);
        };
        var _Relu = Module["_Relu"] = function() {
          return (_Relu = Module["_Relu"] = Module["asm"]["ja"]).apply(null, arguments);
        };
        var _Relu6 = Module["_Relu6"] = function() {
          return (_Relu6 = Module["_Relu6"] = Module["asm"]["ka"]).apply(null, arguments);
        };
        var _ResizeBilinear = Module["_ResizeBilinear"] = function() {
          return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["la"]).apply(null, arguments);
        };
        var _Reverse = Module["_Reverse"] = function() {
          return (_Reverse = Module["_Reverse"] = Module["asm"]["ma"]).apply(null, arguments);
        };
        var _RotateWithOffset = Module["_RotateWithOffset"] = function() {
          return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["na"]).apply(null, arguments);
        };
        var _Round = Module["_Round"] = function() {
          return (_Round = Module["_Round"] = Module["asm"]["oa"]).apply(null, arguments);
        };
        var _Rsqrt = Module["_Rsqrt"] = function() {
          return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["pa"]).apply(null, arguments);
        };
        var _ScatterNd = Module["_ScatterNd"] = function() {
          return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["qa"]).apply(null, arguments);
        };
        var _SelectV2 = Module["_SelectV2"] = function() {
          return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["ra"]).apply(null, arguments);
        };
        var _Sigmoid = Module["_Sigmoid"] = function() {
          return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["sa"]).apply(null, arguments);
        };
        var _Sin = Module["_Sin"] = function() {
          return (_Sin = Module["_Sin"] = Module["asm"]["ta"]).apply(null, arguments);
        };
        var _Softmax = Module["_Softmax"] = function() {
          return (_Softmax = Module["_Softmax"] = Module["asm"]["ua"]).apply(null, arguments);
        };
        var _Sqrt = Module["_Sqrt"] = function() {
          return (_Sqrt = Module["_Sqrt"] = Module["asm"]["va"]).apply(null, arguments);
        };
        var _Square = Module["_Square"] = function() {
          return (_Square = Module["_Square"] = Module["asm"]["wa"]).apply(null, arguments);
        };
        var _SquaredDifference = Module["_SquaredDifference"] = function() {
          return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["xa"]).apply(null, arguments);
        };
        var _Step = Module["_Step"] = function() {
          return (_Step = Module["_Step"] = Module["asm"]["ya"]).apply(null, arguments);
        };
        var _StridedSlice = Module["_StridedSlice"] = function() {
          return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["za"]).apply(null, arguments);
        };
        var _Sub = Module["_Sub"] = function() {
          return (_Sub = Module["_Sub"] = Module["asm"]["Aa"]).apply(null, arguments);
        };
        var _Sum = Module["_Sum"] = function() {
          return (_Sum = Module["_Sum"] = Module["asm"]["Ba"]).apply(null, arguments);
        };
        var _Tanh = Module["_Tanh"] = function() {
          return (_Tanh = Module["_Tanh"] = Module["asm"]["Ca"]).apply(null, arguments);
        };
        var _Tile = Module["_Tile"] = function() {
          return (_Tile = Module["_Tile"] = Module["asm"]["Da"]).apply(null, arguments);
        };
        var _TopK = Module["_TopK"] = function() {
          return (_TopK = Module["_TopK"] = Module["asm"]["Ea"]).apply(null, arguments);
        };
        var _Transpose = Module["_Transpose"] = function() {
          return (_Transpose = Module["_Transpose"] = Module["asm"]["Fa"]).apply(null, arguments);
        };
        var __FusedMatMul = Module["__FusedMatMul"] = function() {
          return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["Ga"]).apply(null, arguments);
        };
        var _malloc = Module["_malloc"] = function() {
          return (_malloc = Module["_malloc"] = Module["asm"]["Ha"]).apply(null, arguments);
        };
        var _free = Module["_free"] = function() {
          return (_free = Module["_free"] = Module["asm"]["Ia"]).apply(null, arguments);
        };
        var stackSave = Module["stackSave"] = function() {
          return (stackSave = Module["stackSave"] = Module["asm"]["Ja"]).apply(null, arguments);
        };
        var stackRestore = Module["stackRestore"] = function() {
          return (stackRestore = Module["stackRestore"] = Module["asm"]["Ka"]).apply(null, arguments);
        };
        var stackAlloc = Module["stackAlloc"] = function() {
          return (stackAlloc = Module["stackAlloc"] = Module["asm"]["La"]).apply(null, arguments);
        };
        Module["cwrap"] = cwrap;
        var calledRun;
        function ExitStatus(status) {
          this.name = "ExitStatus";
          this.message = "Program terminated with exit(" + status + ")";
          this.status = status;
        }
        dependenciesFulfilled = function runCaller() {
          if (!calledRun)
            run();
          if (!calledRun)
            dependenciesFulfilled = runCaller;
        };
        function run(args) {
          args = args || arguments_;
          if (runDependencies > 0) {
            return;
          }
          preRun();
          if (runDependencies > 0) {
            return;
          }
          function doRun() {
            if (calledRun)
              return;
            calledRun = true;
            Module["calledRun"] = true;
            if (ABORT)
              return;
            initRuntime();
            preMain();
            readyPromiseResolve(Module);
            if (Module["onRuntimeInitialized"])
              Module["onRuntimeInitialized"]();
            postRun();
          }
          if (Module["setStatus"]) {
            Module["setStatus"]("Running...");
            setTimeout(function() {
              setTimeout(function() {
                Module["setStatus"]("");
              }, 1);
              doRun();
            }, 1);
          } else {
            doRun();
          }
        }
        Module["run"] = run;
        if (Module["preInit"]) {
          if (typeof Module["preInit"] == "function")
            Module["preInit"] = [Module["preInit"]];
          while (Module["preInit"].length > 0) {
            Module["preInit"].pop()();
          }
        }
        run();
        return WasmBackendModule2.ready;
      };
    }();
    if (typeof exports === "object" && typeof module2 === "object")
      module2.exports = WasmBackendModule;
    else if (typeof define === "function" && define["amd"])
      define([], function() {
        return WasmBackendModule;
      });
    else if (typeof exports === "object")
      exports["WasmBackendModule"] = WasmBackendModule;
  });
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var EPSILON_FLOAT32 = 1e-7;
  var EPSILON_FLOAT16 = 1e-4;
  var DataStorage = class {
    constructor(backend2, dataMover) {
      this.backend = backend2;
      this.dataMover = dataMover;
      this.data = new WeakMap();
      this.dataIdsCount = 0;
    }
    get(dataId) {
      if (!this.data.has(dataId)) {
        this.dataMover.moveData(this.backend, dataId);
      }
      return this.data.get(dataId);
    }
    set(dataId, value) {
      this.dataIdsCount++;
      this.data.set(dataId, value);
    }
    has(dataId) {
      return this.data.has(dataId);
    }
    delete(dataId) {
      this.dataIdsCount--;
      return this.data.delete(dataId);
    }
    numDataIds() {
      return this.dataIdsCount;
    }
  };
  var KernelBackend = class {
    refCount(dataId) {
      return notYetImplemented("refCount");
    }
    incRef(dataId) {
      return notYetImplemented("incRef");
    }
    timerAvailable() {
      return true;
    }
    time(f) {
      return notYetImplemented("time");
    }
    read(dataId) {
      return notYetImplemented("read");
    }
    readSync(dataId) {
      return notYetImplemented("readSync");
    }
    numDataIds() {
      return notYetImplemented("numDataIds");
    }
    disposeData(dataId, force) {
      return notYetImplemented("disposeData");
    }
    write(values, shape, dtype) {
      return notYetImplemented("write");
    }
    move(dataId, values, shape, dtype, refCount) {
      return notYetImplemented("move");
    }
    memory() {
      return notYetImplemented("memory");
    }
    floatPrecision() {
      return notYetImplemented("floatPrecision");
    }
    epsilon() {
      return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
    }
    dispose() {
      return notYetImplemented("dispose");
    }
  };
  function notYetImplemented(kernelName) {
    throw new Error(`'${kernelName}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`);
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function shuffle(array2) {
    let counter = array2.length;
    let temp = 0;
    let index = 0;
    while (counter > 0) {
      index = Math.random() * counter | 0;
      counter--;
      temp = array2[counter];
      array2[counter] = array2[index];
      array2[index] = temp;
    }
  }
  function shuffleCombo(array2, array22) {
    if (array2.length !== array22.length) {
      throw new Error(`Array sizes must match to be shuffled together First array length was ${array2.length}Second array length was ${array22.length}`);
    }
    let counter = array2.length;
    let temp, temp2;
    let index = 0;
    while (counter > 0) {
      index = Math.random() * counter | 0;
      counter--;
      temp = array2[counter];
      temp2 = array22[counter];
      array2[counter] = array2[index];
      array22[counter] = array22[index];
      array2[index] = temp;
      array22[index] = temp2;
    }
  }
  function clamp(min6, x, max6) {
    return Math.max(min6, Math.min(x, max6));
  }
  function nearestLargerEven(val) {
    return val % 2 === 0 ? val : val + 1;
  }
  function sum(arr) {
    let sum6 = 0;
    for (let i = 0; i < arr.length; i++) {
      sum6 += arr[i];
    }
    return sum6;
  }
  function randUniform(a, b) {
    const r = Math.random();
    return b * r + (1 - r) * a;
  }
  function distSquared(a, b) {
    let result = 0;
    for (let i = 0; i < a.length; i++) {
      const diff = Number(a[i]) - Number(b[i]);
      result += diff * diff;
    }
    return result;
  }
  function assert(expr, msg) {
    if (!expr) {
      throw new Error(typeof msg === "string" ? msg : msg());
    }
  }
  function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = "") {
    assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
  }
  function assertNonNull(a) {
    assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);
  }
  function flatten(arr, result = [], skipTypedArray = false) {
    if (result == null) {
      result = [];
    }
    if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {
      for (let i = 0; i < arr.length; ++i) {
        flatten(arr[i], result, skipTypedArray);
      }
    } else {
      result.push(arr);
    }
    return result;
  }
  function sizeFromShape(shape) {
    if (shape.length === 0) {
      return 1;
    }
    let size = shape[0];
    for (let i = 1; i < shape.length; i++) {
      size *= shape[i];
    }
    return size;
  }
  function isScalarShape(shape) {
    return shape.length === 0;
  }
  function arraysEqual(n1, n2) {
    if (n1 === n2) {
      return true;
    }
    if (n1 == null || n2 == null) {
      return false;
    }
    if (n1.length !== n2.length) {
      return false;
    }
    for (let i = 0; i < n1.length; i++) {
      if (n1[i] !== n2[i]) {
        return false;
      }
    }
    return true;
  }
  function isInt(a) {
    return a % 1 === 0;
  }
  function tanh(x) {
    if (Math.tanh != null) {
      return Math.tanh(x);
    }
    if (x === Infinity) {
      return 1;
    } else if (x === -Infinity) {
      return -1;
    } else {
      const e2x = Math.exp(2 * x);
      return (e2x - 1) / (e2x + 1);
    }
  }
  function sizeToSquarishShape(size) {
    const width = Math.ceil(Math.sqrt(size));
    return [width, Math.ceil(size / width)];
  }
  function createShuffledIndices(n) {
    const shuffledIndices = new Uint32Array(n);
    for (let i = 0; i < n; ++i) {
      shuffledIndices[i] = i;
    }
    shuffle(shuffledIndices);
    return shuffledIndices;
  }
  function rightPad(a, size) {
    if (size <= a.length) {
      return a;
    }
    return a + " ".repeat(size - a.length);
  }
  function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {
    return new Promise((resolve, reject) => {
      let tryCount = 0;
      const tryFn = () => {
        if (checkFn()) {
          resolve();
          return;
        }
        tryCount++;
        const nextBackoff = delayFn(tryCount);
        if (maxCounter != null && tryCount >= maxCounter) {
          reject();
          return;
        }
        setTimeout(tryFn, nextBackoff);
      };
      tryFn();
    });
  }
  function inferFromImplicitShape(shape, size) {
    let shapeProd = 1;
    let implicitIdx = -1;
    for (let i = 0; i < shape.length; ++i) {
      if (shape[i] >= 0) {
        shapeProd *= shape[i];
      } else if (shape[i] === -1) {
        if (implicitIdx !== -1) {
          throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`);
        }
        implicitIdx = i;
      } else if (shape[i] < 0) {
        throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);
      }
    }
    if (implicitIdx === -1) {
      if (size > 0 && size !== shapeProd) {
        throw Error(`Size(${size}) must match the product of shape ${shape}`);
      }
      return shape;
    }
    if (shapeProd === 0) {
      throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`);
    }
    if (size % shapeProd !== 0) {
      throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`);
    }
    const newShape = shape.slice();
    newShape[implicitIdx] = size / shapeProd;
    return newShape;
  }
  function parseAxisParam(axis, shape) {
    const rank = shape.length;
    axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);
    assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`);
    assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`);
    return axis.map((a) => a < 0 ? rank + a : a);
  }
  function squeezeShape(shape, axis) {
    const newShape = [];
    const keptDims = [];
    const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;
    const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort();
    let j = 0;
    for (let i = 0; i < shape.length; ++i) {
      if (axes != null) {
        if (axes[j] === i && shape[i] !== 1) {
          throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);
        }
        if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {
          newShape.push(shape[i]);
          keptDims.push(i);
        }
        if (axes[j] <= i) {
          j++;
        }
      }
      if (shape[i] !== 1) {
        newShape.push(shape[i]);
        keptDims.push(i);
      }
    }
    return {newShape, keptDims};
  }
  function getTypedArrayFromDType(dtype, size) {
    let values = null;
    if (dtype == null || dtype === "float32") {
      values = new Float32Array(size);
    } else if (dtype === "int32") {
      values = new Int32Array(size);
    } else if (dtype === "bool") {
      values = new Uint8Array(size);
    } else {
      throw new Error(`Unknown data type ${dtype}`);
    }
    return values;
  }
  function getArrayFromDType(dtype, size) {
    let values = null;
    if (dtype == null || dtype === "float32") {
      values = new Float32Array(size);
    } else if (dtype === "int32") {
      values = new Int32Array(size);
    } else if (dtype === "bool") {
      values = new Uint8Array(size);
    } else if (dtype === "string") {
      values = new Array(size);
    } else {
      throw new Error(`Unknown data type ${dtype}`);
    }
    return values;
  }
  function checkConversionForErrors(vals, dtype) {
    for (let i = 0; i < vals.length; i++) {
      const num = vals[i];
      if (isNaN(num) || !isFinite(num)) {
        throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);
      }
    }
  }
  function isValidDtype(dtype) {
    return dtype === "bool" || dtype === "complex64" || dtype === "float32" || dtype === "int32" || dtype === "string";
  }
  function hasEncodingLoss(oldType, newType) {
    if (newType === "complex64") {
      return false;
    }
    if (newType === "float32" && oldType !== "complex64") {
      return false;
    }
    if (newType === "int32" && oldType !== "float32" && oldType !== "complex64") {
      return false;
    }
    if (newType === "bool" && oldType === "bool") {
      return false;
    }
    return true;
  }
  function isTypedArray(a) {
    return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array;
  }
  function bytesPerElement(dtype) {
    if (dtype === "float32" || dtype === "int32") {
      return 4;
    } else if (dtype === "complex64") {
      return 8;
    } else if (dtype === "bool") {
      return 1;
    } else {
      throw new Error(`Unknown dtype ${dtype}`);
    }
  }
  function bytesFromStringArray(arr) {
    if (arr == null) {
      return 0;
    }
    let bytes = 0;
    arr.forEach((x) => bytes += x.length);
    return bytes;
  }
  function isString(value) {
    return typeof value === "string" || value instanceof String;
  }
  function isBoolean(value) {
    return typeof value === "boolean";
  }
  function isNumber(value) {
    return typeof value === "number";
  }
  function inferDtype(values) {
    if (Array.isArray(values)) {
      return inferDtype(values[0]);
    }
    if (values instanceof Float32Array) {
      return "float32";
    } else if (values instanceof Int32Array || values instanceof Uint8Array) {
      return "int32";
    } else if (isNumber(values)) {
      return "float32";
    } else if (isString(values)) {
      return "string";
    } else if (isBoolean(values)) {
      return "bool";
    }
    return "float32";
  }
  function isFunction(f) {
    return !!(f && f.constructor && f.call && f.apply);
  }
  function nearestDivisor(size, start) {
    for (let i = start; i < size; ++i) {
      if (size % i === 0) {
        return i;
      }
    }
    return size;
  }
  function computeStrides(shape) {
    const rank = shape.length;
    if (rank < 2) {
      return [];
    }
    const strides = new Array(rank - 1);
    strides[rank - 2] = shape[rank - 1];
    for (let i = rank - 3; i >= 0; --i) {
      strides[i] = strides[i + 1] * shape[i + 1];
    }
    return strides;
  }
  function createNestedArray(offset, shape, a) {
    const ret = new Array();
    if (shape.length === 1) {
      const d = shape[0];
      for (let i = 0; i < d; i++) {
        ret[i] = a[offset + i];
      }
    } else {
      const d = shape[0];
      const rest = shape.slice(1);
      const len = rest.reduce((acc, c) => acc * c);
      for (let i = 0; i < d; i++) {
        ret[i] = createNestedArray(offset + i * len, rest, a);
      }
    }
    return ret;
  }
  function toNestedArray(shape, a) {
    if (shape.length === 0) {
      return a[0];
    }
    const size = shape.reduce((acc, c) => acc * c);
    if (size === 0) {
      return [];
    }
    if (size !== a.length) {
      throw new Error(`[${shape}] does not match the input size ${a.length}.`);
    }
    return createNestedArray(0, shape, a);
  }
  function makeOnesTypedArray(size, dtype) {
    const array2 = makeZerosTypedArray(size, dtype);
    for (let i = 0; i < array2.length; i++) {
      array2[i] = 1;
    }
    return array2;
  }
  function makeZerosTypedArray(size, dtype) {
    if (dtype == null || dtype === "float32" || dtype === "complex64") {
      return new Float32Array(size);
    } else if (dtype === "int32") {
      return new Int32Array(size);
    } else if (dtype === "bool") {
      return new Uint8Array(size);
    } else {
      throw new Error(`Unknown data type ${dtype}`);
    }
  }
  function makeZerosNestedTypedArray(shape, dtype) {
    const size = shape.reduce((prev, curr) => prev * curr, 1);
    if (dtype == null || dtype === "float32") {
      return toNestedArray(shape, new Float32Array(size));
    } else if (dtype === "int32") {
      return toNestedArray(shape, new Int32Array(size));
    } else if (dtype === "bool") {
      return toNestedArray(shape, new Uint8Array(size));
    } else {
      throw new Error(`Unknown data type ${dtype}`);
    }
  }
  function assertNonNegativeIntegerDimensions(shape) {
    shape.forEach((dimSize) => {
      assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`);
    });
  }
  function locToIndex(locs, rank, strides) {
    if (rank === 0) {
      return 0;
    } else if (rank === 1) {
      return locs[0];
    }
    let index = locs[locs.length - 1];
    for (let i = 0; i < locs.length - 1; ++i) {
      index += strides[i] * locs[i];
    }
    return index;
  }
  function indexToLoc(index, rank, strides) {
    if (rank === 0) {
      return [];
    } else if (rank === 1) {
      return [index];
    }
    const locs = new Array(rank);
    for (let i = 0; i < locs.length - 1; ++i) {
      locs[i] = Math.floor(index / strides[i]);
      index -= locs[i] * strides[i];
    }
    locs[locs.length - 1] = index;
    return locs;
  }
  function isPromise(object) {
    return object && object.then && typeof object.then === "function";
  }
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags";
  var Environment = class {
    constructor(global2) {
      this.global = global2;
      this.flags = {};
      this.flagRegistry = {};
      this.urlFlags = {};
      this.populateURLFlags();
    }
    setPlatform(platformName, platform) {
      if (this.platform != null) {
        console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platform}.`);
      }
      this.platformName = platformName;
      this.platform = platform;
    }
    registerFlag(flagName, evaluationFn, setHook) {
      this.flagRegistry[flagName] = {evaluationFn, setHook};
      if (this.urlFlags[flagName] != null) {
        const flagValue = this.urlFlags[flagName];
        console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);
        this.set(flagName, flagValue);
      }
    }
    async getAsync(flagName) {
      if (flagName in this.flags) {
        return this.flags[flagName];
      }
      this.flags[flagName] = await this.evaluateFlag(flagName);
      return this.flags[flagName];
    }
    get(flagName) {
      if (flagName in this.flags) {
        return this.flags[flagName];
      }
      const flagValue = this.evaluateFlag(flagName);
      if (isPromise(flagValue)) {
        throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`);
      }
      this.flags[flagName] = flagValue;
      return this.flags[flagName];
    }
    getNumber(flagName) {
      return this.get(flagName);
    }
    getBool(flagName) {
      return this.get(flagName);
    }
    getFlags() {
      return this.flags;
    }
    get features() {
      return this.flags;
    }
    set(flagName, value) {
      if (this.flagRegistry[flagName] == null) {
        throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);
      }
      this.flags[flagName] = value;
      if (this.flagRegistry[flagName].setHook != null) {
        this.flagRegistry[flagName].setHook(value);
      }
    }
    evaluateFlag(flagName) {
      if (this.flagRegistry[flagName] == null) {
        throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);
      }
      return this.flagRegistry[flagName].evaluationFn();
    }
    setFlags(flags) {
      this.flags = Object.assign({}, flags);
    }
    reset() {
      this.flags = {};
      this.urlFlags = {};
      this.populateURLFlags();
    }
    populateURLFlags() {
      if (typeof this.global === "undefined" || typeof this.global.location === "undefined" || typeof this.global.location.search === "undefined") {
        return;
      }
      const urlParams = getQueryParams(this.global.location.search);
      if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
        const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(",");
        keyValues.forEach((keyValue) => {
          const [key, value] = keyValue.split(":");
          this.urlFlags[key] = parseValue(key, value);
        });
      }
    }
  };
  function getQueryParams(queryString) {
    const params = {};
    queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {
      decodeParam(params, t[0], t[1]);
      return t.join("=");
    });
    return params;
  }
  function decodeParam(params, name, value) {
    params[decodeURIComponent(name)] = decodeURIComponent(value || "");
  }
  function parseValue(flagName, value) {
    value = value.toLowerCase();
    if (value === "true" || value === "false") {
      return value === "true";
    } else if (`${+value}` === value) {
      return +value;
    }
    throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);
  }
  function env() {
    return ENV;
  }
  var ENV = null;
  function setEnvironmentGlobal(environment2) {
    ENV = environment2;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var globalNameSpace;
  function getGlobalNamespace() {
    if (globalNameSpace == null) {
      let ns;
      if (typeof window !== "undefined") {
        ns = window;
      } else if (typeof global !== "undefined") {
        ns = global;
      } else if (typeof process !== "undefined") {
        ns = process;
      } else if (typeof self !== "undefined") {
        ns = self;
      } else {
        throw new Error("Could not find a global object");
      }
      globalNameSpace = ns;
    }
    return globalNameSpace;
  }
  function getGlobalMap() {
    const ns = getGlobalNamespace();
    if (ns._tfGlobals == null) {
      ns._tfGlobals = new Map();
    }
    return ns._tfGlobals;
  }
  function getGlobal(key, init2) {
    const globalMap = getGlobalMap();
    if (globalMap.has(key)) {
      return globalMap.get(key);
    } else {
      const singleton = init2();
      globalMap.set(key, singleton);
      return globalMap.get(key);
    }
  }
  var Abs = "Abs";
  var Acos = "Acos";
  var Acosh = "Acosh";
  var Add = "Add";
  var AddN = "AddN";
  var All = "All";
  var Any = "Any";
  var ArgMax = "ArgMax";
  var ArgMin = "ArgMin";
  var Asin = "Asin";
  var Asinh = "Asinh";
  var Atan = "Atan";
  var Atanh = "Atanh";
  var Atan2 = "Atan2";
  var AvgPool = "AvgPool";
  var AvgPoolGrad = "AvgPoolGrad";
  var AvgPool3D = "AvgPool3D";
  var AvgPool3DGrad = "AvgPool3DGrad";
  var BatchMatMul = "BatchMatMul";
  var BatchToSpaceND = "BatchToSpaceND";
  var Bincount = "Bincount";
  var BroadcastTo = "BroadcastTo";
  var Cast = "Cast";
  var Ceil = "Ceil";
  var ClipByValue = "ClipByValue";
  var Complex = "Complex";
  var ComplexAbs = "ComplexAbs";
  var Concat = "Concat";
  var Conv2D = "Conv2D";
  var Conv2DBackpropFilter = "Conv2DBackpropFilter";
  var Conv2DBackpropInput = "Conv2DBackpropInput";
  var Conv3D = "Conv3D";
  var Conv3DBackpropFilterV2 = "Conv3DBackpropFilterV2";
  var Conv3DBackpropInputV2 = "Conv3DBackpropInputV2";
  var Cos = "Cos";
  var Cosh = "Cosh";
  var Cumsum = "Cumsum";
  var CropAndResize = "CropAndResize";
  var DenseBincount = "DenseBincount";
  var DepthToSpace = "DepthToSpace";
  var DepthwiseConv2dNative = "DepthwiseConv2dNative";
  var DepthwiseConv2dNativeBackpropFilter = "DepthwiseConv2dNativeBackpropFilter";
  var DepthwiseConv2dNativeBackpropInput = "DepthwiseConv2dNativeBackpropInput";
  var Diag = "Diag";
  var Dilation2D = "Dilation2D";
  var Dilation2DBackpropInput = "Dilation2DBackpropInput";
  var Dilation2DBackpropFilter = "Dilation2DBackpropFilter";
  var RealDiv = "RealDiv";
  var Elu = "Elu";
  var EluGrad = "EluGrad";
  var Erf = "Erf";
  var Equal = "Equal";
  var Exp = "Exp";
  var ExpandDims = "ExpandDims";
  var Expm1 = "Expm1";
  var FFT = "FFT";
  var Fill = "Fill";
  var FlipLeftRight = "FlipLeftRight";
  var Floor = "Floor";
  var FloorDiv = "FloorDiv";
  var FusedBatchNorm = "FusedBatchNorm";
  var GatherV2 = "GatherV2";
  var GatherNd = "GatherNd";
  var Greater = "Greater";
  var GreaterEqual = "GreaterEqual";
  var Identity = "Identity";
  var IFFT = "IFFT";
  var Imag = "Imag";
  var IsFinite = "IsFinite";
  var IsInf = "IsInf";
  var IsNan = "IsNan";
  var LeakyRelu = "LeakyRelu";
  var Less = "Less";
  var LessEqual = "LessEqual";
  var LinSpace = "LinSpace";
  var Log = "Log";
  var Log1p = "Log1p";
  var LogicalAnd = "LogicalAnd";
  var LogicalNot = "LogicalNot";
  var LogicalOr = "LogicalOr";
  var LogSoftmax = "LogSoftmax";
  var LRN = "LRN";
  var LRNGrad = "LRNGrad";
  var Max = "Max";
  var Maximum = "Maximum";
  var MaxPool = "MaxPool";
  var MaxPoolGrad = "MaxPoolGrad";
  var MaxPool3D = "MaxPool3D";
  var MaxPool3DGrad = "MaxPool3DGrad";
  var MaxPoolWithArgmax = "MaxPoolWithArgmax";
  var Mean = "Mean";
  var Min = "Min";
  var Minimum = "Minimum";
  var MirrorPad = "MirrorPad";
  var Mod = "Mod";
  var Multinomial = "Multinomial";
  var Multiply = "Multiply";
  var Neg = "Neg";
  var NotEqual = "NotEqual";
  var NonMaxSuppressionV3 = "NonMaxSuppressionV3";
  var NonMaxSuppressionV4 = "NonMaxSuppressionV4";
  var NonMaxSuppressionV5 = "NonMaxSuppressionV5";
  var OnesLike = "OnesLike";
  var OneHot = "OneHot";
  var Pack = "Pack";
  var PadV2 = "PadV2";
  var Pool = "Pool";
  var Pow = "Pow";
  var Prelu = "Prelu";
  var Prod = "Prod";
  var Range = "Range";
  var Real = "Real";
  var Reciprocal = "Reciprocal";
  var Relu = "Relu";
  var Reshape = "Reshape";
  var ResizeNearestNeighbor = "ResizeNearestNeighbor";
  var ResizeNearestNeighborGrad = "ResizeNearestNeighborGrad";
  var ResizeBilinear = "ResizeBilinear";
  var ResizeBilinearGrad = "ResizeBilinearGrad";
  var Relu6 = "Relu6";
  var Reverse = "Reverse";
  var Round = "Round";
  var Rsqrt = "Rsqrt";
  var ScatterNd = "ScatterNd";
  var Select = "Select";
  var Selu = "Selu";
  var Slice = "Slice";
  var Sin = "Sin";
  var Sinh = "Sinh";
  var Sign = "Sign";
  var Sigmoid = "Sigmoid";
  var Softplus = "Softplus";
  var Sqrt = "Sqrt";
  var Sum = "Sum";
  var SpaceToBatchND = "SpaceToBatchND";
  var SplitV = "SplitV";
  var Softmax = "Softmax";
  var SquaredDifference = "SquaredDifference";
  var Square = "Square";
  var Sub = "Sub";
  var SparseToDense = "SparseToDense";
  var StridedSlice = "StridedSlice";
  var Tan = "Tan";
  var Tanh = "Tanh";
  var Tile = "Tile";
  var TopK = "TopK";
  var Transform = "Transform";
  var Transpose = "Transpose";
  var Unique = "Unique";
  var Unpack = "Unpack";
  var UnsortedSegmentSum = "UnsortedSegmentSum";
  var ZerosLike = "ZerosLike";
  var Step = "Step";
  var FromPixels = "FromPixels";
  var RotateWithOffset = "RotateWithOffset";
  var _FusedMatMul = "_FusedMatMul";
  var FusedConv2D = "FusedConv2D";
  var FusedDepthwiseConv2D = "FusedDepthwiseConv2D";
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var kernelRegistry = getGlobal("kernelRegistry", () => new Map());
  var gradRegistry = getGlobal("gradRegistry", () => new Map());
  function getKernel(kernelName, backendName) {
    const key = makeKey(kernelName, backendName);
    return kernelRegistry.get(key);
  }
  function getGradient(kernelName) {
    return gradRegistry.get(kernelName);
  }
  function getKernelsForBackend(backendName) {
    const it = kernelRegistry.entries();
    const result = [];
    while (true) {
      const {done, value} = it.next();
      if (done) {
        break;
      }
      const [key, config] = value;
      const [backend2] = key.split("_");
      if (backend2 === backendName) {
        result.push(config);
      }
    }
    return result;
  }
  function registerKernel(config) {
    const {kernelName, backendName} = config;
    const key = makeKey(kernelName, backendName);
    if (kernelRegistry.has(key)) {
      console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`);
    }
    kernelRegistry.set(key, config);
  }
  function registerGradient(config) {
    const {kernelName} = config;
    if (gradRegistry.has(kernelName)) {
      if (env().getBool("DEBUG")) {
        console.warn(`Overriding the gradient for '${kernelName}'`);
      }
    }
    gradRegistry.set(kernelName, config);
  }
  function unregisterKernel(kernelName, backendName) {
    const key = makeKey(kernelName, backendName);
    if (!kernelRegistry.has(key)) {
      throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);
    }
    kernelRegistry.delete(key);
  }
  function unregisterGradient(kernelName) {
    if (!gradRegistry.has(kernelName)) {
      throw new Error(`The gradient '${kernelName}' for backend is not registered`);
    }
    gradRegistry.delete(kernelName);
  }
  function copyRegisteredKernels(registeredBackendName, newBackendName) {
    const kernels = getKernelsForBackend(registeredBackendName);
    kernels.forEach((kernelConfig) => {
      const newKernelConfig = Object.assign({}, kernelConfig, {backendName: newBackendName});
      registerKernel(newKernelConfig);
    });
  }
  function makeKey(kernelName, backendName) {
    return `${backendName}_${kernelName}`;
  }
  var util_exports = {};
  __export2(util_exports, {
    arraysEqual: () => arraysEqual,
    assert: () => assert,
    assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions,
    assertNonNull: () => assertNonNull,
    assertShapesMatch: () => assertShapesMatch,
    bytesFromStringArray: () => bytesFromStringArray,
    bytesPerElement: () => bytesPerElement,
    checkConversionForErrors: () => checkConversionForErrors,
    clamp: () => clamp,
    computeStrides: () => computeStrides,
    createScalarValue: () => createScalarValue,
    createShuffledIndices: () => createShuffledIndices,
    decodeString: () => decodeString,
    distSquared: () => distSquared,
    encodeString: () => encodeString,
    fetch: () => fetch2,
    flatten: () => flatten,
    getArrayFromDType: () => getArrayFromDType,
    getTypedArrayFromDType: () => getTypedArrayFromDType,
    hasEncodingLoss: () => hasEncodingLoss,
    indexToLoc: () => indexToLoc,
    inferDtype: () => inferDtype,
    inferFromImplicitShape: () => inferFromImplicitShape,
    isBoolean: () => isBoolean,
    isFunction: () => isFunction,
    isInt: () => isInt,
    isNumber: () => isNumber,
    isPromise: () => isPromise,
    isScalarShape: () => isScalarShape,
    isString: () => isString,
    isTypedArray: () => isTypedArray,
    isValidDtype: () => isValidDtype,
    locToIndex: () => locToIndex,
    makeOnesTypedArray: () => makeOnesTypedArray,
    makeZerosNestedTypedArray: () => makeZerosNestedTypedArray,
    makeZerosTypedArray: () => makeZerosTypedArray,
    nearestDivisor: () => nearestDivisor,
    nearestLargerEven: () => nearestLargerEven,
    now: () => now,
    parseAxisParam: () => parseAxisParam,
    randUniform: () => randUniform,
    repeatedTry: () => repeatedTry,
    rightPad: () => rightPad,
    shuffle: () => shuffle,
    shuffleCombo: () => shuffleCombo,
    sizeFromShape: () => sizeFromShape,
    sizeToSquarishShape: () => sizeToSquarishShape,
    squeezeShape: () => squeezeShape,
    sum: () => sum,
    tanh: () => tanh,
    toNestedArray: () => toNestedArray,
    toTypedArray: () => toTypedArray
  });
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function createScalarValue(value, dtype) {
    if (dtype === "string") {
      return encodeString(value);
    }
    return toTypedArray([value], dtype);
  }
  function noConversionNeeded(a, dtype) {
    return a instanceof Float32Array && dtype === "float32" || a instanceof Int32Array && dtype === "int32" || a instanceof Uint8Array && dtype === "bool";
  }
  function toTypedArray(a, dtype) {
    if (dtype === "string") {
      throw new Error("Cannot convert a string[] to a TypedArray");
    }
    if (Array.isArray(a)) {
      a = flatten(a);
    }
    if (env().getBool("DEBUG")) {
      checkConversionForErrors(a, dtype);
    }
    if (noConversionNeeded(a, dtype)) {
      return a;
    }
    if (dtype == null || dtype === "float32" || dtype === "complex64") {
      return new Float32Array(a);
    } else if (dtype === "int32") {
      return new Int32Array(a);
    } else if (dtype === "bool") {
      const bool = new Uint8Array(a.length);
      for (let i = 0; i < bool.length; ++i) {
        if (Math.round(a[i]) !== 0) {
          bool[i] = 1;
        }
      }
      return bool;
    } else {
      throw new Error(`Unknown data type ${dtype}`);
    }
  }
  function now() {
    return env().platform.now();
  }
  function fetch2(path, requestInits) {
    return env().platform.fetch(path, requestInits);
  }
  function encodeString(s, encoding = "utf-8") {
    encoding = encoding || "utf-8";
    return env().platform.encode(s, encoding);
  }
  function decodeString(bytes, encoding = "utf-8") {
    encoding = encoding || "utf-8";
    return env().platform.decode(bytes, encoding);
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var Profiler = class {
    constructor(backendTimer, logger) {
      this.backendTimer = backendTimer;
      this.logger = logger;
      if (logger == null) {
        this.logger = new Logger();
      }
    }
    profileKernel(kernelName, inputs, f) {
      let outputs;
      const holdResultWrapperFn = () => {
        outputs = f();
      };
      let timer;
      const start = now();
      if (this.backendTimer.timerAvailable()) {
        timer = this.backendTimer.time(holdResultWrapperFn);
      } else {
        holdResultWrapperFn();
        for (const output of outputs) {
          output.dataSync();
        }
        timer = Promise.resolve({kernelMs: now() - start});
      }
      if (env().getBool("CHECK_COMPUTATION_FOR_ERRORS")) {
        for (let i = 0; i < outputs.length; i++) {
          const output = outputs[i];
          output.data().then((tensorVals) => {
            checkComputationForErrors(tensorVals, output.dtype, kernelName);
          });
        }
      }
      const kernelProfile = {
        kernelName,
        outputs,
        inputs,
        timeMs: timer.then((timing) => timing.kernelMs),
        extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : "")
      };
      return kernelProfile;
    }
    logKernelProfile(kernelProfile) {
      const {kernelName, outputs, timeMs, inputs, extraInfo} = kernelProfile;
      outputs.forEach((result) => {
        Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => {
          this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);
        });
      });
    }
  };
  function checkComputationForErrors(vals, dtype, kernelName) {
    if (dtype !== "float32") {
      return false;
    }
    for (let i = 0; i < vals.length; i++) {
      const num = vals[i];
      if (isNaN(num) || !isFinite(num)) {
        console.warn(`Found ${num} in the result of '${kernelName}'`);
        return true;
      }
    }
    return false;
  }
  var Logger = class {
    logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {
      const time2 = typeof timeMs === "number" ? rightPad(`${timeMs}ms`, 9) : timeMs["error"];
      const paddedName = rightPad(name, 25);
      const rank = result.rank;
      const size = result.size;
      const shape = rightPad(result.shape.toString(), 14);
      let inputShapesDescription = "";
      for (const name2 in inputs) {
        const input2 = inputs[name2];
        if (input2 != null) {
          const inputShape = input2.shape || result.shape;
          const inputRank = inputShape.length;
          inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : ""} `;
        }
      }
      console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
    }
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function getFilteredNodesXToY(tape, xs, y) {
    const tensorsFromX = {};
    const nodesFromX = {};
    for (let i = 0; i < xs.length; i++) {
      tensorsFromX[xs[i].id] = true;
    }
    for (let i = 0; i < tape.length; i++) {
      const node2 = tape[i];
      const nodeInputs = node2.inputs;
      for (const inputName in nodeInputs) {
        const input2 = nodeInputs[inputName];
        let anyInputFromX = false;
        for (let j = 0; j < xs.length; j++) {
          if (tensorsFromX[input2.id]) {
            node2.outputs.forEach((output) => tensorsFromX[output.id] = true);
            anyInputFromX = true;
            nodesFromX[node2.id] = true;
            break;
          }
        }
        if (anyInputFromX) {
          break;
        }
      }
    }
    const tensorsLeadToY = {};
    tensorsLeadToY[y.id] = true;
    const nodesToY = {};
    for (let i = tape.length - 1; i >= 0; i--) {
      const node2 = tape[i];
      const nodeInputs = node2.inputs;
      for (let j = 0; j < node2.outputs.length; j++) {
        if (tensorsLeadToY[node2.outputs[j].id]) {
          for (const inputName in nodeInputs) {
            tensorsLeadToY[nodeInputs[inputName].id] = true;
            nodesToY[node2.id] = true;
          }
          break;
        }
      }
    }
    const filteredTape = [];
    for (let i = 0; i < tape.length; i++) {
      const node2 = tape[i];
      if (nodesFromX[node2.id] && nodesToY[node2.id]) {
        const prunedInputs = {};
        for (const inputName in node2.inputs) {
          const nodeInput = node2.inputs[inputName];
          if (tensorsFromX[nodeInput.id]) {
            prunedInputs[inputName] = nodeInput;
          }
        }
        const prunedNode = Object.assign({}, node2);
        prunedNode.inputs = prunedInputs;
        prunedNode.outputs = node2.outputs;
        filteredTape.push(prunedNode);
      }
    }
    return filteredTape;
  }
  function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add5) {
    for (let i = filteredTape.length - 1; i >= 0; i--) {
      const node2 = filteredTape[i];
      const dys = [];
      node2.outputs.forEach((o) => {
        const gradTensor = tensorAccumulatedGradientMap[o.id];
        if (gradTensor != null) {
          dys.push(gradTensor);
        } else {
          dys.push(null);
        }
      });
      if (node2.gradient == null) {
        throw new Error(`Cannot compute gradient: gradient function not found for ${node2.kernelName}.`);
      }
      const inputGradients = node2.gradient(dys);
      for (const inputName in node2.inputs) {
        if (!(inputName in inputGradients)) {
          throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);
        }
        const dx = tidy2(() => inputGradients[inputName]());
        if (dx.dtype !== "float32") {
          throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);
        }
        const x = node2.inputs[inputName];
        if (!arraysEqual(dx.shape, x.shape)) {
          throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);
        }
        if (tensorAccumulatedGradientMap[x.id] == null) {
          tensorAccumulatedGradientMap[x.id] = dx;
        } else {
          const curGradient = tensorAccumulatedGradientMap[x.id];
          tensorAccumulatedGradientMap[x.id] = add5(curGradient, dx);
          curGradient.dispose();
        }
      }
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var FORMAT_LIMIT_NUM_VALS = 20;
  var FORMAT_NUM_FIRST_LAST_VALS = 3;
  var FORMAT_NUM_SIG_DIGITS = 7;
  function tensorToString(vals, shape, dtype, verbose) {
    const strides = computeStrides(shape);
    const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);
    const rank = shape.length;
    const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);
    const lines = ["Tensor"];
    if (verbose) {
      lines.push(`  dtype: ${dtype}`);
      lines.push(`  rank: ${rank}`);
      lines.push(`  shape: [${shape}]`);
      lines.push(`  values:`);
    }
    lines.push(valsLines.map((l) => "    " + l).join("\n"));
    return lines.join("\n");
  }
  function computeMaxSizePerColumn(vals, shape, dtype, strides) {
    const n = sizeFromShape(shape);
    const numCols = strides[strides.length - 1];
    const padPerCol = new Array(numCols).fill(0);
    const rank = shape.length;
    const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals;
    if (rank > 1) {
      for (let row = 0; row < n / numCols; row++) {
        const offset = row * numCols;
        for (let j = 0; j < numCols; j++) {
          padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);
        }
      }
    }
    return padPerCol;
  }
  function valToString(val, pad3, dtype) {
    let valStr;
    if (Array.isArray(val)) {
      valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;
    } else if (isString(val)) {
      valStr = `'${val}'`;
    } else if (dtype === "bool") {
      valStr = boolNumToString(val);
    } else {
      valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();
    }
    return rightPad(valStr, pad3);
  }
  function boolNumToString(v) {
    return v === 0 ? "false" : "true";
  }
  function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {
    const storagePerElement = dtype === "complex64" ? 2 : 1;
    const size = shape[0];
    const rank = shape.length;
    if (rank === 0) {
      if (dtype === "complex64") {
        const complexTuple = createComplexTuples(vals);
        return [valToString(complexTuple[0], 0, dtype)];
      }
      if (dtype === "bool") {
        return [boolNumToString(vals[0])];
      }
      return [vals[0].toString()];
    }
    if (rank === 1) {
      if (size > FORMAT_LIMIT_NUM_VALS) {
        const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;
        let firstVals = Array.from(vals.slice(0, firstValsSize));
        let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));
        if (dtype === "complex64") {
          firstVals = createComplexTuples(firstVals);
          lastVals = createComplexTuples(lastVals);
        }
        return [
          "[" + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + ", ..., " + lastVals.map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(", ") + "]"
        ];
      }
      const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals);
      return [
        "[" + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + "]"
      ];
    }
    const subshape = shape.slice(1);
    const substrides = strides.slice(1);
    const stride = strides[0] * storagePerElement;
    const lines = [];
    if (size > FORMAT_LIMIT_NUM_VALS) {
      for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {
        const start = i * stride;
        const end = start + stride;
        lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));
      }
      lines.push("...");
      for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {
        const start = i * stride;
        const end = start + stride;
        lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
      }
    } else {
      for (let i = 0; i < size; i++) {
        const start = i * stride;
        const end = start + stride;
        lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
      }
    }
    const sep = rank === 2 ? "," : "";
    lines[0] = "[" + lines[0] + sep;
    for (let i = 1; i < lines.length - 1; i++) {
      lines[i] = " " + lines[i] + sep;
    }
    let newLineSep = ",\n";
    for (let i = 2; i < rank; i++) {
      newLineSep += "\n";
    }
    lines[lines.length - 1] = " " + lines[lines.length - 1] + "]" + (isLast ? "" : newLineSep);
    return lines;
  }
  function createComplexTuples(vals) {
    const complexTuples = [];
    for (let i = 0; i < vals.length; i += 2) {
      complexTuples.push([vals[i], vals[i + 1]]);
    }
    return complexTuples;
  }
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var TensorBuffer = class {
    constructor(shape, dtype, values) {
      this.dtype = dtype;
      this.shape = shape.slice();
      this.size = sizeFromShape(shape);
      if (values != null) {
        const n = values.length;
        assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`);
      }
      if (dtype === "complex64") {
        throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`);
      }
      this.values = values || getArrayFromDType(dtype, this.size);
      this.strides = computeStrides(shape);
    }
    set(value, ...locs) {
      if (locs.length === 0) {
        locs = [0];
      }
      assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);
      const index = this.locToIndex(locs);
      this.values[index] = value;
    }
    get(...locs) {
      if (locs.length === 0) {
        locs = [0];
      }
      let i = 0;
      for (const loc of locs) {
        if (loc < 0 || loc >= this.shape[i]) {
          const msg = `Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;
          throw new Error(msg);
        }
        i++;
      }
      let index = locs[locs.length - 1];
      for (let i2 = 0; i2 < locs.length - 1; ++i2) {
        index += this.strides[i2] * locs[i2];
      }
      return this.values[index];
    }
    locToIndex(locs) {
      if (this.rank === 0) {
        return 0;
      } else if (this.rank === 1) {
        return locs[0];
      }
      let index = locs[locs.length - 1];
      for (let i = 0; i < locs.length - 1; ++i) {
        index += this.strides[i] * locs[i];
      }
      return index;
    }
    indexToLoc(index) {
      if (this.rank === 0) {
        return [];
      } else if (this.rank === 1) {
        return [index];
      }
      const locs = new Array(this.shape.length);
      for (let i = 0; i < locs.length - 1; ++i) {
        locs[i] = Math.floor(index / this.strides[i]);
        index -= locs[i] * this.strides[i];
      }
      locs[locs.length - 1] = index;
      return locs;
    }
    get rank() {
      return this.shape.length;
    }
    toTensor() {
      return trackerFn().makeTensor(this.values, this.shape, this.dtype);
    }
  };
  var trackerFn = null;
  var opHandler = null;
  var deprecationWarningFn = null;
  function setTensorTracker(fn) {
    trackerFn = fn;
  }
  function setOpHandler(handler) {
    opHandler = handler;
  }
  function setDeprecationWarningFn(fn) {
    deprecationWarningFn = fn;
  }
  var Tensor = class {
    constructor(shape, dtype, dataId, id) {
      this.kept = false;
      this.isDisposedInternal = false;
      this.shape = shape.slice();
      this.dtype = dtype || "float32";
      this.size = sizeFromShape(shape);
      this.strides = computeStrides(shape);
      this.dataId = dataId;
      this.id = id;
      this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
    }
    get rank() {
      return this.shape.length;
    }
    async buffer() {
      const vals = await this.data();
      return opHandler.buffer(this.shape, this.dtype, vals);
    }
    bufferSync() {
      return opHandler.buffer(this.shape, this.dtype, this.dataSync());
    }
    async array() {
      const vals = await this.data();
      return toNestedArray(this.shape, vals);
    }
    arraySync() {
      return toNestedArray(this.shape, this.dataSync());
    }
    async data() {
      this.throwIfDisposed();
      const data = trackerFn().read(this.dataId);
      if (this.dtype === "string") {
        const bytes = await data;
        try {
          return bytes.map((b) => decodeString(b));
        } catch (_a) {
          throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
        }
      }
      return data;
    }
    dataSync() {
      this.throwIfDisposed();
      const data = trackerFn().readSync(this.dataId);
      if (this.dtype === "string") {
        try {
          return data.map((b) => decodeString(b));
        } catch (_a) {
          throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
        }
      }
      return data;
    }
    async bytes() {
      this.throwIfDisposed();
      const data = await trackerFn().read(this.dataId);
      if (this.dtype === "string") {
        return data;
      } else {
        return new Uint8Array(data.buffer);
      }
    }
    dispose() {
      if (this.isDisposed) {
        return;
      }
      trackerFn().disposeTensor(this);
      this.isDisposedInternal = true;
    }
    get isDisposed() {
      return this.isDisposedInternal;
    }
    throwIfDisposed() {
      if (this.isDisposed) {
        throw new Error(`Tensor is disposed.`);
      }
    }
    print(verbose = false) {
      return opHandler.print(this, verbose);
    }
    clone() {
      this.throwIfDisposed();
      return opHandler.clone(this);
    }
    toString(verbose = false) {
      const vals = this.dataSync();
      return tensorToString(vals, this.shape, this.dtype, verbose);
    }
    cast(dtype) {
      this.throwIfDisposed();
      return opHandler.cast(this, dtype);
    }
    variable(trainable = true, name, dtype) {
      this.throwIfDisposed();
      return trackerFn().makeVariable(this, trainable, name, dtype);
    }
  };
  Object.defineProperty(Tensor, Symbol.hasInstance, {
    value: (instance) => {
      return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null;
    }
  });
  function getGlobalTensorClass() {
    return getGlobal("Tensor", () => {
      return Tensor;
    });
  }
  getGlobalTensorClass();
  var Variable = class extends Tensor {
    constructor(initialValue, trainable, name, tensorId) {
      super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);
      this.trainable = trainable;
      this.name = name;
    }
    assign(newValue) {
      if (newValue.dtype !== this.dtype) {
        throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);
      }
      if (!arraysEqual(newValue.shape, this.shape)) {
        throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);
      }
      trackerFn().disposeTensor(this);
      this.dataId = newValue.dataId;
      trackerFn().incRef(this, null);
    }
    dispose() {
      trackerFn().disposeVariable(this);
      this.isDisposedInternal = true;
    }
  };
  Object.defineProperty(Variable, Symbol.hasInstance, {
    value: (instance) => {
      return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function;
    }
  });
  var tensor_util_exports = {};
  __export2(tensor_util_exports, {
    assertTypesMatch: () => assertTypesMatch,
    getTensorsInContainer: () => getTensorsInContainer,
    isTensorInList: () => isTensorInList,
    makeTypesMatch: () => makeTypesMatch
  });
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var Rank;
  (function(Rank2) {
    Rank2["R0"] = "R0";
    Rank2["R1"] = "R1";
    Rank2["R2"] = "R2";
    Rank2["R3"] = "R3";
    Rank2["R4"] = "R4";
    Rank2["R5"] = "R5";
    Rank2["R6"] = "R6";
  })(Rank || (Rank = {}));
  var UpcastInt32AndMap;
  (function(UpcastInt32AndMap2) {
    UpcastInt32AndMap2["float32"] = "float32";
    UpcastInt32AndMap2["int32"] = "int32";
    UpcastInt32AndMap2["bool"] = "int32";
    UpcastInt32AndMap2["complex64"] = "complex64";
  })(UpcastInt32AndMap || (UpcastInt32AndMap = {}));
  var UpcastBoolAndMap;
  (function(UpcastBoolAndMap2) {
    UpcastBoolAndMap2["float32"] = "float32";
    UpcastBoolAndMap2["int32"] = "int32";
    UpcastBoolAndMap2["bool"] = "bool";
    UpcastBoolAndMap2["complex64"] = "complex64";
  })(UpcastBoolAndMap || (UpcastBoolAndMap = {}));
  var UpcastFloat32AndMap;
  (function(UpcastFloat32AndMap2) {
    UpcastFloat32AndMap2["float32"] = "float32";
    UpcastFloat32AndMap2["int32"] = "float32";
    UpcastFloat32AndMap2["bool"] = "float32";
    UpcastFloat32AndMap2["complex64"] = "complex64";
  })(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));
  var UpcastComplex64AndMap;
  (function(UpcastComplex64AndMap2) {
    UpcastComplex64AndMap2["float32"] = "complex64";
    UpcastComplex64AndMap2["int32"] = "complex64";
    UpcastComplex64AndMap2["bool"] = "complex64";
    UpcastComplex64AndMap2["complex64"] = "complex64";
  })(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));
  var upcastTypeMap = {
    float32: UpcastFloat32AndMap,
    int32: UpcastInt32AndMap,
    bool: UpcastBoolAndMap,
    complex64: UpcastComplex64AndMap
  };
  function upcastType(typeA, typeB) {
    if (typeA === "string" || typeB === "string") {
      if (typeA === "string" && typeB === "string") {
        return "string";
      }
      throw new Error(`Can not upcast ${typeA} with ${typeB}`);
    }
    return upcastTypeMap[typeA][typeB];
  }
  function sumOutType(type) {
    return upcastType(type, "int32");
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function makeTypesMatch(a, b) {
    if (a.dtype === b.dtype) {
      return [a, b];
    }
    const dtype = upcastType(a.dtype, b.dtype);
    return [a.cast(dtype), b.cast(dtype)];
  }
  function assertTypesMatch(a, b) {
    assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`);
  }
  function isTensorInList(tensor2, tensorList) {
    return tensorList.some((x) => x.id === tensor2.id);
  }
  function getTensorsInContainer(result) {
    const list = [];
    const seen = new Set();
    walkTensorContainer(result, list, seen);
    return list;
  }
  function walkTensorContainer(container, list, seen) {
    if (container == null) {
      return;
    }
    if (container instanceof Tensor) {
      list.push(container);
      return;
    }
    if (!isIterable(container)) {
      return;
    }
    const iterable = container;
    for (const k in iterable) {
      const val = iterable[k];
      if (!seen.has(val)) {
        seen.add(val);
        walkTensorContainer(val, list, seen);
      }
    }
  }
  function isIterable(obj) {
    return Array.isArray(obj) || typeof obj === "object";
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function isRegisteredKernelInvocation(kernelInvocation) {
    return kernelInvocation.kernelName != null;
  }
  var EngineState = class {
    constructor() {
      this.registeredVariables = {};
      this.nextTapeNodeId = 0;
      this.numBytes = 0;
      this.numTensors = 0;
      this.numStringTensors = 0;
      this.numDataBuffers = 0;
      this.gradientDepth = 0;
      this.kernelDepth = 0;
      this.scopeStack = [];
      this.numDataMovesStack = [];
      this.nextScopeId = 0;
      this.tensorInfo = new WeakMap();
      this.profiling = false;
      this.activeProfile = {
        newBytes: 0,
        newTensors: 0,
        peakBytes: 0,
        kernels: [],
        result: null,
        get kernelNames() {
          return Array.from(new Set(this.kernels.map((k) => k.name)));
        }
      };
    }
    dispose() {
      for (const variableName in this.registeredVariables) {
        this.registeredVariables[variableName].dispose();
      }
    }
  };
  var Engine = class {
    constructor(ENV5) {
      this.ENV = ENV5;
      this.registry = {};
      this.registryFactory = {};
      this.pendingBackendInitId = 0;
      this.state = new EngineState();
    }
    async ready() {
      if (this.pendingBackendInit != null) {
        return this.pendingBackendInit.then(() => {
        });
      }
      if (this.backendInstance != null) {
        return;
      }
      const sortedBackends = this.getSortedBackends();
      for (let i = 0; i < sortedBackends.length; i++) {
        const backendName = sortedBackends[i];
        const success = await this.initializeBackend(backendName).success;
        if (success) {
          await this.setBackend(backendName);
          return;
        }
      }
      throw new Error(`Could not initialize any backends, all backend initializations failed.`);
    }
    get backend() {
      if (this.pendingBackendInit != null) {
        throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
      }
      if (this.backendInstance == null) {
        const {name, asyncInit} = this.initializeBackendsAndReturnBest();
        if (asyncInit) {
          throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
        }
        this.setBackend(name);
      }
      return this.backendInstance;
    }
    backendNames() {
      return Object.keys(this.registryFactory);
    }
    findBackend(backendName) {
      if (!(backendName in this.registry)) {
        if (backendName in this.registryFactory) {
          const {asyncInit} = this.initializeBackend(backendName);
          if (asyncInit) {
            return null;
          }
        } else {
          return null;
        }
      }
      return this.registry[backendName];
    }
    findBackendFactory(backendName) {
      if (!(backendName in this.registryFactory)) {
        return null;
      }
      return this.registryFactory[backendName].factory;
    }
    registerBackend(backendName, factory, priority = 1) {
      if (backendName in this.registryFactory) {
        console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`);
        return false;
      }
      this.registryFactory[backendName] = {factory, priority};
      return true;
    }
    async setBackend(backendName) {
      if (this.registryFactory[backendName] == null) {
        throw new Error(`Backend name '${backendName}' not found in registry`);
      }
      this.backendName = backendName;
      if (this.registry[backendName] == null) {
        this.backendInstance = null;
        const {success, asyncInit} = this.initializeBackend(backendName);
        const result = asyncInit ? await success : success;
        if (!result) {
          return false;
        }
      }
      this.backendInstance = this.registry[backendName];
      this.setupRegisteredKernels();
      this.profiler = new Profiler(this.backendInstance);
      return true;
    }
    setupRegisteredKernels() {
      const kernels = getKernelsForBackend(this.backendName);
      kernels.forEach((kernel) => {
        if (kernel.setupFunc != null) {
          kernel.setupFunc(this.backendInstance);
        }
      });
    }
    disposeRegisteredKernels(backendName) {
      const kernels = getKernelsForBackend(backendName);
      kernels.forEach((kernel) => {
        if (kernel.disposeFunc != null) {
          kernel.disposeFunc(this.registry[backendName]);
        }
      });
    }
    initializeBackend(backendName) {
      const registryFactoryEntry = this.registryFactory[backendName];
      if (registryFactoryEntry == null) {
        throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);
      }
      try {
        const backend2 = registryFactoryEntry.factory();
        if (backend2 && !(backend2 instanceof KernelBackend) && typeof backend2.then === "function") {
          const promiseId = ++this.pendingBackendInitId;
          const success = backend2.then((backendInstance) => {
            if (promiseId < this.pendingBackendInitId) {
              return false;
            }
            this.registry[backendName] = backendInstance;
            this.pendingBackendInit = null;
            return true;
          }).catch((err) => {
            if (promiseId < this.pendingBackendInitId) {
              return false;
            }
            this.pendingBackendInit = null;
            console.warn(`Initialization of backend ${backendName} failed`);
            console.warn(err.stack || err.message);
            return false;
          });
          this.pendingBackendInit = success;
          return {success, asyncInit: true};
        } else {
          this.registry[backendName] = backend2;
          return {success: true, asyncInit: false};
        }
      } catch (err) {
        console.warn(`Initialization of backend ${backendName} failed`);
        console.warn(err.stack || err.message);
        return {success: false, asyncInit: false};
      }
    }
    removeBackend(backendName) {
      if (!(backendName in this.registryFactory)) {
        throw new Error(`${backendName} backend not found in registry`);
      }
      if (this.backendName === backendName && this.pendingBackendInit != null) {
        this.pendingBackendInitId++;
      }
      if (backendName in this.registry) {
        this.disposeRegisteredKernels(backendName);
        this.registry[backendName].dispose();
        delete this.registry[backendName];
      }
      delete this.registryFactory[backendName];
      if (this.backendName === backendName) {
        this.pendingBackendInit = null;
        this.backendName = null;
        this.backendInstance = null;
      }
    }
    getSortedBackends() {
      if (Object.keys(this.registryFactory).length === 0) {
        throw new Error("No backend found in registry.");
      }
      return Object.keys(this.registryFactory).sort((a, b) => {
        return this.registryFactory[b].priority - this.registryFactory[a].priority;
      });
    }
    initializeBackendsAndReturnBest() {
      const sortedBackends = this.getSortedBackends();
      for (let i = 0; i < sortedBackends.length; i++) {
        const backendName = sortedBackends[i];
        const {success, asyncInit} = this.initializeBackend(backendName);
        if (asyncInit || success) {
          return {name: backendName, asyncInit};
        }
      }
      throw new Error(`Could not initialize any backends, all backend initializations failed.`);
    }
    moveData(backend2, dataId) {
      const info = this.state.tensorInfo.get(dataId);
      const srcBackend = info.backend;
      const values = this.readSync(dataId);
      const refCount = srcBackend.refCount(dataId);
      srcBackend.disposeData(dataId, true);
      info.backend = backend2;
      backend2.move(dataId, values, info.shape, info.dtype, refCount);
      if (this.shouldCheckForMemLeaks()) {
        this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
      }
    }
    tidy(nameOrFn, fn) {
      let name = null;
      if (fn == null) {
        if (typeof nameOrFn !== "function") {
          throw new Error("Please provide a function to tidy()");
        }
        fn = nameOrFn;
      } else {
        if (typeof nameOrFn !== "string" && !(nameOrFn instanceof String)) {
          throw new Error("When calling with two arguments, the first argument to tidy() must be a string");
        }
        if (typeof fn !== "function") {
          throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");
        }
        name = nameOrFn;
      }
      let result;
      return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {
        result = fn();
        if (result instanceof Promise) {
          console.error("Cannot return a Promise inside of tidy.");
        }
        return result;
      });
    }
    scopedRun(start, end, f) {
      start();
      try {
        const res = f();
        end();
        return res;
      } catch (ex) {
        end();
        throw ex;
      }
    }
    nextTensorId() {
      return Engine.nextTensorId++;
    }
    nextVariableId() {
      return Engine.nextVariableId++;
    }
    clone(x) {
      const y = ENGINE.runKernel(Identity, {x});
      const inputs = {x};
      const grad2 = (dy) => ({
        x: () => {
          const dtype = "float32";
          const gradInputs = {x: dy};
          const attrs = {dtype};
          return ENGINE.runKernel(Cast, gradInputs, attrs);
        }
      });
      const saved = [];
      this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {});
      return y;
    }
    runKernel(kernelName, inputs, attrs) {
      const hasKernel = getKernel(kernelName, this.backendName) != null;
      if (!hasKernel) {
        throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`);
      }
      return this.runKernelFunc({kernelName, inputs, attrs});
    }
    shouldCheckForMemLeaks() {
      return this.ENV.getBool("IS_TEST");
    }
    checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {
      const numDataIdsAfter = this.backend.numDataIds();
      let numOutputDataIds = 0;
      outInfos.forEach((info) => {
        numOutputDataIds += info.dtype === "complex64" ? 3 : 1;
      });
      const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];
      const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;
      if (dataIdsLeaked > 0) {
        throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`);
      }
    }
    runKernelFunc(kernelParams) {
      let outputs;
      let saved = [];
      const isTapeOn = this.isTapeOn();
      const startingBytecount = this.state.numBytes;
      const startingNumTensors = this.state.numTensors;
      if (this.shouldCheckForMemLeaks()) {
        this.state.numDataMovesStack.push(0);
      }
      let kernelFunc3;
      if (this.backendName == null) {
        this.backend;
      }
      let out;
      const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? kernelParams.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
      if (isRegisteredKernelInvocation(kernelParams)) {
        const {kernelName, inputs: inputs2, attrs: attrs2} = kernelParams;
        if (this.backendName == null) {
          this.backend;
        }
        const kernel = getKernel(kernelName, this.backendName);
        assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`);
        kernelFunc3 = () => {
          const numDataIdsBefore = this.backend.numDataIds();
          out = kernel.kernelFunc({inputs: inputs2, attrs: attrs2, backend: this.backend});
          const outInfos = Array.isArray(out) ? out : [out];
          if (this.shouldCheckForMemLeaks()) {
            this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);
          }
          const outTensors = outInfos.map((outInfo) => {
            if (outInfo.rank != null) {
              return outInfo;
            }
            const {dataId, shape, dtype} = outInfo;
            return this.makeTensorFromDataId(dataId, shape, dtype);
          });
          if (isTapeOn) {
            const tensorsToSave = this.getTensorsForGradient(kernelName, inputs2, outTensors);
            saved = this.saveTensorsForBackwardMode(tensorsToSave);
          }
          return outTensors;
        };
      } else {
        const {forwardFunc} = kernelParams;
        const saveFunc = (tensors) => {
          if (!isTapeOn) {
            return;
          }
          saved = tensors.map((tensor2) => this.keep(this.clone(tensor2)));
        };
        kernelFunc3 = () => {
          const numDataIdsBefore = this.backend.numDataIds();
          out = this.tidy(() => forwardFunc(this.backend, saveFunc));
          const outs = Array.isArray(out) ? out : [out];
          if (this.shouldCheckForMemLeaks()) {
            this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs);
          }
          return outs;
        };
      }
      const {inputs, attrs} = kernelParams;
      const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? null : kernelParams.backwardsFunc;
      let kernelProfile;
      this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
        if (!this.ENV.getBool("DEBUG") && !this.state.profiling) {
          outputs = kernelFunc3();
        } else {
          kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc3());
          if (this.ENV.getBool("DEBUG")) {
            this.profiler.logKernelProfile(kernelProfile);
          }
          outputs = kernelProfile.outputs;
        }
      });
      if (isTapeOn) {
        this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs);
      }
      if (this.state.profiling) {
        this.state.activeProfile.kernels.push({
          name: kernelOrScopeName,
          bytesAdded: this.state.numBytes - startingBytecount,
          totalBytesSnapshot: this.state.numBytes,
          tensorsAdded: this.state.numTensors - startingNumTensors,
          totalTensorsSnapshot: this.state.numTensors,
          inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null),
          outputShapes: outputs.map((item) => item.shape),
          kernelTimeMs: kernelProfile.timeMs,
          extraInfo: kernelProfile.extraInfo
        });
      }
      return Array.isArray(out) ? outputs : outputs[0];
    }
    saveTensorsForBackwardMode(tensors) {
      const saved = tensors.map((tensor2) => this.keep(this.clone(tensor2)));
      return saved;
    }
    getTensorsForGradient(kernelName, inputs, outputs) {
      const gradConfig = getGradient(kernelName);
      if (gradConfig != null) {
        const inputsToSave = gradConfig.inputsToSave || [];
        const outputsToSave = gradConfig.outputsToSave || [];
        let inputTensorsToSave;
        if (gradConfig.saveAllInputs) {
          assert(Array.isArray(inputs), () => "saveAllInputs is true, expected inputs to be an array.");
          inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);
        } else {
          inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);
        }
        const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);
        return inputTensorsToSave.concat(outputTensorsToSave);
      }
      return [];
    }
    makeTensor(values, shape, dtype, backend2) {
      if (values == null) {
        throw new Error("Values passed to engine.makeTensor() are null");
      }
      dtype = dtype || "float32";
      backend2 = backend2 || this.backend;
      let backendVals = values;
      if (dtype === "string" && isString(values[0])) {
        backendVals = values.map((d) => encodeString(d));
      }
      const dataId = backend2.write(backendVals, shape, dtype);
      const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
      this.trackTensor(t, backend2);
      if (dtype === "string") {
        const info = this.state.tensorInfo.get(dataId);
        const newBytes = bytesFromStringArray(backendVals);
        this.state.numBytes += newBytes - info.bytes;
        info.bytes = newBytes;
      }
      return t;
    }
    makeTensorFromDataId(dataId, shape, dtype, backend2) {
      dtype = dtype || "float32";
      const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
      this.trackTensor(t, backend2);
      return t;
    }
    makeVariable(initialValue, trainable = true, name, dtype) {
      name = name || this.nextVariableId().toString();
      if (dtype != null && dtype !== initialValue.dtype) {
        initialValue = initialValue.cast(dtype);
      }
      const v = new Variable(initialValue, trainable, name, this.nextTensorId());
      if (this.state.registeredVariables[v.name] != null) {
        throw new Error(`Variable with name ${v.name} was already registered`);
      }
      this.state.registeredVariables[v.name] = v;
      this.incRef(v, this.backend);
      return v;
    }
    trackTensor(a, backend2) {
      this.state.numTensors++;
      if (a.dtype === "string") {
        this.state.numStringTensors++;
      }
      let bytes = 0;
      if (a.dtype !== "complex64" && a.dtype !== "string") {
        bytes = a.size * bytesPerElement(a.dtype);
      }
      this.state.numBytes += bytes;
      if (!this.state.tensorInfo.has(a.dataId)) {
        this.state.numDataBuffers++;
        this.state.tensorInfo.set(a.dataId, {
          backend: backend2 || this.backend,
          dtype: a.dtype,
          shape: a.shape,
          bytes
        });
      }
      if (!(a instanceof Variable)) {
        this.track(a);
      }
    }
    incRef(a, backend2) {
      this.trackTensor(a, backend2);
      this.backend.incRef(a.dataId);
    }
    removeDataId(dataId, backend2) {
      if (this.state.tensorInfo.has(dataId) && this.state.tensorInfo.get(dataId).backend === backend2) {
        this.state.tensorInfo.delete(dataId);
        this.state.numDataBuffers--;
      }
    }
    disposeTensor(a) {
      if (!this.state.tensorInfo.has(a.dataId)) {
        return;
      }
      const info = this.state.tensorInfo.get(a.dataId);
      this.state.numTensors--;
      if (a.dtype === "string") {
        this.state.numStringTensors--;
        this.state.numBytes -= info.bytes;
      }
      if (a.dtype !== "complex64" && a.dtype !== "string") {
        const bytes = a.size * bytesPerElement(a.dtype);
        this.state.numBytes -= bytes;
      }
      if (info.backend.disposeData(a.dataId)) {
        this.removeDataId(a.dataId, info.backend);
      }
    }
    disposeVariables() {
      for (const varName in this.state.registeredVariables) {
        const v = this.state.registeredVariables[varName];
        this.disposeVariable(v);
      }
    }
    disposeVariable(v) {
      this.disposeTensor(v);
      if (this.state.registeredVariables[v.name] != null) {
        delete this.state.registeredVariables[v.name];
      }
    }
    memory() {
      const info = this.backend.memory();
      info.numTensors = this.state.numTensors;
      info.numDataBuffers = this.state.numDataBuffers;
      info.numBytes = this.state.numBytes;
      if (this.state.numStringTensors > 0) {
        info.unreliable = true;
        if (info.reasons == null) {
          info.reasons = [];
        }
        info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)");
      }
      return info;
    }
    async profile(query) {
      this.state.profiling = true;
      const startBytes = this.state.numBytes;
      const startNumTensors = this.state.numTensors;
      this.state.activeProfile.kernels = [];
      this.state.activeProfile.result = await query();
      this.state.profiling = false;
      this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot));
      this.state.activeProfile.newBytes = this.state.numBytes - startBytes;
      this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors;
      for (const kernel of this.state.activeProfile.kernels) {
        kernel.kernelTimeMs = await kernel.kernelTimeMs;
        kernel.extraInfo = await kernel.extraInfo;
      }
      return this.state.activeProfile;
    }
    isTapeOn() {
      return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
    }
    addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {
      const tapeNode = {id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved};
      const gradConfig = getGradient(kernelName);
      if (gradConfig != null) {
        gradientsFunc = gradConfig.gradFunc;
      }
      if (gradientsFunc != null) {
        tapeNode.gradient = (dys) => {
          dys = dys.map((dy, i) => {
            if (dy == null) {
              const output = outputs[i];
              const vals = makeZerosTypedArray(output.size, output.dtype);
              return this.makeTensor(vals, output.shape, output.dtype);
            }
            return dy;
          });
          return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);
        };
      }
      this.state.activeTape.push(tapeNode);
    }
    keep(result) {
      result.kept = true;
      return result;
    }
    startTape() {
      if (this.state.gradientDepth === 0) {
        this.state.activeTape = [];
      }
      this.state.gradientDepth++;
    }
    endTape() {
      this.state.gradientDepth--;
    }
    startScope(name) {
      const scopeInfo = {
        track: [],
        name: "unnamed scope",
        id: this.state.nextScopeId++
      };
      if (name) {
        scopeInfo.name = name;
      }
      this.state.scopeStack.push(scopeInfo);
      this.state.activeScope = scopeInfo;
    }
    endScope(result) {
      const tensorsToTrackInParent = getTensorsInContainer(result);
      const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t) => t.id));
      for (let i = 0; i < this.state.activeScope.track.length; i++) {
        const tensor2 = this.state.activeScope.track[i];
        if (!tensor2.kept && !tensorsToTrackInParentSet.has(tensor2.id)) {
          tensor2.dispose();
        }
      }
      const oldScope = this.state.scopeStack.pop();
      this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1];
      tensorsToTrackInParent.forEach((tensor2) => {
        if (!tensor2.kept && tensor2.scopeId === oldScope.id) {
          this.track(tensor2);
        }
      });
    }
    gradients(f, xs, dy, allowNoGradients = false) {
      assert(xs.length > 0, () => "gradients() received an empty list of xs.");
      if (dy != null && dy.dtype !== "float32") {
        throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);
      }
      const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", f));
      assert(y instanceof Tensor, () => "The result y returned by f() must be a tensor.");
      const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);
      if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {
        throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");
      }
      return this.tidy("backward", () => {
        const accumulatedGradientMap = {};
        accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy;
        backpropagateGradients(accumulatedGradientMap, filteredTape, (f2) => this.tidy(f2), add);
        const grads2 = xs.map((x) => accumulatedGradientMap[x.id]);
        if (this.state.gradientDepth === 0) {
          this.state.activeTape.forEach((node2) => {
            for (const tensor2 of node2.saved) {
              tensor2.dispose();
            }
          });
          this.state.activeTape = null;
        }
        return {value: y, grads: grads2};
      });
    }
    customGrad(f) {
      assert(isFunction(f), () => "The f passed in customGrad(f) must be a function.");
      return (...inputs) => {
        assert(inputs.every((t) => t instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
        let res;
        const inputMap = {};
        inputs.forEach((input2, i) => {
          inputMap[i] = input2;
        });
        const forwardFunc = (_, save) => {
          res = f(...[...inputs, save]);
          assert(res.value instanceof Tensor, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor");
          assert(isFunction(res.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.");
          return res.value;
        };
        const backwardsFunc = (dy, saved) => {
          const gradRes = res.gradFunc(dy, saved);
          const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes];
          assert(grads2.length === inputs.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).");
          assert(grads2.every((t) => t instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
          const gradMap = {};
          grads2.forEach((grad2, i) => {
            gradMap[i] = () => grad2;
          });
          return gradMap;
        };
        return this.runKernelFunc({
          forwardFunc,
          backwardsFunc,
          inputs: inputMap
        });
      };
    }
    readSync(dataId) {
      const info = this.state.tensorInfo.get(dataId);
      return info.backend.readSync(dataId);
    }
    read(dataId) {
      const info = this.state.tensorInfo.get(dataId);
      return info.backend.read(dataId);
    }
    async time(query) {
      const start = now();
      const timingInfo = await this.backend.time(query);
      timingInfo.wallMs = now() - start;
      return timingInfo;
    }
    track(result) {
      if (this.state.activeScope != null) {
        result.scopeId = this.state.activeScope.id;
        this.state.activeScope.track.push(result);
      }
      return result;
    }
    get registeredVariables() {
      return this.state.registeredVariables;
    }
    reset() {
      this.pendingBackendInitId++;
      this.state.dispose();
      this.ENV.reset();
      this.state = new EngineState();
      for (const backendName in this.registry) {
        this.disposeRegisteredKernels(backendName);
        this.registry[backendName].dispose();
        delete this.registry[backendName];
      }
      this.backendName = null;
      this.backendInstance = null;
      this.pendingBackendInit = null;
    }
  };
  Engine.nextTensorId = 0;
  Engine.nextVariableId = 0;
  function ones(shape) {
    const values = makeOnesTypedArray(sizeFromShape(shape), "float32");
    return ENGINE.makeTensor(values, shape, "float32");
  }
  function getOrMakeEngine() {
    const ns = getGlobalNamespace();
    if (ns._tfengine == null) {
      const environment2 = new Environment(ns);
      ns._tfengine = new Engine(environment2);
    }
    setEnvironmentGlobal(ns._tfengine.ENV);
    setTensorTracker(() => ns._tfengine);
    return ns._tfengine;
  }
  var ENGINE = getOrMakeEngine();
  function add(a, b) {
    const inputs = {a, b};
    return ENGINE.runKernel(Add, inputs);
  }
  var device_util_exports = {};
  __export2(device_util_exports, {
    isBrowser: () => isBrowser,
    isMobile: () => isMobile
  });
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function _isNavigatorDefined() {
    return typeof navigator !== "undefined" && navigator != null;
  }
  function isMobile() {
    if (_isNavigatorDefined()) {
      const a = navigator.userAgent || navigator.vendor || window.opera;
      return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0, 4));
    }
    return false;
  }
  function isBrowser() {
    return typeof window !== "undefined" && window.document != null || typeof WorkerGlobalScope !== "undefined";
  }
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ENV2 = env();
  ENV2.registerFlag("DEBUG", () => false, (debugValue) => {
    if (debugValue) {
      console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.");
    }
  });
  ENV2.registerFlag("IS_BROWSER", () => isBrowser());
  ENV2.registerFlag("IS_NODE", () => typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined");
  ENV2.registerFlag("IS_CHROME", () => typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
  ENV2.registerFlag("PROD", () => false);
  ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => ENV2.getBool("DEBUG"));
  ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
  ENV2.registerFlag("IS_TEST", () => false);
  ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true);
  ENV2.registerFlag("WRAP_TO_IMAGEBITMAP", () => false);
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function inferShape(val, dtype) {
    let firstElem = val;
    if (isTypedArray(val)) {
      return dtype === "string" ? [] : [val.length];
    }
    if (!Array.isArray(val)) {
      return [];
    }
    const shape = [];
    while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== "string") {
      shape.push(firstElem.length);
      firstElem = firstElem[0];
    }
    if (Array.isArray(val) && env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")) {
      deepAssertShapeConsistency(val, shape, []);
    }
    return shape;
  }
  function deepAssertShapeConsistency(val, shape, indices) {
    indices = indices || [];
    if (!Array.isArray(val) && !isTypedArray(val)) {
      assert(shape.length === 0, () => `Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);
      return;
    }
    assert(shape.length > 0, () => `Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`);
    assert(val.length === shape[0], () => `Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);
    const subShape = shape.slice(1);
    for (let i = 0; i < val.length; ++i) {
      deepAssertShapeConsistency(val[i], subShape, indices.concat(i));
    }
  }
  function assertDtype(expectedDtype, actualDType, argName, functionName) {
    if (expectedDtype === "string_or_numeric") {
      return;
    }
    if (expectedDtype == null) {
      throw new Error(`Expected dtype cannot be null.`);
    }
    if (expectedDtype !== "numeric" && expectedDtype !== actualDType || expectedDtype === "numeric" && actualDType === "string") {
      throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`);
    }
  }
  function convertToTensor(x, argName, functionName, parseAsDtype = "numeric") {
    if (x instanceof Tensor) {
      assertDtype(parseAsDtype, x.dtype, argName, functionName);
      return x;
    }
    let inferredDtype = inferDtype(x);
    if (inferredDtype !== "string" && ["bool", "int32", "float32"].indexOf(parseAsDtype) >= 0) {
      inferredDtype = parseAsDtype;
    }
    assertDtype(parseAsDtype, inferredDtype, argName, functionName);
    if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== "number" && typeof x !== "boolean" && typeof x !== "string") {
      const type = x == null ? "null" : x.constructor.name;
      throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`);
    }
    const inferredShape = inferShape(x, inferredDtype);
    if (!isTypedArray(x) && !Array.isArray(x)) {
      x = [x];
    }
    const skipTypedArray = true;
    const values = inferredDtype !== "string" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray);
    return ENGINE.makeTensor(values, inferredShape, inferredDtype);
  }
  function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeric") {
    if (!Array.isArray(arg)) {
      throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);
    }
    const tensors = arg;
    return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype));
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var OP_SCOPE_SUFFIX = "__op";
  function op(f) {
    const keys = Object.keys(f);
    if (keys.length !== 1) {
      throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);
    }
    let opName = keys[0];
    const fn = f[opName];
    if (opName.endsWith("_")) {
      opName = opName.substring(0, opName.length - 1);
    }
    opName = opName + OP_SCOPE_SUFFIX;
    const f2 = (...args) => {
      ENGINE.startScope(opName);
      try {
        const result = fn(...args);
        if (isPromise(result)) {
          console.error("Cannot return a Promise inside of tidy.");
        }
        ENGINE.endScope(result);
        return result;
      } catch (ex) {
        ENGINE.endScope(null);
        throw ex;
      }
    };
    Object.defineProperty(f2, "name", {value: opName, configurable: true});
    return f2;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function complex_(real4, imag4) {
    const $real = convertToTensor(real4, "real", "complex");
    const $imag = convertToTensor(imag4, "imag", "complex");
    assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);
    const inputs = {real: $real, imag: $imag};
    return ENGINE.runKernel(Complex, inputs);
  }
  var complex = op({complex_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function makeTensor(values, shape, inferredShape, dtype) {
    if (dtype == null) {
      dtype = inferDtype(values);
    }
    if (dtype === "complex64") {
      throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`);
    }
    if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== "number" && typeof values !== "boolean" && typeof values !== "string") {
      throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
    }
    if (shape != null) {
      assertNonNegativeIntegerDimensions(shape);
      const providedSize = sizeFromShape(shape);
      const inferredSize = sizeFromShape(inferredShape);
      assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);
      for (let i = 0; i < inferredShape.length; ++i) {
        const inferred = inferredShape[i];
        const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true;
        assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);
      }
    }
    if (!isTypedArray(values) && !Array.isArray(values)) {
      values = [values];
    }
    shape = shape || inferredShape;
    values = dtype !== "string" ? toTypedArray(values, dtype) : flatten(values, [], true);
    return ENGINE.makeTensor(values, shape, dtype);
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function tensor(values, shape, dtype) {
    const inferredShape = inferShape(values, dtype);
    return makeTensor(values, shape, inferredShape, dtype);
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var DTYPE_VALUE_SIZE_MAP = {
    float32: 4,
    float16: 2,
    int32: 4,
    uint16: 2,
    uint8: 1,
    bool: 1,
    complex64: 8
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var NUM_BYTES_STRING_LENGTH = 4;
  async function encodeWeights(tensors, group) {
    const specs = [];
    const dataPromises = [];
    const names = Array.isArray(tensors) ? tensors.map((tensor2) => tensor2.name) : Object.keys(tensors);
    for (let i = 0; i < names.length; ++i) {
      const name = names[i];
      const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];
      if (t.dtype !== "float32" && t.dtype !== "int32" && t.dtype !== "bool" && t.dtype !== "string" && t.dtype !== "complex64") {
        throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);
      }
      const spec = {name, shape: t.shape, dtype: t.dtype};
      if (t.dtype === "string") {
        const utf8bytes = new Promise(async (resolve) => {
          const vals = await t.bytes();
          const totalNumBytes = vals.reduce((p2, c) => p2 + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length;
          const bytes = new Uint8Array(totalNumBytes);
          let offset = 0;
          for (let i2 = 0; i2 < vals.length; i2++) {
            const val = vals[i2];
            const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);
            bytes.set(bytesOfLength, offset);
            offset += NUM_BYTES_STRING_LENGTH;
            bytes.set(val, offset);
            offset += val.length;
          }
          resolve(bytes);
        });
        dataPromises.push(utf8bytes);
      } else {
        dataPromises.push(t.data());
      }
      if (group != null) {
        spec.group = group;
      }
      specs.push(spec);
    }
    const tensorValues = await Promise.all(dataPromises);
    return {data: concatenateTypedArrays(tensorValues), specs};
  }
  function decodeWeights(buffer2, specs) {
    const out = {};
    let float16Decode;
    let offset = 0;
    for (const spec of specs) {
      const name = spec.name;
      const dtype = spec.dtype;
      const shape = spec.shape;
      const size = sizeFromShape(shape);
      let values;
      if ("quantization" in spec) {
        const quantization = spec.quantization;
        if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
          if (!("min" in quantization && "scale" in quantization)) {
            throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`);
          }
        } else if (quantization.dtype === "float16") {
          if (dtype !== "float32") {
            throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`);
          }
        } else {
          throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);
        }
        const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];
        const byteBuffer = buffer2.slice(offset, offset + size * quantizationSizeFactor);
        const quantizedArray = quantization.dtype === "uint8" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer);
        if (dtype === "float32") {
          if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
            values = new Float32Array(quantizedArray.length);
            for (let i = 0; i < quantizedArray.length; i++) {
              const v = quantizedArray[i];
              values[i] = v * quantization.scale + quantization.min;
            }
          } else if (quantization.dtype === "float16") {
            if (float16Decode === void 0) {
              float16Decode = getFloat16Decoder();
            }
            values = float16Decode(quantizedArray);
          } else {
            throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);
          }
        } else if (dtype === "int32") {
          if (quantization.dtype !== "uint8" && quantization.dtype !== "uint16") {
            throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);
          }
          values = new Int32Array(quantizedArray.length);
          for (let i = 0; i < quantizedArray.length; i++) {
            const v = quantizedArray[i];
            values[i] = Math.round(v * quantization.scale + quantization.min);
          }
        } else {
          throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
        }
        offset += size * quantizationSizeFactor;
      } else if (dtype === "string") {
        const size2 = sizeFromShape(spec.shape);
        values = [];
        for (let i = 0; i < size2; i++) {
          const byteLength = new Uint32Array(buffer2.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];
          offset += NUM_BYTES_STRING_LENGTH;
          const bytes = new Uint8Array(buffer2.slice(offset, offset + byteLength));
          values.push(bytes);
          offset += byteLength;
        }
      } else {
        const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];
        const byteBuffer = buffer2.slice(offset, offset + size * dtypeFactor);
        if (dtype === "float32") {
          values = new Float32Array(byteBuffer);
        } else if (dtype === "int32") {
          values = new Int32Array(byteBuffer);
        } else if (dtype === "bool") {
          values = new Uint8Array(byteBuffer);
        } else if (dtype === "complex64") {
          values = new Float32Array(byteBuffer);
          const real4 = new Float32Array(values.length / 2);
          const image3 = new Float32Array(values.length / 2);
          for (let i = 0; i < real4.length; i++) {
            real4[i] = values[i * 2];
            image3[i] = values[i * 2 + 1];
          }
          const realTensor = tensor(real4, shape, "float32");
          const imageTensor = tensor(image3, shape, "float32");
          out[name] = complex(realTensor, imageTensor);
          realTensor.dispose();
          imageTensor.dispose();
        } else {
          throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
        }
        offset += size * dtypeFactor;
      }
      if (dtype !== "complex64") {
        out[name] = tensor(values, shape, dtype);
      }
    }
    return out;
  }
  function concatenateTypedArrays(xs) {
    if (xs === null) {
      throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);
    }
    let totalByteLength = 0;
    const normalizedXs = [];
    xs.forEach((x) => {
      totalByteLength += x.byteLength;
      normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x));
      if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) {
        throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);
      }
    });
    const y = new Uint8Array(totalByteLength);
    let offset = 0;
    normalizedXs.forEach((x) => {
      y.set(new Uint8Array(x.buffer), offset);
      offset += x.byteLength;
    });
    return y.buffer;
  }
  var useNodeBuffer = typeof Buffer !== "undefined" && (typeof Blob === "undefined" || typeof atob === "undefined" || typeof btoa === "undefined");
  function stringByteLength(str) {
    if (useNodeBuffer) {
      return Buffer.byteLength(str);
    }
    return new Blob([str]).size;
  }
  function arrayBufferToBase64String(buffer2) {
    if (useNodeBuffer) {
      return Buffer.from(buffer2).toString("base64");
    }
    const buf = new Uint8Array(buffer2);
    let s = "";
    for (let i = 0, l = buf.length; i < l; i++) {
      s += String.fromCharCode(buf[i]);
    }
    return btoa(s);
  }
  function base64StringToArrayBuffer(str) {
    if (useNodeBuffer) {
      const buf = Buffer.from(str, "base64");
      return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);
    }
    const s = atob(str);
    const buffer2 = new Uint8Array(s.length);
    for (let i = 0; i < s.length; ++i) {
      buffer2.set([s.charCodeAt(i)], i);
    }
    return buffer2.buffer;
  }
  function concatenateArrayBuffers(buffers) {
    if (buffers.length === 1) {
      return buffers[0];
    }
    let totalByteLength = 0;
    buffers.forEach((buffer2) => {
      totalByteLength += buffer2.byteLength;
    });
    const temp = new Uint8Array(totalByteLength);
    let offset = 0;
    buffers.forEach((buffer2) => {
      temp.set(new Uint8Array(buffer2), offset);
      offset += buffer2.byteLength;
    });
    return temp.buffer;
  }
  function basename(path) {
    const SEPARATOR = "/";
    path = path.trim();
    while (path.endsWith(SEPARATOR)) {
      path = path.slice(0, path.length - 1);
    }
    const items = path.split(SEPARATOR);
    return items[items.length - 1];
  }
  function getModelArtifactsInfoForJSON(modelArtifacts) {
    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
      throw new Error("Expected JSON model topology, received ArrayBuffer.");
    }
    return {
      dateSaved: new Date(),
      modelTopologyType: "JSON",
      modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),
      weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),
      weightDataBytes: modelArtifacts.weightData == null ? 0 : modelArtifacts.weightData.byteLength
    };
  }
  function computeFloat16MantisaTable() {
    const convertMantissa = (i) => {
      let m = i << 13;
      let e = 0;
      while ((m & 8388608) === 0) {
        e -= 8388608;
        m <<= 1;
      }
      m &= ~8388608;
      e += 947912704;
      return m | e;
    };
    const mantisaTable = new Uint32Array(2048);
    mantisaTable[0] = 0;
    for (let i = 1; i < 1024; i++) {
      mantisaTable[i] = convertMantissa(i);
    }
    for (let i = 1024; i < 2048; i++) {
      mantisaTable[i] = 939524096 + (i - 1024 << 13);
    }
    return mantisaTable;
  }
  function computeFloat16ExponentTable() {
    const exponentTable = new Uint32Array(64);
    exponentTable[0] = 0;
    exponentTable[31] = 1199570944;
    exponentTable[32] = 2147483648;
    exponentTable[63] = 3347054592;
    for (let i = 1; i < 31; i++) {
      exponentTable[i] = i << 23;
    }
    for (let i = 33; i < 63; i++) {
      exponentTable[i] = 2147483648 + (i - 32 << 23);
    }
    return exponentTable;
  }
  function computeFloat16OffsetTable() {
    const offsetTable = new Uint32Array(64);
    for (let i = 0; i < 64; i++) {
      offsetTable[i] = 1024;
    }
    offsetTable[0] = offsetTable[32] = 0;
    return offsetTable;
  }
  function getFloat16Decoder() {
    const mantisaTable = computeFloat16MantisaTable();
    const exponentTable = computeFloat16ExponentTable();
    const offsetTable = computeFloat16OffsetTable();
    return (quantizedArray) => {
      const buffer2 = new ArrayBuffer(4 * quantizedArray.length);
      const bufferUint32View = new Uint32Array(buffer2);
      for (let index = 0; index < quantizedArray.length; index++) {
        const float16Bits = quantizedArray[index];
        const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10];
        bufferUint32View[index] = float32Bits;
      }
      return new Float32Array(buffer2);
    };
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var IORouterRegistry = class {
    constructor() {
      this.saveRouters = [];
      this.loadRouters = [];
    }
    static getInstance() {
      if (IORouterRegistry.instance == null) {
        IORouterRegistry.instance = new IORouterRegistry();
      }
      return IORouterRegistry.instance;
    }
    static registerSaveRouter(saveRouter) {
      IORouterRegistry.getInstance().saveRouters.push(saveRouter);
    }
    static registerLoadRouter(loadRouter) {
      IORouterRegistry.getInstance().loadRouters.push(loadRouter);
    }
    static getSaveHandlers(url) {
      return IORouterRegistry.getHandlers(url, "save");
    }
    static getLoadHandlers(url, loadOptions) {
      return IORouterRegistry.getHandlers(url, "load", loadOptions);
    }
    static getHandlers(url, handlerType, loadOptions) {
      const validHandlers = [];
      const routers = handlerType === "load" ? IORouterRegistry.getInstance().loadRouters : IORouterRegistry.getInstance().saveRouters;
      routers.forEach((router) => {
        const handler = router(url, loadOptions);
        if (handler !== null) {
          validHandlers.push(handler);
        }
      });
      return validHandlers;
    }
  };
  var registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);
  var registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);
  var getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);
  var getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var DATABASE_NAME = "tensorflowjs";
  var DATABASE_VERSION = 1;
  var MODEL_STORE_NAME = "models_store";
  var INFO_STORE_NAME = "model_info_store";
  function getIndexedDBFactory() {
    if (!env().getBool("IS_BROWSER")) {
      throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");
    }
    const theWindow = typeof window === "undefined" ? self : window;
    const factory = theWindow.indexedDB || theWindow.mozIndexedDB || theWindow.webkitIndexedDB || theWindow.msIndexedDB || theWindow.shimIndexedDB;
    if (factory == null) {
      throw new Error("The current browser does not appear to support IndexedDB.");
    }
    return factory;
  }
  function setUpDatabase(openRequest) {
    const db = openRequest.result;
    db.createObjectStore(MODEL_STORE_NAME, {keyPath: "modelPath"});
    db.createObjectStore(INFO_STORE_NAME, {keyPath: "modelPath"});
  }
  var BrowserIndexedDB = class {
    constructor(modelPath) {
      this.indexedDB = getIndexedDBFactory();
      if (modelPath == null || !modelPath) {
        throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");
      }
      this.modelPath = modelPath;
    }
    async save(modelArtifacts) {
      if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
        throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
      }
      return this.databaseAction(this.modelPath, modelArtifacts);
    }
    async load() {
      return this.databaseAction(this.modelPath);
    }
    databaseAction(modelPath, modelArtifacts) {
      return new Promise((resolve, reject) => {
        const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
        openRequest.onupgradeneeded = () => setUpDatabase(openRequest);
        openRequest.onsuccess = () => {
          const db = openRequest.result;
          if (modelArtifacts == null) {
            const modelTx = db.transaction(MODEL_STORE_NAME, "readonly");
            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
            const getRequest = modelStore.get(this.modelPath);
            getRequest.onsuccess = () => {
              if (getRequest.result == null) {
                db.close();
                return reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));
              } else {
                resolve(getRequest.result.modelArtifacts);
              }
            };
            getRequest.onerror = (error) => {
              db.close();
              return reject(getRequest.error);
            };
            modelTx.oncomplete = () => db.close();
          } else {
            const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);
            const infoTx = db.transaction(INFO_STORE_NAME, "readwrite");
            let infoStore = infoTx.objectStore(INFO_STORE_NAME);
            const putInfoRequest = infoStore.put({modelPath: this.modelPath, modelArtifactsInfo});
            let modelTx;
            putInfoRequest.onsuccess = () => {
              modelTx = db.transaction(MODEL_STORE_NAME, "readwrite");
              const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
              const putModelRequest = modelStore.put({
                modelPath: this.modelPath,
                modelArtifacts,
                modelArtifactsInfo
              });
              putModelRequest.onsuccess = () => resolve({modelArtifactsInfo});
              putModelRequest.onerror = (error) => {
                infoStore = infoTx.objectStore(INFO_STORE_NAME);
                const deleteInfoRequest = infoStore.delete(this.modelPath);
                deleteInfoRequest.onsuccess = () => {
                  db.close();
                  return reject(putModelRequest.error);
                };
                deleteInfoRequest.onerror = (error2) => {
                  db.close();
                  return reject(putModelRequest.error);
                };
              };
            };
            putInfoRequest.onerror = (error) => {
              db.close();
              return reject(putInfoRequest.error);
            };
            infoTx.oncomplete = () => {
              if (modelTx == null) {
                db.close();
              } else {
                modelTx.oncomplete = () => db.close();
              }
            };
          }
        };
        openRequest.onerror = (error) => reject(openRequest.error);
      });
    }
  };
  BrowserIndexedDB.URL_SCHEME = "indexeddb://";
  var indexedDBRouter = (url) => {
    if (!env().getBool("IS_BROWSER")) {
      return null;
    } else {
      if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {
        return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));
      } else {
        return null;
      }
    }
  };
  IORouterRegistry.registerSaveRouter(indexedDBRouter);
  IORouterRegistry.registerLoadRouter(indexedDBRouter);
  function browserIndexedDB(modelPath) {
    return new BrowserIndexedDB(modelPath);
  }
  function maybeStripScheme(key) {
    return key.startsWith(BrowserIndexedDB.URL_SCHEME) ? key.slice(BrowserIndexedDB.URL_SCHEME.length) : key;
  }
  var BrowserIndexedDBManager = class {
    constructor() {
      this.indexedDB = getIndexedDBFactory();
    }
    async listModels() {
      return new Promise((resolve, reject) => {
        const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
        openRequest.onupgradeneeded = () => setUpDatabase(openRequest);
        openRequest.onsuccess = () => {
          const db = openRequest.result;
          const tx = db.transaction(INFO_STORE_NAME, "readonly");
          const store = tx.objectStore(INFO_STORE_NAME);
          const getAllInfoRequest = store.getAll();
          getAllInfoRequest.onsuccess = () => {
            const out = {};
            for (const item of getAllInfoRequest.result) {
              out[item.modelPath] = item.modelArtifactsInfo;
            }
            resolve(out);
          };
          getAllInfoRequest.onerror = (error) => {
            db.close();
            return reject(getAllInfoRequest.error);
          };
          tx.oncomplete = () => db.close();
        };
        openRequest.onerror = (error) => reject(openRequest.error);
      });
    }
    async removeModel(path) {
      path = maybeStripScheme(path);
      return new Promise((resolve, reject) => {
        const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
        openRequest.onupgradeneeded = () => setUpDatabase(openRequest);
        openRequest.onsuccess = () => {
          const db = openRequest.result;
          const infoTx = db.transaction(INFO_STORE_NAME, "readwrite");
          const infoStore = infoTx.objectStore(INFO_STORE_NAME);
          const getInfoRequest = infoStore.get(path);
          let modelTx;
          getInfoRequest.onsuccess = () => {
            if (getInfoRequest.result == null) {
              db.close();
              return reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));
            } else {
              const deleteInfoRequest = infoStore.delete(path);
              const deleteModelData = () => {
                modelTx = db.transaction(MODEL_STORE_NAME, "readwrite");
                const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
                const deleteModelRequest = modelStore.delete(path);
                deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);
                deleteModelRequest.onerror = (error) => reject(getInfoRequest.error);
              };
              deleteInfoRequest.onsuccess = deleteModelData;
              deleteInfoRequest.onerror = (error) => {
                deleteModelData();
                db.close();
                return reject(getInfoRequest.error);
              };
            }
          };
          getInfoRequest.onerror = (error) => {
            db.close();
            return reject(getInfoRequest.error);
          };
          infoTx.oncomplete = () => {
            if (modelTx == null) {
              db.close();
            } else {
              modelTx.oncomplete = () => db.close();
            }
          };
        };
        openRequest.onerror = (error) => reject(openRequest.error);
      });
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var PATH_SEPARATOR = "/";
  var PATH_PREFIX = "tensorflowjs_models";
  var INFO_SUFFIX = "info";
  var MODEL_TOPOLOGY_SUFFIX = "model_topology";
  var WEIGHT_SPECS_SUFFIX = "weight_specs";
  var WEIGHT_DATA_SUFFIX = "weight_data";
  var MODEL_METADATA_SUFFIX = "model_metadata";
  function getModelKeys(path) {
    return {
      info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),
      topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),
      weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),
      weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),
      modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)
    };
  }
  function getModelPathFromKey(key) {
    const items = key.split(PATH_SEPARATOR);
    if (items.length < 3) {
      throw new Error(`Invalid key format: ${key}`);
    }
    return items.slice(1, items.length - 1).join(PATH_SEPARATOR);
  }
  function maybeStripScheme2(key) {
    return key.startsWith(BrowserLocalStorage.URL_SCHEME) ? key.slice(BrowserLocalStorage.URL_SCHEME.length) : key;
  }
  var BrowserLocalStorage = class {
    constructor(modelPath) {
      if (!env().getBool("IS_BROWSER") || typeof window === "undefined" || typeof window.localStorage === "undefined") {
        throw new Error("The current environment does not support local storage.");
      }
      this.LS = window.localStorage;
      if (modelPath == null || !modelPath) {
        throw new Error("For local storage, modelPath must not be null, undefined or empty.");
      }
      this.modelPath = modelPath;
      this.keys = getModelKeys(this.modelPath);
    }
    async save(modelArtifacts) {
      if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
        throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
      } else {
        const topology = JSON.stringify(modelArtifacts.modelTopology);
        const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);
        const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);
        try {
          this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));
          this.LS.setItem(this.keys.topology, topology);
          this.LS.setItem(this.keys.weightSpecs, weightSpecs);
          this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));
          const result = {
            format: modelArtifacts.format,
            generatedBy: modelArtifacts.generatedBy,
            convertedBy: modelArtifacts.convertedBy
          };
          if (modelArtifacts.signature != null) {
            result.signature = modelArtifacts.signature;
          }
          if (modelArtifacts.userDefinedMetadata != null) {
            result.userDefinedMetadata = modelArtifacts.userDefinedMetadata;
          }
          if (modelArtifacts.modelInitializer != null) {
            result.modelInitializer = modelArtifacts.modelInitializer;
          }
          this.LS.setItem(this.keys.modelMetadata, JSON.stringify(result));
          return {modelArtifactsInfo};
        } catch (err) {
          this.LS.removeItem(this.keys.info);
          this.LS.removeItem(this.keys.topology);
          this.LS.removeItem(this.keys.weightSpecs);
          this.LS.removeItem(this.keys.weightData);
          this.LS.removeItem(this.keys.modelMetadata);
          throw new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);
        }
      }
    }
    async load() {
      const info = JSON.parse(this.LS.getItem(this.keys.info));
      if (info == null) {
        throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);
      }
      if (info.modelTopologyType !== "JSON") {
        throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");
      }
      const out = {};
      const topology = JSON.parse(this.LS.getItem(this.keys.topology));
      if (topology == null) {
        throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);
      }
      out.modelTopology = topology;
      const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));
      if (weightSpecs == null) {
        throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);
      }
      out.weightSpecs = weightSpecs;
      const metadataString = this.LS.getItem(this.keys.modelMetadata);
      if (metadataString != null) {
        const metadata = JSON.parse(metadataString);
        out.format = metadata["format"];
        out.generatedBy = metadata["generatedBy"];
        out.convertedBy = metadata["convertedBy"];
        if (metadata["signature"] != null) {
          out.signature = metadata["signature"];
        }
        if (metadata["userDefinedMetadata"] != null) {
          out.userDefinedMetadata = metadata["userDefinedMetadata"];
        }
        if (metadata["modelInitializer"] != null) {
          out.modelInitializer = metadata["modelInitializer"];
        }
      }
      const weightDataBase64 = this.LS.getItem(this.keys.weightData);
      if (weightDataBase64 == null) {
        throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);
      }
      out.weightData = base64StringToArrayBuffer(weightDataBase64);
      return out;
    }
  };
  BrowserLocalStorage.URL_SCHEME = "localstorage://";
  var localStorageRouter = (url) => {
    if (!env().getBool("IS_BROWSER")) {
      return null;
    } else {
      if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {
        return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));
      } else {
        return null;
      }
    }
  };
  IORouterRegistry.registerSaveRouter(localStorageRouter);
  IORouterRegistry.registerLoadRouter(localStorageRouter);
  function browserLocalStorage(modelPath) {
    return new BrowserLocalStorage(modelPath);
  }
  var BrowserLocalStorageManager = class {
    constructor() {
      assert(env().getBool("IS_BROWSER"), () => "Current environment is not a web browser");
      assert(typeof window === "undefined" || typeof window.localStorage !== "undefined", () => "Current browser does not appear to support localStorage");
      this.LS = window.localStorage;
    }
    async listModels() {
      const out = {};
      const prefix = PATH_PREFIX + PATH_SEPARATOR;
      const suffix = PATH_SEPARATOR + INFO_SUFFIX;
      for (let i = 0; i < this.LS.length; ++i) {
        const key = this.LS.key(i);
        if (key.startsWith(prefix) && key.endsWith(suffix)) {
          const modelPath = getModelPathFromKey(key);
          out[modelPath] = JSON.parse(this.LS.getItem(key));
        }
      }
      return out;
    }
    async removeModel(path) {
      path = maybeStripScheme2(path);
      const keys = getModelKeys(path);
      if (this.LS.getItem(keys.info) == null) {
        throw new Error(`Cannot find model at path '${path}'`);
      }
      const info = JSON.parse(this.LS.getItem(keys.info));
      this.LS.removeItem(keys.info);
      this.LS.removeItem(keys.topology);
      this.LS.removeItem(keys.weightSpecs);
      this.LS.removeItem(keys.weightData);
      return info;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var URL_SCHEME_SUFFIX = "://";
  var ModelStoreManagerRegistry = class {
    constructor() {
      this.managers = {};
    }
    static getInstance() {
      if (ModelStoreManagerRegistry.instance == null) {
        ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();
      }
      return ModelStoreManagerRegistry.instance;
    }
    static registerManager(scheme, manager) {
      assert(scheme != null, () => "scheme must not be undefined or null.");
      if (scheme.endsWith(URL_SCHEME_SUFFIX)) {
        scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));
      }
      assert(scheme.length > 0, () => "scheme must not be an empty string.");
      const registry = ModelStoreManagerRegistry.getInstance();
      assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);
      registry.managers[scheme] = manager;
    }
    static getManager(scheme) {
      const manager = this.getInstance().managers[scheme];
      if (manager == null) {
        throw new Error(`Cannot find model manager for scheme '${scheme}'`);
      }
      return manager;
    }
    static getSchemes() {
      return Object.keys(this.getInstance().managers);
    }
  };
  function parseURL(url) {
    if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {
      throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);
    }
    return {
      scheme: url.split(URL_SCHEME_SUFFIX)[0],
      path: url.split(URL_SCHEME_SUFFIX)[1]
    };
  }
  async function cloneModelInternal(sourceURL, destURL, deleteSource = false) {
    assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);
    const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);
    assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);
    assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);
    const loadHandler = loadHandlers[0];
    const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);
    assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`);
    assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);
    const saveHandler = saveHandlers[0];
    const sourceScheme = parseURL(sourceURL).scheme;
    const sourcePath = parseURL(sourceURL).path;
    const sameMedium = sourceScheme === parseURL(sourceURL).scheme;
    const modelArtifacts = await loadHandler.load();
    if (deleteSource && sameMedium) {
      await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
    }
    const saveResult = await saveHandler.save(modelArtifacts);
    if (deleteSource && !sameMedium) {
      await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
    }
    return saveResult.modelArtifactsInfo;
  }
  async function listModels() {
    const schemes = ModelStoreManagerRegistry.getSchemes();
    const out = {};
    for (const scheme of schemes) {
      const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();
      for (const path in schemeOut) {
        const url = scheme + URL_SCHEME_SUFFIX + path;
        out[url] = schemeOut[path];
      }
    }
    return out;
  }
  async function removeModel(url) {
    const schemeAndPath = parseURL(url);
    const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);
    return manager.removeModel(schemeAndPath.path);
  }
  async function copyModel(sourceURL, destURL) {
    const deleteSource = false;
    return cloneModelInternal(sourceURL, destURL, deleteSource);
  }
  async function moveModel(sourceURL, destURL) {
    const deleteSource = true;
    return cloneModelInternal(sourceURL, destURL, deleteSource);
  }
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var PlatformBrowser = class {
    fetch(path, init2) {
      return fetch(path, init2);
    }
    now() {
      return performance.now();
    }
    encode(text, encoding) {
      if (encoding !== "utf-8" && encoding !== "utf8") {
        throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);
      }
      if (this.textEncoder == null) {
        this.textEncoder = new TextEncoder();
      }
      return this.textEncoder.encode(text);
    }
    decode(bytes, encoding) {
      return new TextDecoder(encoding).decode(bytes);
    }
  };
  if (env().get("IS_BROWSER")) {
    env().setPlatform("browser", new PlatformBrowser());
    try {
      ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());
    } catch (err) {
    }
    try {
      ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());
    } catch (err) {
    }
  }
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var getNodeFetch = {
    importFetch: () => require_browser()
  };
  var systemFetch;
  var PlatformNode = class {
    constructor() {
      this.util = require("util");
      this.textEncoder = new this.util.TextEncoder();
    }
    fetch(path, requestInits) {
      if (env().global.fetch != null) {
        return env().global.fetch(path, requestInits);
      }
      if (systemFetch == null) {
        systemFetch = getNodeFetch.importFetch();
      }
      return systemFetch(path, requestInits);
    }
    now() {
      const time2 = process.hrtime();
      return time2[0] * 1e3 + time2[1] / 1e6;
    }
    encode(text, encoding) {
      if (encoding !== "utf-8" && encoding !== "utf8") {
        throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);
      }
      return this.textEncoder.encode(text);
    }
    decode(bytes, encoding) {
      if (bytes.length === 0) {
        return "";
      }
      return new this.util.TextDecoder(encoding).decode(bytes);
    }
  };
  if (env().get("IS_NODE")) {
    env().setPlatform("node", new PlatformNode());
  }
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function buffer(shape, dtype = "float32", values) {
    dtype = dtype || "float32";
    assertNonNegativeIntegerDimensions(shape);
    return new TensorBuffer(shape, dtype, values);
  }
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function cast_(x, dtype) {
    const $x = convertToTensor(x, "x", "cast");
    if (!isValidDtype(dtype)) {
      throw new Error(`Failed to cast to unknown dtype ${dtype}`);
    }
    if (dtype === "string" && $x.dtype !== "string" || dtype !== "string" && $x.dtype === "string") {
      throw new Error("Only strings can be casted to strings");
    }
    const inputs = {x: $x};
    const attrs = {dtype};
    return ENGINE.runKernel(Cast, inputs, attrs);
  }
  var cast = op({cast_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function clone_(x) {
    const $x = convertToTensor(x, "x", "clone", "string_or_numeric");
    const inputs = {x: $x};
    return ENGINE.runKernel(Identity, inputs);
  }
  var clone = op({clone_});
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function print2(x, verbose = false) {
    console.log(x.toString(verbose));
  }
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getOrMakeEngine();
  var opHandler2 = {
    buffer,
    cast,
    clone,
    print: print2
  };
  setOpHandler(opHandler2);
  var io_exports = {};
  __export2(io_exports, {
    browserFiles: () => browserFiles,
    browserHTTPRequest: () => browserHTTPRequest,
    concatenateArrayBuffers: () => concatenateArrayBuffers,
    copyModel: () => copyModel,
    decodeWeights: () => decodeWeights,
    encodeWeights: () => encodeWeights,
    fromMemory: () => fromMemory,
    getLoadHandlers: () => getLoadHandlers,
    getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON,
    getSaveHandlers: () => getSaveHandlers,
    http: () => http,
    isHTTPScheme: () => isHTTPScheme,
    listModels: () => listModels,
    loadWeights: () => loadWeights,
    moveModel: () => moveModel,
    registerLoadRouter: () => registerLoadRouter,
    registerSaveRouter: () => registerSaveRouter,
    removeModel: () => removeModel,
    weightsLoaderFactory: () => weightsLoaderFactory,
    withSaveHandler: () => withSaveHandler
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var DEFAULT_FILE_NAME_PREFIX = "model";
  var DEFAULT_JSON_EXTENSION_NAME = ".json";
  var DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin";
  function defer(f) {
    return new Promise((resolve) => setTimeout(resolve)).then(f);
  }
  var BrowserDownloads = class {
    constructor(fileNamePrefix) {
      if (!env().getBool("IS_BROWSER")) {
        throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
      }
      if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {
        fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);
      }
      if (fileNamePrefix == null || fileNamePrefix.length === 0) {
        fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;
      }
      this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;
      this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;
    }
    async save(modelArtifacts) {
      if (typeof document === "undefined") {
        throw new Error("Browser downloads are not supported in this environment since `document` is not present");
      }
      const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], {type: "application/octet-stream"}));
      if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
        throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");
      } else {
        const weightsManifest = [{
          paths: ["./" + this.weightDataFileName],
          weights: modelArtifacts.weightSpecs
        }];
        const modelTopologyAndWeightManifest = {
          modelTopology: modelArtifacts.modelTopology,
          format: modelArtifacts.format,
          generatedBy: modelArtifacts.generatedBy,
          convertedBy: modelArtifacts.convertedBy,
          weightsManifest
        };
        if (modelArtifacts.signature != null) {
          modelTopologyAndWeightManifest.signature = modelArtifacts.signature;
        }
        if (modelArtifacts.userDefinedMetadata != null) {
          modelTopologyAndWeightManifest.userDefinedMetadata = modelArtifacts.userDefinedMetadata;
        }
        if (modelArtifacts.modelInitializer != null) {
          modelTopologyAndWeightManifest.modelInitializer = modelArtifacts.modelInitializer;
        }
        const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], {type: "application/json"}));
        const jsonAnchor = this.jsonAnchor == null ? document.createElement("a") : this.jsonAnchor;
        jsonAnchor.download = this.modelTopologyFileName;
        jsonAnchor.href = modelTopologyAndWeightManifestURL;
        await defer(() => jsonAnchor.dispatchEvent(new MouseEvent("click")));
        if (modelArtifacts.weightData != null) {
          const weightDataAnchor = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
          weightDataAnchor.download = this.weightDataFileName;
          weightDataAnchor.href = weightsURL;
          await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent("click")));
        }
        return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)};
      }
    }
  };
  BrowserDownloads.URL_SCHEME = "downloads://";
  var BrowserFiles = class {
    constructor(files) {
      if (files == null || files.length < 1) {
        throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);
      }
      this.files = files;
    }
    async load() {
      const jsonFile = this.files[0];
      const weightFiles = this.files.slice(1);
      return new Promise((resolve, reject) => {
        const jsonReader = new FileReader();
        jsonReader.onload = (event) => {
          const modelJSON = JSON.parse(event.target.result);
          const modelTopology = modelJSON.modelTopology;
          if (modelTopology == null) {
            reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));
            return;
          }
          if (weightFiles.length === 0) {
            resolve({modelTopology});
          }
          const weightsManifest = modelJSON.weightsManifest;
          if (weightsManifest == null) {
            reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));
            return;
          }
          let pathToFile;
          try {
            pathToFile = this.checkManifestAndWeightFiles(weightsManifest, weightFiles);
          } catch (err) {
            reject(err);
            return;
          }
          const weightSpecs = [];
          const paths = [];
          const perFileBuffers = [];
          weightsManifest.forEach((weightsGroup) => {
            weightsGroup.paths.forEach((path) => {
              paths.push(path);
              perFileBuffers.push(null);
            });
            weightSpecs.push(...weightsGroup.weights);
          });
          weightsManifest.forEach((weightsGroup) => {
            weightsGroup.paths.forEach((path) => {
              const weightFileReader = new FileReader();
              weightFileReader.onload = (event2) => {
                const weightData = event2.target.result;
                const index = paths.indexOf(path);
                perFileBuffers[index] = weightData;
                if (perFileBuffers.indexOf(null) === -1) {
                  const result = {
                    modelTopology,
                    weightSpecs,
                    weightData: concatenateArrayBuffers(perFileBuffers),
                    format: modelJSON.format,
                    generatedBy: modelJSON.generatedBy,
                    convertedBy: modelJSON.convertedBy
                  };
                  if (modelJSON.signature != null) {
                    result.signature = modelJSON.signature;
                  }
                  if (modelJSON.userDefinedMetadata != null) {
                    result.userDefinedMetadata = modelJSON.userDefinedMetadata;
                  }
                  if (modelJSON.modelInitializer != null) {
                    result.modelInitializer = modelJSON.modelInitializer;
                  }
                  resolve(result);
                }
              };
              weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`);
              weightFileReader.readAsArrayBuffer(pathToFile[path]);
            });
          });
        };
        jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`);
        jsonReader.readAsText(jsonFile);
      });
    }
    checkManifestAndWeightFiles(manifest, files) {
      const basenames = [];
      const fileNames = files.map((file) => basename(file.name));
      const pathToFile = {};
      for (const group of manifest) {
        group.paths.forEach((path) => {
          const pathBasename = basename(path);
          if (basenames.indexOf(pathBasename) !== -1) {
            throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);
          }
          basenames.push(pathBasename);
          if (fileNames.indexOf(pathBasename) === -1) {
            throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);
          } else {
            pathToFile[path] = files[fileNames.indexOf(pathBasename)];
          }
        });
      }
      if (basenames.length !== files.length) {
        throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);
      }
      return pathToFile;
    }
  };
  var browserDownloadsRouter = (url) => {
    if (!env().getBool("IS_BROWSER")) {
      return null;
    } else {
      if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {
        return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));
      } else {
        return null;
      }
    }
  };
  IORouterRegistry.registerSaveRouter(browserDownloadsRouter);
  function browserDownloads(fileNamePrefix = "model") {
    return new BrowserDownloads(fileNamePrefix);
  }
  function browserFiles(files) {
    return new BrowserFiles(files);
  }
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {
    checkPromises(promises);
    startFraction = startFraction == null ? 0 : startFraction;
    endFraction = endFraction == null ? 1 : endFraction;
    checkFraction(startFraction, endFraction);
    let resolvedPromise = 0;
    const registerMonitor = (promise) => {
      promise.then((value) => {
        const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction);
        onProgress(fraction);
        return value;
      });
      return promise;
    };
    function checkPromises(promises2) {
      assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => "promises must be a none empty array");
    }
    function checkFraction(startFraction2, endFraction2) {
      assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`);
      assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`);
      assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`);
    }
    return Promise.all(promises.map(registerMonitor));
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {
    if (loadOptions == null) {
      loadOptions = {};
    }
    const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc;
    const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, {isBinary: true}));
    const fetchStartFraction = 0;
    const fetchEndFraction = 0.5;
    const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);
    const bufferPromises = responses.map((response) => response.arrayBuffer());
    const bufferStartFraction = 0.5;
    const bufferEndFraction = 1;
    const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);
    return buffers;
  }
  async function loadWeights(manifest, filePathPrefix = "", weightNames, requestInit) {
    const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, {requestInit});
    const loadWeights2 = weightsLoaderFactory(fetchWeights);
    return loadWeights2(manifest, filePathPrefix, weightNames);
  }
  function weightsLoaderFactory(fetchWeightsFunction) {
    return async (manifest, filePathPrefix = "", weightNames) => {
      const groupIndicesToFetchMap = manifest.map(() => false);
      const groupWeightsToFetch = {};
      const weightsFound = weightNames != null ? weightNames.map(() => false) : [];
      const allManifestWeightNames = [];
      manifest.forEach((manifestGroupConfig, groupIndex) => {
        let groupOffset = 0;
        manifestGroupConfig.weights.forEach((weightsEntry) => {
          const rawDtype = "quantization" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype;
          const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape);
          const enqueueWeightsForFetchingFn = () => {
            groupIndicesToFetchMap[groupIndex] = true;
            if (groupWeightsToFetch[groupIndex] == null) {
              groupWeightsToFetch[groupIndex] = [];
            }
            groupWeightsToFetch[groupIndex].push({
              manifestEntry: weightsEntry,
              groupOffset,
              sizeBytes: weightsBytes
            });
          };
          if (weightNames != null) {
            weightNames.forEach((weightName, weightIndex) => {
              if (weightName === weightsEntry.name) {
                enqueueWeightsForFetchingFn();
                weightsFound[weightIndex] = true;
              }
            });
          } else {
            enqueueWeightsForFetchingFn();
          }
          allManifestWeightNames.push(weightsEntry.name);
          groupOffset += weightsBytes;
        });
      });
      if (!weightsFound.every((found) => found)) {
        const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);
        throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`);
      }
      const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {
        if (shouldFetch) {
          accumulator.push(i);
        }
        return accumulator;
      }, []);
      const fetchUrls = [];
      groupIndicesToFetch.forEach((i) => {
        manifest[i].paths.forEach((filepath) => {
          const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath;
          fetchUrls.push(fetchUrl);
        });
      });
      const buffers = await fetchWeightsFunction(fetchUrls);
      const weightsTensorMap = {};
      let bufferIndexOffset = 0;
      groupIndicesToFetch.forEach((i) => {
        const numBuffers = manifest[i].paths.length;
        let groupBytes = 0;
        for (let i2 = 0; i2 < numBuffers; i2++) {
          groupBytes += buffers[bufferIndexOffset + i2].byteLength;
        }
        const groupBuffer = new ArrayBuffer(groupBytes);
        const groupByteBuffer = new Uint8Array(groupBuffer);
        let groupBufferOffset = 0;
        for (let i2 = 0; i2 < numBuffers; i2++) {
          const buffer2 = new Uint8Array(buffers[bufferIndexOffset + i2]);
          groupByteBuffer.set(buffer2, groupBufferOffset);
          groupBufferOffset += buffer2.byteLength;
        }
        const weightsEntries = groupWeightsToFetch[i];
        weightsEntries.forEach((weightsEntry) => {
          const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
          const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
          for (const name in nameToTensorMap) {
            weightsTensorMap[name] = nameToTensorMap[name];
          }
        });
        bufferIndexOffset += numBuffers;
      });
      return weightsTensorMap;
    };
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var OCTET_STREAM_MIME_TYPE = "application/octet-stream";
  var JSON_TYPE = "application/json";
  var HTTPRequest = class {
    constructor(path, loadOptions) {
      this.DEFAULT_METHOD = "POST";
      if (loadOptions == null) {
        loadOptions = {};
      }
      this.weightPathPrefix = loadOptions.weightPathPrefix;
      this.onProgress = loadOptions.onProgress;
      this.weightUrlConverter = loadOptions.weightUrlConverter;
      if (loadOptions.fetchFunc != null) {
        assert(typeof loadOptions.fetchFunc === "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)");
        this.fetch = loadOptions.fetchFunc;
      } else {
        this.fetch = env().platform.fetch;
      }
      assert(path != null && path.length > 0, () => "URL path for http must not be null, undefined or empty.");
      if (Array.isArray(path)) {
        assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`);
      }
      this.path = path;
      if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) {
        throw new Error("requestInit is expected to have no pre-existing body, but has one.");
      }
      this.requestInit = loadOptions.requestInit || {};
    }
    async save(modelArtifacts) {
      if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
        throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");
      }
      const init2 = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit);
      init2.body = new FormData();
      const weightsManifest = [{
        paths: ["./model.weights.bin"],
        weights: modelArtifacts.weightSpecs
      }];
      const modelTopologyAndWeightManifest = {
        modelTopology: modelArtifacts.modelTopology,
        format: modelArtifacts.format,
        generatedBy: modelArtifacts.generatedBy,
        convertedBy: modelArtifacts.convertedBy,
        weightsManifest
      };
      if (modelArtifacts.signature != null) {
        modelTopologyAndWeightManifest.signature = modelArtifacts.signature;
      }
      if (modelArtifacts.userDefinedMetadata != null) {
        modelTopologyAndWeightManifest.userDefinedMetadata = modelArtifacts.userDefinedMetadata;
      }
      if (modelArtifacts.modelInitializer != null) {
        modelTopologyAndWeightManifest.modelInitializer = modelArtifacts.modelInitializer;
      }
      init2.body.append("model.json", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], {type: JSON_TYPE}), "model.json");
      if (modelArtifacts.weightData != null) {
        init2.body.append("model.weights.bin", new Blob([modelArtifacts.weightData], {type: OCTET_STREAM_MIME_TYPE}), "model.weights.bin");
      }
      const response = await this.fetch(this.path, init2);
      if (response.ok) {
        return {
          modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),
          responses: [response]
        };
      } else {
        throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`);
      }
    }
    async load() {
      const modelConfigRequest = await this.fetch(this.path, this.requestInit);
      if (!modelConfigRequest.ok) {
        throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);
      }
      let modelConfig;
      try {
        modelConfig = await modelConfigRequest.json();
      } catch (e) {
        let message = `Failed to parse model JSON of response from ${this.path}.`;
        if (this.path.endsWith(".pb")) {
          message += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.";
        } else {
          message += " Please make sure the server is serving valid JSON for this request.";
        }
        throw new Error(message);
      }
      const modelTopology = modelConfig.modelTopology;
      const weightsManifest = modelConfig.weightsManifest;
      const generatedBy = modelConfig.generatedBy;
      const convertedBy = modelConfig.convertedBy;
      const format = modelConfig.format;
      const signature = modelConfig.signature;
      const userDefinedMetadata = modelConfig.userDefinedMetadata;
      if (modelTopology == null && weightsManifest == null) {
        throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);
      }
      let weightSpecs;
      let weightData;
      if (weightsManifest != null) {
        const results = await this.loadWeights(weightsManifest);
        [weightSpecs, weightData] = results;
      }
      const artifacts = {
        modelTopology,
        weightSpecs,
        weightData,
        generatedBy,
        convertedBy,
        format
      };
      if (signature != null) {
        artifacts.signature = signature;
      }
      if (userDefinedMetadata != null) {
        artifacts.userDefinedMetadata = userDefinedMetadata;
      }
      const initializer = modelConfig.modelInitializer;
      if (initializer) {
        artifacts.modelInitializer = initializer;
      }
      return artifacts;
    }
    async loadWeights(weightsManifest) {
      const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
      const [prefix, suffix] = parseUrl(weightPath);
      const pathPrefix = this.weightPathPrefix || prefix;
      const weightSpecs = [];
      for (const entry of weightsManifest) {
        weightSpecs.push(...entry.weights);
      }
      const fetchURLs = [];
      const urlPromises = [];
      for (const weightsGroup of weightsManifest) {
        for (const path of weightsGroup.paths) {
          if (this.weightUrlConverter != null) {
            urlPromises.push(this.weightUrlConverter(path));
          } else {
            fetchURLs.push(pathPrefix + path + suffix);
          }
        }
      }
      if (this.weightUrlConverter) {
        fetchURLs.push(...await Promise.all(urlPromises));
      }
      const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {
        requestInit: this.requestInit,
        fetchFunc: this.fetch,
        onProgress: this.onProgress
      });
      return [weightSpecs, concatenateArrayBuffers(buffers)];
    }
  };
  HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//;
  function parseUrl(url) {
    const lastSlash = url.lastIndexOf("/");
    const lastSearchParam = url.lastIndexOf("?");
    const prefix = url.substring(0, lastSlash);
    const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : "";
    return [prefix + "/", suffix];
  }
  function isHTTPScheme(url) {
    return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;
  }
  var httpRouter = (url, loadOptions) => {
    if (typeof fetch === "undefined" && (loadOptions == null || loadOptions.fetchFunc == null)) {
      return null;
    } else {
      let isHTTP = true;
      if (Array.isArray(url)) {
        isHTTP = url.every((urlItem) => isHTTPScheme(urlItem));
      } else {
        isHTTP = isHTTPScheme(url);
      }
      if (isHTTP) {
        return http(url, loadOptions);
      }
    }
    return null;
  };
  IORouterRegistry.registerSaveRouter(httpRouter);
  IORouterRegistry.registerLoadRouter(httpRouter);
  function http(path, loadOptions) {
    return new HTTPRequest(path, loadOptions);
  }
  function browserHTTPRequest(path, loadOptions) {
    return http(path, loadOptions);
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var PassthroughLoader = class {
    constructor(modelArtifacts) {
      this.modelArtifacts = modelArtifacts;
    }
    async load() {
      return this.modelArtifacts;
    }
  };
  var PassthroughSaver = class {
    constructor(saveHandler) {
      this.saveHandler = saveHandler;
    }
    async save(modelArtifacts) {
      return this.saveHandler(modelArtifacts);
    }
  };
  function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {
    if (arguments.length === 1) {
      const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null;
      if (isModelArtifacts) {
        return new PassthroughLoader(modelArtifacts);
      } else {
        console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
        return new PassthroughLoader({modelTopology: modelArtifacts});
      }
    } else {
      console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
      return new PassthroughLoader({
        modelTopology: modelArtifacts,
        weightSpecs,
        weightData,
        trainingConfig
      });
    }
  }
  function withSaveHandler(saveHandler) {
    return new PassthroughSaver(saveHandler);
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var math_exports = {};
  __export2(math_exports, {
    confusionMatrix: () => confusionMatrix
  });
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function matMul_(a, b, transposeA = false, transposeB = false) {
    let $a = convertToTensor(a, "a", "matMul");
    let $b = convertToTensor(b, "b", "matMul");
    [$a, $b] = makeTypesMatch($a, $b);
    const inputs = {a: $a, b: $b};
    const attrs = {transposeA, transposeB};
    return ENGINE.runKernel(BatchMatMul, inputs, attrs);
  }
  var matMul = op({matMul_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function oneHot_(indices, depth, onValue = 1, offValue = 0) {
    if (depth < 2) {
      throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);
    }
    const $indices = convertToTensor(indices, "indices", "oneHot", "int32");
    const inputs = {indices: $indices};
    const attrs = {depth, onValue, offValue};
    return ENGINE.runKernel(OneHot, inputs, attrs);
  }
  var oneHot = op({oneHot_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function transpose_(x, perm) {
    const $x = convertToTensor(x, "x", "transpose");
    if (perm == null) {
      perm = $x.shape.map((s, i) => i).reverse();
    }
    assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`);
    perm.forEach((axis) => {
      assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`);
    });
    if ($x.rank <= 1) {
      return $x.clone();
    }
    const inputs = {x: $x};
    const attrs = {perm};
    return ENGINE.runKernel(Transpose, inputs, attrs);
  }
  var transpose = op({transpose_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function confusionMatrix_(labels, predictions, numClasses) {
    const $labels = convertToTensor(labels, "labels", "confusionMatrix");
    const $predictions = convertToTensor(predictions, "predictions", "confusionMatrix");
    assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`);
    assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);
    assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`);
    assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`);
    assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`);
    const oneHotLabels = oneHot(cast($labels, "int32"), numClasses);
    const oneHotPredictions = oneHot(cast($predictions, "int32"), numClasses);
    const oneHotLabelsT = transpose(oneHotLabels);
    const product = matMul(oneHotLabelsT, oneHotPredictions);
    return cast(product, "int32");
  }
  var confusionMatrix = op({confusionMatrix_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var browser_exports = {};
  __export2(browser_exports, {
    fromPixels: () => fromPixels,
    fromPixelsAsync: () => fromPixelsAsync,
    toPixels: () => toPixels
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function tensor3d(values, shape, dtype) {
    assertNonNull(values);
    if (shape != null && shape.length !== 3) {
      throw new Error("tensor3d() requires shape to have three numbers");
    }
    const inferredShape = inferShape(values, dtype);
    if (inferredShape.length !== 3 && inferredShape.length !== 1) {
      throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");
    }
    if (inferredShape.length === 1 && shape == null) {
      throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");
    }
    return makeTensor(values, shape, inferredShape, dtype);
  }
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var fromPixels2DContext;
  function fromPixels_(pixels, numChannels = 3) {
    if (numChannels > 4) {
      throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");
    }
    if (pixels == null) {
      throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
    }
    let isPixelData2 = false;
    let isImageData = false;
    let isVideo = false;
    let isImage = false;
    let isCanvasLike = false;
    let isImageBitmap = false;
    if (pixels.data instanceof Uint8Array) {
      isPixelData2 = true;
    } else if (typeof ImageData !== "undefined" && pixels instanceof ImageData) {
      isImageData = true;
    } else if (typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement) {
      isVideo = true;
    } else if (typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement) {
      isImage = true;
    } else if (pixels.getContext != null) {
      isCanvasLike = true;
    } else if (typeof ImageBitmap !== "undefined" && pixels instanceof ImageBitmap) {
      isImageBitmap = true;
    } else {
      throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);
    }
    if (isVideo) {
      const HAVE_CURRENT_DATA_READY_STATE = 2;
      if (isVideo && pixels.readyState < HAVE_CURRENT_DATA_READY_STATE) {
        throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.");
      }
    }
    const kernel = getKernel(FromPixels, ENGINE.backendName);
    if (kernel != null) {
      const inputs = {pixels};
      const attrs = {numChannels};
      return ENGINE.runKernel(FromPixels, inputs, attrs);
    }
    const [width, height] = isVideo ? [
      pixels.videoWidth,
      pixels.videoHeight
    ] : [pixels.width, pixels.height];
    let vals;
    if (isCanvasLike) {
      vals = pixels.getContext("2d").getImageData(0, 0, width, height).data;
    } else if (isImageData || isPixelData2) {
      vals = pixels.data;
    } else if (isImage || isVideo || isImageBitmap) {
      if (fromPixels2DContext == null) {
        fromPixels2DContext = document.createElement("canvas").getContext("2d");
      }
      fromPixels2DContext.canvas.width = width;
      fromPixels2DContext.canvas.height = height;
      fromPixels2DContext.drawImage(pixels, 0, 0, width, height);
      vals = fromPixels2DContext.getImageData(0, 0, width, height).data;
    }
    let values;
    if (numChannels === 4) {
      values = new Int32Array(vals);
    } else {
      const numPixels = width * height;
      values = new Int32Array(numPixels * numChannels);
      for (let i = 0; i < numPixels; i++) {
        for (let channel = 0; channel < numChannels; ++channel) {
          values[i * numChannels + channel] = vals[i * 4 + channel];
        }
      }
    }
    const outShape = [height, width, numChannels];
    return tensor3d(values, outShape, "int32");
  }
  function isPixelData(pixels) {
    return pixels != null && pixels.data instanceof Uint8Array;
  }
  function isImageBitmapFullySupported() {
    return typeof window !== "undefined" && typeof ImageBitmap !== "undefined" && window.hasOwnProperty("createImageBitmap");
  }
  function isNonEmptyPixels(pixels) {
    return pixels != null && pixels.width !== 0 && pixels.height !== 0;
  }
  function canWrapPixelsToImageBitmap(pixels) {
    return isImageBitmapFullySupported() && !(pixels instanceof ImageBitmap) && isNonEmptyPixels(pixels) && !isPixelData(pixels);
  }
  async function fromPixelsAsync(pixels, numChannels = 3) {
    let inputs = null;
    if (env().getBool("WRAP_TO_IMAGEBITMAP") && canWrapPixelsToImageBitmap(pixels)) {
      let imageBitmap;
      try {
        imageBitmap = await createImageBitmap(pixels, {premultiplyAlpha: "none"});
      } catch (e) {
        imageBitmap = null;
      }
      if (imageBitmap != null && imageBitmap.width === pixels.width && imageBitmap.height === pixels.height) {
        inputs = imageBitmap;
      } else {
        inputs = pixels;
      }
    } else {
      inputs = pixels;
    }
    return fromPixels_(inputs, numChannels);
  }
  async function toPixels(img, canvas) {
    let $img = convertToTensor(img, "img", "toPixels");
    if (!(img instanceof Tensor)) {
      const originalImgTensor = $img;
      $img = cast(originalImgTensor, "int32");
      originalImgTensor.dispose();
    }
    if ($img.rank !== 2 && $img.rank !== 3) {
      throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);
    }
    const [height, width] = $img.shape.slice(0, 2);
    const depth = $img.rank === 2 ? 1 : $img.shape[2];
    if (depth > 4 || depth === 2) {
      throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);
    }
    if ($img.dtype !== "float32" && $img.dtype !== "int32") {
      throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);
    }
    const data = await $img.data();
    const multiplier = $img.dtype === "float32" ? 255 : 1;
    const bytes = new Uint8ClampedArray(width * height * 4);
    for (let i = 0; i < height * width; ++i) {
      const rgba = [0, 0, 0, 255];
      for (let d = 0; d < depth; d++) {
        const value = data[i * depth + d];
        if ($img.dtype === "float32") {
          if (value < 0 || value > 1) {
            throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`);
          }
        } else if ($img.dtype === "int32") {
          if (value < 0 || value > 255) {
            throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);
          }
        }
        if (depth === 1) {
          rgba[0] = value * multiplier;
          rgba[1] = value * multiplier;
          rgba[2] = value * multiplier;
        } else {
          rgba[d] = value * multiplier;
        }
      }
      const j = i * 4;
      bytes[j + 0] = Math.round(rgba[0]);
      bytes[j + 1] = Math.round(rgba[1]);
      bytes[j + 2] = Math.round(rgba[2]);
      bytes[j + 3] = Math.round(rgba[3]);
    }
    if (canvas != null) {
      canvas.width = width;
      canvas.height = height;
      const ctx = canvas.getContext("2d");
      const imageData = new ImageData(bytes, width, height);
      ctx.putImageData(imageData, 0, 0);
    }
    if ($img !== img) {
      $img.dispose();
    }
    return bytes;
  }
  var fromPixels = op({fromPixels_});
  var gather_nd_util_exports = {};
  __export2(gather_nd_util_exports, {
    prepareAndValidate: () => prepareAndValidate
  });
  function prepareAndValidate(tensor2, indices) {
    const tensorRank = tensor2.shape.length;
    const indicesRank = indices.shape.length;
    if (tensorRank < 1) {
      throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);
    }
    if (indicesRank < 1) {
      throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);
    }
    if (indices.dtype !== "int32") {
      throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);
    }
    if (indices.shape[indicesRank - 1] > tensorRank) {
      throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank - 1]} vs. ${tensorRank}`);
    }
    if (sizeFromShape(tensor2.shape) === 0) {
      throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);
    }
    const indicesShape = indices.shape;
    const sliceRank = indicesShape[indicesShape.length - 1];
    let nResult = 1;
    for (let i = 0; i < indicesShape.length - 1; ++i) {
      nResult *= indicesShape[i];
    }
    const inputShape = tensor2.shape;
    const resultShape = indicesShape.slice();
    resultShape.pop();
    let sliceSize = 1;
    for (let i = sliceRank; i < tensorRank; ++i) {
      sliceSize *= inputShape[i];
      resultShape.push(inputShape[i]);
    }
    const strides = [
      ...computeStrides(tensor2.shape).map((stride) => stride / sliceSize),
      1
    ].slice(0, sliceRank);
    return [resultShape, nResult, sliceSize, strides];
  }
  var scatter_nd_util_exports = {};
  __export2(scatter_nd_util_exports, {
    calculateShapes: () => calculateShapes,
    validateInput: () => validateInput,
    validateUpdateShape: () => validateUpdateShape
  });
  function validateUpdateShape(shape, indices, updates) {
    const sliceDim = indices.rank > 1 ? indices.shape[indices.rank - 1] : 1;
    const batchDim = indices.rank > 1 ? indices.rank - 1 : 1;
    const shapeError = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;
    if (updates.rank < batchDim) {
      throw new Error(shapeError + ` update.rank < ${batchDim}. `);
    }
    if (shape.length < sliceDim + (updates.rank - batchDim)) {
      throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);
    }
    if (updates.rank !== batchDim + shape.length - sliceDim) {
      throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);
    }
    for (let d = 0; d < batchDim; ++d) {
      if (updates.shape[d] !== indices.shape[d]) {
        throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);
      }
    }
    for (let d = 0; d < updates.rank - batchDim; ++d) {
      if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {
        throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);
      }
    }
  }
  function validateInput(updates, indices, shape) {
    if (indices.rank < 1) {
      throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);
    }
    if (updates.rank < 1) {
      throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);
    }
    if (indices.dtype !== "int32") {
      throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);
    }
    if (shape.length < 1) {
      throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);
    }
    if (shape.length === 0) {
      if (indices.size === 0) {
        throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);
      }
      if (updates.size === 0) {
        throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);
      }
    }
    validateUpdateShape(shape, indices, updates);
  }
  function calculateShapes(updates, indices, shape) {
    const indicesRank = indices.shape.length;
    const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;
    const totalNd = shape.length;
    let sliceSize = 1;
    for (let i = sliceRank; i < totalNd; ++i) {
      sliceSize *= shape[i];
    }
    const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;
    const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;
    const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];
    const outputSize = sizeFromShape(shape);
    return {sliceRank, numUpdates, sliceSize, strides, outputSize};
  }
  var slice_util_exports = {};
  __export2(slice_util_exports, {
    assertParamsValid: () => assertParamsValid,
    computeFlatOffset: () => computeFlatOffset,
    computeOutShape: () => computeOutShape,
    getNormalizedAxes: () => getNormalizedAxes,
    isSliceContinous: () => isSliceContinous,
    maskToAxes: () => maskToAxes,
    parseSliceParams: () => parseSliceParams,
    sliceInfo: () => sliceInfo,
    startForAxis: () => startForAxis,
    startIndicesWithElidedDims: () => startIndicesWithElidedDims,
    stopForAxis: () => stopForAxis,
    stopIndicesWithElidedDims: () => stopIndicesWithElidedDims,
    stridesForAxis: () => stridesForAxis,
    stridesWithElidedDims: () => stridesWithElidedDims
  });
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function assertParamsValid(input2, begin, size) {
    const inputRank = input2.shape.length;
    assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`);
    assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);
    for (let i = 0; i < inputRank; ++i) {
      assert(begin[i] + size[i] <= input2.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i] + size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`);
    }
  }
  function maskToAxes(mask) {
    const axes = [];
    let axis = 0;
    while (mask > 0) {
      if (mask & 1) {
        axes.push(axis);
      }
      mask /= 2;
      axis++;
    }
    return axes;
  }
  function computeOutShape(begin, end, strides) {
    const size = [];
    for (let axis = 0; axis < begin.length; axis++) {
      size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);
    }
    return size;
  }
  function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {
    const newStrides = [...strides];
    for (let i = newStrides.length; i < inputShape.length; i++) {
      newStrides.push(1);
    }
    for (let i = 0; i < numElidedAxes; i++) {
      if (i === 0) {
        newStrides[ellipsisInsertionIndex] = 1;
      } else {
        newStrides.splice(ellipsisInsertionIndex, 0, 1);
        newStrides.pop();
      }
    }
    return newStrides;
  }
  function unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {
    if (normalizedAxis <= ellipsisInsertionIndex) {
      return normalizedAxis;
    }
    return normalizedAxis - (numElidedAxes - 1);
  }
  function getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {
    const elidedAxes = [];
    for (let i = 0; i < numElidedAxes; i++) {
      elidedAxes.push(ellipsisInsertionIndex + i);
    }
    return elidedAxes;
  }
  function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {
    const inputRank = inputShape.length;
    let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);
    if (ellipsisAxes.length && numInterpolatedAxes > 0) {
      const fullIndex = ellipsisAxes[0];
      const numElidedAxes = numInterpolatedAxes + 1;
      normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);
      normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);
      normalizedStrides = stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);
    } else {
      for (let axis = 0; axis < inputRank; axis++) {
        normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);
        normalizedEnd[axis] = stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);
        normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);
      }
    }
    return {
      begin: normalizedBegin,
      end: normalizedEnd,
      strides: normalizedStrides
    };
  }
  function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {
    const newIndices = [...inputShape];
    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
    for (let axis = 0; axis < newIndices.length; axis++) {
      if (elidedAxes.indexOf(axis) > -1) {
        newIndices[axis] = 0;
      } else {
        const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
        let originalValue = originalBegin[originalAxis];
        if (beginMask & 1 << originalAxis) {
          originalValue = 0;
        }
        newIndices[axis] = originalValue;
      }
    }
    return newIndices;
  }
  function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {
    const newIndices = [...inputShape];
    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
    for (let axis = 0; axis < newIndices.length; axis++) {
      if (elidedAxes.indexOf(axis) > -1) {
        newIndices[axis] = Number.MAX_SAFE_INTEGER;
      } else {
        const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
        let originalValue = originalEnd[originalAxis];
        if (endMask & 1 << originalAxis) {
          originalValue = Number.MAX_SAFE_INTEGER;
        }
        newIndices[axis] = originalValue;
      }
    }
    for (let i = 0; i < newIndices.length; i++) {
      const axisSize = inputShape[i];
      if (newIndices[i] < 0) {
        newIndices[i] += axisSize;
      }
      newIndices[i] = clamp(0, newIndices[i], inputShape[i]);
    }
    return newIndices;
  }
  function stridesForAxis(strides, axis, ellipsisMask) {
    let stride = strides[axis];
    if (ellipsisMask & 1 << axis || stride == null) {
      stride = 1;
    }
    return stride;
  }
  function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {
    let start = startIndices[axis];
    const stride = strides[axis] || 1;
    if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {
      if (stride > 0) {
        start = Number.MIN_SAFE_INTEGER;
      } else {
        start = Number.MAX_SAFE_INTEGER;
      }
    }
    const axisSize = inputShape[axis];
    if (start < 0) {
      start += axisSize;
    }
    start = clamp(0, start, axisSize - 1);
    return start;
  }
  function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {
    let stop = stopIndices[axis];
    const stride = strides[axis] || 1;
    if (endMask & 1 << axis || ellipsisMask & 1 << axis || stop == null) {
      if (stride > 0) {
        stop = Number.MAX_SAFE_INTEGER;
      } else {
        stop = Number.MIN_SAFE_INTEGER;
      }
    }
    const axisSize = inputShape[axis];
    if (stop < 0) {
      stop += axisSize;
    }
    if (stride > 0) {
      stop = clamp(0, stop, axisSize);
    } else {
      stop = clamp(-1, stop, axisSize - 1);
    }
    return stop;
  }
  function isSliceContinous(shape, begin, size) {
    let firstNonOneAxis = size.length;
    for (let i = 0; i < size.length; i++) {
      if (size[i] > 1) {
        firstNonOneAxis = i;
        break;
      }
    }
    for (let i = firstNonOneAxis + 1; i < size.length; i++) {
      if (begin[i] > 0 || size[i] !== shape[i]) {
        return false;
      }
    }
    return true;
  }
  function computeFlatOffset(begin, strides) {
    let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;
    for (let i = 0; i < begin.length - 1; i++) {
      flatOffset += begin[i] * strides[i];
    }
    return flatOffset;
  }
  function parseSliceParams(x, begin, size) {
    let begin_;
    const xRank = x.shape.length;
    if (typeof begin === "number") {
      begin_ = [begin, ...new Array(xRank - 1).fill(0)];
    } else if (begin.length < xRank) {
      begin_ = begin.concat(new Array(xRank - begin.length).fill(0));
    } else {
      begin_ = begin.slice();
    }
    begin_.forEach((d) => {
      assert(d !== -1, () => "slice() does not support negative begin indexing.");
    });
    let size_;
    if (size == null) {
      size_ = new Array(xRank).fill(-1);
    } else if (typeof size === "number") {
      size_ = [size, ...new Array(xRank - 1).fill(-1)];
    } else if (size.length < xRank) {
      size_ = size.concat(new Array(xRank - size.length).fill(-1));
    } else {
      size_ = size;
    }
    size_ = size_.map((d, i) => {
      if (d >= 0) {
        return d;
      } else {
        assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`);
        return x.shape[i] - begin_[i];
      }
    });
    return [begin_, size_];
  }
  function sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
    let $begin = begin.slice();
    let $end = end.slice();
    let $strides = strides;
    if (strides == null) {
      $strides = new Array($begin.length);
    }
    const ellipsisAxes = maskToAxes(ellipsisMask);
    if (ellipsisAxes.length > 1) {
      throw new Error("Multiple ellipses in slice is not allowed.");
    }
    if (ellipsisMask !== 0 && newAxisMask !== 0) {
      throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");
    }
    if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {
      throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");
    }
    const numInterpolatedAxes = xShape.length - $begin.length;
    const expandAxes = maskToAxes(newAxisMask);
    const newShape = xShape.slice();
    expandAxes.forEach((axis) => {
      $begin[axis] = 0;
      $end[axis] = 1;
      newShape.splice(axis, 0, 1);
    });
    const {begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides} = getNormalizedAxes(newShape, ellipsisAxes, numInterpolatedAxes, $begin, $end, $strides, beginMask, endMask, ellipsisMask);
    $begin = normalizedBegin;
    $end = normalizedEnd;
    $strides = normalizedStrides;
    const shrinkAxes = maskToAxes(shrinkAxisMask);
    shrinkAxes.forEach((axis) => {
      $end[axis] = $begin[axis] + 1;
      $strides[axis] = 1;
    });
    const size = computeOutShape($begin, $end, $strides);
    const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);
    const nonStrided = $strides.every((v) => v === 1);
    return {nonStrided, $begin, $end, $strides, size, newShape, outShape};
  }
  var serialization_exports = {};
  __export2(serialization_exports, {
    Serializable: () => Serializable,
    SerializationMap: () => SerializationMap,
    registerClass: () => registerClass
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var Serializable = class {
    getClassName() {
      return this.constructor.className;
    }
    static fromConfig(cls, config) {
      return new cls(config);
    }
  };
  var SerializationMap = class {
    constructor() {
      this.classNameMap = {};
    }
    static getMap() {
      if (SerializationMap.instance == null) {
        SerializationMap.instance = new SerializationMap();
      }
      return SerializationMap.instance;
    }
    static register(cls) {
      SerializationMap.getMap().classNameMap[cls.className] = [cls, cls.fromConfig];
    }
  };
  function registerClass(cls) {
    assert(cls.className != null, () => `Class being registered does not have the static className property defined.`);
    assert(typeof cls.className === "string", () => `className is required to be a string, but got type ` + typeof cls.className);
    assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, which is disallowed.`);
    SerializationMap.register(cls);
  }
  var test_util_exports = {};
  __export2(test_util_exports, {
    TEST_EPSILON_FLOAT16: () => TEST_EPSILON_FLOAT16,
    encodeStrings: () => encodeStrings,
    expectArrayBuffersEqual: () => expectArrayBuffersEqual,
    expectArraysClose: () => expectArraysClose,
    expectArraysEqual: () => expectArraysEqual,
    expectNumbersClose: () => expectNumbersClose,
    expectPromiseToFail: () => expectPromiseToFail,
    expectValuesInRange: () => expectValuesInRange,
    testEpsilon: () => testEpsilon
  });
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var TEST_EPSILON_FLOAT32 = 1e-3;
  var TEST_EPSILON_FLOAT16 = 0.1;
  function expectArraysClose(actual, expected, epsilon32) {
    if (epsilon32 == null) {
      epsilon32 = testEpsilon();
    }
    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon32));
  }
  function testEpsilon() {
    return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16;
  }
  function expectArraysPredicate(actual, expected, predicate) {
    let checkClassType = true;
    if (isTypedArray(actual) || isTypedArray(expected)) {
      checkClassType = false;
    }
    if (isTypedArray(actual) && isTypedArray(expected)) {
      checkClassType = true;
    }
    if (checkClassType) {
      const aType = actual.constructor.name;
      const bType = expected.constructor.name;
      if (aType !== bType) {
        throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`);
      }
    }
    if (Array.isArray(actual) && Array.isArray(expected)) {
      const actualShape = inferShape(actual);
      const expectedShape = inferShape(expected);
      if (!arraysEqual(actualShape, expectedShape)) {
        throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`);
      }
    }
    const actualFlat = isTypedArray(actual) ? actual : flatten(actual);
    const expectedFlat = isTypedArray(expected) ? expected : flatten(expected);
    if (actualFlat.length !== expectedFlat.length) {
      throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
Actual:   ${actualFlat}.
Expected: ${expectedFlat}.`);
    }
    for (let i = 0; i < expectedFlat.length; ++i) {
      const a = actualFlat[i];
      const e = expectedFlat[i];
      if (!predicate(a, e)) {
        throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.
Actual:   ${actualFlat}.
Expected: ${expectedFlat}.`);
      }
    }
  }
  function expectPromiseToFail(fn, done) {
    fn().then(() => done.fail(), () => done());
  }
  function expectArraysEqual(actual, expected) {
    const exp4 = typeof expected === "string" || typeof expected === "number" || typeof expected === "boolean" ? [expected] : expected;
    if (isString(actual) || isString(actual[0]) || isString(expected) || isString(expected[0])) {
      return expectArraysPredicate(actual, exp4, (a, b) => a == b);
    }
    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));
  }
  function expectNumbersClose(a, e, epsilon32) {
    if (epsilon32 == null) {
      epsilon32 = testEpsilon();
    }
    if (!areClose(a, e, epsilon32)) {
      throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);
    }
  }
  function areClose(a, e, epsilon32) {
    if (!isFinite(a) && !isFinite(e)) {
      return true;
    }
    if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon32) {
      return false;
    }
    return true;
  }
  function expectValuesInRange(actual, low, high) {
    for (let i = 0; i < actual.length; i++) {
      if (actual[i] < low || actual[i] > high) {
        throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);
      }
    }
  }
  function expectArrayBuffersEqual(actual, expected) {
    expect(new Float32Array(actual)).toEqual(new Float32Array(expected));
  }
  function encodeStrings(a) {
    for (let i = 0; i < a.length; i++) {
      const val = a[i];
      if (Array.isArray(val)) {
        encodeStrings(val);
      } else {
        a[i] = encodeString(val);
      }
    }
    return a;
  }
  /** @license See the LICENSE file. */
  var version = "3.3.0";
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function enableProdMode() {
    env().set("PROD", true);
  }
  function enableDebugMode() {
    env().set("DEBUG", true);
  }
  function disableDeprecationWarnings() {
    env().set("DEPRECATION_WARNINGS_ENABLED", false);
    console.warn(`TensorFlow.js deprecation warnings have been disabled.`);
  }
  function deprecationWarn(msg) {
    if (env().getBool("DEPRECATION_WARNINGS_ENABLED")) {
      console.warn(msg + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
    }
  }
  setDeprecationWarningFn(deprecationWarn);
  function disposeVariables() {
    ENGINE.disposeVariables();
  }
  function engine() {
    return ENGINE;
  }
  function memory() {
    return ENGINE.memory();
  }
  function profile(f) {
    return ENGINE.profile(f);
  }
  function tidy(nameOrFn, fn) {
    return ENGINE.tidy(nameOrFn, fn);
  }
  function dispose(container) {
    const tensors = getTensorsInContainer(container);
    tensors.forEach((tensor2) => tensor2.dispose());
  }
  function keep(result) {
    return ENGINE.keep(result);
  }
  function time(f) {
    return ENGINE.time(f);
  }
  function setBackend(backendName) {
    return ENGINE.setBackend(backendName);
  }
  function ready() {
    return ENGINE.ready();
  }
  function getBackend() {
    return ENGINE.backendName;
  }
  function removeBackend(name) {
    ENGINE.removeBackend(name);
  }
  function findBackend(name) {
    return ENGINE.findBackend(name);
  }
  function findBackendFactory(name) {
    return ENGINE.findBackendFactory(name);
  }
  function registerBackend(name, factory, priority = 1) {
    return ENGINE.registerBackend(name, factory, priority);
  }
  function backend() {
    return ENGINE.backend;
  }
  function setPlatform(platformName, platform) {
    env().setPlatform(platformName, platform);
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function add_(a, b) {
    let $a = convertToTensor(a, "a", "add");
    let $b = convertToTensor(b, "b", "add");
    [$a, $b] = makeTypesMatch($a, $b);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(Add, inputs);
  }
  var add2 = op({add_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function floorDiv_(a, b) {
    let $a = convertToTensor(a, "a", "floorDiv");
    let $b = convertToTensor(b, "b", "floorDiv");
    [$a, $b] = makeTypesMatch($a, $b);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(FloorDiv, inputs);
  }
  var floorDiv = op({floorDiv_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function div_(a, b) {
    let $a = convertToTensor(a, "a", "div");
    let $b = convertToTensor(b, "b", "div");
    [$a, $b] = makeTypesMatch($a, $b);
    if ($a.dtype === "int32" && $b.dtype === "int32") {
      return floorDiv($a, $b);
    }
    const inputs = {a: $a, b: $b};
    const attrs = {};
    return ENGINE.runKernel(RealDiv, inputs, attrs);
  }
  var div = op({div_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function mul_(a, b) {
    let $a = convertToTensor(a, "a", "mul");
    let $b = convertToTensor(b, "b", "mul");
    [$a, $b] = makeTypesMatch($a, $b);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(Multiply, inputs);
  }
  var mul = op({mul_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function abs_(x) {
    const $x = convertToTensor(x, "x", "abs");
    if ($x.dtype === "complex64") {
      const inputs = {x: $x};
      return ENGINE.runKernel(ComplexAbs, inputs);
    } else {
      const inputs = {x: $x};
      return ENGINE.runKernel(Abs, inputs);
    }
  }
  var abs = op({abs_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function acos_(x) {
    const $x = convertToTensor(x, "x", "acos");
    const inputs = {x: $x};
    return ENGINE.runKernel(Acos, inputs);
  }
  var acos = op({acos_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function acosh_(x) {
    const $x = convertToTensor(x, "x", "acosh");
    const inputs = {x: $x};
    return ENGINE.runKernel(Acosh, inputs);
  }
  var acosh = op({acosh_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function addN_(tensors) {
    assert(Array.isArray(tensors), () => "The argument passed to tf.addN() must be a list of tensors");
    assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);
    const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, "addN"));
    const firstTensor = $tensors[0];
    $tensors.forEach((t) => {
      if (t.dtype !== firstTensor.dtype) {
        throw new Error("All tensors passed to tf.addN() must have the same dtype");
      }
    });
    $tensors.forEach((t) => {
      if (!arraysEqual(t.shape, firstTensor.shape)) {
        throw new Error("All tensors passed to tf.addN() must have the same shape");
      }
    });
    const inputs = $tensors;
    return ENGINE.runKernel(AddN, inputs);
  }
  var addN = op({addN_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function all_(x, axis = null, keepDims = false) {
    const $x = convertToTensor(x, "x", "all", "bool");
    const inputs = {x: $x};
    const attrs = {axis, keepDims};
    return ENGINE.runKernel(All, inputs, attrs);
  }
  var all = op({all_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function any_(x, axis = null, keepDims = false) {
    const $x = convertToTensor(x, "x", "any", "bool");
    const inputs = {x: $x};
    const attrs = {axis, keepDims};
    return ENGINE.runKernel(Any, inputs, attrs);
  }
  var any = op({any_});
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function argMax_(x, axis = 0) {
    const $x = convertToTensor(x, "x", "argMax");
    const inputs = {x: $x};
    const attrs = {axis};
    return ENGINE.runKernel(ArgMax, inputs, attrs);
  }
  var argMax = op({argMax_});
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function argMin_(x, axis = 0) {
    const $x = convertToTensor(x, "x", "argMin");
    const inputs = {x: $x};
    const attrs = {axis};
    return ENGINE.runKernel(ArgMin, inputs, attrs);
  }
  var argMin = op({argMin_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function asin_(x) {
    const $x = convertToTensor(x, "x", "asin");
    const inputs = {x: $x};
    return ENGINE.runKernel(Asin, inputs);
  }
  var asin = op({asin_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function asinh_(x) {
    const $x = convertToTensor(x, "x", "asinh");
    const inputs = {x: $x};
    return ENGINE.runKernel(Asinh, inputs);
  }
  var asinh = op({asinh_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function atan_(x) {
    const $x = convertToTensor(x, "x", "atan");
    const inputs = {x: $x};
    return ENGINE.runKernel(Atan, inputs);
  }
  var atan = op({atan_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function atan2_(a, b) {
    let $a = convertToTensor(a, "a", "atan2");
    let $b = convertToTensor(b, "b", "atan2");
    [$a, $b] = makeTypesMatch($a, $b);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(Atan2, inputs);
  }
  var atan2 = op({atan2_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function atanh_(x) {
    const $x = convertToTensor(x, "x", "atanh");
    const inputs = {x: $x};
    return ENGINE.runKernel(Atanh, inputs);
  }
  var atanh = op({atanh_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function computeDilation2DInfo(inputShape, filterShape, strides, pad3, dataFormat = "NHWC", dilations) {
    const inputChannels = inputShape[3];
    const $filterShape = [...filterShape, inputChannels];
    const $dataFormat = convertConv2DDataFormat(dataFormat);
    return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad3, null, null, $dataFormat);
  }
  function computePool2DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = "channelsLast") {
    const [filterHeight, filterWidth] = parseTupleParam(filterSize);
    let filterShape;
    if (dataFormat === "channelsLast") {
      filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];
    } else if (dataFormat === "channelsFirst") {
      filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];
    } else {
      throw new Error(`Unknown dataFormat ${dataFormat}`);
    }
    return computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, false, dataFormat);
  }
  function computePool3DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = "NDHWC") {
    const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);
    let filterShape;
    let $dataFormat;
    if (dataFormat === "NDHWC") {
      $dataFormat = "channelsLast";
      filterShape = [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];
    } else if (dataFormat === "NCDHW") {
      $dataFormat = "channelsFirst";
      filterShape = [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];
    } else {
      throw new Error(`Unknown dataFormat ${dataFormat}`);
    }
    return computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, false, $dataFormat, roundingMode);
  }
  function computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, depthwise = false, dataFormat = "channelsLast") {
    let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];
    if (dataFormat === "channelsLast") {
      [batchSize, inHeight, inWidth, inChannels] = inShape;
    } else if (dataFormat === "channelsFirst") {
      [batchSize, inChannels, inHeight, inWidth] = inShape;
    } else {
      throw new Error(`Unknown dataFormat ${dataFormat}`);
    }
    const [filterHeight, filterWidth, , filterChannels] = filterShape;
    const [strideHeight, strideWidth] = parseTupleParam(strides);
    const [dilationHeight, dilationWidth] = parseTupleParam(dilations);
    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
    const {padInfo, outHeight, outWidth} = getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);
    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
    let outShape;
    if (dataFormat === "channelsFirst") {
      outShape = [batchSize, outChannels, outHeight, outWidth];
    } else if (dataFormat === "channelsLast") {
      outShape = [batchSize, outHeight, outWidth, outChannels];
    }
    return {
      batchSize,
      dataFormat,
      inHeight,
      inWidth,
      inChannels,
      outHeight,
      outWidth,
      outChannels,
      padInfo,
      strideHeight,
      strideWidth,
      filterHeight,
      filterWidth,
      effectiveFilterHeight,
      effectiveFilterWidth,
      dilationHeight,
      dilationWidth,
      inShape,
      outShape,
      filterShape
    };
  }
  function computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, depthwise = false, dataFormat = "channelsLast", roundingMode) {
    let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];
    if (dataFormat === "channelsLast") {
      [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;
    } else if (dataFormat === "channelsFirst") {
      [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;
    } else {
      throw new Error(`Unknown dataFormat ${dataFormat}`);
    }
    const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;
    const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);
    const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);
    const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);
    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
    const {padInfo, outDepth, outHeight, outWidth} = get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);
    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
    let outShape;
    if (dataFormat === "channelsFirst") {
      outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];
    } else if (dataFormat === "channelsLast") {
      outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];
    }
    return {
      batchSize,
      dataFormat,
      inDepth,
      inHeight,
      inWidth,
      inChannels,
      outDepth,
      outHeight,
      outWidth,
      outChannels,
      padInfo,
      strideDepth,
      strideHeight,
      strideWidth,
      filterDepth,
      filterHeight,
      filterWidth,
      effectiveFilterDepth,
      effectiveFilterHeight,
      effectiveFilterWidth,
      dilationDepth,
      dilationHeight,
      dilationWidth,
      inShape,
      outShape,
      filterShape
    };
  }
  function computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {
    if (zeroPad == null) {
      zeroPad = computeDefaultPad(inShape, fieldSize, stride);
    }
    const inputRows = inShape[0];
    const inputCols = inShape[1];
    const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
    const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
    return [outputRows, outputCols];
  }
  function computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {
    if (zeroPad == null) {
      zeroPad = computeDefaultPad(inShape, fieldSize, stride);
    }
    const inputDepth = inShape[0];
    const inputRows = inShape[1];
    const inputCols = inShape[2];
    const outputDepths = round((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
    const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
    const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
    return [outputDepths, outputRows, outputCols, outChannels];
  }
  function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {
    const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);
    return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);
  }
  function parseTupleParam(param) {
    if (typeof param === "number") {
      return [param, param, param];
    }
    if (param.length === 2) {
      return [param[0], param[1], 1];
    }
    return param;
  }
  function parse3TupleParam(param) {
    return typeof param === "number" ? [param, param, param] : param;
  }
  function getEffectiveFilterSize(filterSize, dilation) {
    if (dilation <= 1) {
      return filterSize;
    }
    return filterSize + (filterSize - 1) * (dilation - 1);
  }
  function getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {
    let padInfo;
    let outHeight;
    let outWidth;
    if (typeof pad3 === "number") {
      const padType = pad3 === 0 ? "VALID" : "NUMBER";
      padInfo = {top: pad3, bottom: pad3, left: pad3, right: pad3, type: padType};
      const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad3, roundingMode);
      outHeight = outShape[0];
      outWidth = outShape[1];
    } else if (pad3 === "same") {
      outHeight = Math.ceil(inHeight / strideHeight);
      outWidth = Math.ceil(inWidth / strideWidth);
      const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);
      const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);
      const top = Math.floor(padAlongHeight / 2);
      const bottom = padAlongHeight - top;
      const left = Math.floor(padAlongWidth / 2);
      const right = padAlongWidth - left;
      padInfo = {top, bottom, left, right, type: "SAME"};
    } else if (pad3 === "valid") {
      padInfo = {top: 0, bottom: 0, left: 0, right: 0, type: "VALID"};
      outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
      outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
    } else if (typeof pad3 === "object") {
      const top = dataFormat === "channelsLast" ? pad3[1][0] : pad3[2][0];
      const bottom = dataFormat === "channelsLast" ? pad3[1][1] : pad3[2][1];
      const left = dataFormat === "channelsLast" ? pad3[2][0] : pad3[3][0];
      const right = dataFormat === "channelsLast" ? pad3[2][1] : pad3[3][1];
      const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? "VALID" : "EXPLICIT";
      padInfo = {top, bottom, left, right, type: padType};
      outHeight = round((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);
      outWidth = round((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);
    } else {
      throw Error(`Unknown padding parameter: ${pad3}`);
    }
    return {padInfo, outHeight, outWidth};
  }
  function get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {
    let padInfo;
    let outDepth;
    let outHeight;
    let outWidth;
    if (typeof pad3 === "number") {
      const padType = pad3 === 0 ? "VALID" : "NUMBER";
      padInfo = {
        top: pad3,
        bottom: pad3,
        left: pad3,
        right: pad3,
        front: pad3,
        back: pad3,
        type: padType
      };
      const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad3, roundingMode);
      outDepth = outShape[0];
      outHeight = outShape[1];
      outWidth = outShape[2];
    } else if (pad3 === "same") {
      outDepth = Math.ceil(inDepth / strideDepth);
      outHeight = Math.ceil(inHeight / strideHeight);
      outWidth = Math.ceil(inWidth / strideWidth);
      const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;
      const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;
      const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;
      const front = Math.floor(padAlongDepth / 2);
      const back = padAlongDepth - front;
      const top = Math.floor(padAlongHeight / 2);
      const bottom = padAlongHeight - top;
      const left = Math.floor(padAlongWidth / 2);
      const right = padAlongWidth - left;
      padInfo = {top, bottom, left, right, front, back, type: "SAME"};
    } else if (pad3 === "valid") {
      padInfo = {
        top: 0,
        bottom: 0,
        left: 0,
        right: 0,
        front: 0,
        back: 0,
        type: "VALID"
      };
      outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);
      outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
      outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
    } else {
      throw Error(`Unknown padding parameter: ${pad3}`);
    }
    return {padInfo, outDepth, outHeight, outWidth};
  }
  function round(value, roundingMode) {
    if (!roundingMode) {
      return Math.trunc(value);
    }
    switch (roundingMode) {
      case "round":
        return Math.round(value);
      case "ceil":
        return Math.ceil(value);
      case "floor":
        return Math.floor(value);
      default:
        throw new Error(`Unknown roundingMode ${roundingMode}`);
    }
  }
  function tupleValuesAreOne(param) {
    const [dimA, dimB, dimC] = parseTupleParam(param);
    return dimA === 1 && dimB === 1 && dimC === 1;
  }
  function eitherStridesOrDilationsAreOne(strides, dilations) {
    return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);
  }
  function convertConv2DDataFormat(dataFormat) {
    if (dataFormat === "NHWC") {
      return "channelsLast";
    } else if (dataFormat === "NCHW") {
      return "channelsFirst";
    } else {
      throw new Error(`Unknown dataFormat ${dataFormat}`);
    }
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function reshape_(x, shape) {
    const $x = convertToTensor(x, "x", "reshape", "string_or_numeric");
    const inputs = {x: $x};
    const attrs = {shape};
    return ENGINE.runKernel(Reshape, inputs, attrs);
  }
  var reshape = op({reshape_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function avgPool_(x, filterSize, strides, pad3, dimRoundingMode) {
    const $x = convertToTensor(x, "x", "avgPool", "float32");
    const dilations = 1;
    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
      reshapedTo4D = true;
      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
    }
    assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);
    if (dimRoundingMode != null) {
      assert(isInt(pad3), () => `Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
    }
    const inputs = {x: x4D};
    const attrs = {filterSize, strides, pad: pad3, dimRoundingMode};
    let res = ENGINE.runKernel(AvgPool, inputs, attrs);
    res = cast(res, $x.dtype);
    if (reshapedTo4D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
    }
    return res;
  }
  var avgPool = op({avgPool_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function avgPool3d_(x, filterSize, strides, pad3, dimRoundingMode, dataFormat = "NDHWC") {
    const $x = convertToTensor(x, "x", "avgPool3d", "float32");
    let x5D = $x;
    let reshapedTo5D = false;
    if ($x.rank === 4) {
      reshapedTo5D = true;
      x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
    }
    assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
    assert(dataFormat === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
    if (dimRoundingMode != null) {
      assert(isInt(pad3), () => `Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
    }
    const inputs = {x: x5D};
    const attrs = {filterSize, strides, pad: pad3, dimRoundingMode, dataFormat};
    let res = ENGINE.runKernel(AvgPool3D, inputs, attrs);
    res = cast(res, x5D.dtype);
    if (reshapedTo5D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
    }
    return res;
  }
  var avgPool3d = op({avgPool3d_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function concat_(tensors, axis = 0) {
    assert(tensors.length >= 1, () => "Pass at least one tensor to concat");
    const $tensors = convertToTensorArray(tensors, "tensors", "concat", "string_or_numeric");
    if ($tensors[0].dtype === "complex64") {
      $tensors.forEach((tensor2) => {
        if (tensor2.dtype !== "complex64") {
          throw new Error(`Cannot concatenate complex64 tensors with a tensor
          with dtype ${tensor2.dtype}. `);
        }
      });
    }
    if ($tensors.length === 1) {
      return clone($tensors[0]);
    }
    const inputs = $tensors;
    const attr = {axis};
    return ENGINE.runKernel(Concat, inputs, attr);
  }
  var concat = op({concat_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sigmoid_(x) {
    const $x = convertToTensor(x, "x", "sigmoid");
    const inputs = {x: $x};
    return ENGINE.runKernel(Sigmoid, inputs);
  }
  var sigmoid = op({sigmoid_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function slice_(x, begin, size) {
    const $x = convertToTensor(x, "x", "slice", "string_or_numeric");
    if ($x.rank === 0) {
      throw new Error("Slicing scalar is not possible");
    }
    const inputs = {x: $x};
    const attrs = {begin, size};
    return ENGINE.runKernel(Slice, inputs, attrs);
  }
  var slice = op({slice_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function tanh_(x) {
    const $x = convertToTensor(x, "x", "tanh");
    const inputs = {x: $x};
    return ENGINE.runKernel(Tanh, inputs);
  }
  var tanh2 = op({tanh_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
    const $forgetBias = convertToTensor(forgetBias, "forgetBias", "basicLSTMCell");
    const $lstmKernel = convertToTensor(lstmKernel, "lstmKernel", "basicLSTMCell");
    const $lstmBias = convertToTensor(lstmBias, "lstmBias", "basicLSTMCell");
    const $data = convertToTensor(data, "data", "basicLSTMCell");
    const $c = convertToTensor(c, "c", "basicLSTMCell");
    const $h = convertToTensor(h, "h", "basicLSTMCell");
    const combined = concat([$data, $h], 1);
    const weighted = matMul(combined, $lstmKernel);
    const res = add2(weighted, $lstmBias);
    const batchSize = res.shape[0];
    const sliceCols = res.shape[1] / 4;
    const sliceSize = [batchSize, sliceCols];
    const i = slice(res, [0, 0], sliceSize);
    const j = slice(res, [0, sliceCols], sliceSize);
    const f = slice(res, [0, sliceCols * 2], sliceSize);
    const o = slice(res, [0, sliceCols * 3], sliceSize);
    const newC = add2(mul(sigmoid(i), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));
    const newH = mul(tanh2(newC), sigmoid(o));
    return [newC, newH];
  }
  var basicLSTMCell = op({basicLSTMCell_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function batchToSpaceND_(x, blockShape, crops) {
    const $x = convertToTensor(x, "x", "batchToSpaceND");
    const prod5 = blockShape.reduce((a, b) => a * b);
    assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);
    assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);
    assert($x.shape[0] % prod5 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod5}`);
    const inputs = {x: $x};
    const attrs = {blockShape, crops};
    return ENGINE.runKernel(BatchToSpaceND, inputs, attrs);
  }
  var batchToSpaceND = op({batchToSpaceND_});
  function xAs4D(x) {
    let x4D;
    if (x.rank === 0 || x.rank === 1) {
      x4D = reshape(x, [1, 1, 1, x.size]);
    } else if (x.rank === 2) {
      x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);
    } else if (x.rank === 3) {
      x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
    } else {
      x4D = x;
    }
    return x4D;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function batchNorm_(x, mean4, variance, offset, scale22, varianceEpsilon) {
    if (varianceEpsilon == null) {
      varianceEpsilon = 1e-3;
    }
    const $x = convertToTensor(x, "x", "batchNorm");
    const $mean = convertToTensor(mean4, "mean", "batchNorm");
    const $variance = convertToTensor(variance, "variance", "batchNorm");
    let $scale;
    if (scale22 != null) {
      $scale = convertToTensor(scale22, "scale", "batchNorm");
    }
    let $offset;
    if (offset != null) {
      $offset = convertToTensor(offset, "offset", "batchNorm");
    }
    assert($mean.rank === $variance.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
    assert($offset == null || $mean.rank === $offset.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
    assert($scale == null || $mean.rank === $scale.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
    const x4D = xAs4D($x);
    const inputs = {
      x: x4D,
      scale: $scale,
      offset: $offset,
      mean: $mean,
      variance: $variance
    };
    const attrs = {varianceEpsilon};
    const res = ENGINE.runKernel(FusedBatchNorm, inputs, attrs);
    return reshape(res, $x.shape);
  }
  var batchNorm = op({batchNorm_});
  function batchNorm2d_(x, mean4, variance, offset, scale22, varianceEpsilon) {
    const $x = convertToTensor(x, "x", "batchNorm");
    const $mean = convertToTensor(mean4, "mean", "batchNorm");
    const $variance = convertToTensor(variance, "variance", "batchNorm");
    let $scale;
    if (scale22 != null) {
      $scale = convertToTensor(scale22, "scale", "batchNorm");
    }
    let $offset;
    if (offset != null) {
      $offset = convertToTensor(offset, "offset", "batchNorm");
    }
    assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`);
    assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`);
    assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`);
    if ($scale != null) {
      assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`);
    }
    if ($offset != null) {
      assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`);
    }
    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
  }
  var batchNorm2d = op({batchNorm2d_});
  function batchNorm3d_(x, mean4, variance, offset, scale22, varianceEpsilon) {
    const $x = convertToTensor(x, "x", "batchNorm");
    const $mean = convertToTensor(mean4, "mean", "batchNorm");
    const $variance = convertToTensor(variance, "variance", "batchNorm");
    let $scale;
    if (scale22 != null) {
      $scale = convertToTensor(scale22, "scale", "batchNorm");
    }
    let $offset;
    if (offset != null) {
      $offset = convertToTensor(offset, "offset", "batchNorm");
    }
    assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`);
    assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`);
    assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`);
    if ($scale != null) {
      assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`);
    }
    if ($offset != null) {
      assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`);
    }
    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
  }
  var batchNorm3d = op({batchNorm3d_});
  function batchNorm4d_(x, mean4, variance, offset, scale22, varianceEpsilon) {
    const $x = convertToTensor(x, "x", "batchNorm");
    const $mean = convertToTensor(mean4, "mean", "batchNorm");
    const $variance = convertToTensor(variance, "variance", "batchNorm");
    let $scale;
    if (scale22 != null) {
      $scale = convertToTensor(scale22, "scale", "batchNorm");
    }
    let $offset;
    if (offset != null) {
      $offset = convertToTensor(offset, "offset", "batchNorm");
    }
    assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`);
    assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`);
    assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`);
    if ($scale != null) {
      assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`);
    }
    if ($offset != null) {
      assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`);
    }
    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
  }
  var batchNorm4d = op({batchNorm4d_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function bincount_(x, weights, size) {
    const $x = convertToTensor(x, "x", "bincount");
    const $weights = convertToTensor(weights, "weights", "bincount");
    assert($x.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${$x.dtype}`);
    assert(size >= 0, () => `size must be non-negative, but got ${size}.`);
    assert($weights.size === $x.size || $weights.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`);
    const inputs = {x: $x, weights: $weights};
    const attrs = {size};
    return ENGINE.runKernel(Bincount, inputs, attrs);
  }
  var bincount = op({bincount_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function broadcastTo_(x, shape) {
    let input2 = convertToTensor(x, "broadcastTo", "x");
    const xShape = input2.shape;
    if (shape.some((d) => !(d > 0) || d % 1 !== 0)) {
      throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);
    }
    if (shape.length < input2.rank) {
      throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);
    }
    if (shape.length > input2.rank) {
      const newShape = input2.shape.slice();
      while (newShape.length < shape.length) {
        newShape.unshift(1);
      }
      input2 = reshape(input2, newShape);
    }
    const inputShape = input2.shape;
    const reps = Array.from(shape);
    for (let i = shape.length - 1; i >= 0; i--) {
      if (inputShape[i] === shape[i]) {
        reps[i] = 1;
      } else if (input2.shape[i] !== 1) {
        throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);
      }
    }
    const axes = reps.map((n, i) => n > 1 ? i : -1).filter((i) => i >= 0);
    if (axes.length === 0) {
      return clone(input2);
    }
    const inputs = {x: input2};
    const attrs = {reps};
    return ENGINE.runKernel(Tile, inputs, attrs);
  }
  var broadcastTo = op({broadcastTo_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function ceil_(x) {
    const $x = convertToTensor(x, "x", "ceil");
    const inputs = {x: $x};
    return ENGINE.runKernel(Ceil, inputs);
  }
  var ceil = op({ceil_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function clipByValue_(x, clipValueMin, clipValueMax) {
    const $x = convertToTensor(x, "x", "clipByValue");
    assert(clipValueMin <= clipValueMax, () => `Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);
    const inputs = {x: $x};
    const attrs = {clipValueMin, clipValueMax};
    return ENGINE.runKernel(ClipByValue, inputs, attrs);
  }
  var clipByValue = op({clipByValue_});
  function concat1d_(tensors) {
    return concat(tensors, 0);
  }
  var concat1d = op({concat1d_});
  function concat2d_(tensors, axis) {
    return concat(tensors, axis);
  }
  var concat2d = op({concat2d_});
  function concat3d_(tensors, axis) {
    return concat(tensors, axis);
  }
  var concat3d = op({concat3d_});
  function concat4d_(tensors, axis) {
    return concat(tensors, axis);
  }
  var concat4d = op({concat4d_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv2d_(x, filter, strides, pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
    const $x = convertToTensor(x, "x", "conv2d");
    const $filter = convertToTensor(filter, "filter", "conv2d");
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
      reshapedTo4D = true;
      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
    }
    assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
    assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
    if (dimRoundingMode != null) {
      assert(isInt(pad3), () => `Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
    }
    const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
    assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`);
    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
    const inputs = {x: x4D, filter: $filter};
    const attrs = {strides, pad: pad3, dataFormat, dilations, dimRoundingMode};
    const res = ENGINE.runKernel(Conv2D, inputs, attrs);
    if (reshapedTo4D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
    }
    return res;
  }
  var conv2d = op({conv2d_});
  function conv1d_(x, filter, stride, pad3, dataFormat = "NWC", dilation = 1, dimRoundingMode) {
    const $x = convertToTensor(x, "x", "conv1d");
    const $filter = convertToTensor(filter, "filter", "conv1d");
    let x3D = $x;
    let reshapedTo3D = false;
    if ($x.rank === 2) {
      reshapedTo3D = true;
      x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);
    }
    assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);
    assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`);
    if (dimRoundingMode != null) {
      assert(isInt(pad3), () => `Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
    }
    assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`);
    assert(eitherStridesOrDilationsAreOne(stride, dilation), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`);
    assert(dataFormat === "NWC", () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);
    const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);
    const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);
    const strides = [1, stride];
    const dilations = [1, dilation];
    const conv2dDataFormat = "NHWC";
    const res = conv2d(input4D, filter4D, strides, pad3, conv2dDataFormat, dilations, dimRoundingMode);
    if (reshapedTo3D) {
      return reshape(res, [res.shape[2], res.shape[3]]);
    }
    return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);
  }
  var conv1d = op({conv1d_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv2DBackpropInput_(xShape, dy, filter, strides, pad3, dataFormat = "NHWC", dimRoundingMode) {
    assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
    let xShape4D = xShape;
    let dy4D = dy;
    let reshapedTo4D = false;
    if (dy.rank === 3) {
      reshapedTo4D = true;
      dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
      xShape4D = [1, xShape[0], xShape[1], xShape[2]];
    }
    assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`);
    assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`);
    assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);
    const inDepth = dataFormat === "NHWC" ? xShape4D[3] : xShape4D[1];
    const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
    assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`);
    assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`);
    if (dimRoundingMode != null) {
      assert(isInt(pad3), () => `Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
    }
    const inputs = {dy: dy4D, filter};
    const attrs = {strides, pad: pad3, dataFormat, dimRoundingMode, inputShape: xShape4D};
    const res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs);
    if (reshapedTo4D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
    }
    return res;
  }
  var conv2DBackpropInput = op({conv2DBackpropInput_});
  function conv2dTranspose_(x, filter, outputShape, strides, pad3, dimRoundingMode) {
    const $x = convertToTensor(x, "x", "conv2dTranspose");
    const $filter = convertToTensor(filter, "filter", "conv2dTranspose");
    return conv2DBackpropInput(outputShape, $x, $filter, strides, pad3, "NHWC", dimRoundingMode);
  }
  var conv2dTranspose = op({conv2dTranspose_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv3d_(x, filter, strides, pad3, dataFormat = "NDHWC", dilations = [1, 1, 1]) {
    const $x = convertToTensor(x, "x", "conv3d");
    const $filter = convertToTensor(filter, "filter", "conv3d");
    let x5D = $x;
    let reshapedTo5D = false;
    if ($x.rank === 4) {
      reshapedTo5D = true;
      x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
    }
    assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);
    assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`);
    assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`);
    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
    assert(dataFormat === "NDHWC", () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);
    const inputs = {x: x5D, filter: $filter};
    const attrs = {strides, pad: pad3, dataFormat, dilations};
    const res = ENGINE.runKernel(Conv3D, inputs, attrs);
    if (reshapedTo5D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
    }
    return res;
  }
  var conv3d = op({conv3d_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv3DBackpropInput_(xShape, dy, filter, strides, pad3) {
    assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
    let xShape5D = xShape;
    let dy5D = dy;
    let reshapedTo5D = false;
    if (dy.rank === 4) {
      reshapedTo5D = true;
      dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
      xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];
    }
    const inDepth = xShape5D[4];
    const outDepth = dy5D.shape[4];
    assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`);
    assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`);
    assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`);
    assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`);
    assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);
    const inputs = {dy: dy5D, filter};
    const attrs = {pad: pad3, strides, inputShape: xShape5D};
    const res = ENGINE.runKernel(Conv3DBackpropInputV2, inputs, attrs);
    if (reshapedTo5D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
    }
    return res;
  }
  var conv3DBackpropInput = op({conv3DBackpropInput_});
  function conv3dTranspose_(x, filter, outputShape, strides, pad3) {
    const $x = convertToTensor(x, "x", "conv3dTranspose");
    const $filter = convertToTensor(filter, "filter", "conv3dTranspose");
    return conv3DBackpropInput(outputShape, $x, $filter, strides, pad3);
  }
  var conv3dTranspose = op({conv3dTranspose_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function cos_(x) {
    const $x = convertToTensor(x, "x", "cos");
    const inputs = {x: $x};
    return ENGINE.runKernel(Cos, inputs);
  }
  var cos = op({cos_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function cosh_(x) {
    const $x = convertToTensor(x, "x", "cosh");
    const inputs = {x: $x};
    return ENGINE.runKernel(Cosh, inputs);
  }
  var cosh = op({cosh_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function cumsum_(x, axis = 0, exclusive = false, reverse5 = false) {
    const $x = convertToTensor(x, "x", "cumsum");
    const inputs = {x: $x};
    const attrs = {axis, exclusive, reverse: reverse5};
    return ENGINE.runKernel(Cumsum, inputs, attrs);
  }
  var cumsum = op({cumsum_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function denseBincount_(x, weights, size, binaryOutput = false) {
    const $x = convertToTensor(x, "x", "denseBincount");
    const $weights = convertToTensor(weights, "weights", "denseBincount");
    assert($x.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${$x.dtype}`);
    assert($x.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${$x.rank}.`);
    assert(size >= 0, () => `size must be non-negative, but got ${size}.`);
    assert($weights.size === $x.size || $weights.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${$x.shape}, weights shape: ${$weights.shape}.`);
    const inputs = {x: $x, weights: $weights};
    const attrs = {size, binaryOutput};
    return ENGINE.runKernel(DenseBincount, inputs, attrs);
  }
  var denseBincount = op({denseBincount_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function depthToSpace_(x, blockSize, dataFormat = "NHWC") {
    const $x = convertToTensor(x, "x", "depthToSpace");
    const inputHeight = dataFormat === "NHWC" ? $x.shape[1] : $x.shape[2];
    const inputWidth = dataFormat === "NHWC" ? $x.shape[2] : $x.shape[3];
    const inputDepth = dataFormat === "NHWC" ? $x.shape[3] : $x.shape[1];
    assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
    ${inputHeight} and ${blockSize}  for depthToSpace with input shape
    ${$x.shape}`);
    assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
    ${inputWidth} and ${blockSize} for depthToSpace with input shape
        ${$x.shape}`);
    assert(inputDepth % (blockSize * blockSize) === 0, () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);
    const inputs = {x: $x};
    const attrs = {blockSize, dataFormat};
    return ENGINE.runKernel(DepthToSpace, inputs, attrs);
  }
  var depthToSpace = op({depthToSpace_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function depthwiseConv2d_(x, filter, strides, pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
    const $x = convertToTensor(x, "x", "depthwiseConv2d");
    const $filter = convertToTensor(filter, "filter", "depthwiseConv2d");
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
      reshapedTo4D = true;
      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
    }
    assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
    assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
    assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
    if (dimRoundingMode != null) {
      assert(isInt(pad3), () => `Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
    }
    const inputs = {x: x4D, filter: $filter};
    const attrs = {strides, pad: pad3, dataFormat, dilations, dimRoundingMode};
    const res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs);
    if (reshapedTo4D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
    }
    return res;
  }
  var depthwiseConv2d = op({depthwiseConv2d_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function diag_(x) {
    const $x = convertToTensor(x, "x", "diag");
    const inputs = {x: $x};
    return ENGINE.runKernel(Diag, inputs);
  }
  var diag = op({diag_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function dilation2d_(x, filter, strides, pad3, dilations = [1, 1], dataFormat = "NHWC") {
    const $x = convertToTensor(x, "x", "dilation2d");
    const $filter = convertToTensor(filter, "filter", "dilation2d");
    assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`);
    assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`);
    assert(dataFormat === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
      reshapedTo4D = true;
    }
    const inputs = {x: x4D, filter: $filter};
    const attrs = {strides, pad: pad3, dilations};
    const res = ENGINE.runKernel(Dilation2D, inputs, attrs);
    if (reshapedTo4D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
    }
    return res;
  }
  var dilation2d = op({dilation2d_});
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function getBroadcastDims(inShape, outShape) {
    const inRank = inShape.length;
    const dims = [];
    for (let i = 0; i < inRank; i++) {
      const dim = inRank - 1 - i;
      const a = inShape[dim] || 1;
      const b = outShape[outShape.length - 1 - i] || 1;
      if (b > 1 && a === 1) {
        dims.unshift(dim);
      }
    }
    return dims;
  }
  function getReductionAxes(inShape, outShape) {
    const result = [];
    for (let i = 0; i < outShape.length; i++) {
      const inDim = inShape[inShape.length - i - 1];
      const outAxis = outShape.length - i - 1;
      const outDim = outShape[outAxis];
      if (inDim == null || inDim === 1 && outDim > 1) {
        result.unshift(outAxis);
      }
    }
    return result;
  }
  function assertAndGetBroadcastShape(shapeA, shapeB) {
    const result = [];
    const l = Math.max(shapeA.length, shapeB.length);
    for (let i = 0; i < l; i++) {
      let a = shapeA[shapeA.length - i - 1];
      if (a == null) {
        a = 1;
      }
      let b = shapeB[shapeB.length - i - 1];
      if (b == null) {
        b = 1;
      }
      if (a === 1) {
        result.unshift(b);
      } else if (b === 1) {
        result.unshift(a);
      } else if (a !== b) {
        const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;
        throw Error(errMsg);
      } else {
        result.unshift(a);
      }
    }
    return result;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function equal_(a, b) {
    let $a = convertToTensor(a, "a", "equal");
    let $b = convertToTensor(b, "b", "equal");
    [$a, $b] = makeTypesMatch($a, $b);
    assertAndGetBroadcastShape($a.shape, $b.shape);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(Equal, inputs);
  }
  var equal = op({equal_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function where_(condition, a, b) {
    const $a = convertToTensor(a, "a", "where");
    const $b = convertToTensor(b, "b", "where");
    const $condition = convertToTensor(condition, "condition", "where", "bool");
    const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);
    const $broadcastedA = broadcastTo($a, broadcastShape);
    const $broadcastedB = broadcastTo($b, broadcastShape);
    if ($condition.rank === 1) {
      assert($condition.shape[0] === $a.shape[0], () => "The first dimension of `a` must match the size of `condition`.");
    }
    if ($condition.rank !== 1) {
      assertShapesMatch($condition.shape, $broadcastedB.shape, "Error in where: ");
    }
    const inputs = {
      condition: $condition,
      t: $broadcastedA,
      e: $broadcastedB
    };
    return ENGINE.runKernel(Select, inputs);
  }
  var where = op({where_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function zerosLike_(x) {
    const $x = convertToTensor(x, "x", "zerosLike");
    const inputs = {x: $x};
    return ENGINE.runKernel(ZerosLike, inputs);
  }
  var zerosLike = op({zerosLike_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function divNoNan_(a, b) {
    let $a = convertToTensor(a, "a", "div");
    let $b = convertToTensor(b, "b", "div");
    [$a, $b] = makeTypesMatch($a, $b);
    const divResult = div($a, $b);
    const zeros4 = zerosLike(divResult);
    const bEqualsZero = equal($b, zeros4);
    return where(bEqualsZero, zeros4, divResult);
  }
  var divNoNan = op({divNoNan_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function dot_(t1, t2) {
    const $t1 = convertToTensor(t1, "t1", "dot");
    const $t2 = convertToTensor(t2, "t2", "dot");
    assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);
    const t1Inner = $t1.rank === 1 ? $t1.size : $t1.shape[1];
    const t2Inner = $t2.rank === 1 ? $t2.size : $t2.shape[0];
    assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`);
    if ($t1.rank === 1 && $t2.rank === 1) {
      const t12D = reshape($t1, [1, -1]);
      const t22D = reshape($t2, [-1, 1]);
      const t1t2 = matMul(t12D, t22D);
      return reshape(t1t2, []);
    } else if ($t1.rank === 1 && $t2.rank === 2) {
      const t12D = reshape($t1, [1, -1]);
      const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
      const t1t2 = matMul(t12D, t22D);
      return reshape(t1t2, [t1t2.size]);
    } else if ($t1.rank === 2 && $t2.rank === 1) {
      const t22D = reshape($t2, [-1, 1]);
      const t1t2 = matMul($t1, t22D);
      return reshape(t1t2, [t1t2.size]);
    } else {
      const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
      const t1t2 = matMul($t1, t22D);
      return t1t2;
    }
  }
  var dot = op({dot_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function elu_(x) {
    const $x = convertToTensor(x, "x", "elu");
    const inputs = {x: $x};
    return ENGINE.runKernel(Elu, inputs);
  }
  var elu = op({elu_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function erf_(x) {
    let $x = convertToTensor(x, "x", "erf");
    assert($x.dtype === "int32" || $x.dtype === "float32", () => "Input dtype must be `int32` or `float32`.");
    if ($x.dtype === "int32") {
      $x = cast($x, "float32");
    }
    const inputs = {x: $x};
    return ENGINE.runKernel(Erf, inputs);
  }
  var erf = op({erf_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function exp_(x) {
    const $x = convertToTensor(x, "x", "exp");
    const inputs = {x: $x};
    return ENGINE.runKernel(Exp, inputs);
  }
  var exp = op({exp_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function expandDims_(x, axis = 0) {
    const $x = convertToTensor(x, "x", "expandDims", "string_or_numeric");
    assert(axis <= $x.rank, () => "Axis must be <= rank of the tensor");
    const inputs = {input: $x};
    const attrs = {dim: axis};
    return ENGINE.runKernel(ExpandDims, inputs, attrs);
  }
  var expandDims = op({expandDims_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function expm1_(x) {
    const $x = convertToTensor(x, "x", "expm1");
    const inputs = {x: $x};
    return ENGINE.runKernel(Expm1, inputs);
  }
  var expm1 = op({expm1_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function tile_(x, reps) {
    const $x = convertToTensor(x, "x", "tile", "string_or_numeric");
    assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);
    const inputs = {x: $x};
    const attrs = {reps};
    return ENGINE.runKernel(Tile, inputs, attrs);
  }
  var tile = op({tile_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function eye_(numRows, numColumns, batchShape, dtype = "float32") {
    if (numColumns == null) {
      numColumns = numRows;
    }
    const buff = buffer([numRows, numColumns], dtype);
    const n = numRows <= numColumns ? numRows : numColumns;
    for (let i = 0; i < n; ++i) {
      buff.set(1, i, i);
    }
    const out = reshape(buff.toTensor(), [numRows, numColumns]);
    if (batchShape == null) {
      return out;
    } else {
      if (batchShape.length === 1) {
        return tile(expandDims(out, 0), [batchShape[0], 1, 1]);
      } else if (batchShape.length === 2) {
        return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);
      } else if (batchShape.length === 3) {
        return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [
          batchShape[0],
          batchShape[1],
          batchShape[2],
          1,
          1
        ]);
      } else {
        throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`);
      }
    }
  }
  var eye = op({eye_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fill(shape, value, dtype) {
    const attrs = {shape, value, dtype};
    return ENGINE.runKernel(Fill, {}, attrs);
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function floor_(x) {
    const $x = convertToTensor(x, "x", "floor");
    const inputs = {x: $x};
    return ENGINE.runKernel(Floor, inputs);
  }
  var floor = op({floor_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function gather_(x, indices, axis = 0, batchDims = 0) {
    const $x = convertToTensor(x, "x", "gather");
    const $indices = convertToTensor(indices, "indices", "gather", "int32");
    const inputs = {x: $x, indices: $indices};
    const attrs = {axis, batchDims};
    return ENGINE.runKernel(GatherV2, inputs, attrs);
  }
  var gather = op({gather_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function greater_(a, b) {
    let $a = convertToTensor(a, "a", "greater");
    let $b = convertToTensor(b, "b", "greater");
    [$a, $b] = makeTypesMatch($a, $b);
    assertAndGetBroadcastShape($a.shape, $b.shape);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(Greater, inputs);
  }
  var greater = op({greater_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function greaterEqual_(a, b) {
    let $a = convertToTensor(a, "a", "greaterEqual");
    let $b = convertToTensor(b, "b", "greaterEqual");
    [$a, $b] = makeTypesMatch($a, $b);
    assertAndGetBroadcastShape($a.shape, $b.shape);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(GreaterEqual, inputs);
  }
  var greaterEqual = op({greaterEqual_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function imag_(input2) {
    const $input = convertToTensor(input2, "input", "imag");
    const inputs = {input: $input};
    return ENGINE.runKernel(Imag, inputs);
  }
  var imag = op({imag_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function isFinite_(x) {
    const $x = convertToTensor(x, "x", "isFinite");
    const inputs = {x: $x};
    return ENGINE.runKernel(IsFinite, inputs);
  }
  var isFinite2 = op({isFinite_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function isInf_(x) {
    const $x = convertToTensor(x, "x", "isInf");
    const inputs = {x: $x};
    return ENGINE.runKernel(IsInf, inputs);
  }
  var isInf = op({isInf_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function isNaN_(x) {
    const $x = convertToTensor(x, "x", "isNaN");
    const inputs = {x: $x};
    return ENGINE.runKernel(IsNan, inputs);
  }
  var isNaN2 = op({isNaN_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function leakyRelu_(x, alpha = 0.2) {
    const $x = convertToTensor(x, "x", "leakyRelu");
    const inputs = {x: $x};
    const attrs = {alpha};
    return ENGINE.runKernel(LeakyRelu, inputs, attrs);
  }
  var leakyRelu = op({leakyRelu_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function less_(a, b) {
    let $a = convertToTensor(a, "a", "less");
    let $b = convertToTensor(b, "b", "less");
    [$a, $b] = makeTypesMatch($a, $b);
    assertAndGetBroadcastShape($a.shape, $b.shape);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(Less, inputs);
  }
  var less = op({less_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function lessEqual_(a, b) {
    let $a = convertToTensor(a, "a", "lessEqual");
    let $b = convertToTensor(b, "b", "lessEqual");
    [$a, $b] = makeTypesMatch($a, $b);
    assertAndGetBroadcastShape($a.shape, $b.shape);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(LessEqual, inputs);
  }
  var lessEqual = op({lessEqual_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function linspace(start, stop, num) {
    if (num <= 0) {
      throw new Error("The number of values should be positive.");
    }
    const attrs = {start, stop, num};
    return ENGINE.runKernel(LinSpace, {}, attrs);
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
    const $x = convertToTensor(x, "x", "localResponseNormalization");
    assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
               rank ${$x.rank}.`);
    assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
      reshapedTo4D = true;
      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
    }
    const inputs = {x: x4D};
    const attrs = {depthRadius, bias, alpha, beta};
    const res = ENGINE.runKernel(LRN, inputs, attrs);
    if (reshapedTo4D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
    } else {
      return res;
    }
  }
  var localResponseNormalization = op({localResponseNormalization_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function log_(x) {
    const $x = convertToTensor(x, "x", "log");
    const inputs = {x: $x};
    return ENGINE.runKernel(Log, inputs);
  }
  var log = op({log_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function log1p_(x) {
    const $x = convertToTensor(x, "x", "log1p");
    const inputs = {x: $x};
    return ENGINE.runKernel(Log1p, inputs);
  }
  var log1p = op({log1p_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function grad(f) {
    assert(isFunction(f), () => "The f passed in grad(f) must be a function");
    return (x, dy) => {
      const $x = convertToTensor(x, "x", "tf.grad", "string_or_numeric");
      const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grad") : null;
      return ENGINE.tidy(() => {
        const {value, grads: grads2} = ENGINE.gradients(() => f($x), [$x], $dy);
        if ($dy != null) {
          assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)");
        }
        checkGrads(grads2);
        return grads2[0];
      });
    };
  }
  function grads(f) {
    assert(isFunction(f), () => "The f passed in grads(f) must be a function");
    return (args, dy) => {
      assert(Array.isArray(args), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
      const $args = convertToTensorArray(args, "args", "tf.grads", "string_or_numeric");
      const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grads") : null;
      return ENGINE.tidy(() => {
        const {value, grads: grads2} = ENGINE.gradients(() => f(...$args), $args, $dy);
        if ($dy != null) {
          assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
        }
        checkGrads(grads2);
        return grads2;
      });
    };
  }
  function valueAndGrad(f) {
    assert(isFunction(f), () => "The f passed in valueAndGrad(f) must be a function");
    return (x, dy) => {
      assert(x instanceof Tensor, () => "The x passed in valueAndGrad(f)(x) must be a tensor");
      assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
      const {grads: grads2, value} = ENGINE.gradients(() => f(x), [x], dy);
      checkGrads(grads2);
      return {grad: grads2[0], value};
    };
  }
  function valueAndGrads(f) {
    assert(isFunction(f), () => "The f passed in valueAndGrads(f) must be a function");
    return (args, dy) => {
      assert(Array.isArray(args) && args.every((arg) => arg instanceof Tensor), () => "The args passed in valueAndGrads(f)(args) must be array of tensors");
      assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
      const res = ENGINE.gradients(() => f(...args), args, dy);
      if (dy != null) {
        assertShapesMatch(res.value.shape, dy.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
      }
      checkGrads(res.grads);
      return res;
    };
  }
  function variableGrads(f, varList) {
    assert(isFunction(f), () => "The f passed in variableGrads(f) must be a function");
    assert(varList == null || Array.isArray(varList) && varList.every((v) => v instanceof Variable), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
    const specifiedVarList = varList != null;
    if (!specifiedVarList) {
      varList = [];
      for (const varName in ENGINE.registeredVariables) {
        varList.push(ENGINE.registeredVariables[varName]);
      }
    }
    const specifiedNonTrainable = specifiedVarList ? varList.filter((variable2) => !variable2.trainable) : null;
    const originalVarCount = varList.length;
    varList = varList.filter((variable2) => variable2.trainable);
    assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);
    const allowNoGradients = true;
    const {value, grads: grads2} = ENGINE.gradients(f, varList, null, allowNoGradients);
    assert(grads2.some((g) => g != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().");
    assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);
    const namedGrads = {};
    varList.forEach((v, i) => {
      if (grads2[i] != null) {
        namedGrads[v.name] = grads2[i];
      }
    });
    if (specifiedNonTrainable != null) {
      specifiedNonTrainable.forEach((v) => namedGrads[v.name] = null);
    }
    return {value, grads: namedGrads};
  }
  function customGrad(f) {
    return ENGINE.customGrad(f);
  }
  function checkGrads(grads2) {
    const numNullGradients = grads2.filter((g) => g == null).length;
    if (numNullGradients > 0) {
      throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
    the f you passed encloses all operations that lead from x to y.`);
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function neg_(x) {
    const $x = convertToTensor(x, "x", "neg");
    const inputs = {x: $x};
    return ENGINE.runKernel(Neg, inputs);
  }
  var neg = op({neg_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function softplus_(x) {
    const $x = convertToTensor(x, "x", "softplus");
    const inputs = {x: $x};
    return ENGINE.runKernel(Softplus, inputs);
  }
  var softplus = op({softplus_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function logSigmoid_(x) {
    const $x = convertToTensor(x, "x", "logSigmoid");
    const customOp = customGrad((x2) => {
      const value = neg(softplus(neg(x2)));
      const gradFunc = (dy) => {
        const derX = mul(dy, sigmoid(neg(x2)));
        return derX;
      };
      return {value, gradFunc};
    });
    return customOp($x);
  }
  var logSigmoid = op({logSigmoid_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function max_(x, axis = null, keepDims = false) {
    const $x = convertToTensor(x, "x", "max");
    const inputs = {x: $x};
    const attrs = {reductionIndices: axis, keepDims};
    return ENGINE.runKernel(Max, inputs, attrs);
  }
  var max = op({max_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sub_(a, b) {
    let $a = convertToTensor(a, "a", "sub");
    let $b = convertToTensor(b, "b", "sub");
    [$a, $b] = makeTypesMatch($a, $b);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(Sub, inputs);
  }
  var sub = op({sub_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sum_(x, axis = null, keepDims = false) {
    let $x = convertToTensor(x, "x", "sum");
    if ($x.dtype === "bool") {
      $x = cast($x, "int32");
    }
    const inputs = {x: $x};
    const attrs = {axis, keepDims};
    return ENGINE.runKernel(Sum, inputs, attrs);
  }
  var sum2 = op({sum_});
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function logSoftmax_(logits, axis = -1) {
    const $logits = convertToTensor(logits, "logits", "logSoftmax");
    if (axis === -1) {
      axis = $logits.rank - 1;
    }
    if (axis !== $logits.rank - 1) {
      throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);
    }
    const customOp = customGrad((logits2, save) => {
      const keepDims = true;
      const xMax = max(logits2, axis, true);
      const shifted = sub(logits2, xMax);
      const value = sub(cast(shifted, "float32"), log(sum2(exp(shifted), axis, keepDims)));
      save([value]);
      const gradFunc = (dy, saved) => {
        const [value2] = saved;
        const keepDims2 = true;
        const softmax6 = exp(value2);
        return sub(dy, mul(sum2(dy, axis, keepDims2), softmax6));
      };
      return {value, gradFunc};
    });
    return customOp($logits);
  }
  var logSoftmax = op({logSoftmax_});
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function axesAreInnerMostDims(axes, rank) {
    for (let i = 0; i < axes.length; ++i) {
      if (axes[axes.length - i - 1] !== rank - 1 - i) {
        return false;
      }
    }
    return true;
  }
  function combineLocations(outputLoc, reduceLoc, axes) {
    const rank = outputLoc.length + reduceLoc.length;
    const loc = [];
    let outIdx = 0;
    let reduceIdx = 0;
    for (let dim = 0; dim < rank; dim++) {
      if (axes.indexOf(dim) === -1) {
        loc.push(outputLoc[outIdx++]);
      } else {
        loc.push(reduceLoc[reduceIdx++]);
      }
    }
    return loc;
  }
  function computeOutAndReduceShapes(aShape, axes) {
    const outShape = [];
    const rank = aShape.length;
    for (let dim = 0; dim < rank; dim++) {
      if (axes.indexOf(dim) === -1) {
        outShape.push(aShape[dim]);
      }
    }
    const reduceShape = axes.map((dim) => aShape[dim]);
    return [outShape, reduceShape];
  }
  function expandShapeToKeepDim(shape, axes) {
    const reduceSubShape = axes.map((x) => 1);
    return combineLocations(shape, reduceSubShape, axes);
  }
  function assertAxesAreInnerMostDims(msg, axes, rank) {
    assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`);
  }
  function getAxesPermutation(axes, rank) {
    if (axesAreInnerMostDims(axes, rank)) {
      return null;
    }
    const result = [];
    for (let i = 0; i < rank; ++i) {
      if (axes.indexOf(i) === -1) {
        result.push(i);
      }
    }
    axes.forEach((axis) => result.push(axis));
    return result;
  }
  function getUndoAxesPermutation(axes) {
    return axes.map((axis, i) => [i, axis]).sort((a, b) => a[1] - b[1]).map((x) => x[0]);
  }
  function getInnerMostAxes(numAxes, rank) {
    const res = [];
    for (let i = rank - numAxes; i < rank; ++i) {
      res.push(i);
    }
    return res;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function logSumExp_(x, axis = null, keepDims = false) {
    const $x = convertToTensor(x, "x", "logSumExp");
    const axes = parseAxisParam(axis, $x.shape);
    const xMax = max($x, axes, true);
    const a = sub($x, xMax);
    const b = exp(a);
    const c = sum2(b, axes);
    const d = log(c);
    const res = add2(reshape(xMax, d.shape), d);
    if (keepDims) {
      const newShape = expandShapeToKeepDim(res.shape, axes);
      return reshape(res, newShape);
    }
    return res;
  }
  var logSumExp = op({logSumExp_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function logicalAnd_(a, b) {
    const $a = convertToTensor(a, "a", "logicalAnd", "bool");
    const $b = convertToTensor(b, "b", "logicalAnd", "bool");
    assertAndGetBroadcastShape($a.shape, $b.shape);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(LogicalAnd, inputs);
  }
  var logicalAnd = op({logicalAnd_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function logicalNot_(x) {
    const $x = convertToTensor(x, "x", "logicalNot", "bool");
    const inputs = {x: $x};
    return ENGINE.runKernel(LogicalNot, inputs);
  }
  var logicalNot = op({logicalNot_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function logicalOr_(a, b) {
    const $a = convertToTensor(a, "a", "logicalOr", "bool");
    const $b = convertToTensor(b, "b", "logicalOr", "bool");
    assertAndGetBroadcastShape($a.shape, $b.shape);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(LogicalOr, inputs);
  }
  var logicalOr = op({logicalOr_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function logicalXor_(a, b) {
    const $a = convertToTensor(a, "a", "logicalXor", "bool");
    const $b = convertToTensor(b, "b", "logicalXor", "bool");
    assertAndGetBroadcastShape($a.shape, $b.shape);
    return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));
  }
  var logicalXor = op({logicalXor_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxPool_(x, filterSize, strides, pad3, dimRoundingMode) {
    const $x = convertToTensor(x, "x", "maxPool");
    const dilations = 1;
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
      reshapedTo4D = true;
      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
    }
    assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);
    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
    if (dimRoundingMode != null) {
      assert(isInt(pad3), () => `Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
    }
    const inputs = {x: x4D};
    const attrs = {filterSize, strides, pad: pad3, dimRoundingMode};
    const res = ENGINE.runKernel(MaxPool, inputs, attrs);
    if (reshapedTo4D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
    }
    return res;
  }
  var maxPool = op({maxPool_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxPool3d_(x, filterSize = [1, 1, 1], strides, pad3, dimRoundingMode, dataFormat = "NDHWC") {
    const $x = convertToTensor(x, "x", "maxPool3d");
    let x5D = $x;
    let reshapedTo5D = false;
    if ($x.rank === 4) {
      reshapedTo5D = true;
      x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
    }
    assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
    assert(dataFormat === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
    if (dimRoundingMode != null) {
      assert(isInt(pad3), () => `Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
    }
    const inputs = {x: x5D};
    const attrs = {filterSize, strides, pad: pad3, dimRoundingMode, dataFormat};
    const res = ENGINE.runKernel(MaxPool3D, inputs, attrs);
    if (reshapedTo5D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
    }
    return res;
  }
  var maxPool3d = op({maxPool3d_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxPoolWithArgmax_(x, filterSize, strides, pad3, includeBatchInIndex = false) {
    const $x = convertToTensor(x, "x", "maxPoolWithArgmax");
    const inputs = {x: $x};
    const attrs = {filterSize, strides, pad: pad3, includeBatchInIndex};
    const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);
    return {result: result[0], indexes: result[1]};
  }
  var maxPoolWithArgmax = op({maxPoolWithArgmax_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maximum_(a, b) {
    let $a = convertToTensor(a, "a", "maximum");
    let $b = convertToTensor(b, "b", "maximum");
    [$a, $b] = makeTypesMatch($a, $b);
    if ($a.dtype === "bool") {
      $a = cast($a, "int32");
      $b = cast($b, "int32");
    }
    assertAndGetBroadcastShape($a.shape, $b.shape);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(Maximum, inputs);
  }
  var maximum = op({maximum_});
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function mean_(x, axis = null, keepDims = false) {
    const $x = convertToTensor(x, "x", "mean");
    const inputs = {x: $x};
    const attrs = {axis, keepDims};
    return ENGINE.runKernel(Mean, inputs, attrs);
  }
  var mean = op({mean_});
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function min_(x, axis = null, keepDims = false) {
    const $x = convertToTensor(x, "x", "min");
    const inputs = {x: $x};
    const attrs = {axis, keepDims};
    return ENGINE.runKernel(Min, inputs, attrs);
  }
  var min = op({min_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function minimum_(a, b) {
    let $a = convertToTensor(a, "a", "minimum");
    let $b = convertToTensor(b, "b", "minimum");
    [$a, $b] = makeTypesMatch($a, $b);
    if ($a.dtype === "bool") {
      $a = cast($a, "int32");
      $b = cast($b, "int32");
    }
    assertAndGetBroadcastShape($a.shape, $b.shape);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(Minimum, inputs);
  }
  var minimum = op({minimum_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function mirrorPad_(x, paddings, mode) {
    assert(mode === "reflect" || mode === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);
    const $x = convertToTensor(x, "x", "mirrorPad");
    if ($x.rank === 0) {
      throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");
    }
    assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);
    const shapeOffset = mode === "reflect" ? 1 : 0;
    for (let i = 0; i < $x.rank; i++) {
      assert(paddings[i].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);
      assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset && paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, () => `Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i] - shapeOffset} or less than 0 for input of shape ${$x.shape}`);
    }
    const attrs = {paddings, mode};
    const inputs = {x: $x};
    return ENGINE.runKernel(MirrorPad, inputs, attrs);
  }
  var mirrorPad = op({mirrorPad_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function mod_(a, b) {
    let $a = convertToTensor(a, "a", "mod");
    let $b = convertToTensor(b, "b", "mod");
    [$a, $b] = makeTypesMatch($a, $b);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(Mod, inputs);
  }
  var mod = op({mod_});
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function square_(x) {
    const $x = convertToTensor(x, "x", "square");
    const attrs = {};
    return ENGINE.runKernel("Square", {x: $x}, attrs);
  }
  var square = op({square_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function moments_(x, axis = null, keepDims = false) {
    x = convertToTensor(x, "x", "moments");
    const axes = parseAxisParam(axis, x.shape);
    const xMean = mean(x, axes, keepDims);
    let keepDimsShape = xMean.shape;
    if (!keepDims) {
      keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);
    }
    const devSquared = square(sub(cast(x, "float32"), reshape(xMean, keepDimsShape)));
    const variance = mean(devSquared, axes, keepDims);
    return {mean: xMean, variance};
  }
  var moments = op({moments_});
  function multiRNNCell_(lstmCells, data, c, h) {
    const $data = convertToTensor(data, "data", "multiRNNCell");
    const $c = convertToTensorArray(c, "c", "multiRNNCell");
    const $h = convertToTensorArray(h, "h", "multiRNNCell");
    let input2 = $data;
    const newStates = [];
    for (let i = 0; i < lstmCells.length; i++) {
      const output = lstmCells[i](input2, $c[i], $h[i]);
      newStates.push(output[0]);
      newStates.push(output[1]);
      input2 = output[1];
    }
    const newC = [];
    const newH = [];
    for (let i = 0; i < newStates.length; i += 2) {
      newC.push(newStates[i]);
      newH.push(newStates[i + 1]);
    }
    return [newC, newH];
  }
  var multiRNNCell = op({multiRNNCell_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function multinomial_(logits, numSamples, seed, normalized = false) {
    const $logits = convertToTensor(logits, "logits", "multinomial");
    const numOutcomes = $logits.size;
    const origRank = $logits.rank;
    if (numOutcomes < 2) {
      throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);
    }
    if (origRank > 2) {
      throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);
    }
    seed = seed || Math.random();
    const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;
    const inputs = {logits: logits2D};
    const attrs = {numSamples, seed, normalized};
    const res = ENGINE.runKernel(Multinomial, inputs, attrs);
    return origRank === 1 ? reshape(res, [res.size]) : res;
  }
  var multinomial = op({multinomial_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function notEqual_(a, b) {
    let $a = convertToTensor(a, "a", "notEqual");
    let $b = convertToTensor(b, "b", "notEqual");
    [$a, $b] = makeTypesMatch($a, $b);
    assertAndGetBroadcastShape($a.shape, $b.shape);
    const inputs = {a: $a, b: $b};
    return ENGINE.runKernel(NotEqual, inputs);
  }
  var notEqual = op({notEqual_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function zeros(shape, dtype = "float32") {
    if (dtype === "complex64") {
      const real4 = zeros(shape, "float32");
      const imag4 = zeros(shape, "float32");
      return complex(real4, imag4);
    }
    const values = makeZerosTypedArray(sizeFromShape(shape), dtype);
    return ENGINE.makeTensor(values, shape, dtype);
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function ones2(shape, dtype = "float32") {
    if (dtype === "complex64") {
      const real4 = ones2(shape, "float32");
      const imag4 = zeros(shape, "float32");
      return complex(real4, imag4);
    }
    const values = makeOnesTypedArray(sizeFromShape(shape), dtype);
    return ENGINE.makeTensor(values, shape, dtype);
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function onesLike_(x) {
    const $x = convertToTensor(x, "x", "onesLike");
    const inputs = {x: $x};
    return ENGINE.runKernel(OnesLike, inputs);
  }
  var onesLike = op({onesLike_});
  function outerProduct_(v1, v2) {
    const $v1 = convertToTensor(v1, "v1", "outerProduct");
    const $v2 = convertToTensor(v2, "v2", "outerProduct");
    assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);
    const v12D = reshape($v1, [-1, 1]);
    const v22D = reshape($v2, [1, -1]);
    return matMul(v12D, v22D);
  }
  var outerProduct = op({outerProduct_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function pad_(x, paddings, constantValue = 0) {
    const $x = convertToTensor(x, "x", "pad");
    if ($x.rank === 0) {
      throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
    }
    const attrs = {paddings, constantValue};
    const inputs = {x: $x};
    return ENGINE.runKernel(PadV2, inputs, attrs);
  }
  var pad = op({pad_});
  function pad1d_(x, paddings, constantValue = 0) {
    assert(paddings.length === 2, () => "Invalid number of paddings. Must be length of 2.");
    return pad(x, [paddings], constantValue);
  }
  var pad1d = op({pad1d_});
  function pad2d_(x, paddings, constantValue = 0) {
    assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
    return pad(x, paddings, constantValue);
  }
  var pad2d = op({pad2d_});
  function pad3d_(x, paddings, constantValue = 0) {
    assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
    return pad(x, paddings, constantValue);
  }
  var pad3d = op({pad3d_});
  function pad4d_(x, paddings, constantValue = 0) {
    assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
    return pad(x, paddings, constantValue);
  }
  var pad4d = op({pad4d_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function spaceToBatchND_(x, blockShape, paddings) {
    const $x = convertToTensor(x, "x", "spaceToBatchND");
    assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);
    assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);
    assert($x.shape.reduce((a, b, i) => {
      if (i > 0 && i <= blockShape.length) {
        return a && (b + paddings[i - 1][0] + paddings[i - 1][1]) % blockShape[i - 1] === 0;
      }
      return a;
    }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);
    const inputs = {x: $x};
    const attrs = {blockShape, paddings};
    return ENGINE.runKernel(SpaceToBatchND, inputs, attrs);
  }
  var spaceToBatchND = op({spaceToBatchND_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function pool_(input2, windowShape, poolingType, pad3, dilations, strides) {
    if (dilations == null) {
      dilations = [1, 1];
    }
    if (strides == null) {
      strides = 1;
    }
    if (pad3 === 0) {
      pad3 = "valid";
    }
    const $x = convertToTensor(input2, "x", "maxPool");
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
      reshapedTo4D = true;
      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
    }
    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
    const convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad3);
    const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];
    let basePadding;
    if (pad3 === "same") {
      basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);
    } else {
      basePadding = [[0, 0], [0, 0]];
    }
    const isDilationOne = dilation[0] === 1 && dilation[1] === 1;
    const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);
    const convertedPad = isDilationOne ? pad3 : "valid";
    const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);
    const forwardOp = poolingType === "avg" ? () => avgPool(convertedX, windowShape, strides, convertedPad) : () => maxPool(convertedX, windowShape, strides, convertedPad);
    const y = forwardOp();
    const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);
    if (reshapedTo4D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
    }
    return res;
  }
  function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {
    const padStart = basePadding.map((b) => b[0]);
    const origPadEnd = basePadding.map((b) => b[1]);
    const fullInputShape = inputShape.concat(padStart, origPadEnd);
    const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);
    const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);
    const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);
    const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);
    return [paddings, crops];
  }
  function withSpaceToBatchBasePaddings(filterShape, dilation) {
    const dilatedFilterShape = filterShape.map((s, i) => {
      return s + (s - 1) * (dilation[i] - 1);
    });
    const padExtraShape = dilatedFilterShape.map((s) => s - 1);
    const padExtraStart = padExtraShape.map((s) => Math.floor(s / 2));
    const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);
    return padExtraShape.map((_, i) => {
      return [padExtraStart[i], padExtraEnd[i]];
    });
  }
  var pool = op({pool_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function pow_(base2, exp4) {
    let $base = convertToTensor(base2, "base", "pow");
    let $exp = convertToTensor(exp4, "exp", "pow");
    [$base, $exp] = makeTypesMatch($base, $exp);
    const inputs = {a: $base, b: $exp};
    return ENGINE.runKernel(Pow, inputs);
  }
  var pow = op({pow_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function prelu_(x, alpha) {
    const $x = convertToTensor(x, "x", "prelu");
    const $alpha = convertToTensor(alpha, "alpha", "prelu");
    const inputs = {x: $x, alpha: $alpha};
    return ENGINE.runKernel(Prelu, inputs);
  }
  var prelu = op({prelu_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function prod_(x, axis = null, keepDims = false) {
    let $x = convertToTensor(x, "x", "prod");
    if ($x.dtype === "bool") {
      $x = cast($x, "int32");
    }
    const inputs = {x: $x};
    const attrs = {axis, keepDims};
    return ENGINE.runKernel(Prod, inputs, attrs);
  }
  var prod = op({prod_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function rand_(shape, randFunction, dtype) {
    const size = sizeFromShape(shape);
    let values = null;
    if (dtype == null || dtype === "float32") {
      values = new Float32Array(size);
    } else if (dtype === "int32") {
      values = new Int32Array(size);
    } else if (dtype === "bool") {
      values = new Uint8Array(size);
    } else {
      throw new Error(`Unknown data type ${dtype}`);
    }
    for (let i = 0; i < size; i++) {
      values[i] = randFunction();
    }
    return ENGINE.makeTensor(values, shape, dtype);
  }
  var rand = op({rand_});
  var seedrandom = __toModule(require_seedrandom2());
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var MPRandGauss = class {
    constructor(mean4, stdDeviation, dtype, truncated, seed) {
      this.mean = mean4;
      this.stdDev = stdDeviation;
      this.dtype = dtype;
      this.nextVal = NaN;
      this.truncated = truncated;
      if (this.truncated) {
        this.upper = this.mean + this.stdDev * 2;
        this.lower = this.mean - this.stdDev * 2;
      }
      const seedValue = seed ? seed : Math.random();
      this.random = seedrandom.alea(seedValue.toString());
    }
    nextValue() {
      if (!isNaN(this.nextVal)) {
        const value = this.nextVal;
        this.nextVal = NaN;
        return value;
      }
      let resultX, resultY;
      let isValid = false;
      while (!isValid) {
        let v1, v2, s;
        do {
          v1 = 2 * this.random() - 1;
          v2 = 2 * this.random() - 1;
          s = v1 * v1 + v2 * v2;
        } while (s >= 1 || s === 0);
        const mul2 = Math.sqrt(-2 * Math.log(s) / s);
        resultX = this.mean + this.stdDev * v1 * mul2;
        resultY = this.mean + this.stdDev * v2 * mul2;
        if (!this.truncated || this.isValidTruncated(resultX)) {
          isValid = true;
        }
      }
      if (!this.truncated || this.isValidTruncated(resultY)) {
        this.nextVal = this.convertValue(resultY);
      }
      return this.convertValue(resultX);
    }
    convertValue(value) {
      if (this.dtype == null || this.dtype === "float32") {
        return value;
      }
      return Math.round(value);
    }
    isValidTruncated(value) {
      return value <= this.upper && value >= this.lower;
    }
  };
  var RandGamma = class {
    constructor(alpha, beta, dtype, seed) {
      this.alpha = alpha;
      this.beta = 1 / beta;
      this.dtype = dtype;
      const seedValue = seed ? seed : Math.random();
      this.randu = seedrandom.alea(seedValue.toString());
      this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());
      if (alpha < 1) {
        this.d = alpha + 2 / 3;
      } else {
        this.d = alpha - 1 / 3;
      }
      this.c = 1 / Math.sqrt(9 * this.d);
    }
    nextValue() {
      let x2, v0, v1, x, u, v;
      while (true) {
        do {
          x = this.randn.nextValue();
          v = 1 + this.c * x;
        } while (v <= 0);
        v *= v * v;
        x2 = x * x;
        v0 = 1 - 0.331 * x2 * x2;
        v1 = 0.5 * x2 + this.d * (1 - v + Math.log(v));
        u = this.randu();
        if (u < v0 || Math.log(u) < v1) {
          break;
        }
      }
      v = 1 / this.beta * this.d * v;
      if (this.alpha < 1) {
        v *= Math.pow(this.randu(), 1 / this.alpha);
      }
      return this.convertValue(v);
    }
    convertValue(value) {
      if (this.dtype === "float32") {
        return value;
      }
      return Math.round(value);
    }
  };
  var UniformRandom = class {
    constructor(min6 = 0, max6 = 1, dtype, seed) {
      this.canReturnFloat = () => this.dtype == null || this.dtype === "float32";
      this.min = min6;
      this.range = max6 - min6;
      this.dtype = dtype;
      if (seed == null) {
        seed = Math.random();
      }
      if (typeof seed === "number") {
        seed = seed.toString();
      }
      if (!this.canReturnFloat() && this.range <= 1) {
        throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`);
      }
      this.random = seedrandom.alea(seed);
    }
    convertValue(value) {
      if (this.canReturnFloat()) {
        return value;
      }
      return Math.round(value);
    }
    nextValue() {
      return this.convertValue(this.min + this.range * this.random());
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function randomGamma_(shape, alpha, beta = 1, dtype = "float32", seed) {
    if (beta == null) {
      beta = 1;
    }
    if (dtype == null) {
      dtype = "float32";
    }
    if (dtype !== "float32" && dtype !== "int32") {
      throw new Error(`Unsupported data type ${dtype}`);
    }
    const rgamma = new RandGamma(alpha, beta, dtype, seed);
    const res = buffer(shape, dtype);
    for (let i = 0; i < res.values.length; i++) {
      res.values[i] = rgamma.nextValue();
    }
    return res.toTensor();
  }
  var randomGamma = op({randomGamma_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function randomNormal_(shape, mean4 = 0, stdDev = 1, dtype, seed) {
    if (dtype != null && dtype === "bool") {
      throw new Error(`Unsupported data type ${dtype}`);
    }
    const randGauss = new MPRandGauss(mean4, stdDev, dtype, false, seed);
    const res = buffer(shape, dtype);
    for (let i = 0; i < res.values.length; i++) {
      res.values[i] = randGauss.nextValue();
    }
    return res.toTensor();
  }
  var randomNormal = op({randomNormal_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function randomUniform_(shape, minval = 0, maxval = 1, dtype = "float32", seed) {
    const res = buffer(shape, dtype);
    const random = new UniformRandom(minval, maxval, null, seed);
    for (let i = 0; i < res.values.length; i++) {
      res.values[i] = random.nextValue();
    }
    return res.toTensor();
  }
  var randomUniform = op({randomUniform_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function range(start, stop, step5 = 1, dtype = "float32") {
    if (step5 === 0) {
      throw new Error("Cannot have a step of zero");
    }
    const attrs = {start, stop, step: step5, dtype};
    return ENGINE.runKernel(Range, {}, attrs);
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function real_(input2) {
    const $input = convertToTensor(input2, "input", "real");
    const inputs = {input: $input};
    return ENGINE.runKernel(Real, inputs);
  }
  var real = op({real_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function reciprocal_(x) {
    const $x = convertToTensor(x, "x", "reciprocal");
    const inputs = {x: $x};
    return ENGINE.runKernel(Reciprocal, inputs);
  }
  var reciprocal = op({reciprocal_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function relu_(x) {
    const $x = convertToTensor(x, "x", "relu");
    const inputs = {x: $x};
    return ENGINE.runKernel(Relu, inputs);
  }
  var relu = op({relu_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function relu6_(x) {
    const $x = convertToTensor(x, "x", "relu6");
    const inputs = {x: $x};
    return ENGINE.runKernel(Relu6, inputs);
  }
  var relu6 = op({relu6_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function reverse_(x, axis) {
    const $x = convertToTensor(x, "x", "reverse");
    const inputs = {x: $x};
    const attrs = {dims: axis};
    return ENGINE.runKernel(Reverse, inputs, attrs);
  }
  var reverse = op({reverse_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function reverse1d_(x) {
    const $x = convertToTensor(x, "x", "reverse");
    assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);
    return reverse($x, 0);
  }
  var reverse1d = op({reverse1d_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function reverse2d_(x, axis) {
    const $x = convertToTensor(x, "x", "reverse");
    assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);
    return reverse($x, axis);
  }
  var reverse2d = op({reverse2d_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function reverse3d_(x, axis) {
    const $x = convertToTensor(x, "x", "reverse");
    assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);
    return reverse($x, axis);
  }
  var reverse3d = op({reverse3d_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function reverse4d_(x, axis) {
    const $x = convertToTensor(x, "x", "reverse");
    assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);
    return reverse($x, axis);
  }
  var reverse4d = op({reverse4d_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function round_(x) {
    const $x = convertToTensor(x, "x", "round");
    const inputs = {x: $x};
    return ENGINE.runKernel(Round, inputs);
  }
  var round2 = op({round_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function rsqrt_(x) {
    const $x = convertToTensor(x, "x", "rsqrt");
    const inputs = {x: $x};
    return ENGINE.runKernel(Rsqrt, inputs);
  }
  var rsqrt = op({rsqrt_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function scalar(value, dtype) {
    if ((isTypedArray(value) && dtype !== "string" || Array.isArray(value)) && dtype !== "complex64") {
      throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");
    }
    if (dtype === "string" && isTypedArray(value) && !(value instanceof Uint8Array)) {
      throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
    }
    const shape = [];
    const inferredShape = [];
    return makeTensor(value, shape, inferredShape, dtype);
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function selu_(x) {
    const $x = convertToTensor(x, "x", "selu");
    const inputs = {x: $x};
    return ENGINE.runKernel(Selu, inputs);
  }
  var selu = op({selu_});
  function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad3, dilation = [1, 1], dataFormat = "NHWC") {
    const $x = convertToTensor(x, "x", "separableConv2d");
    const $depthwiseFilter = convertToTensor(depthwiseFilter, "depthwiseFilter", "separableConv2d");
    const $pointwiseFilter = convertToTensor(pointwiseFilter, "pointwiseFilter", "separableConv2d");
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
      reshapedTo4D = true;
      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
    }
    if (dataFormat === "NCHW") {
      throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
    }
    assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
    assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
    assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
    assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`);
    assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);
    const inChannels = $depthwiseFilter.shape[2];
    const channelMultiplier = $depthwiseFilter.shape[3];
    assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels * channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);
    const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad3, dataFormat, dilation);
    const pointwiseStride = 1;
    const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, "valid", dataFormat);
    if (reshapedTo4D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
    }
    return res;
  }
  var separableConv2d = op({separableConv2d_});
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  async function setdiff1dAsync_(x, y) {
    const $x = convertToTensor(x, "x", "setdiff1d");
    const $y = convertToTensor(y, "y", "setdiff1d");
    assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);
    assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);
    assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);
    const xVals = await $x.data();
    const yVals = await $y.data();
    const ySet = new Set(yVals);
    let outputSize = 0;
    for (let i = 0; i < xVals.length; i++) {
      if (!ySet.has(xVals[i])) {
        outputSize++;
      }
    }
    const buffer2 = new TensorBuffer([outputSize], $x.dtype);
    const indices = new TensorBuffer([outputSize], "int32");
    for (let i = 0, p2 = 0; i < xVals.length; i++) {
      if (!ySet.has(xVals[i])) {
        buffer2.values[p2] = xVals[i];
        indices.values[p2] = i;
        p2++;
      }
    }
    return [buffer2.toTensor(), indices.toTensor()];
  }
  var setdiff1dAsync = setdiff1dAsync_;
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sign_(x) {
    const $x = convertToTensor(x, "x", "sign");
    const inputs = {x: $x};
    return ENGINE.runKernel(Sign, inputs);
  }
  var sign = op({sign_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sin_(x) {
    const $x = convertToTensor(x, "x", "sin");
    const inputs = {x: $x};
    return ENGINE.runKernel(Sin, inputs);
  }
  var sin = op({sin_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sinh_(x) {
    const $x = convertToTensor(x, "x", "sinh");
    const inputs = {x: $x};
    return ENGINE.runKernel(Sinh, inputs);
  }
  var sinh = op({sinh_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function slice1d_(x, begin, size) {
    const $x = convertToTensor(x, "x", "slice1d");
    assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);
    return slice($x, [begin], [size]);
  }
  var slice1d = op({slice1d_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function slice2d_(x, begin, size) {
    const $x = convertToTensor(x, "x", "slice2d");
    assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);
    return slice($x, begin, size);
  }
  var slice2d = op({slice2d_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function slice3d_(x, begin, size) {
    const $x = convertToTensor(x, "x", "slice3d");
    assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);
    return slice($x, begin, size);
  }
  var slice3d = op({slice3d_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function slice4d_(x, begin, size) {
    const $x = convertToTensor(x, "x", "slice4d");
    assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);
    return slice($x, begin, size);
  }
  var slice4d = op({slice4d_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function softmax_(logits, dim = -1) {
    const $logits = convertToTensor(logits, "logits", "softmax", "float32");
    if (dim === -1) {
      dim = $logits.rank - 1;
    }
    if (dim !== $logits.rank - 1) {
      throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);
    }
    const inputs = {logits: $logits};
    const attrs = {dim};
    return ENGINE.runKernel(Softmax, inputs, attrs);
  }
  var softmax = op({softmax_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fft_(input2) {
    assert(input2.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);
    const inputs = {input: input2};
    return ENGINE.runKernel(FFT, inputs);
  }
  var fft = op({fft_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function ifft_(input2) {
    assert(input2.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);
    const inputs = {input: input2};
    return ENGINE.runKernel(IFFT, inputs);
  }
  var ifft = op({ifft_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function irfft_(input2) {
    const innerDimensionSize = input2.shape[input2.shape.length - 1];
    const batch = input2.size / innerDimensionSize;
    let ret;
    if (innerDimensionSize <= 2) {
      const complexInput = reshape(input2, [batch, innerDimensionSize]);
      ret = ifft(complexInput);
    } else {
      const outputShape = [batch, 2 * (innerDimensionSize - 1)];
      const realInput = reshape(real(input2), [batch, innerDimensionSize]);
      const imagInput = reshape(imag(input2), [batch, innerDimensionSize]);
      const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);
      const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));
      const r = concat([realInput, realConjugate], 1);
      const i = concat([imagInput, imagConjugate], 1);
      const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);
      ret = ifft(complexInput);
    }
    ret = real(ret);
    if (input2.rank === 3 && input2.shape[0] !== 0) {
      const temp = ret;
      const batch2 = input2.shape[0];
      ret = reshape(ret, [batch2, ret.shape[0] / batch2, ret.shape[1]]);
      temp.dispose();
    }
    return ret;
  }
  var irfft = op({irfft_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function split_(x, numOrSizeSplits, axis = 0) {
    const $x = convertToTensor(x, "x", "split");
    const inputs = {x: $x};
    const attr = {numOrSizeSplits, axis};
    return ENGINE.runKernel(SplitV, inputs, attr);
  }
  var split = op({split_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function rfft_(input2, fftLength) {
    assert(input2.dtype === "float32", () => `The dtype for rfft() must be real value but got ${input2.dtype}`);
    let innerDimensionSize = input2.shape[input2.shape.length - 1];
    const batch = input2.size / innerDimensionSize;
    let adjustedInput;
    if (fftLength != null && fftLength < innerDimensionSize) {
      const begin = input2.shape.map((v) => 0);
      const size = input2.shape.map((v) => v);
      size[input2.shape.length - 1] = fftLength;
      adjustedInput = slice(input2, begin, size);
      innerDimensionSize = fftLength;
    } else if (fftLength != null && fftLength > innerDimensionSize) {
      const zerosShape = input2.shape.map((v) => v);
      zerosShape[input2.shape.length - 1] = fftLength - innerDimensionSize;
      adjustedInput = concat([input2, zeros(zerosShape)], input2.shape.length - 1);
      innerDimensionSize = fftLength;
    } else {
      adjustedInput = input2;
    }
    const zerosInput = zerosLike(adjustedInput);
    const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);
    const ret = fft(complexInput);
    const half = Math.floor(innerDimensionSize / 2) + 1;
    const realValues = real(ret);
    const imagValues = imag(ret);
    const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);
    const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);
    const outputShape = adjustedInput.shape.slice();
    outputShape[adjustedInput.shape.length - 1] = half;
    return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);
  }
  var rfft = op({rfft_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sqrt_(x) {
    const $x = convertToTensor(x, "x", "sqrt");
    const inputs = {x: $x};
    return ENGINE.runKernel(Sqrt, inputs);
  }
  var sqrt = op({sqrt_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function squaredDifference_(a, b) {
    let $a = convertToTensor(a, "a", "squaredDifference");
    let $b = convertToTensor(b, "b", "squaredDifference");
    [$a, $b] = makeTypesMatch($a, $b);
    assertAndGetBroadcastShape($a.shape, $b.shape);
    const inputs = {a: $a, b: $b};
    const attrs = {};
    return ENGINE.runKernel(SquaredDifference, inputs, attrs);
  }
  var squaredDifference = op({squaredDifference_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function squeeze_(x, axis) {
    const $x = convertToTensor(x, "x", "squeeze");
    return reshape($x, squeezeShape($x.shape, axis).newShape);
  }
  var squeeze = op({squeeze_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function stack_(tensors, axis = 0) {
    const $tensors = convertToTensorArray(tensors, "tensors", "stack", "string_or_numeric");
    assert($tensors.length >= 1, () => "Pass at least one tensor to tf.stack");
    if ($tensors.length > 0) {
      assert(axis <= $tensors[0].rank, () => "Axis must be <= rank of the tensor");
    }
    const inputs = $tensors;
    const attrs = {axis};
    return ENGINE.runKernel(Pack, inputs, attrs);
  }
  var stack = op({stack_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function step_(x, alpha = 0) {
    const $x = convertToTensor(x, "x", "step");
    const inputs = {x: $x};
    const attrs = {alpha};
    return ENGINE.runKernel(Step, inputs, attrs);
  }
  var step = op({step_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {
    const $x = convertToTensor(x, "x", "stridedSlice");
    const inputs = {x: $x};
    const attrs = {
      begin,
      end,
      strides,
      beginMask,
      endMask,
      ellipsisMask,
      newAxisMask,
      shrinkAxisMask
    };
    return ENGINE.runKernel(StridedSlice, inputs, attrs);
  }
  var stridedSlice = op({stridedSlice_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function tan_(x) {
    const $x = convertToTensor(x, "x", "tan");
    const inputs = {x: $x};
    return ENGINE.runKernel(Tan, inputs);
  }
  var tan = op({tan_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function tensor1d(values, dtype) {
    assertNonNull(values);
    const inferredShape = inferShape(values, dtype);
    if (inferredShape.length !== 1) {
      throw new Error("tensor1d() requires values to be a flat/TypedArray");
    }
    const shape = null;
    return makeTensor(values, shape, inferredShape, dtype);
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function tensor2d(values, shape, dtype) {
    assertNonNull(values);
    if (shape != null && shape.length !== 2) {
      throw new Error("tensor2d() requires shape to have two numbers");
    }
    const inferredShape = inferShape(values, dtype);
    if (inferredShape.length !== 2 && inferredShape.length !== 1) {
      throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");
    }
    if (inferredShape.length === 1 && shape == null) {
      throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
    }
    return makeTensor(values, shape, inferredShape, dtype);
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function tensor4d(values, shape, dtype) {
    assertNonNull(values);
    if (shape != null && shape.length !== 4) {
      throw new Error("tensor4d() requires shape to have four numbers");
    }
    const inferredShape = inferShape(values, dtype);
    if (inferredShape.length !== 4 && inferredShape.length !== 1) {
      throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");
    }
    if (inferredShape.length === 1 && shape == null) {
      throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
    }
    return makeTensor(values, shape, inferredShape, dtype);
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function tensor5d(values, shape, dtype) {
    assertNonNull(values);
    if (shape != null && shape.length !== 5) {
      throw new Error("tensor5d() requires shape to have five numbers");
    }
    const inferredShape = inferShape(values, dtype);
    if (inferredShape.length !== 5 && inferredShape.length !== 1) {
      throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");
    }
    if (inferredShape.length === 1 && shape == null) {
      throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
    }
    return makeTensor(values, shape, inferredShape, dtype);
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function tensor6d(values, shape, dtype) {
    assertNonNull(values);
    if (shape != null && shape.length !== 6) {
      throw new Error("tensor6d() requires shape to have six numbers");
    }
    const inferredShape = inferShape(values, dtype);
    if (inferredShape.length !== 6 && inferredShape.length !== 1) {
      throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");
    }
    if (inferredShape.length === 1 && shape == null) {
      throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");
    }
    shape = shape || inferredShape;
    return makeTensor(values, shape, inferredShape, dtype);
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function topk_(x, k = 1, sorted = true) {
    const $x = convertToTensor(x, "x", "topk");
    if ($x.rank === 0) {
      throw new Error("topk() expects the input to be of rank 1 or higher");
    }
    const lastDim = $x.shape[$x.shape.length - 1];
    if (k > lastDim) {
      throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);
    }
    const inputs = {x: $x};
    const attrs = {k, sorted};
    const [values, indices] = ENGINE.runKernel(TopK, inputs, attrs);
    return {values, indices};
  }
  var topk = op({topk_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function truncatedNormal_(shape, mean4 = 0, stdDev = 1, dtype, seed) {
    if (dtype != null && dtype === "bool") {
      throw new Error(`Unsupported data type $ { dtype }`);
    }
    const randGauss = new MPRandGauss(mean4, stdDev, dtype, true, seed);
    const res = buffer(shape, dtype);
    for (let i = 0; i < res.values.length; i++) {
      res.values[i] = randGauss.nextValue();
    }
    return res.toTensor();
  }
  var truncatedNormal = op({truncatedNormal_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function unique_(x, axis = 0) {
    const $x = convertToTensor(x, "x", "unique", "string_or_numeric");
    assert($x.rank > 0, () => "The input tensor must be at least 1D");
    const inputs = {x: $x};
    const attrs = {axis};
    const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);
    return {values, indices};
  }
  var unique = op({unique_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function unsortedSegmentSum_(x, segmentIds, numSegments) {
    const $x = convertToTensor(x, "x", "unsortedSegmentSum");
    const $segmentIds = convertToTensor(segmentIds, "segmentIds", "unsortedSegmentSum", "int32");
    assert(isInt(numSegments), () => "numSegments must be of dtype int");
    const inputs = {x: $x, segmentIds: $segmentIds};
    const attrs = {numSegments};
    return ENGINE.runKernel(UnsortedSegmentSum, inputs, attrs);
  }
  var unsortedSegmentSum = op({unsortedSegmentSum_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function unstack_(x, axis = 0) {
    const $x = convertToTensor(x, "x", "unstack", "string_or_numeric");
    assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);
    const inputs = {value: $x};
    const attrs = {axis};
    return ENGINE.runKernel(Unpack, inputs, attrs);
  }
  var unstack = op({unstack_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function variable(initialValue, trainable = true, name, dtype) {
    return ENGINE.makeVariable(initialValue, trainable, name, dtype);
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function whereImpl(condShape, condVals) {
    const indices = [];
    for (let i = 0; i < condVals.length; i++) {
      if (condVals[i]) {
        indices.push(i);
      }
    }
    const inBuffer = buffer(condShape, "int32");
    const out = buffer([indices.length, condShape.length], "int32");
    for (let i = 0; i < indices.length; i++) {
      const loc = inBuffer.indexToLoc(indices[i]);
      const offset = i * condShape.length;
      out.values.set(loc, offset);
    }
    return out.toTensor();
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  async function whereAsync_(condition) {
    const $condition = convertToTensor(condition, "condition", "whereAsync", "bool");
    const vals = await $condition.data();
    const res = whereImpl($condition.shape, vals);
    if (condition !== $condition) {
      $condition.dispose();
    }
    return res;
  }
  var whereAsync = whereAsync_;
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  async function booleanMaskAsync_(tensor2, mask, axis) {
    const $tensor = convertToTensor(tensor2, "tensor", "boolMask");
    const $mask = convertToTensor(mask, "mask", "boolMask", "bool");
    const axisFrom = axis == null ? 0 : axis;
    const maskDim = $mask.rank;
    const tensorShape = $tensor.shape;
    assert(maskDim > 0, () => "mask cannot be scalar");
    assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);
    let leadingSize = 1;
    for (let i = axisFrom; i < axisFrom + maskDim; i++) {
      leadingSize *= tensorShape[i];
    }
    const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim));
    const reshapedTensor = reshape($tensor, targetTensorShape);
    const reshapedMask = reshape($mask, [-1]);
    const positivePositions = await whereAsync(reshapedMask);
    const indices = squeeze(positivePositions, [1]);
    const res = gather(reshapedTensor, indices, axisFrom);
    if (tensor2 !== $tensor) {
      $tensor.dispose();
    }
    if (mask !== $mask) {
      $mask.dispose();
    }
    indices.dispose();
    reshapedTensor.dispose();
    reshapedMask.dispose();
    positivePositions.dispose();
    return res;
  }
  var booleanMaskAsync = booleanMaskAsync_;
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function norm_(x, ord = "euclidean", axis = null, keepDims = false) {
    x = convertToTensor(x, "x", "norm");
    const norm2 = normImpl(x, ord, axis);
    let keepDimsShape = norm2.shape;
    if (keepDims) {
      const axes = parseAxisParam(axis, x.shape);
      keepDimsShape = expandShapeToKeepDim(norm2.shape, axes);
    }
    return reshape(norm2, keepDimsShape);
  }
  function normImpl(x, p2, axis = null) {
    if (x.rank === 0) {
      return abs(x);
    }
    if (x.rank !== 1 && axis === null) {
      return normImpl(reshape(x, [-1]), p2, axis);
    }
    if (x.rank === 1 || typeof axis === "number" || Array.isArray(axis) && axis.length === 1) {
      if (p2 === 1) {
        return sum2(abs(x), axis);
      }
      if (p2 === Infinity) {
        return max(abs(x), axis);
      }
      if (p2 === -Infinity) {
        return min(abs(x), axis);
      }
      if (p2 === "euclidean" || p2 === 2) {
        return sqrt(sum2(pow(abs(x), scalar(2, "int32")), axis));
      }
      throw new Error(`Error in norm: invalid ord value: ${p2}`);
    }
    if (Array.isArray(axis) && axis.length === 2) {
      if (p2 === 1) {
        return max(sum2(abs(x), axis[0]), axis[1] - 1);
      }
      if (p2 === Infinity) {
        return max(sum2(abs(x), axis[1]), axis[0]);
      }
      if (p2 === -Infinity) {
        return min(sum2(abs(x), axis[1]), axis[0]);
      }
      if (p2 === "fro" || p2 === "euclidean") {
        return sqrt(sum2(square(x), axis));
      }
      throw new Error(`Error in norm: invalid ord value: ${p2}`);
    }
    throw new Error(`Error in norm: invalid axis: ${axis}`);
  }
  var norm = op({norm_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function movingAverage_(v, x, decay, step5, zeroDebias = true) {
    const $v = convertToTensor(v, "v", "movingAverage");
    const $x = convertToTensor(x, "x", "movingAverage");
    const $decay = convertToTensor(decay, "decay", "movingAverage");
    assertTypesMatch($v, $x);
    assert(arraysEqual($v.shape, $x.shape), () => "Shape mismatch in v and x");
    const one = scalar(1);
    const oneMinusDecay = sub(one, $decay);
    let update = mul(sub($x, $v), oneMinusDecay);
    if (zeroDebias) {
      assert(step5 != null, () => "When using zeroDebias: true, step is required.");
      const $step = convertToTensor(step5, "step", "movingAverage");
      update = div(update, sub(one, pow($decay, $step)));
    }
    return add2($v, update);
  }
  var movingAverage = op({movingAverage_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function scatterND_(indices, updates, shape) {
    const $indices = convertToTensor(indices, "indices", "scatterND", "int32");
    const $updates = convertToTensor(updates, "updates", "scatterND");
    validateInput($updates, $indices, shape);
    const inputs = {indices: $indices, updates: $updates};
    const attrs = {shape};
    return ENGINE.runKernel(ScatterNd, inputs, attrs);
  }
  var scatterND = op({scatterND_});
  function validateInput2(sparseIndices, sparseValues, outputShape, defaultValues) {
    if (sparseIndices.dtype !== "int32") {
      throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);
    }
    if (sparseIndices.rank > 2) {
      throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);
    }
    const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
    const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
    if (outputShape.length !== numDims) {
      throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);
    }
    const numValues = sparseValues.size;
    if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) {
      throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);
    }
    if (sparseValues.dtype !== defaultValues.dtype) {
      throw new Error("sparseValues.dtype must match defaultValues.dtype");
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {
    const $sparseIndices = convertToTensor(sparseIndices, "sparseIndices", "sparseToDense", "int32");
    const $sparseValues = convertToTensor(sparseValues, "sparseValues", "sparseToDense");
    const $defaultValue = convertToTensor(defaultValue, "defaultValue", "sparseToDense", $sparseValues.dtype);
    validateInput2($sparseIndices, $sparseValues, outputShape, $defaultValue);
    const inputs = {
      sparseIndices: $sparseIndices,
      sparseValues: $sparseValues,
      defaultValue: $defaultValue
    };
    const attrs = {outputShape};
    return ENGINE.runKernel(SparseToDense, inputs, attrs);
  }
  var sparseToDense = op({sparseToDense_});
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function gatherND_(x, indices) {
    const $indices = convertToTensor(indices, "indices", "gatherND", "int32");
    const $x = convertToTensor(x, "x", "gatherND");
    const inputs = {params: $x, indices: $indices};
    return ENGINE.runKernel(GatherNd, inputs);
  }
  var gatherND = op({gatherND_});
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function getNoiseShape(x, noiseShape) {
    if (noiseShape == null) {
      return x.shape.slice();
    }
    if (arraysEqual(x.shape, noiseShape)) {
      return noiseShape;
    }
    if (x.shape.length === noiseShape.length) {
      const newDimension = [];
      for (let i = 0; i < x.shape.length; i++) {
        if (noiseShape[i] == null && x.shape[i] != null) {
          newDimension.push(x.shape[i]);
        } else {
          newDimension.push(noiseShape[i]);
        }
      }
      return newDimension;
    }
    return noiseShape;
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function dropout_(x, rate, noiseShape, seed) {
    const $x = convertToTensor(x, "x", "dropout");
    assert($x.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`);
    assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);
    if (rate === 0) {
      return x instanceof Tensor ? $x.clone() : $x;
    }
    const $noiseShape = getNoiseShape($x, noiseShape);
    const keepProb = 1 - rate;
    const multiplier = div(floor(add2(randomUniform($noiseShape, 0, 1, "float32", seed), keepProb)), keepProb);
    return mul($x, multiplier);
  }
  var dropout = op({dropout_});
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function enclosingPowerOfTwo(value) {
    return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2))));
  }
  function cosineWindow(windowLength, a, b) {
    const even = 1 - windowLength % 2;
    const newValues = new Float32Array(windowLength);
    for (let i = 0; i < windowLength; ++i) {
      const cosArg = 2 * Math.PI * i / (windowLength + even - 1);
      newValues[i] = a - b * Math.cos(cosArg);
    }
    return tensor1d(newValues, "float32");
  }
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  async function inTopKAsync_(predictions, targets, k = 1) {
    const $predictions = convertToTensor(predictions, "predictions", "inTopK");
    const $targets = convertToTensor(targets, "targets", "inTopK");
    assert($predictions.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`);
    assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`);
    assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, except the last dimension.`);
    const lastDim = $predictions.shape[$predictions.shape.length - 1];
    assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);
    const predictionsVals = await $predictions.data();
    const targetsVals = await $targets.data();
    const [batch, size] = [predictionsVals.length / lastDim, lastDim];
    const precision3 = getTypedArrayFromDType("bool", batch);
    for (let b = 0; b < batch; b++) {
      const offset = b * size;
      const vals = predictionsVals.subarray(offset, offset + size);
      const valAndInd = [];
      for (let i = 0; i < vals.length; i++) {
        valAndInd.push({value: vals[i], index: i});
      }
      valAndInd.sort((a, b2) => b2.value - a.value);
      precision3[b] = 0;
      for (let i = 0; i < k; i++) {
        if (valAndInd[i].index === targetsVals[b]) {
          precision3[b] = 1;
          break;
        }
      }
    }
    if (predictions !== $predictions) {
      $predictions.dispose();
    }
    if (targets !== $targets) {
      $targets.dispose();
    }
    return tensor(precision3, $targets.shape, "bool");
  }
  var inTopKAsync = inTopKAsync_;
  var fused_ops_exports = {};
  __export2(fused_ops_exports, {
    conv2d: () => conv2d2,
    depthwiseConv2d: () => depthwiseConv2d2,
    matMul: () => matMul2
  });
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv2DBackpropFilter_(x, dy, filterShape, strides, pad3, dataFormat = "NHWC", dimRoundingMode) {
    let x4D = x;
    if (x.rank === 3) {
      x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
    }
    let dy4D = dy;
    if (dy4D.rank === 3) {
      dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
    }
    assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`);
    assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`);
    assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);
    const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
    const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
    assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`);
    assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`);
    if (dimRoundingMode != null) {
      assert(isInt(pad3), () => `Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
    }
    const inputs = {x: x4D, dy: dy4D};
    const attrs = {strides, pad: pad3, dataFormat, dimRoundingMode, filterShape};
    return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs);
  }
  var conv2DBackpropFilter = op({conv2DBackpropFilter_});
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function getFusedDyActivation(dy, y, activation2) {
    if (activation2 == null || activation2 === "linear") {
      return dy;
    }
    if (activation2 === "relu") {
      return mul(dy, step(y));
    }
    throw new Error(`Cannot compute gradient for fused activation ${activation2}.`);
  }
  function getFusedBiasGradient(bias, dyActivation) {
    let res = dyActivation;
    const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape);
    if (reduceAxes.length > 0) {
      res = sum2(res, reduceAxes);
    }
    return reshape(res, bias.shape);
  }
  function applyActivation(x, activation2, preluActivationWeights, leakyreluAlpha) {
    if (activation2 === "linear") {
      return x;
    } else if (activation2 === "relu") {
      return relu(x);
    } else if (activation2 === "elu") {
      return elu(x);
    } else if (activation2 === "relu6") {
      return relu6(x);
    } else if (activation2 === "prelu") {
      return prelu(x, preluActivationWeights);
    } else if (activation2 === "leakyrelu") {
      return leakyRelu(x, leakyreluAlpha);
    }
    throw new Error(`Unknown fused activation ${activation2}.`);
  }
  var shouldFuse = (gradientDepth, activation2) => {
    const gradientMode = gradientDepth > 0;
    return !gradientMode || activation2 === "linear";
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fusedConv2d_({x, filter, strides, pad: pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha}) {
    activation2 = activation2 || "linear";
    if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {
      let result = conv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);
      if (bias != null) {
        result = add2(result, bias);
      }
      return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);
    }
    const $x = convertToTensor(x, "x", "conv2d");
    const $filter = convertToTensor(filter, "filter", "conv2d");
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
      reshapedTo4D = true;
      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
    }
    assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
    assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
    if (dimRoundingMode != null) {
      assert(isInt(pad3), () => `Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
    }
    assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`);
    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
    assert(dataFormat === "NHWC", () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);
    const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad3, dimRoundingMode);
    let $bias;
    if (bias != null) {
      $bias = convertToTensor(bias, "bias", "fused conv2d");
      [$bias] = makeTypesMatch($bias, $x);
      assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
    }
    let $preluActivationWeights;
    if (preluActivationWeights != null) {
      $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused conv2d");
    }
    const grad2 = (dy, saved) => {
      const [$filter2, x4D2, y, $bias2] = saved;
      const dyActivation = getFusedDyActivation(dy, y, activation2);
      assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
      const xDer = conv2DBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3);
      const filterDer = conv2DBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3);
      const der = [xDer, filterDer];
      if ($bias2 != null) {
        const biasDer = getFusedBiasGradient($bias2, dyActivation);
        der.push(biasDer);
      }
      return der;
    };
    const inputs = {
      x: x4D,
      filter: $filter,
      bias: $bias,
      preluActivationWeights: $preluActivationWeights
    };
    const attrs = {
      strides,
      pad: pad3,
      dataFormat,
      dilations,
      dimRoundingMode,
      activation: activation2,
      leakyreluAlpha
    };
    if (bias == null) {
      const customOp = customGrad((x4D2, filter2, save) => {
        let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);
        save([filter2, x4D2, res]);
        if (reshapedTo4D) {
          res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
        }
        return {value: res, gradFunc: grad2};
      });
      return customOp(x4D, $filter);
    } else {
      const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
        let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);
        save([filter2, x4D2, res, bias2]);
        if (reshapedTo4D) {
          res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
        }
        return {value: res, gradFunc: grad2};
      });
      return customOpWithBias(x4D, $filter, $bias);
    }
  }
  var conv2d2 = op({fusedConv2d_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad3, dilations = [1, 1], dimRoundingMode) {
    let x4D = x;
    if (x.rank === 3) {
      x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
    }
    let dy4D = dy;
    if (dy4D.rank === 3) {
      dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
    }
    const inputs = {x: x4D, dy: dy4D};
    const attrs = {strides, pad: pad3, dimRoundingMode, dilations, filterShape};
    return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter, inputs, attrs);
  }
  var depthwiseConv2dNativeBackpropFilter = op({depthwiseConv2dNativeBackpropFilter_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad3, dilations = [1, 1], dimRoundingMode) {
    let dy4D = dy;
    let reshapedTo4D = false;
    if (dy.rank === 3) {
      reshapedTo4D = true;
      dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
    }
    const inputs = {dy: dy4D, filter};
    const attrs = {strides, pad: pad3, dimRoundingMode, dilations, inputShape: xShape};
    const res = ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput, inputs, attrs);
    if (reshapedTo4D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
    }
    return res;
  }
  var depthwiseConv2dNativeBackpropInput = op({depthwiseConv2dNativeBackpropInput_});
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fusedDepthwiseConv2d_({x, filter, strides, pad: pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha}) {
    if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {
      let result = depthwiseConv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);
      if (bias != null) {
        result = add2(result, bias);
      }
      return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);
    }
    const $x = convertToTensor(x, "x", "depthwiseConv2d");
    const $filter = convertToTensor(filter, "filter", "depthwiseConv2d");
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
      reshapedTo4D = true;
      x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
    }
    assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
    assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
    assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
    if (dilations == null) {
      dilations = [1, 1];
    }
    assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
    if (dimRoundingMode != null) {
      assert(isInt(pad3), () => `Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
    }
    const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad3, dimRoundingMode, true);
    let $bias;
    if (bias != null) {
      $bias = convertToTensor(bias, "bias", "fused conv2d");
      [$bias] = makeTypesMatch($bias, $x);
      assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
    }
    let $preluActivationWeights;
    if (preluActivationWeights != null) {
      $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused depthwiseConv2d");
    }
    const grad2 = (dy, saved) => {
      assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);
      const [$filter2, x4D2, y, bias2] = saved;
      const dyActivation = getFusedDyActivation(dy, y, activation2);
      const xDer = depthwiseConv2dNativeBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3, dilations, dimRoundingMode);
      const filterDer = depthwiseConv2dNativeBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3, dilations, dimRoundingMode);
      if (bias2 != null) {
        const biasDer = getFusedBiasGradient($bias, dyActivation);
        return [xDer, filterDer, biasDer];
      }
      return [xDer, filterDer];
    };
    const inputs = {
      x: x4D,
      filter: $filter,
      bias: $bias,
      preluActivationWeights: $preluActivationWeights
    };
    const attrs = {
      strides,
      pad: pad3,
      dataFormat,
      dilations,
      dimRoundingMode,
      activation: activation2,
      leakyreluAlpha
    };
    if (bias == null) {
      const customOp = customGrad((x4D2, filter2, save) => {
        let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);
        save([filter2, x4D2, res]);
        if (reshapedTo4D) {
          res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
        }
        return {value: res, gradFunc: grad2};
      });
      return customOp(x4D, $filter);
    } else {
      const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
        let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);
        save([filter2, x4D2, res, bias2]);
        if (reshapedTo4D) {
          res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
        }
        return {value: res, gradFunc: grad2};
      });
      return customOpWithBias(x4D, $filter, $bias);
    }
  }
  var depthwiseConv2d2 = op({fusedDepthwiseConv2d_});
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fusedMatMul_({a, b, transposeA = false, transposeB = false, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha}) {
    if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {
      let result = matMul(a, b, transposeA, transposeB);
      if (bias != null) {
        result = add2(result, bias);
      }
      return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);
    }
    let $a = convertToTensor(a, "a", "fused matMul");
    let $b = convertToTensor(b, "b", "fused matMul");
    [$a, $b] = makeTypesMatch($a, $b);
    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
    const outerDimsA = $a.shape.slice(0, -2);
    const outerDimsB = $b.shape.slice(0, -2);
    const batchDimA = sizeFromShape(outerDimsA);
    const batchDimB = sizeFromShape(outerDimsB);
    assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`);
    assert(arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`);
    assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
    const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);
    const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);
    let $bias;
    if (bias != null) {
      $bias = convertToTensor(bias, "bias", "fused matMul");
      [$bias] = makeTypesMatch($bias, $a);
      assertAndGetBroadcastShape(outShape, $bias.shape);
    }
    let $preluActivationWeights;
    if (preluActivationWeights != null) {
      $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused matMul");
    }
    const grad2 = (dy, saved) => {
      const [a3D2, b3D2, y, $bias2] = saved;
      const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation2);
      let aDer;
      let bDer;
      if (!transposeA && !transposeB) {
        aDer = matMul(dyActivation, b3D2, false, true);
        bDer = matMul(a3D2, dyActivation, true, false);
      } else if (!transposeA && transposeB) {
        aDer = matMul(dyActivation, b3D2, false, false);
        bDer = matMul(dyActivation, a3D2, true, false);
      } else if (transposeA && !transposeB) {
        aDer = matMul(b3D2, dyActivation, false, true);
        bDer = matMul(a3D2, dyActivation, false, false);
      } else {
        aDer = matMul(b3D2, dyActivation, true, true);
        bDer = matMul(dyActivation, a3D2, true, true);
      }
      if (bias != null) {
        const biasDer = getFusedBiasGradient($bias2, dyActivation);
        return [aDer, bDer, biasDer];
      } else {
        return [aDer, bDer];
      }
    };
    const inputs = {
      a: a3D,
      b: b3D,
      bias: $bias,
      preluActivationWeights: $preluActivationWeights
    };
    const attrs = {transposeA, transposeB, activation: activation2, leakyreluAlpha};
    if (bias == null) {
      const customOp = customGrad((a3D2, b3D2, save) => {
        const res = ENGINE.runKernel(_FusedMatMul, inputs, attrs);
        save([a3D2, b3D2, res]);
        return {value: reshape(res, outShape), gradFunc: grad2};
      });
      return customOp(a3D, b3D);
    } else {
      const customOpWithBias = customGrad((a3D2, b3D2, $bias2, save) => {
        const res = ENGINE.runKernel(_FusedMatMul, inputs, attrs);
        save([a3D2, b3D2, res, $bias2]);
        return {value: reshape(res, outShape), gradFunc: grad2};
      });
      return customOpWithBias(a3D, b3D, $bias);
    }
  }
  var matMul2 = op({fusedMatMul_});
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function hammingWindow_(windowLength) {
    return cosineWindow(windowLength, 0.54, 0.46);
  }
  var hammingWindow = op({hammingWindow_});
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function hannWindow_(windowLength) {
    return cosineWindow(windowLength, 0.5, 0.5);
  }
  var hannWindow = op({hannWindow_});
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function frame_(signal2, frameLength, frameStep, padEnd = false, padValue = 0) {
    let start = 0;
    const output = [];
    while (start + frameLength <= signal2.size) {
      output.push(slice(signal2, start, frameLength));
      start += frameStep;
    }
    if (padEnd) {
      while (start < signal2.size) {
        const padLen = start + frameLength - signal2.size;
        const pad3 = concat([
          slice(signal2, start, frameLength - padLen),
          fill([padLen], padValue)
        ]);
        output.push(pad3);
        start += frameStep;
      }
    }
    if (output.length === 0) {
      return tensor2d([], [0, frameLength]);
    }
    return reshape(concat(output), [output.length, frameLength]);
  }
  var frame = op({frame_});
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function stft_(signal2, frameLength, frameStep, fftLength, windowFn = hannWindow) {
    if (fftLength == null) {
      fftLength = enclosingPowerOfTwo(frameLength);
    }
    const framedSignal = frame(signal2, frameLength, frameStep);
    const windowedSignal = mul(framedSignal, windowFn(frameLength));
    const output = [];
    for (let i = 0; i < framedSignal.shape[0]; i++) {
      output.push(rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));
    }
    return concat(output);
  }
  var stft = op({stft_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function cropAndResize_(image3, boxes, boxInd, cropSize, method = "bilinear", extrapolationValue = 0) {
    const $image = convertToTensor(image3, "image", "cropAndResize");
    const $boxes = convertToTensor(boxes, "boxes", "cropAndResize", "float32");
    const $boxInd = convertToTensor(boxInd, "boxInd", "cropAndResize", "int32");
    const numBoxes = $boxes.shape[0];
    assert($image.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`);
    assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`);
    assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`);
    assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`);
    assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);
    assert(method === "bilinear" || method === "nearest", () => `method must be bilinear or nearest, but was ${method}`);
    const inputs = {image: $image, boxes: $boxes, boxInd: $boxInd};
    const attrs = {method, extrapolationValue, cropSize};
    const res = ENGINE.runKernel(CropAndResize, inputs, attrs);
    return res;
  }
  var cropAndResize = op({cropAndResize_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function flipLeftRight_(image3) {
    const $image = convertToTensor(image3, "image", "flipLeftRight", "float32");
    assert($image.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);
    const inputs = {image: $image};
    const res = ENGINE.runKernel(FlipLeftRight, inputs, {});
    return res;
  }
  var flipLeftRight = op({flipLeftRight_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function rotateWithOffset_(image3, radians, fillValue = 0, center = 0.5) {
    const $image = convertToTensor(image3, "image", "rotateWithOffset", "float32");
    assert($image.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);
    const inputs = {image: $image};
    const attrs = {radians, fillValue, center};
    const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);
    return res;
  }
  var rotateWithOffset = op({rotateWithOffset_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
    if (iouThreshold == null) {
      iouThreshold = 0.5;
    }
    if (scoreThreshold == null) {
      scoreThreshold = Number.NEGATIVE_INFINITY;
    }
    if (softNmsSigma == null) {
      softNmsSigma = 0;
    }
    const numBoxes = boxes.shape[0];
    maxOutputSize = Math.min(maxOutputSize, numBoxes);
    assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);
    assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);
    assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);
    assert(scores.rank === 1, () => "scores must be a 1D tensor");
    assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`);
    assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);
    return {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
    const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
    const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
    maxOutputSize = inputs.maxOutputSize;
    iouThreshold = inputs.iouThreshold;
    scoreThreshold = inputs.scoreThreshold;
    const attrs = {maxOutputSize, iouThreshold, scoreThreshold};
    return ENGINE.runKernel(NonMaxSuppressionV3, {boxes: $boxes, scores: $scores}, attrs);
  }
  var nonMaxSuppression = op({nonMaxSuppression_});
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function binaryInsert(arr, element, comparator) {
    const index = binarySearch(arr, element, comparator);
    const insertionPoint = index < 0 ? -(index + 1) : index;
    arr.splice(insertionPoint, 0, element);
  }
  function binarySearch(arr, target, comparator) {
    return binarySearch_(arr, target, comparator || defaultComparator);
  }
  function defaultComparator(a, b) {
    return a > b ? 1 : a < b ? -1 : 0;
  }
  function binarySearch_(arr, target, comparator) {
    let left = 0;
    let right = arr.length;
    let middle = 0;
    let found = false;
    while (left < right) {
      middle = left + (right - left >>> 1);
      const compareResult = comparator(target, arr[middle]);
      if (compareResult > 0) {
        left = middle + 1;
      } else {
        right = middle;
        found = !compareResult;
      }
    }
    return found ? left : -left - 1;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0);
  }
  function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {
    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0, false, padToMaxOutputSize, true);
  }
  function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true);
  }
  function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {
    const candidates = [];
    for (let i = 0; i < scores.length; i++) {
      if (scores[i] > scoreThreshold) {
        candidates.push({score: scores[i], boxIndex: i, suppressBeginIndex: 0});
      }
    }
    candidates.sort(ascendingComparator);
    const scale22 = softNmsSigma > 0 ? -0.5 / softNmsSigma : 0;
    const selectedIndices = [];
    const selectedScores = [];
    while (selectedIndices.length < maxOutputSize && candidates.length > 0) {
      const candidate = candidates.pop();
      const {score: originalScore, boxIndex, suppressBeginIndex} = candidate;
      if (originalScore < scoreThreshold) {
        break;
      }
      let ignoreCandidate = false;
      for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {
        const iou2 = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);
        if (iou2 >= iouThreshold) {
          ignoreCandidate = true;
          break;
        }
        candidate.score = candidate.score * suppressWeight(iouThreshold, scale22, iou2);
        if (candidate.score <= scoreThreshold) {
          break;
        }
      }
      candidate.suppressBeginIndex = selectedIndices.length;
      if (!ignoreCandidate) {
        if (candidate.score === originalScore) {
          selectedIndices.push(boxIndex);
          selectedScores.push(candidate.score);
        } else if (candidate.score > scoreThreshold) {
          binaryInsert(candidates, candidate, ascendingComparator);
        }
      }
    }
    const validOutputs = selectedIndices.length;
    const elemsToPad = maxOutputSize - validOutputs;
    if (padToMaxOutputSize && elemsToPad > 0) {
      selectedIndices.push(...new Array(elemsToPad).fill(0));
      selectedScores.push(...new Array(elemsToPad).fill(0));
    }
    const result = {selectedIndices};
    if (returnScoresTensor) {
      result["selectedScores"] = selectedScores;
    }
    if (returnValidOutputs) {
      result["validOutputs"] = validOutputs;
    }
    return result;
  }
  function intersectionOverUnion(boxes, i, j) {
    const iCoord = boxes.subarray(i * 4, i * 4 + 4);
    const jCoord = boxes.subarray(j * 4, j * 4 + 4);
    const yminI = Math.min(iCoord[0], iCoord[2]);
    const xminI = Math.min(iCoord[1], iCoord[3]);
    const ymaxI = Math.max(iCoord[0], iCoord[2]);
    const xmaxI = Math.max(iCoord[1], iCoord[3]);
    const yminJ = Math.min(jCoord[0], jCoord[2]);
    const xminJ = Math.min(jCoord[1], jCoord[3]);
    const ymaxJ = Math.max(jCoord[0], jCoord[2]);
    const xmaxJ = Math.max(jCoord[1], jCoord[3]);
    const areaI = (ymaxI - yminI) * (xmaxI - xminI);
    const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
    if (areaI <= 0 || areaJ <= 0) {
      return 0;
    }
    const intersectionYmin = Math.max(yminI, yminJ);
    const intersectionXmin = Math.max(xminI, xminJ);
    const intersectionYmax = Math.min(ymaxI, ymaxJ);
    const intersectionXmax = Math.min(xmaxI, xmaxJ);
    const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0);
    return intersectionArea / (areaI + areaJ - intersectionArea);
  }
  function suppressWeight(iouThreshold, scale22, iou2) {
    const weight = Math.exp(scale22 * iou2 * iou2);
    return iou2 <= iouThreshold ? weight : 0;
  }
  function ascendingComparator(c1, c2) {
    return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  async function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
    const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
    const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
    maxOutputSize = inputs.maxOutputSize;
    iouThreshold = inputs.iouThreshold;
    scoreThreshold = inputs.scoreThreshold;
    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
    const boxesVals = boxesAndScores[0];
    const scoresVals = boxesAndScores[1];
    const {selectedIndices} = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
    if ($boxes !== boxes) {
      $boxes.dispose();
    }
    if ($scores !== scores) {
      $scores.dispose();
    }
    return tensor1d(selectedIndices, "int32");
  }
  var nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
    const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
    const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
    maxOutputSize = params.maxOutputSize;
    iouThreshold = params.iouThreshold;
    scoreThreshold = params.scoreThreshold;
    softNmsSigma = params.softNmsSigma;
    const inputs = {boxes: $boxes, scores: $scores};
    const attrs = {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};
    const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);
    return {selectedIndices: result[0], selectedScores: result[1]};
  }
  var nonMaxSuppressionWithScore = op({nonMaxSuppressionWithScore_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  async function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
    const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
    const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
    maxOutputSize = params.maxOutputSize;
    iouThreshold = params.iouThreshold;
    scoreThreshold = params.scoreThreshold;
    softNmsSigma = params.softNmsSigma;
    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
    const boxesVals = boxesAndScores[0];
    const scoresVals = boxesAndScores[1];
    const {selectedIndices, selectedScores} = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
    if ($boxes !== boxes) {
      $boxes.dispose();
    }
    if ($scores !== scores) {
      $scores.dispose();
    }
    return {
      selectedIndices: tensor1d(selectedIndices, "int32"),
      selectedScores: tensor1d(selectedScores)
    };
  }
  var nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
    const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
    const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
    const $maxOutputSize = params.maxOutputSize;
    const $iouThreshold = params.iouThreshold;
    const $scoreThreshold = params.scoreThreshold;
    const inputs = {boxes: $boxes, scores: $scores};
    const attrs = {
      maxOutputSize: $maxOutputSize,
      iouThreshold: $iouThreshold,
      scoreThreshold: $scoreThreshold,
      padToMaxOutputSize
    };
    const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);
    return {selectedIndices: result[0], validOutputs: result[1]};
  }
  var nonMaxSuppressionPadded = op({nonMaxSuppressionPadded_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  async function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
    const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
    const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
    const $maxOutputSize = params.maxOutputSize;
    const $iouThreshold = params.iouThreshold;
    const $scoreThreshold = params.scoreThreshold;
    const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);
    const {selectedIndices, validOutputs} = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);
    if ($boxes !== boxes) {
      $boxes.dispose();
    }
    if ($scores !== scores) {
      $scores.dispose();
    }
    return {
      selectedIndices: tensor1d(selectedIndices, "int32"),
      validOutputs: scalar(validOutputs, "int32")
    };
  }
  var nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function resizeBilinear_(images, size, alignCorners = false, halfPixelCenters = false) {
    const $images = convertToTensor(images, "images", "resizeBilinear");
    assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`);
    assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);
    assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.`);
    let batchImages = $images;
    let reshapedTo4D = false;
    if ($images.rank === 3) {
      reshapedTo4D = true;
      batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
    }
    const [] = size;
    const inputs = {images: batchImages};
    const attrs = {alignCorners, halfPixelCenters, size};
    const res = ENGINE.runKernel(ResizeBilinear, inputs, attrs);
    if (reshapedTo4D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
    }
    return res;
  }
  var resizeBilinear = op({resizeBilinear_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function resizeNearestNeighbor_(images, size, alignCorners = false, halfPixelCenters = false) {
    const $images = convertToTensor(images, "images", "resizeNearestNeighbor");
    assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`);
    assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`);
    assert($images.dtype === "float32" || $images.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype");
    assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.`);
    let batchImages = $images;
    let reshapedTo4D = false;
    if ($images.rank === 3) {
      reshapedTo4D = true;
      batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
    }
    const [] = size;
    const inputs = {images: batchImages};
    const attrs = {alignCorners, halfPixelCenters, size};
    const res = ENGINE.runKernel(ResizeNearestNeighbor, inputs, attrs);
    if (reshapedTo4D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
    }
    return res;
  }
  var resizeNearestNeighbor = op({resizeNearestNeighbor_});
  /**
   * @license
   * Copyright 2021 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function transform_(image3, transforms, interpolation = "nearest", fillMode = "constant", fillValue = 0, outputShape) {
    const $image = convertToTensor(image3, "image", "transform", "float32");
    const $transforms = convertToTensor(transforms, "transforms", "transform", "float32");
    assert($image.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${$image.rank}.`);
    assert($transforms.rank === 2 && ($transforms.shape[0] === $image.shape[0] || $transforms.shape[0] === 1) && $transforms.shape[1] === 8, () => `Error in transform: Input transform should be batch x 8 or 1 x 8`);
    assert(outputShape == null || outputShape.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${outputShape}.`);
    const inputs = {image: $image, transforms: $transforms};
    const attrs = {interpolation, fillMode, fillValue, outputShape};
    return ENGINE.runKernel(Transform, inputs, attrs);
  }
  var transform = op({transform_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function bandPart_(a, numLower, numUpper) {
    assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);
    assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);
    const $a = convertToTensor(a, "a", "bandPart");
    assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);
    const shape = $a.shape;
    const [M, N] = $a.shape.slice(-2);
    if (!(numLower <= M)) {
      throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);
    }
    if (!(numUpper <= N)) {
      throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);
    }
    if (numLower < 0) {
      numLower = M;
    }
    if (numUpper < 0) {
      numUpper = N;
    }
    const i = reshape(range(0, M, 1, "int32"), [-1, 1]);
    const j = range(0, N, 1, "int32");
    const ij = sub(i, j);
    const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, "int32")), greaterEqual(ij, scalar(-numUpper, "int32")));
    const zero = zeros([M, N], $a.dtype);
    return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape);
  }
  var bandPart = op({bandPart_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function gramSchmidt_(xs) {
    let inputIsTensor2D;
    if (Array.isArray(xs)) {
      inputIsTensor2D = false;
      assert(xs != null && xs.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
      const dim = xs[0].shape[0];
      for (let i = 1; i < xs.length; ++i) {
        assert(xs[i].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`);
      }
    } else {
      inputIsTensor2D = true;
      xs = split(xs, xs.shape[0], 0).map((x) => squeeze(x, [0]));
    }
    assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);
    const ys = [];
    const xs1d = xs;
    for (let i = 0; i < xs.length; ++i) {
      ys.push(ENGINE.tidy(() => {
        let x = xs1d[i];
        if (i > 0) {
          for (let j = 0; j < i; ++j) {
            const proj = mul(sum2(mul(ys[j], x)), ys[j]);
            x = sub(x, proj);
          }
        }
        return div(x, norm(x, "euclidean"));
      }));
    }
    if (inputIsTensor2D) {
      return stack(ys, 0);
    } else {
      return ys;
    }
  }
  var gramSchmidt = op({gramSchmidt_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function qr_(x, fullMatrices = false) {
    assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);
    if (x.rank === 2) {
      return qr2d(x, fullMatrices);
    } else {
      const outerDimsProd = x.shape.slice(0, x.shape.length - 2).reduce((value, prev) => value * prev);
      const x2ds = unstack(reshape(x, [
        outerDimsProd,
        x.shape[x.shape.length - 2],
        x.shape[x.shape.length - 1]
      ]), 0);
      const q2ds = [];
      const r2ds = [];
      x2ds.forEach((x2d) => {
        const [q2d, r2d] = qr2d(x2d, fullMatrices);
        q2ds.push(q2d);
        r2ds.push(r2d);
      });
      const q = reshape(stack(q2ds, 0), x.shape);
      const r = reshape(stack(r2ds, 0), x.shape);
      return [q, r];
    }
  }
  function qr2d(x, fullMatrices = false) {
    return ENGINE.tidy(() => {
      assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);
      const m = x.shape[0];
      const n = x.shape[1];
      let q = eye(m);
      let r = clone(x);
      const one2D = tensor2d([[1]], [1, 1]);
      let w = clone(one2D);
      const iters = m >= n ? n : m;
      for (let j = 0; j < iters; ++j) {
        const rTemp = r;
        const wTemp = w;
        const qTemp = q;
        [w, r, q] = ENGINE.tidy(() => {
          const rjEnd1 = slice(r, [j, j], [m - j, 1]);
          const normX = norm(rjEnd1);
          const rjj = slice(r, [j, j], [1, 1]);
          const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));
          const u1 = sub(rjj, mul(s, normX));
          const wPre = div(rjEnd1, u1);
          if (wPre.shape[0] === 1) {
            w = clone(one2D);
          } else {
            w = concat([
              one2D,
              slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])
            ], 0);
          }
          const tau = neg(div(matMul(s, u1), normX));
          const rjEndAll = slice(r, [j, 0], [m - j, n]);
          const tauTimesW = mul(tau, w);
          const wT = transpose(w);
          if (j === 0) {
            r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
          } else {
            const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
            r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);
          }
          const tawTimesWT = transpose(tauTimesW);
          const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);
          if (j === 0) {
            q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
          } else {
            const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
            q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);
          }
          return [w, r, q];
        });
        dispose([rTemp, wTemp, qTemp]);
      }
      if (!fullMatrices && m > n) {
        q = slice(q, [0, 0], [m, n]);
        r = slice(r, [0, 0], [n, n]);
      }
      return [q, r];
    });
  }
  var qr = op({qr_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var Reduction;
  (function(Reduction2) {
    Reduction2[Reduction2["NONE"] = 0] = "NONE";
    Reduction2[Reduction2["MEAN"] = 1] = "MEAN";
    Reduction2[Reduction2["SUM"] = 2] = "SUM";
    Reduction2[Reduction2["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS";
  })(Reduction || (Reduction = {}));
  function computeWeightedLoss_(losses4, weights, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
    const $losses = convertToTensor(losses4, "losses", "computeWeightedLoss");
    let $weights = null;
    if (weights != null) {
      $weights = convertToTensor(weights, "weights", "computeWeightedLoss");
    }
    const weightedLoss = $weights == null ? $losses : mul($losses, $weights);
    if (reduction2 === Reduction.NONE) {
      return weightedLoss;
    }
    if (reduction2 === Reduction.SUM) {
      return sum2(weightedLoss);
    }
    if (reduction2 === Reduction.MEAN) {
      if ($weights == null) {
        return mean(weightedLoss);
      } else {
        const broadcastFactor = $losses.size / $weights.size;
        const result = div(sum2(weightedLoss), sum2($weights));
        return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : result;
      }
    }
    if (reduction2 === Reduction.SUM_BY_NONZERO_WEIGHTS) {
      if ($weights == null) {
        return div(sum2(weightedLoss), scalar($losses.size));
      } else {
        const broadcastedWeights = mul($weights, ones2($losses.shape));
        const numNonZeros = cast(sum2(notEqual(broadcastedWeights, scalar(0))), "float32");
        return div(sum2(weightedLoss), numNonZeros);
      }
    }
    throw Error(`Unknown reduction: ${reduction2}`);
  }
  var computeWeightedLoss = op({computeWeightedLoss_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function absoluteDifference_(labels, predictions, weights, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
    const $labels = convertToTensor(labels, "labels", "absoluteDifference");
    const $predictions = convertToTensor(predictions, "predictions", "absoluteDifference");
    let $weights = null;
    if (weights != null) {
      $weights = convertToTensor(weights, "weights", "absoluteDifference");
    }
    assertShapesMatch($labels.shape, $predictions.shape, "Error in absoluteDifference: ");
    const losses4 = abs(sub($labels, $predictions));
    return computeWeightedLoss(losses4, $weights, reduction2);
  }
  var absoluteDifference = op({absoluteDifference_});
  function cosineDistance_(labels, predictions, axis, weights, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
    const $labels = convertToTensor(labels, "labels", "cosineDistance");
    const $predictions = convertToTensor(predictions, "predictions", "cosineDistance");
    let $weights = null;
    if (weights != null) {
      $weights = convertToTensor(weights, "weights", "cosineDistance");
    }
    assertShapesMatch($labels.shape, $predictions.shape, "Error in cosineDistance: ");
    const one = scalar(1);
    const losses4 = sub(one, sum2(mul($labels, $predictions), axis, true));
    return computeWeightedLoss(losses4, $weights, reduction2);
  }
  var cosineDistance = op({cosineDistance_});
  function hingeLoss_(labels, predictions, weights, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
    let $labels = convertToTensor(labels, "labels", "hingeLoss");
    const $predictions = convertToTensor(predictions, "predictions", "hingeLoss");
    let $weights = null;
    if (weights != null) {
      $weights = convertToTensor(weights, "weights", "hingeLoss");
    }
    assertShapesMatch($labels.shape, $predictions.shape, "Error in hingeLoss: ");
    const one = scalar(1);
    $labels = sub(mul(scalar(2), $labels), one);
    const losses4 = relu(sub(one, mul($labels, $predictions)));
    return computeWeightedLoss(losses4, $weights, reduction2);
  }
  var hingeLoss = op({hingeLoss_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function huberLoss_(labels, predictions, weights, delta = 1, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
    const $labels = convertToTensor(labels, "labels", "huberLoss");
    const $predictions = convertToTensor(predictions, "predictions", "huberLoss");
    let $weights = null;
    if (weights != null) {
      $weights = convertToTensor(weights, "weights", "huberLoss");
    }
    assertShapesMatch($labels.shape, $predictions.shape, "Error in huberLoss: ");
    const deltaScalar = scalar(delta);
    const error = abs(sub($predictions, $labels));
    const quadratic = minimum(error, deltaScalar);
    const linear = sub(error, quadratic);
    const losses4 = add2(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));
    return computeWeightedLoss(losses4, $weights, reduction2);
  }
  var huberLoss = op({huberLoss_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function logLoss_(labels, predictions, weights, epsilon32 = 1e-7, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
    const $labels = convertToTensor(labels, "labels", "logLoss");
    const $predictions = convertToTensor(predictions, "predictions", "logLoss");
    let $weights = null;
    if (weights != null) {
      $weights = convertToTensor(weights, "weights", "logLoss");
    }
    assertShapesMatch($labels.shape, $predictions.shape, "Error in logLoss: ");
    const one = scalar(1);
    const epsilonScalar = scalar(epsilon32);
    const l13 = neg(mul($labels, log(add2($predictions, epsilonScalar))));
    const l23 = mul(sub(one, $labels), log(add2(sub(one, $predictions), epsilonScalar)));
    const losses4 = sub(l13, l23);
    return computeWeightedLoss(losses4, $weights, reduction2);
  }
  var logLoss = op({logLoss_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function meanSquaredError_(labels, predictions, weights, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
    const $labels = convertToTensor(labels, "labels", "meanSquaredError");
    const $predictions = convertToTensor(predictions, "predictions", "meanSquaredError");
    let $weights = null;
    if (weights != null) {
      $weights = convertToTensor(weights, "weights", "meanSquaredError");
    }
    assertShapesMatch($labels.shape, $predictions.shape, "Error in meanSquaredError: ");
    const losses4 = squaredDifference($labels, $predictions);
    return computeWeightedLoss(losses4, $weights, reduction2);
  }
  var meanSquaredError = op({meanSquaredError_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sigmoidCrossEntropyWithLogits_(labels, logits) {
    const $labels = convertToTensor(labels, "labels", "sigmoidCrossEntropyWithLogits");
    const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropyWithLogits");
    assertShapesMatch($labels.shape, $logits.shape, "Error in sigmoidCrossEntropyWithLogits: ");
    const maxOutput = relu($logits);
    const outputXTarget = mul($logits, $labels);
    const sigmoidOutput = log1p(exp(neg(abs($logits))));
    return add2(sub(maxOutput, outputXTarget), sigmoidOutput);
  }
  function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
    let $multiClassLabels = convertToTensor(multiClassLabels, "multiClassLabels", "sigmoidCrossEntropy");
    const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropy");
    let $weights = null;
    if (weights != null) {
      $weights = convertToTensor(weights, "weights", "sigmoidCrossEntropy");
    }
    assertShapesMatch($multiClassLabels.shape, $logits.shape, "Error in sigmoidCrossEntropy: ");
    if (labelSmoothing > 0) {
      const labelSmoothingScalar = scalar(labelSmoothing);
      const one = scalar(1);
      const half = scalar(0.5);
      $multiClassLabels = add2(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));
    }
    const losses4 = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
    return computeWeightedLoss(losses4, $weights, reduction2);
  }
  var sigmoidCrossEntropy = op({sigmoidCrossEntropy_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {
    if (dim === -1) {
      dim = logits.rank - 1;
    }
    if (dim !== logits.rank - 1) {
      throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);
    }
    const customOp = customGrad((labels2, logits2, save) => {
      const keepDims = true;
      const lse = logSumExp(logits2, [dim], keepDims);
      const logResult = sub(cast(logits2, "float32"), lse);
      save([labels2, logResult]);
      const costVector = neg(mul(logResult, labels2));
      const value = sum2(costVector, [dim]);
      const gradFunc = (dy, saved) => {
        const [labels3, logResult2] = saved;
        const dyShape = expandShapeToKeepDim(dy.shape, [dim]);
        return [
          mul(reshape(dy, dyShape), sub(cast(labels3, "float32"), exp(logResult2))),
          mul(reshape(dy, dyShape), sub(exp(logResult2), cast(labels3, "float32")))
        ];
      };
      return {value, gradFunc};
    });
    return customOp(labels, logits);
  }
  function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction2 = Reduction.SUM_BY_NONZERO_WEIGHTS) {
    let $onehotLabels = convertToTensor(onehotLabels, "onehotLabels", "softmaxCrossEntropy");
    const $logits = convertToTensor(logits, "logits", "softmaxCrossEntropy");
    let $weights = null;
    if (weights != null) {
      $weights = convertToTensor(weights, "weights", "softmaxCrossEntropy");
    }
    assertShapesMatch($onehotLabels.shape, $logits.shape, "Error in softmaxCrossEntropy: ");
    if (labelSmoothing > 0) {
      const labelSmoothingScalar = scalar(labelSmoothing);
      const one = scalar(1);
      const numClasses = scalar($onehotLabels.shape[1]);
      $onehotLabels = add2(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));
    }
    const losses4 = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);
    return computeWeightedLoss(losses4, $weights, reduction2);
  }
  var softmaxCrossEntropy = op({softmaxCrossEntropy_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var spectral = {
    fft,
    ifft,
    rfft,
    irfft
  };
  var signal = {
    hammingWindow,
    hannWindow,
    frame,
    stft
  };
  var image = {
    flipLeftRight,
    resizeNearestNeighbor,
    resizeBilinear,
    rotateWithOffset,
    cropAndResize,
    nonMaxSuppression,
    nonMaxSuppressionAsync,
    nonMaxSuppressionWithScore,
    nonMaxSuppressionWithScoreAsync,
    nonMaxSuppressionPadded,
    nonMaxSuppressionPaddedAsync,
    transform
  };
  var linalg = {
    bandPart,
    gramSchmidt,
    qr
  };
  var losses = {
    absoluteDifference,
    computeWeightedLoss,
    cosineDistance,
    hingeLoss,
    huberLoss,
    logLoss,
    meanSquaredError,
    sigmoidCrossEntropy,
    softmaxCrossEntropy
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var Optimizer = class extends Serializable {
    minimize(f, returnCost = false, varList) {
      const {value, grads: grads2} = this.computeGradients(f, varList);
      if (varList != null) {
        const gradArray = varList.map((v) => ({name: v.name, tensor: grads2[v.name]}));
        this.applyGradients(gradArray);
      } else {
        this.applyGradients(grads2);
      }
      dispose(grads2);
      if (returnCost) {
        return value;
      } else {
        value.dispose();
        return null;
      }
    }
    get iterations() {
      if (this.iterations_ == null) {
        this.iterations_ = 0;
      }
      return this.iterations_;
    }
    incrementIterations() {
      this.iterations_ = this.iterations + 1;
    }
    computeGradients(f, varList) {
      return variableGrads(f, varList);
    }
    dispose() {
      if (this.iterations_ != null) {
        dispose(this.iterations_);
      }
    }
    async saveIterations() {
      if (this.iterations_ == null) {
        this.iterations_ = 0;
      }
      return {
        name: "iter",
        tensor: scalar(this.iterations_, "int32")
      };
    }
    async getWeights() {
      throw new Error("getWeights() is not implemented for this optimizer yet.");
    }
    async setWeights(weightValues) {
      throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`);
    }
    async extractIterations(weightValues) {
      this.iterations_ = (await weightValues[0].tensor.data())[0];
      return weightValues.slice(1);
    }
  };
  Object.defineProperty(Optimizer, Symbol.hasInstance, {
    value: (instance) => {
      return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null;
    }
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var AdadeltaOptimizer = class extends Optimizer {
    constructor(learningRate, rho, epsilon32 = null) {
      super();
      this.learningRate = learningRate;
      this.rho = rho;
      this.epsilon = epsilon32;
      this.accumulatedGrads = [];
      this.accumulatedUpdates = [];
      if (epsilon32 == null) {
        this.epsilon = ENGINE.backend.epsilon();
      }
    }
    applyGradients(variableGradients) {
      const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
      variableNames.forEach((name, i) => {
        const value = ENGINE.registeredVariables[name];
        const trainable = false;
        if (this.accumulatedGrads[i] == null) {
          this.accumulatedGrads[i] = {
            originalName: `${name}/accum_grad`,
            variable: tidy(() => zerosLike(value).variable(trainable))
          };
        }
        if (this.accumulatedUpdates[i] == null) {
          this.accumulatedUpdates[i] = {
            originalName: `${name}/accum_var`,
            variable: tidy(() => zerosLike(value).variable(trainable))
          };
        }
        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
        if (gradient == null) {
          return;
        }
        const accumulatedGrad = this.accumulatedGrads[i].variable;
        const accumulatedUpdate = this.accumulatedUpdates[i].variable;
        tidy(() => {
          const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));
          const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient);
          const newAccumulatedUpdate = add2(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));
          accumulatedGrad.assign(newAccumulatedGrad);
          accumulatedUpdate.assign(newAccumulatedUpdate);
          const newValue = add2(mul(updates, -this.learningRate), value);
          value.assign(newValue);
        });
      });
      this.incrementIterations();
    }
    dispose() {
      if (this.accumulatedUpdates != null) {
        dispose(this.accumulatedGrads.map((v) => v.variable));
        dispose(this.accumulatedUpdates.map((v) => v.variable));
      }
    }
    async getWeights() {
      const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];
      return [await this.saveIterations()].concat(variables.map((v) => ({name: v.originalName, tensor: v.variable})));
    }
    async setWeights(weightValues) {
      weightValues = await this.extractIterations(weightValues);
      const variableCount = weightValues.length / 2;
      const trainable = false;
      this.accumulatedGrads = weightValues.slice(0, variableCount).map((v) => ({
        originalName: v.name,
        variable: v.tensor.variable(trainable)
      }));
      this.accumulatedUpdates = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
        originalName: v.name,
        variable: v.tensor.variable(trainable)
      }));
    }
    getConfig() {
      return {
        learningRate: this.learningRate,
        rho: this.rho,
        epsilon: this.epsilon
      };
    }
    static fromConfig(cls, config) {
      return new cls(config["learningRate"], config["rho"], config["epsilon"]);
    }
  };
  AdadeltaOptimizer.className = "Adadelta";
  registerClass(AdadeltaOptimizer);
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var AdagradOptimizer = class extends Optimizer {
    constructor(learningRate, initialAccumulatorValue = 0.1) {
      super();
      this.learningRate = learningRate;
      this.initialAccumulatorValue = initialAccumulatorValue;
      this.accumulatedGrads = [];
    }
    applyGradients(variableGradients) {
      const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
      variableNames.forEach((name, i) => {
        const value = ENGINE.registeredVariables[name];
        if (this.accumulatedGrads[i] == null) {
          const trainable = false;
          this.accumulatedGrads[i] = {
            originalName: `${name}/accumulator`,
            variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable))
          };
        }
        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
        if (gradient == null) {
          return;
        }
        const accumulatedGrad = this.accumulatedGrads[i].variable;
        tidy(() => {
          const newAccumulatedGrad = add2(accumulatedGrad, square(gradient));
          accumulatedGrad.assign(newAccumulatedGrad);
          const newValue = add2(mul(div(gradient, sqrt(add2(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);
          value.assign(newValue);
        });
      });
      this.incrementIterations();
    }
    dispose() {
      if (this.accumulatedGrads != null) {
        dispose(this.accumulatedGrads.map((v) => v.variable));
      }
    }
    async getWeights() {
      return [await this.saveIterations()].concat(this.accumulatedGrads.map((v) => ({name: v.originalName, tensor: v.variable})));
    }
    async setWeights(weightValues) {
      weightValues = await this.extractIterations(weightValues);
      const trainable = false;
      this.accumulatedGrads = weightValues.map((v) => ({originalName: v.name, variable: v.tensor.variable(trainable)}));
    }
    getConfig() {
      return {
        learningRate: this.learningRate,
        initialAccumulatorValue: this.initialAccumulatorValue
      };
    }
    static fromConfig(cls, config) {
      return new cls(config["learningRate"], config["initialAccumulatorValue"]);
    }
  };
  AdagradOptimizer.className = "Adagrad";
  registerClass(AdagradOptimizer);
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var AdamOptimizer = class extends Optimizer {
    constructor(learningRate, beta1, beta2, epsilon32 = null) {
      super();
      this.learningRate = learningRate;
      this.beta1 = beta1;
      this.beta2 = beta2;
      this.epsilon = epsilon32;
      this.accumulatedFirstMoment = [];
      this.accumulatedSecondMoment = [];
      tidy(() => {
        this.accBeta1 = scalar(beta1).variable();
        this.accBeta2 = scalar(beta2).variable();
      });
      if (epsilon32 == null) {
        this.epsilon = ENGINE.backend.epsilon();
      }
    }
    applyGradients(variableGradients) {
      const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
      tidy(() => {
        const oneMinusAccBeta1 = sub(1, this.accBeta1);
        const oneMinusAccBeta2 = sub(1, this.accBeta2);
        varNames.forEach((name, i) => {
          const value = ENGINE.registeredVariables[name];
          const trainable = false;
          if (this.accumulatedFirstMoment[i] == null) {
            this.accumulatedFirstMoment[i] = {
              originalName: `${name}/m`,
              variable: tidy(() => zerosLike(value).variable(trainable))
            };
          }
          if (this.accumulatedSecondMoment[i] == null) {
            this.accumulatedSecondMoment[i] = {
              originalName: `${name}/v`,
              variable: tidy(() => zerosLike(value).variable(trainable))
            };
          }
          const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
          if (gradient == null) {
            return;
          }
          const firstMoment = this.accumulatedFirstMoment[i].variable;
          const secondMoment = this.accumulatedSecondMoment[i].variable;
          const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
          const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));
          const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);
          const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);
          firstMoment.assign(newFirstMoment);
          secondMoment.assign(newSecondMoment);
          const newValue = add2(mul(div(biasCorrectedFirstMoment, add2(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);
          value.assign(newValue);
        });
        this.accBeta1.assign(mul(this.accBeta1, this.beta1));
        this.accBeta2.assign(mul(this.accBeta2, this.beta2));
      });
      this.incrementIterations();
    }
    dispose() {
      this.accBeta1.dispose();
      this.accBeta2.dispose();
      if (this.accumulatedFirstMoment != null) {
        dispose(this.accumulatedFirstMoment.map((v) => v.variable));
      }
      if (this.accumulatedSecondMoment != null) {
        dispose(this.accumulatedSecondMoment.map((v) => v.variable));
      }
    }
    async getWeights() {
      const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];
      return [await this.saveIterations()].concat(variables.map((v) => ({name: v.originalName, tensor: v.variable})));
    }
    async setWeights(weightValues) {
      weightValues = await this.extractIterations(weightValues);
      tidy(() => {
        this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));
        this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));
      });
      const variableCount = weightValues.length / 2;
      const trainable = false;
      this.accumulatedFirstMoment = weightValues.slice(0, variableCount).map((v) => ({
        originalName: v.name,
        variable: v.tensor.variable(trainable)
      }));
      this.accumulatedSecondMoment = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
        originalName: v.name,
        variable: v.tensor.variable(trainable)
      }));
    }
    getConfig() {
      return {
        learningRate: this.learningRate,
        beta1: this.beta1,
        beta2: this.beta2,
        epsilon: this.epsilon
      };
    }
    static fromConfig(cls, config) {
      return new cls(config["learningRate"], config["beta1"], config["beta2"], config["epsilon"]);
    }
  };
  AdamOptimizer.className = "Adam";
  registerClass(AdamOptimizer);
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var AdamaxOptimizer = class extends Optimizer {
    constructor(learningRate, beta1, beta2, epsilon32 = null, decay = 0) {
      super();
      this.learningRate = learningRate;
      this.beta1 = beta1;
      this.beta2 = beta2;
      this.epsilon = epsilon32;
      this.decay = decay;
      this.accumulatedFirstMoment = [];
      this.accumulatedWeightedInfNorm = [];
      tidy(() => {
        this.iteration = scalar(0).variable();
        this.accBeta1 = scalar(beta1).variable();
      });
      if (epsilon32 == null) {
        this.epsilon = ENGINE.backend.epsilon();
      }
    }
    applyGradients(variableGradients) {
      const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
      tidy(() => {
        const oneMinusAccBeta1 = sub(1, this.accBeta1);
        const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1));
        variableNames.forEach((name, i) => {
          const value = ENGINE.registeredVariables[name];
          const trainable = false;
          if (this.accumulatedFirstMoment[i] == null) {
            this.accumulatedFirstMoment[i] = {
              originalName: `${name}/m`,
              variable: zerosLike(value).variable(trainable)
            };
          }
          if (this.accumulatedWeightedInfNorm[i] == null) {
            this.accumulatedWeightedInfNorm[i] = {
              originalName: `${name}/v`,
              variable: zerosLike(value).variable(trainable)
            };
          }
          const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
          if (gradient == null) {
            return;
          }
          const firstMoment = this.accumulatedFirstMoment[i].variable;
          const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;
          const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
          const ut0 = mul(weightedInfNorm, this.beta2);
          const ut1 = abs(gradient);
          const newWeightedInfNorm = maximum(ut0, ut1);
          firstMoment.assign(newFirstMoment);
          weightedInfNorm.assign(newWeightedInfNorm);
          const newValue = add2(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add2(newWeightedInfNorm, this.epsilon))), value);
          value.assign(newValue);
        });
        this.iteration.assign(add2(this.iteration, 1));
        this.accBeta1.assign(mul(this.accBeta1, this.beta1));
      });
      this.incrementIterations();
    }
    dispose() {
      this.accBeta1.dispose();
      this.iteration.dispose();
      if (this.accumulatedFirstMoment != null) {
        dispose(this.accumulatedFirstMoment.map((v) => v.variable));
      }
      if (this.accumulatedWeightedInfNorm != null) {
        dispose(this.accumulatedWeightedInfNorm.map((v) => v.variable));
      }
    }
    async getWeights() {
      throw new Error("getWeights() is not implemented for Adamax yet.");
    }
    async setWeights(weightValues) {
      throw new Error("setWeights() is not implemented for Adamax yet.");
    }
    getConfig() {
      return {
        learningRate: this.learningRate,
        beta1: this.beta1,
        beta2: this.beta2,
        epsilon: this.epsilon,
        decay: this.decay
      };
    }
    static fromConfig(cls, config) {
      return new cls(config["learningRate"], config["beta1"], config["beta2"], config["epsilon"], config["decay"]);
    }
  };
  AdamaxOptimizer.className = "Adamax";
  registerClass(AdamaxOptimizer);
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SGDOptimizer = class extends Optimizer {
    constructor(learningRate) {
      super();
      this.learningRate = learningRate;
      this.setLearningRate(learningRate);
    }
    applyGradients(variableGradients) {
      const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
      varNames.forEach((name, i) => {
        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
        if (gradient == null) {
          return;
        }
        const value = ENGINE.registeredVariables[name];
        tidy(() => {
          const newValue = add2(mul(this.c, gradient), value);
          value.assign(newValue);
        });
      });
      this.incrementIterations();
    }
    setLearningRate(learningRate) {
      this.learningRate = learningRate;
      if (this.c != null) {
        this.c.dispose();
      }
      this.c = keep(scalar(-learningRate));
    }
    dispose() {
      this.c.dispose();
    }
    async getWeights() {
      return [await this.saveIterations()];
    }
    async setWeights(weightValues) {
      weightValues = await this.extractIterations(weightValues);
      if (weightValues.length !== 0) {
        throw new Error("SGD optimizer does not have settable weights.");
      }
    }
    getConfig() {
      return {learningRate: this.learningRate};
    }
    static fromConfig(cls, config) {
      return new cls(config["learningRate"]);
    }
  };
  SGDOptimizer.className = "SGD";
  registerClass(SGDOptimizer);
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var MomentumOptimizer = class extends SGDOptimizer {
    constructor(learningRate, momentum, useNesterov = false) {
      super(learningRate);
      this.learningRate = learningRate;
      this.momentum = momentum;
      this.useNesterov = useNesterov;
      this.accumulations = [];
      this.m = scalar(this.momentum);
    }
    applyGradients(variableGradients) {
      const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
      variableNames.forEach((name, i) => {
        const value = ENGINE.registeredVariables[name];
        if (this.accumulations[i] == null) {
          const trainable = false;
          this.accumulations[i] = {
            originalName: `${name}/momentum`,
            variable: tidy(() => zerosLike(value).variable(trainable))
          };
        }
        const accumulation = this.accumulations[i].variable;
        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
        if (gradient == null) {
          return;
        }
        tidy(() => {
          let newValue;
          const newAccumulation = add2(mul(this.m, accumulation), gradient);
          if (this.useNesterov) {
            newValue = add2(mul(this.c, add2(gradient, mul(newAccumulation, this.m))), value);
          } else {
            newValue = add2(mul(this.c, newAccumulation), value);
          }
          accumulation.assign(newAccumulation);
          value.assign(newValue);
        });
      });
      this.incrementIterations();
    }
    dispose() {
      this.m.dispose();
      if (this.accumulations != null) {
        dispose(this.accumulations.map((v) => v.variable));
      }
    }
    setMomentum(momentum) {
      this.momentum = momentum;
    }
    async getWeights() {
      return [await this.saveIterations()].concat(this.accumulations.map((v) => ({name: v.originalName, tensor: v.variable})));
    }
    async setWeights(weightValues) {
      weightValues = await this.extractIterations(weightValues);
      const trainable = false;
      this.accumulations = weightValues.map((v) => ({originalName: v.name, variable: v.tensor.variable(trainable)}));
    }
    getConfig() {
      return {
        learningRate: this.learningRate,
        momentum: this.momentum,
        useNesterov: this.useNesterov
      };
    }
    static fromConfig(cls, config) {
      return new cls(config["learningRate"], config["momentum"], config["useNesterov"]);
    }
  };
  MomentumOptimizer.className = "Momentum";
  registerClass(MomentumOptimizer);
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var RMSPropOptimizer = class extends Optimizer {
    constructor(learningRate, decay = 0.9, momentum = 0, epsilon32 = null, centered = false) {
      super();
      this.learningRate = learningRate;
      this.decay = decay;
      this.momentum = momentum;
      this.epsilon = epsilon32;
      this.accumulatedMeanSquares = [];
      this.accumulatedMoments = [];
      this.accumulatedMeanGrads = [];
      this.centered = centered;
      if (epsilon32 == null) {
        this.epsilon = ENGINE.backend.epsilon();
      }
      if (learningRate == null) {
        throw new Error(`learningRate for RMSPropOptimizer must be defined.`);
      }
    }
    applyGradients(variableGradients) {
      const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
      variableNames.forEach((name, i) => {
        const value = ENGINE.registeredVariables[name];
        const trainable = false;
        if (this.accumulatedMeanSquares[i] == null) {
          this.accumulatedMeanSquares[i] = {
            originalName: `${name}/rms`,
            variable: tidy(() => zerosLike(value).variable(trainable))
          };
        }
        if (this.accumulatedMoments[i] == null) {
          this.accumulatedMoments[i] = {
            originalName: `${name}/momentum`,
            variable: tidy(() => zerosLike(value).variable(trainable))
          };
        }
        if (this.accumulatedMeanGrads[i] == null && this.centered) {
          this.accumulatedMeanGrads[i] = {
            originalName: `${name}/mg`,
            variable: tidy(() => zerosLike(value).variable(trainable))
          };
        }
        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
        if (gradient == null) {
          return;
        }
        const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;
        const accumulatedMoments = this.accumulatedMoments[i].variable;
        tidy(() => {
          const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
          if (this.centered) {
            const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;
            const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));
            const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon))));
            const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution);
            accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
            accumulatedMeanGrad.assign(newAccumulatedMeanGrad);
            accumulatedMoments.assign(newAccumulatedMoments);
            const newValue = sub(value, newAccumulatedMoments);
            value.assign(newValue);
          } else {
            const newAccumulatedMeanSquare2 = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
            const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add2(newAccumulatedMeanSquare2, this.epsilon))));
            accumulatedMeanSquare.assign(newAccumulatedMeanSquare2);
            accumulatedMoments.assign(newAccumulatedMoments);
            const newValue = sub(value, newAccumulatedMoments);
            value.assign(newValue);
          }
        });
      });
      this.incrementIterations();
    }
    dispose() {
      if (this.accumulatedMeanSquares != null) {
        dispose(this.accumulatedMeanSquares.map((v) => v.variable));
      }
      if (this.accumulatedMeanGrads != null && this.centered) {
        dispose(this.accumulatedMeanGrads.map((v) => v.variable));
      }
      if (this.accumulatedMoments != null) {
        dispose(this.accumulatedMoments.map((v) => v.variable));
      }
    }
    async getWeights() {
      const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];
      if (this.centered) {
        variables.push(...this.accumulatedMeanGrads);
      }
      return [await this.saveIterations()].concat(variables.map((v) => ({name: v.originalName, tensor: v.variable})));
    }
    async setWeights(weightValues) {
      weightValues = await this.extractIterations(weightValues);
      const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;
      const trainable = false;
      this.accumulatedMeanSquares = weightValues.slice(0, variableCount).map((v) => ({
        originalName: v.name,
        variable: v.tensor.variable(trainable)
      }));
      this.accumulatedMoments = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
        originalName: v.name,
        variable: v.tensor.variable(trainable)
      }));
      if (this.centered) {
        this.accumulatedMeanGrads = weightValues.slice(variableCount * 2, variableCount * 3).map((v) => ({
          originalName: v.name,
          variable: v.tensor.variable(trainable)
        }));
      }
    }
    getConfig() {
      return {
        learningRate: this.learningRate,
        decay: this.decay,
        momentum: this.momentum,
        epsilon: this.epsilon,
        centered: this.centered
      };
    }
    static fromConfig(cls, config) {
      return new cls(config["learningRate"], config["decay"], config["momentum"], config["epsilon"], config["centered"]);
    }
  };
  RMSPropOptimizer.className = "RMSProp";
  registerClass(RMSPropOptimizer);
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var OptimizerConstructors = class {
    static sgd(learningRate) {
      return new SGDOptimizer(learningRate);
    }
    static momentum(learningRate, momentum, useNesterov = false) {
      return new MomentumOptimizer(learningRate, momentum, useNesterov);
    }
    static rmsprop(learningRate, decay = 0.9, momentum = 0, epsilon32 = null, centered = false) {
      return new RMSPropOptimizer(learningRate, decay, momentum, epsilon32, centered);
    }
    static adam(learningRate = 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon32 = null) {
      return new AdamOptimizer(learningRate, beta1, beta2, epsilon32);
    }
    static adadelta(learningRate = 1e-3, rho = 0.95, epsilon32 = null) {
      return new AdadeltaOptimizer(learningRate, rho, epsilon32);
    }
    static adamax(learningRate = 2e-3, beta1 = 0.9, beta2 = 0.999, epsilon32 = null, decay = 0) {
      return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon32, decay);
    }
    static adagrad(learningRate, initialAccumulatorValue = 0.1) {
      return new AdagradOptimizer(learningRate, initialAccumulatorValue);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var train = {
    sgd: OptimizerConstructors.sgd,
    momentum: OptimizerConstructors.momentum,
    adadelta: OptimizerConstructors.adadelta,
    adagrad: OptimizerConstructors.adagrad,
    rmsprop: OptimizerConstructors.rmsprop,
    adamax: OptimizerConstructors.adamax,
    adam: OptimizerConstructors.adam
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var delayCallback = (() => {
    if (typeof requestAnimationFrame !== "undefined") {
      return requestAnimationFrame;
    } else if (typeof setImmediate !== "undefined") {
      return setImmediate;
    }
    return (f) => f();
  })();
  function nextFrame() {
    return new Promise((resolve) => delayCallback(() => resolve()));
  }
  var backend_util_exports = {};
  __export2(backend_util_exports, {
    ERF_A1: () => ERF_A1,
    ERF_A2: () => ERF_A2,
    ERF_A3: () => ERF_A3,
    ERF_A4: () => ERF_A4,
    ERF_A5: () => ERF_A5,
    ERF_P: () => ERF_P,
    PARALLELIZE_THRESHOLD: () => PARALLELIZE_THRESHOLD,
    SELU_SCALE: () => SELU_SCALE,
    SELU_SCALEALPHA: () => SELU_SCALEALPHA,
    applyActivation: () => applyActivation,
    assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,
    assertAxesAreInnerMostDims: () => assertAxesAreInnerMostDims,
    assertParamsConsistent: () => assertParamsConsistent,
    assignToTypedArray: () => assignToTypedArray,
    axesAreInnerMostDims: () => axesAreInnerMostDims,
    calculateShapes: () => calculateShapes,
    combineLocations: () => combineLocations,
    complexWithEvenIndex: () => complexWithEvenIndex,
    complexWithOddIndex: () => complexWithOddIndex,
    computeConv2DInfo: () => computeConv2DInfo,
    computeConv3DInfo: () => computeConv3DInfo,
    computeDefaultPad: () => computeDefaultPad,
    computeDilation2DInfo: () => computeDilation2DInfo,
    computeOptimalWindowSize: () => computeOptimalWindowSize,
    computeOutAndReduceShapes: () => computeOutAndReduceShapes,
    computeOutShape: () => computeOutShape2,
    computePool2DInfo: () => computePool2DInfo,
    computePool3DInfo: () => computePool3DInfo,
    convertConv2DDataFormat: () => convertConv2DDataFormat,
    eitherStridesOrDilationsAreOne: () => eitherStridesOrDilationsAreOne,
    expandShapeToKeepDim: () => expandShapeToKeepDim,
    exponent: () => exponent,
    exponents: () => exponents,
    fromStringArrayToUint8: () => fromStringArrayToUint8,
    fromUint8ToStringArray: () => fromUint8ToStringArray,
    getAxesPermutation: () => getAxesPermutation,
    getBroadcastDims: () => getBroadcastDims,
    getComplexWithIndex: () => getComplexWithIndex,
    getFusedBiasGradient: () => getFusedBiasGradient,
    getFusedDyActivation: () => getFusedDyActivation,
    getImageCenter: () => getImageCenter,
    getInnerMostAxes: () => getInnerMostAxes,
    getPermuted: () => getPermuted,
    getReductionAxes: () => getReductionAxes,
    getReshaped: () => getReshaped,
    getReshapedPermuted: () => getReshapedPermuted,
    getSliceBeginCoords: () => getSliceBeginCoords,
    getSliceSize: () => getSliceSize,
    getUndoAxesPermutation: () => getUndoAxesPermutation,
    log: () => log2,
    mergeRealAndImagArrays: () => mergeRealAndImagArrays,
    prepareAndValidate: () => prepareAndValidate,
    prepareSplitSize: () => prepareSplitSize,
    segment_util: () => segment_util_exports,
    shouldFuse: () => shouldFuse,
    slice_util: () => slice_util_exports,
    splitRealAndImagArrays: () => splitRealAndImagArrays,
    tupleValuesAreOne: () => tupleValuesAreOne,
    upcastType: () => upcastType,
    validateInput: () => validateInput,
    validateUpdateShape: () => validateUpdateShape,
    warn: () => warn
  });
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function assertParamsConsistent(shapes, axis) {
    const rank = shapes[0].length;
    shapes.forEach((shape, i) => {
      assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`);
    });
    assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);
    const firstShape = shapes[0];
    shapes.forEach((shape, i) => {
      for (let r = 0; r < rank; r++) {
        assert(r === axis || shape[r] === firstShape[r], () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`);
      }
    });
  }
  function computeOutShape2(shapes, axis) {
    const outputShape = shapes[0].slice();
    for (let i = 1; i < shapes.length; i++) {
      outputShape[axis] += shapes[i][axis];
    }
    return outputShape;
  }
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var PARALLELIZE_THRESHOLD = 30;
  function computeOptimalWindowSize(inSize) {
    if (inSize <= PARALLELIZE_THRESHOLD) {
      return inSize;
    }
    return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function getImageCenter(center, imageHeight, imageWidth) {
    const centerX = imageWidth * (typeof center === "number" ? center : center[0]);
    const centerY = imageHeight * (typeof center === "number" ? center : center[1]);
    return [centerX, centerY];
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function getReshaped(inputShape, blockShape, prod5, batchToSpace = true) {
    let reshaped = [];
    if (batchToSpace) {
      reshaped = reshaped.concat(blockShape.slice(0));
      reshaped.push(inputShape[0] / prod5);
      reshaped = reshaped.concat(inputShape.slice(1));
    } else {
      reshaped = reshaped.concat(inputShape[0]);
      const spatialLength = blockShape.length;
      for (let i = 0; i < spatialLength; ++i) {
        reshaped = reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);
      }
      reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
    }
    return reshaped;
  }
  function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {
    const permuted = [];
    if (batchToSpace) {
      permuted.push(blockShapeRank);
      for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {
        if (i <= 2 * blockShapeRank) {
          permuted.push(i);
          permuted.push(i - (blockShapeRank + 1));
        } else {
          permuted.push(i);
        }
      }
    } else {
      const permutedBeforeBatch = [];
      const permutedAfterBatch = [];
      for (let i = 1; i < reshapedRank; ++i) {
        if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {
          permutedAfterBatch.push(i);
        } else {
          permutedBeforeBatch.push(i);
        }
      }
      permuted.push(...permutedBeforeBatch);
      permuted.push(0);
      permuted.push(...permutedAfterBatch);
    }
    return permuted;
  }
  function getReshapedPermuted(inputShape, blockShape, prod5, batchToSpace = true) {
    const reshapedPermuted = [];
    if (batchToSpace) {
      reshapedPermuted.push(inputShape[0] / prod5);
    } else {
      reshapedPermuted.push(inputShape[0] * prod5);
    }
    for (let i = 1; i < inputShape.length; ++i) {
      if (i <= blockShape.length) {
        if (batchToSpace) {
          reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);
        } else {
          reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);
        }
      } else {
        reshapedPermuted.push(inputShape[i]);
      }
    }
    return reshapedPermuted;
  }
  function getSliceBeginCoords(crops, blockShape) {
    const sliceBeginCoords = [0];
    for (let i = 0; i < blockShape; ++i) {
      sliceBeginCoords.push(crops[i][0]);
    }
    return sliceBeginCoords;
  }
  function getSliceSize(uncroppedShape, crops, blockShape) {
    const sliceSize = uncroppedShape.slice(0, 1);
    for (let i = 0; i < blockShape; ++i) {
      sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);
    }
    return sliceSize;
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SELU_SCALEALPHA = 1.7580993408473768;
  var SELU_SCALE = 1.0507009873554805;
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ERF_P = 0.3275911;
  var ERF_A1 = 0.254829592;
  var ERF_A2 = -0.284496736;
  var ERF_A3 = 1.421413741;
  var ERF_A4 = -1.453152027;
  var ERF_A5 = 1.061405429;
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function warn(...msg) {
    if (!env().getBool("IS_TEST")) {
      console.warn(...msg);
    }
  }
  function log2(...msg) {
    if (!env().getBool("IS_TEST")) {
      console.log(...msg);
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function mergeRealAndImagArrays(real4, imag4) {
    if (real4.length !== imag4.length) {
      throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real4.length}, imag: ${imag4.length}.`);
    }
    const result = new Float32Array(real4.length * 2);
    for (let i = 0; i < result.length; i += 2) {
      result[i] = real4[i / 2];
      result[i + 1] = imag4[i / 2];
    }
    return result;
  }
  function splitRealAndImagArrays(complex4) {
    const real4 = new Float32Array(complex4.length / 2);
    const imag4 = new Float32Array(complex4.length / 2);
    for (let i = 0; i < complex4.length; i += 2) {
      real4[i / 2] = complex4[i];
      imag4[i / 2] = complex4[i + 1];
    }
    return {real: real4, imag: imag4};
  }
  function complexWithEvenIndex(complex4) {
    const len = Math.ceil(complex4.length / 4);
    const real4 = new Float32Array(len);
    const imag4 = new Float32Array(len);
    for (let i = 0; i < complex4.length; i += 4) {
      real4[Math.floor(i / 4)] = complex4[i];
      imag4[Math.floor(i / 4)] = complex4[i + 1];
    }
    return {real: real4, imag: imag4};
  }
  function complexWithOddIndex(complex4) {
    const len = Math.floor(complex4.length / 4);
    const real4 = new Float32Array(len);
    const imag4 = new Float32Array(len);
    for (let i = 2; i < complex4.length; i += 4) {
      real4[Math.floor(i / 4)] = complex4[i];
      imag4[Math.floor(i / 4)] = complex4[i + 1];
    }
    return {real: real4, imag: imag4};
  }
  function getComplexWithIndex(complex4, index) {
    const real4 = complex4[index * 2];
    const imag4 = complex4[index * 2 + 1];
    return {real: real4, imag: imag4};
  }
  function assignToTypedArray(data, real4, imag4, index) {
    data[index * 2] = real4;
    data[index * 2 + 1] = imag4;
  }
  function exponents(n, inverse) {
    const real4 = new Float32Array(n / 2);
    const imag4 = new Float32Array(n / 2);
    for (let i = 0; i < Math.ceil(n / 2); i++) {
      const x = (inverse ? 2 : -2) * Math.PI * (i / n);
      real4[i] = Math.cos(x);
      imag4[i] = Math.sin(x);
    }
    return {real: real4, imag: imag4};
  }
  function exponent(k, n, inverse) {
    const x = (inverse ? 2 : -2) * Math.PI * (k / n);
    const real4 = Math.cos(x);
    const imag4 = Math.sin(x);
    return {real: real4, imag: imag4};
  }
  function prepareSplitSize(x, numOrSizeSplits, axis = 0) {
    let splitSizes = [];
    if (typeof numOrSizeSplits === "number") {
      assert(x.shape[axis] % numOrSizeSplits === 0, () => "Number of splits must evenly divide the axis.");
      splitSizes = new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);
    } else {
      const numOfNegs = numOrSizeSplits.reduce((count2, value) => {
        if (value === -1) {
          count2 += 1;
        }
        return count2;
      }, 0);
      assert(numOfNegs <= 1, () => "There should be only one negative value in split array.");
      const negIndex = numOrSizeSplits.indexOf(-1);
      if (negIndex !== -1) {
        const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);
        numOrSizeSplits[negIndex] = x.shape[axis] - total;
      }
      assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => "The sum of sizes must match the size of the axis dimension.");
      splitSizes = numOrSizeSplits;
    }
    return splitSizes;
  }
  var segment_util_exports = {};
  __export2(segment_util_exports, {
    collectGatherOpShapeInfo: () => collectGatherOpShapeInfo,
    computeOutShape: () => computeOutShape3,
    segOpComputeOptimalWindowSize: () => segOpComputeOptimalWindowSize
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function segOpComputeOptimalWindowSize(inSize, numSegments) {
    let done = false;
    let res;
    if (inSize <= PARALLELIZE_THRESHOLD) {
      res = inSize;
      done = true;
    } else {
      res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
    }
    while (!done) {
      if (res > numSegments || res === inSize) {
        done = true;
      } else {
        res = nearestDivisor(inSize, res + 1);
      }
    }
    return res;
  }
  function computeOutShape3(aShape, axis, numSegments) {
    const outShape = [];
    const rank = aShape.length;
    for (let dim = 0; dim < rank; dim++) {
      if (dim !== axis) {
        outShape.push(aShape[dim]);
      } else {
        outShape.push(numSegments);
      }
    }
    return outShape;
  }
  function collectGatherOpShapeInfo(x, indices, axis, batchDims) {
    const indicesRank = indices.shape.length;
    const xRank = x.shape.length;
    if (batchDims !== 0) {
      if (batchDims < -indicesRank || batchDims > indicesRank) {
        throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);
      }
    }
    if (batchDims < 0) {
      batchDims += indicesRank;
    }
    if (batchDims > xRank) {
      throw new Error(`batchDims (${batchDims}) must be less than rank(x) (
    ${xRank}).`);
    }
    if (axis < batchDims) {
      throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`);
    }
    for (let i = 0; i < batchDims; ++i) {
      if (x.shape[i] !== indices.shape[i]) {
        throw new Error(`x.shape[${i}]: ${x.shape[i]} should be equal to indices.shape[${i}]: ${indices.shape[i]}.`);
      }
    }
    const dimSize = x.shape[axis];
    const outputShape = [];
    let batchSize = 1;
    let outerSize = 1;
    let sliceSize = 1;
    for (let i = 0; i < batchDims; ++i) {
      outputShape.push(x.shape[i]);
      batchSize *= x.shape[i];
    }
    for (let i = batchDims; i < axis; i++) {
      outputShape.push(x.shape[i]);
      outerSize *= x.shape[i];
    }
    for (let i = batchDims; i < indicesRank; i++) {
      outputShape.push(indices.shape[i]);
    }
    for (let i = axis + 1; i < xRank; i++) {
      outputShape.push(x.shape[i]);
      sliceSize *= x.shape[i];
    }
    return {batchSize, sliceSize, outerSize, dimSize, outputShape};
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fromUint8ToStringArray(vals) {
    try {
      return vals.map((val) => decodeString(val));
    } catch (err) {
      throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`);
    }
  }
  function fromStringArrayToUint8(strings) {
    return strings.map((s) => encodeString(s));
  }
  var kernel_impls_exports = {};
  __export2(kernel_impls_exports, {
    nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl,
    nonMaxSuppressionV4Impl: () => nonMaxSuppressionV4Impl,
    nonMaxSuppressionV5Impl: () => nonMaxSuppressionV5Impl,
    whereImpl: () => whereImpl
  });
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var absGradConfig = {
    kernelName: Abs,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => mul(dy, step(cast(x, "float32"), -1))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var acosGradConfig = {
    kernelName: Acos,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {
        x: () => {
          const a = square(cast(x, "float32"));
          const b = sqrt(sub(scalar(1), a));
          return neg(div(dy, b));
        }
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var acoshGradConfig = {
    kernelName: Acosh,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {
        x: () => {
          const a = sqrt(sub(square(cast(x, "float32")), 1));
          return div(dy, a);
        }
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var addGradConfig = {
    kernelName: Add,
    inputsToSave: ["a", "b"],
    gradFunc: (dy, saved) => {
      const [a, b] = saved;
      const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
      const derA = () => {
        let res = dy;
        const reduceAxes = getReductionAxes(a.shape, outShape);
        if (reduceAxes.length > 0) {
          res = sum2(res, reduceAxes);
        }
        return reshape(res, a.shape);
      };
      const derB = () => {
        let res = dy;
        const reduceAxes = getReductionAxes(b.shape, outShape);
        if (reduceAxes.length > 0) {
          res = sum2(res, reduceAxes);
        }
        return reshape(res, b.shape);
      };
      return {a: derA, b: derB};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var addNGradConfig = {
    kernelName: AddN,
    saveAllInputs: true,
    gradFunc: (dy, saved) => {
      const ders = {};
      saved.forEach((_, i) => {
        ders[i] = () => dy.clone();
      });
      return ders;
    }
  };
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var argMaxGradConfig = {
    kernelName: ArgMax,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => zerosLike(x)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var argMinGradConfig = {
    kernelName: ArgMin,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => zerosLike(x)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var asinGradConfig = {
    kernelName: Asin,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, "float32")))))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var asinhGradConfig = {
    kernelName: Asinh,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {
        x: () => {
          const a = sqrt(add2(scalar(1), square(cast(x, "float32"))));
          return div(dy, a);
        }
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var atan2GradConfig = {
    kernelName: Atan2,
    inputsToSave: ["a", "b"],
    gradFunc: (dy, saved) => {
      const [a, b] = saved;
      const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
      const derA = () => {
        const d = add2(square(a), square(b));
        let res = mul(dy, div(b, d));
        const reduceAxes = getReductionAxes(a.shape, outShape);
        if (reduceAxes.length > 0) {
          res = sum2(res, reduceAxes);
        }
        return reshape(res, a.shape);
      };
      const derB = () => {
        const d = add2(square(a), square(b));
        let res = neg(mul(dy, div(a, d)));
        const reduceAxes = getReductionAxes(b.shape, outShape);
        if (reduceAxes.length > 0) {
          res = sum2(res, reduceAxes);
        }
        return reshape(res, b.shape);
      };
      return {a: derA, b: derB};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var atanGradConfig = {
    kernelName: Atan,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => div(dy, add2(square(cast(x, "float32")), 1))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var atanhGradConfig = {
    kernelName: Atanh,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => div(dy, sub(scalar(1), square(cast(x, "float32"))))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function avgPool3dGrad_(dy, input2, filterSize, strides, pad3, dimRoundingMode) {
    const $dy = convertToTensor(dy, "dy", "avgPool3dGrad");
    const $input = convertToTensor(input2, "input", "avgPool3dGrad");
    let dy5D = $dy;
    let input5D = $input;
    let reshapedTo5D = false;
    if ($input.rank === 4) {
      reshapedTo5D = true;
      dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
      input5D = reshape($input, [
        1,
        $input.shape[0],
        $input.shape[1],
        $input.shape[2],
        $input.shape[3]
      ]);
    }
    assert(dy5D.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);
    assert(input5D.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);
    if (dimRoundingMode != null) {
      assert(isInt(pad3), () => `Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
    }
    const inputs = {dy: dy5D, input: input5D};
    const attrs = {filterSize, strides, pad: pad3, dimRoundingMode};
    const res = ENGINE.runKernel(AvgPool3DGrad, inputs, attrs);
    if (reshapedTo5D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
    }
    return res;
  }
  var avgPool3dGrad = op({avgPool3dGrad_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var avgPool3DGradConfig = {
    kernelName: AvgPool3D,
    inputsToSave: ["x"],
    gradFunc: (dy, saved, attrs) => {
      const [x] = saved;
      const {filterSize, strides, pad: pad3, dimRoundingMode} = attrs;
      return {
        x: () => avgPool3dGrad(dy, x, filterSize, strides, pad3, dimRoundingMode)
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function avgPoolGrad_(dy, input2, filterSize, strides, pad3) {
    const $dy = convertToTensor(dy, "dy", "avgPoolGrad");
    const $input = convertToTensor(input2, "input", "avgPoolGrad");
    assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
    let input4D = $input;
    let dy4D = $dy;
    let reshapedTo4D = false;
    if ($input.rank === 3) {
      reshapedTo4D = true;
      input4D = reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);
      dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);
    }
    assert(dy4D.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`);
    assert(input4D.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`);
    const inputs = {dy: dy4D, input: input4D};
    const attrs = {filterSize, strides, pad: pad3};
    const res = ENGINE.runKernel(AvgPoolGrad, inputs, attrs);
    if (reshapedTo4D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
    }
    return res;
  }
  var avgPoolGrad = op({avgPoolGrad_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var avgPoolGradConfig = {
    kernelName: AvgPool,
    inputsToSave: ["x"],
    gradFunc: (dy, saved, attrs) => {
      const [x] = saved;
      const {filterSize, strides, pad: pad3} = attrs;
      return {x: () => avgPoolGrad(dy, x, filterSize, strides, pad3)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var batchMatMulGradConfig = {
    kernelName: BatchMatMul,
    inputsToSave: ["a", "b"],
    gradFunc: (dy, saved, attrs) => {
      const [a, b] = saved;
      const {transposeA, transposeB} = attrs;
      if (!transposeA && !transposeB) {
        return {
          a: () => matMul(dy, b, false, true),
          b: () => matMul(a, dy, true, false)
        };
      } else if (!transposeA && transposeB) {
        return {
          a: () => matMul(dy, b, false, false),
          b: () => matMul(dy, a, true, false)
        };
      } else if (transposeA && !transposeB) {
        return {
          a: () => matMul(b, dy, false, true),
          b: () => matMul(a, dy, false, false)
        };
      } else {
        return {
          a: () => matMul(b, dy, true, true),
          b: () => matMul(dy, a, true, true)
        };
      }
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var batchToSpaceNDGradConfig = {
    kernelName: BatchToSpaceND,
    gradFunc: (dy, saved, attrs) => {
      const {blockShape, crops} = attrs;
      return {x: () => spaceToBatchND(dy, blockShape, crops)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var broadcastToGradConfig = {
    kernelName: BroadcastTo,
    gradFunc: (dy, saved, attrs) => {
      const broadCastToAttrs = attrs;
      const inputShape = broadCastToAttrs.inputShape;
      const outputShape = broadCastToAttrs.shape;
      const reps = Array.from(outputShape);
      for (let i = inputShape.length - 1; i >= 0; i--) {
        if (inputShape[i] === outputShape[i]) {
          reps[i] = 1;
        } else if (inputShape[i] !== 1) {
          throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);
        }
      }
      const axes = [];
      for (let i = 0; i < reps.length; i++) {
        if (reps[i] > 1) {
          axes.push(i);
        }
      }
      return {x: () => sum2(dy, axes, true)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var castGradConfig = {
    kernelName: Cast,
    gradFunc: (dy) => {
      return {x: () => dy.clone()};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ceilGradConfig = {
    kernelName: Ceil,
    gradFunc: (dy) => {
      return {x: () => zerosLike(dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var clipByValueGradConfig = {
    kernelName: ClipByValue,
    inputsToSave: ["x"],
    gradFunc: (dy, saved, attrs) => {
      const [x] = saved;
      const {clipValueMin, clipValueMax} = attrs;
      return {
        x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy))
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var complexAbsGradConfig = {
    kernelName: ComplexAbs,
    inputsToSave: ["x"],
    gradFunc: absGradConfig.gradFunc
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var concatGradConfig = {
    kernelName: Concat,
    saveAllInputs: true,
    gradFunc: (dy, saved, attrs) => {
      const shapes = saved.map((t) => t.shape);
      const {axis} = attrs;
      const $axis = parseAxisParam(axis, saved[0].shape)[0];
      const sizeSplits = shapes.map((s) => s[$axis]);
      const derTensors = split(dy, sizeSplits, $axis);
      return derTensors.map((t) => () => t);
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var conv2DGradConfig = {
    kernelName: Conv2D,
    inputsToSave: ["x", "filter"],
    gradFunc: (dy, saved, attrs) => {
      const [x4D, $filter] = saved;
      const {dilations, strides, pad: pad3, dataFormat} = attrs;
      assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
      return {
        x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad3, dataFormat),
        filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad3, dataFormat)
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var conv2DBackpropInputGradConfig = {
    kernelName: Conv2DBackpropInput,
    inputsToSave: ["dy", "filter"],
    gradFunc: (ddx, saved, attrs) => {
      const [dy, filter] = saved;
      const {strides, pad: pad3, dataFormat, dimRoundingMode} = attrs;
      return {
        dy: () => conv2d(ddx, filter, strides, pad3, dataFormat, 1, dimRoundingMode),
        filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad3, dataFormat, dimRoundingMode)
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv3DBackpropFilter_(x, dy, filterShape, strides, pad3) {
    let x5D = x;
    if (x.rank === 4) {
      x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);
    }
    let dy5D = dy;
    if (dy5D.rank === 4) {
      dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
    }
    assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`);
    assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`);
    assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`);
    assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`);
    assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);
    const inputs = {x: x5D, dy: dy5D};
    const attrs = {strides, pad: pad3, filterShape};
    return ENGINE.runKernel(Conv3DBackpropFilterV2, inputs, attrs);
  }
  var conv3DBackpropFilter = op({conv3DBackpropFilter_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var conv3DGradConfig = {
    kernelName: Conv3D,
    inputsToSave: ["x", "filter"],
    gradFunc: (dy, saved, attrs) => {
      const {dilations, strides, pad: pad3} = attrs;
      assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
      const [x5D, $filter] = saved;
      return {
        x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad3),
        filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad3)
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var cosGradConfig = {
    kernelName: Cos,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => mul(neg(sin(cast(x, "float32"))), dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var coshGradConfig = {
    kernelName: Cosh,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => mul(sinh(cast(x, "float32")), dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var cumsumGradConfig = {
    kernelName: Cumsum,
    inputsToSave: ["x"],
    gradFunc: (dy, saved, attrs) => {
      const [x] = saved;
      const {axis, exclusive, reverse: reverse5} = attrs;
      return {
        x: () => {
          const permutation = getAxesPermutation([axis], x.rank);
          let out = cumsum(dy, axis, exclusive, !reverse5);
          if (permutation != null) {
            out = transpose(out, permutation);
          }
          return out;
        }
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var depthwiseConv2dNativeGradConfig = {
    kernelName: DepthwiseConv2dNative,
    inputsToSave: ["x", "filter"],
    gradFunc: (dy, saved, attrs) => {
      const {dilations, strides, pad: pad3, dimRoundingMode} = attrs;
      const $dilations = dilations == null ? [1, 1] : dilations;
      assert(tupleValuesAreOne($dilations), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);
      const [x, filter] = saved;
      assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`);
      assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`);
      assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`);
      assert(eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`);
      if (dimRoundingMode != null) {
        assert(isInt(pad3), () => `Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
      }
      return {
        x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides, pad3, dilations, dimRoundingMode),
        filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides, pad3, dilations, dimRoundingMode)
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var dilation2dGradConfig = {
    kernelName: Dilation2D,
    inputsToSave: ["x", "filter"],
    gradFunc: (dy, saved, attrs) => {
      const [x, filter] = saved;
      const inputInputs = {x, filter, dy};
      const filterInputs = {x, filter, dy};
      return {
        x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),
        filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var eluGradConfig = {
    kernelName: Elu,
    outputsToSave: [true],
    gradFunc: (dy, saved) => {
      const [y] = saved;
      const inputs = {dy, y};
      return {x: () => ENGINE.runKernel(EluGrad, inputs)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var erfGradConfig = {
    kernelName: Erf,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));
      return {x: () => mul(dy, a)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var expGradConfig = {
    kernelName: Exp,
    outputsToSave: [true],
    gradFunc: (dy, saved) => {
      const [y] = saved;
      return {x: () => mul(dy, y)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var expandDimsGradConfig = {
    kernelName: ExpandDims,
    inputsToSave: ["input"],
    gradFunc: (dy, saved) => {
      const [input2] = saved;
      return {input: () => reshape(dy, input2.shape)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var expm1GradConfig = {
    kernelName: Expm1,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => mul(dy, exp(x))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var floorGradConfig = {
    kernelName: Floor,
    gradFunc: (dy) => {
      return {x: () => zerosLike(dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var floorDivGradConfig = {
    kernelName: FloorDiv,
    inputsToSave: ["a", "b"],
    gradFunc: (dy, saved) => {
      const [a, b] = saved;
      const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
      const derA = () => {
        const res = div(dy, cast(b, "float32"));
        const reduceAxes = getReductionAxes(a.shape, outShape);
        if (reduceAxes.length > 0) {
          return reshape(sum2(res, reduceAxes), a.shape);
        }
        return res;
      };
      const derB = () => {
        let res = mul(dy, cast(a, "float32"));
        const reduceAxes = getReductionAxes(b.shape, outShape);
        if (reduceAxes.length > 0) {
          res = reshape(sum2(res, reduceAxes), b.shape);
        }
        const tmp = square(b);
        return neg(div(res, cast(tmp, "float32")));
      };
      return {a: derA, b: derB};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var fusedBatchNormGradConfig = {
    kernelName: FusedBatchNorm,
    inputsToSave: ["x", "mean", "variance", "scale"],
    gradFunc: (dy, saved, attrs) => {
      const {varianceEpsilon} = attrs;
      const [x, mean4, variance, scale22] = saved;
      const scaleValue = scale22 == null ? scalar(1) : scale22;
      const reductionAxes = getReductionAxes(mean4.shape, x.shape);
      const tileShape = [];
      if (mean4.rank === 1) {
        for (let i = 0; i < x.shape.length - 1; ++i) {
          tileShape.push(x.shape[i]);
        }
        tileShape.push(1);
      }
      const xMinusMean = sub(x, mean4);
      const dyTimesScaleValue = mul(dy, scaleValue);
      const oneOverSqrtVariance = rsqrt(add2(variance, scalar(varianceEpsilon)));
      const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));
      const derX = () => {
        if (mean4.rank === 1) {
          return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean4.shape[0]]), tileShape)), scaleValue), x.shape);
        } else {
          return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);
        }
      };
      const derMean = () => {
        let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);
        if (mean4.rank === 1) {
          meanDer = sum2(meanDer, reductionAxes);
        }
        return reshape(meanDer, mean4.shape);
      };
      const derVariance = () => {
        let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);
        if (mean4.rank === 1) {
          varianceDer = sum2(varianceDer, reductionAxes);
        }
        return reshape(varianceDer, mean4.shape);
      };
      const derScale = () => {
        const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);
        let scaleDer = mul(dy, xMinusMean2TimesRsqrt);
        if (mean4.rank === 1) {
          scaleDer = sum2(scaleDer, reductionAxes);
        }
        return reshape(scaleDer, mean4.shape);
      };
      const derOffset = () => {
        let offsetDer = dy;
        if (mean4.rank === 1) {
          offsetDer = sum2(offsetDer, reductionAxes);
        }
        return reshape(offsetDer, mean4.shape);
      };
      return {
        x: derX,
        mean: derMean,
        variance: derVariance,
        scale: derScale,
        offset: derOffset
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var gatherGradConfig = {
    kernelName: GatherV2,
    inputsToSave: ["x", "indices"],
    gradFunc: (dy, saved, attrs) => {
      const [x, indices] = saved;
      const {axis} = attrs;
      const parsedAxis = parseAxisParam(axis, x.shape)[0];
      const derX = () => {
        const paramsShape = x.shape;
        const indicesSize = indices.size;
        const outerShape = paramsShape.slice(0, parsedAxis);
        const outerDims = outerShape.length;
        const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);
        const innerDims = innerShape.length;
        const outerAxesIndices = arrayRange(0, outerDims);
        const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);
        const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);
        const values = reshape(dy, valuesShape);
        const reshapedIndices = reshape(indices, [indicesSize]);
        const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);
        const valuesTranspose = transpose(values, transposeDims);
        let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);
        const invertTransposeDims = getUndoAxesPermutation(transposeDims);
        paramsGrad = transpose(paramsGrad, invertTransposeDims);
        return paramsGrad;
      };
      return {x: derX, indices: () => indices};
    }
  };
  function arrayRange(start, stop) {
    const result = [];
    for (let i = start; i < stop; ++i) {
      result.push(i);
    }
    return result;
  }
  function arrayConcat(arrays) {
    const result = [];
    for (let i = 0; i < arrays.length; ++i) {
      for (let j = 0; j < arrays[i].length; ++j) {
        result.push(arrays[i][j]);
      }
    }
    return result;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var greaterEqualGradConfig = {
    kernelName: GreaterEqual,
    inputsToSave: ["a", "b"],
    gradFunc: (dy, saved) => {
      const [a, b] = saved;
      return {a: () => zerosLike(a), b: () => zerosLike(b)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var identityGradConfig = {
    kernelName: Identity,
    gradFunc: (dy) => {
      return {x: () => cast(dy, "float32")};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var isFiniteGradConfig = {
    kernelName: IsFinite,
    gradFunc: (dy) => {
      return {x: () => zerosLike(dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var isInfGradConfig = {
    kernelName: IsInf,
    gradFunc: (dy) => {
      return {x: () => zerosLike(dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var isNanGradConfig = {
    kernelName: IsNan,
    gradFunc: (dy) => {
      return {x: () => zerosLike(dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var leakyReluGradConfig = {
    kernelName: LeakyRelu,
    inputsToSave: ["x"],
    gradFunc: (dy, saved, attrs) => {
      const [x] = saved;
      const {alpha} = attrs;
      const mask = greater(x, 0);
      return {x: () => where(mask, dy, mul(dy, alpha))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var log1pGradConfig = {
    kernelName: Log1p,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => div(dy, add2(x, 1))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var logGradConfig = {
    kernelName: Log,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => div(dy, cast(x, "float32"))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var logSoftmaxGradConfig = {
    kernelName: LogSoftmax,
    inputsToSave: [],
    outputsToSave: [true],
    gradFunc: (dy, saved, attrs) => {
      const [value] = saved;
      const {axis} = attrs;
      return {
        logits: () => {
          const keepDims = true;
          const softmax6 = exp(value);
          return sub(dy, mul(sum2(dy, axis, keepDims), softmax6));
        }
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
    const inputs = {x, y, dy};
    const attrs = {depthRadius, bias, alpha, beta};
    return ENGINE.runKernel(LRNGrad, inputs, attrs);
  }
  var localResponseNormalizationBackprop = op({localResponseNormalizationBackprop_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var lrnGradConfig = {
    kernelName: LRN,
    inputsToSave: ["x"],
    outputsToSave: [true],
    gradFunc: (dy, saved, attrs) => {
      const [x, y] = saved;
      const {depthRadius, bias, alpha, beta} = attrs;
      return {
        x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function gradForMinAndMax(dy, y, xOrig, origAxes) {
    if (y.rank < xOrig.rank) {
      y = reshape(y, expandShapeToKeepDim(y.shape, origAxes));
    }
    if (dy.rank < xOrig.rank) {
      dy = reshape(dy, expandShapeToKeepDim(dy.shape, origAxes));
    }
    return {
      x: () => {
        const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));
        return dx;
      }
    };
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var maxGradConfig = {
    kernelName: Max,
    inputsToSave: ["x"],
    outputsToSave: [true],
    gradFunc: (dy, saved, attrs) => {
      const maxAttrs = attrs;
      const {reductionIndices} = maxAttrs;
      const x = saved[0];
      const y = saved[1];
      const origAxes = parseAxisParam(reductionIndices, x.shape);
      const maxGrad = gradForMinAndMax(dy, y, x, origAxes);
      return {
        x: () => {
          return maxGrad["x"]();
        }
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var maximumGradConfig = {
    kernelName: Maximum,
    inputsToSave: ["a", "b"],
    gradFunc: (dy, saved) => {
      const [a, b] = saved;
      const derA = () => mul(dy, cast(greaterEqual(a, b), "float32"));
      const derB = () => mul(dy, cast(less(a, b), "float32"));
      return {a: derA, b: derB};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxPool3dGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) {
    const $dy = convertToTensor(dy, "dy", "maxPool3dGrad");
    const $input = convertToTensor(input2, "input", "maxPool3dGrad");
    const $output = convertToTensor(output, "output", "maxPool3dGrad");
    let dy5D = $dy;
    let input5D = $input;
    let output5D = $output;
    let reshapedTo5D = false;
    if ($input.rank === 4) {
      reshapedTo5D = true;
      dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
      input5D = reshape($input, [
        1,
        $input.shape[0],
        $input.shape[1],
        $input.shape[2],
        $input.shape[3]
      ]);
      output5D = reshape($output, [
        1,
        $output.shape[0],
        $output.shape[1],
        $output.shape[2],
        $output.shape[3]
      ]);
    }
    assert(dy5D.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);
    assert(input5D.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);
    assert(output5D.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`);
    if (dimRoundingMode != null) {
      assert(isInt(pad3), () => `Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
    }
    const inputs = {dy: dy5D, input: input5D, output: output5D};
    const attrs = {filterSize, strides, pad: pad3, dimRoundingMode};
    const res = ENGINE.runKernel(MaxPool3DGrad, inputs, attrs);
    if (reshapedTo5D) {
      return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
    }
    return res;
  }
  var maxPool3dGrad = op({maxPool3dGrad_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var maxPool3DGradConfig = {
    kernelName: MaxPool3D,
    inputsToSave: ["x"],
    outputsToSave: [true],
    gradFunc: (dy, saved, attrs) => {
      const [x, y] = saved;
      const {filterSize, strides, pad: pad3, dimRoundingMode} = attrs;
      return {
        x: () => maxPool3dGrad(dy, x, y, filterSize, strides, pad3, dimRoundingMode)
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxPoolGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) {
    const $dy = convertToTensor(dy, "dy", "maxPoolGrad");
    const $input = convertToTensor(input2, "input", "maxPoolGrad");
    const $output = convertToTensor(output, "output", "maxPoolGrad");
    assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
    assert($dy.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`);
    assert($input.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`);
    if (dimRoundingMode != null) {
      assert(isInt(pad3), () => `Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
    }
    const inputs = {dy: $dy, input: $input, output: $output};
    const attrs = {filterSize, strides, pad: pad3, dimRoundingMode};
    return ENGINE.runKernel(MaxPoolGrad, inputs, attrs);
  }
  var maxPoolGrad = op({maxPoolGrad_});
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var maxPoolGradConfig = {
    kernelName: MaxPool,
    inputsToSave: ["x"],
    outputsToSave: [true],
    gradFunc: (dy, saved, attrs) => {
      const [x, y] = saved;
      const {filterSize, strides, pad: pad3} = attrs;
      return {
        x: () => maxPoolGrad(dy, x, y, filterSize, strides, pad3)
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var meanGradConfig = {
    kernelName: Mean,
    inputsToSave: ["x"],
    gradFunc: (dy, saved, attrs) => {
      const [x] = saved;
      const {axis} = attrs;
      const axes = parseAxisParam(axis, x.shape);
      const shapes = computeOutAndReduceShapes(x.shape, axes);
      const reduceShape = shapes[1];
      const reduceSize = sizeFromShape(reduceShape);
      const derX = () => {
        const expandedDyShape = x.shape.slice();
        axes.forEach((axis2) => {
          expandedDyShape[axis2] = 1;
        });
        const expandedDy = reshape(dy, expandedDyShape);
        const res = div(mul(expandedDy, ones2(x.shape, "float32")), reduceSize);
        return res;
      };
      return {x: derX};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var minGradConfig = {
    kernelName: Min,
    inputsToSave: ["x"],
    outputsToSave: [true],
    gradFunc: (dy, saved, attrs) => {
      const minAttrs = attrs;
      const {axis} = minAttrs;
      const [x, y] = saved;
      const origAxes = parseAxisParam(axis, x.shape);
      const minGrad = gradForMinAndMax(dy, y, x, origAxes);
      return {
        x: () => {
          return minGrad["x"]();
        }
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var minimumGradConfig = {
    kernelName: Minimum,
    inputsToSave: ["a", "b"],
    gradFunc: (dy, saved) => {
      const [a, b] = saved;
      const derA = () => mul(dy, cast(lessEqual(a, b), "float32"));
      const derB = () => mul(dy, cast(greater(a, b), "float32"));
      return {a: derA, b: derB};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var mirrorPadGradConfig = {
    kernelName: MirrorPad,
    inputsToSave: ["x"],
    gradFunc: (dy, saved, attrs) => {
      const x = saved[0];
      const {paddings} = attrs;
      const begin = paddings.map((p2) => p2[0]);
      return {x: () => slice(dy, begin, x.shape)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var modGradConfig = {
    kernelName: Mod,
    inputsToSave: ["a", "b"],
    gradFunc: (dy, saved) => {
      const [a, b] = saved;
      const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
      const derA = () => {
        const reduceAxes = getReductionAxes(a.shape, outShape);
        if (reduceAxes.length > 0) {
          return reshape(sum2(dy, reduceAxes), a.shape);
        }
        return dy;
      };
      const derB = () => {
        const res = mul(dy, neg(floor(div(a, b))));
        const reduceAxes = getReductionAxes(b.shape, outShape);
        if (reduceAxes.length > 0) {
          return reshape(sum2(res, reduceAxes), b.shape);
        }
        return res;
      };
      return {a: derA, b: derB};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var multiplyGradConfig = {
    kernelName: Multiply,
    inputsToSave: ["a", "b"],
    gradFunc: (dy, saved) => {
      const [a, b] = saved;
      const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
      const derA = () => {
        const res = mul(dy, cast(b, "float32"));
        const reduceAxes = getReductionAxes(a.shape, outShape);
        if (reduceAxes.length > 0) {
          return reshape(sum2(res, reduceAxes), a.shape);
        }
        return res;
      };
      const derB = () => {
        const res = mul(dy, cast(a, "float32"));
        const reduceAxes = getReductionAxes(b.shape, outShape);
        if (reduceAxes.length > 0) {
          return reshape(sum2(res, reduceAxes), b.shape);
        }
        return res;
      };
      return {a: derA, b: derB};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var negGradConfig = {
    kernelName: Neg,
    gradFunc: (dy) => {
      return {x: () => neg(dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var oneHotGradConfig = {
    kernelName: OneHot,
    inputsToSave: ["indices"],
    gradFunc: (dy, saved) => {
      const indices = saved[0];
      return {indices: () => zeros(indices.shape, "float32")};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var onesLikeGradConfig = {
    kernelName: OnesLike,
    gradFunc: (dy) => {
      return {x: () => zerosLike(dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var packGradConfig = {
    kernelName: Pack,
    saveAllInputs: true,
    gradFunc: (dy, saved, attrs) => {
      const {axis} = attrs;
      const derTensors = unstack(dy, axis);
      return derTensors.map((t) => () => t);
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var padV2GradConfig = {
    kernelName: PadV2,
    inputsToSave: ["x"],
    gradFunc: (dy, saved, attrs) => {
      const x = saved[0];
      const {paddings} = attrs;
      const begin = paddings.map((p2) => p2[0]);
      return {x: () => slice(dy, begin, x.shape)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var powGradConfig = {
    kernelName: Pow,
    inputsToSave: ["a", "b"],
    outputsToSave: [true],
    gradFunc: (dy, saved) => {
      const [a, b, y] = saved;
      const base2 = a;
      const exp4 = b;
      const outShape = assertAndGetBroadcastShape(base2.shape, exp4.shape);
      const derBase = () => {
        const expFloat = cast(exp4, "float32");
        let res = mul(dy, mul(expFloat, pow(base2, sub(expFloat, scalar(1)))));
        const reduceAxes = getReductionAxes(base2.shape, outShape);
        if (reduceAxes.length > 0) {
          res = sum2(res, reduceAxes);
        }
        return reshape(res, base2.shape);
      };
      const derExp = () => {
        const condition = greater(base2, 0);
        const logBase = where(condition, log(base2), zerosLike(base2));
        let res = mul(dy, mul(y, logBase));
        const reduceAxes = getReductionAxes(exp4.shape, outShape);
        if (reduceAxes.length > 0) {
          res = sum2(res, reduceAxes);
        }
        return reshape(res, exp4.shape);
      };
      return {a: derBase, b: derExp};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var preluGradConfig = {
    kernelName: Prelu,
    inputsToSave: ["x", "alpha"],
    gradFunc: (dy, saved) => {
      const [x, alpha] = saved;
      const mask = greater(x, 0);
      return {
        x: () => where(mask, dy, mul(dy, alpha)),
        alpha: () => {
          let res = where(mask, zerosLike(dy), mul(dy, x));
          const reduceAxes = getReductionAxes(alpha.shape, dy.shape);
          if (reduceAxes.length > 0) {
            res = sum2(res, reduceAxes);
          }
          return reshape(res, alpha.shape);
        }
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var divGradConfig = {
    kernelName: RealDiv,
    inputsToSave: ["a", "b"],
    gradFunc: (dy, saved) => {
      const [a, b] = saved;
      const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
      const derA = () => {
        const res = div(dy, cast(b, "float32"));
        const reduceAxes = getReductionAxes(a.shape, outShape);
        if (reduceAxes.length > 0) {
          return reshape(sum2(res, reduceAxes), a.shape);
        }
        return res;
      };
      const derB = () => {
        let res = mul(dy, cast(a, "float32"));
        const reduceAxes = getReductionAxes(b.shape, outShape);
        if (reduceAxes.length > 0) {
          res = reshape(sum2(res, reduceAxes), b.shape);
        }
        const tmp = square(b);
        return neg(div(res, cast(tmp, "float32")));
      };
      return {a: derA, b: derB};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var reciprocalGradConfig = {
    kernelName: Reciprocal,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => div(dy, neg(square(x)))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var relu6GradConfig = {
    kernelName: Relu6,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      const mask = mul(lessEqual(x, 6), step(x));
      return {x: () => mul(dy, cast(mask, "float32"))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var reluGradConfig = {
    kernelName: Relu,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => mul(dy, cast(step(x), "float32"))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var reshapeGradConfig = {
    kernelName: Reshape,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => reshape(dy, x.shape)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var resizeBilinearGradConfig = {
    kernelName: ResizeBilinear,
    inputsToSave: ["images"],
    gradFunc: (dy, saved, attrs) => {
      const [images] = saved;
      const inputs = {dy, images};
      const imagesDer = () => ENGINE.runKernel(ResizeBilinearGrad, inputs, attrs);
      return {images: imagesDer};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var resizeNearestNeighborGradConfig = {
    kernelName: ResizeNearestNeighbor,
    inputsToSave: ["images"],
    gradFunc: (dy, saved, attrs) => {
      const [images] = saved;
      const inputs = {dy, images};
      const imagesDer = () => ENGINE.runKernel(ResizeNearestNeighborGrad, inputs, attrs);
      return {images: imagesDer};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var reverseGradConfig = {
    kernelName: Reverse,
    gradFunc: (dy, saved, attrs) => {
      const {dims} = attrs;
      const axes = parseAxisParam(dims, dy.shape);
      return {x: () => reverse(dy, axes)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var roundGradConfig = {
    kernelName: Round,
    gradFunc: (dy) => {
      return {x: () => zerosLike(dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var rsqrtGradConfig = {
    kernelName: Rsqrt,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => neg(div(dy, mul(pow(x, 1.5), 2)))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var selectGradConfig = {
    kernelName: Select,
    inputsToSave: ["condition"],
    gradFunc: (dy, saved) => {
      const [condition] = saved;
      return {
        condition: () => cast(zerosLike(condition), "float32"),
        t: () => mul(dy, cast(condition, dy.dtype)),
        e: () => mul(dy, cast(logicalNot(condition), dy.dtype))
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var seluGradConfig = {
    kernelName: Selu,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {
        x: () => {
          const mask = greater(x, scalar(0));
          const scaleAlpha2 = scalar(SELU_SCALEALPHA);
          const scale22 = scalar(SELU_SCALE);
          const greaterThanZeroDer = mul(dy, scale22);
          const lessEqualZeroDer = mul(mul(dy, scaleAlpha2), exp(cast(x, "float32")));
          return where(mask, greaterThanZeroDer, lessEqualZeroDer);
        }
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var sigmoidGradConfig = {
    kernelName: Sigmoid,
    outputsToSave: [true],
    gradFunc: (dy, saved) => {
      const [y] = saved;
      return {x: () => mul(dy, mul(y, sub(scalar(1), y)))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var signGradConfig = {
    kernelName: Sign,
    gradFunc: (dy) => {
      return {x: () => zerosLike(dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var sinGradConfig = {
    kernelName: Sin,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => mul(cos(cast(x, "float32")), dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var sinhGradConfig = {
    kernelName: Sinh,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => mul(cosh(cast(x, "float32")), dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var sliceGradConfig = {
    kernelName: Slice,
    inputsToSave: ["x"],
    gradFunc: (dy, saved, attrs) => {
      const [x] = saved;
      const {begin, size} = attrs;
      const inputShape = x.shape;
      const [begin_, size_] = parseSliceParams(x, begin, size);
      const paddings = [];
      for (let i = 0; i < dy.rank; i++) {
        paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);
      }
      return {x: () => pad(dy, paddings)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var softmaxGradConfig = {
    kernelName: Softmax,
    outputsToSave: [true],
    gradFunc: (dy, saved, attrs) => {
      const [y] = saved;
      const {dim} = attrs;
      const keepDims = true;
      const dyTimesY = mul(dy, y);
      return {
        logits: () => sub(dyTimesY, mul(sum2(dyTimesY, [dim], keepDims), y))
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var softplusGradConfig = {
    kernelName: Softplus,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => mul(dy, sigmoid(x))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var spaceToBatchNDGradConfig = {
    kernelName: SpaceToBatchND,
    gradFunc: (dy, saved, attrs) => {
      const {blockShape, paddings} = attrs;
      return {x: () => batchToSpaceND(dy, blockShape, paddings)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var splitVGradConfig = {
    kernelName: SplitV,
    gradFunc: (dy, saved, attrs) => {
      const {axis} = attrs;
      return {x: () => concat(dy, axis)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var sqrtGradConfig = {
    kernelName: Sqrt,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => div(dy, mul(sqrt(cast(x, "float32")), 2))};
    }
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var squareGradConfig = {
    kernelName: Square,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => mul(dy, mul(cast(x, "float32"), 2))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var squaredDifferenceGradConfig = {
    kernelName: SquaredDifference,
    inputsToSave: ["a", "b"],
    gradFunc: (dy, saved) => {
      const [a, b] = saved;
      const two = scalar(2);
      const derA = () => mul(dy, mul(two, sub(a, b)));
      const derB = () => mul(dy, mul(two, sub(b, a)));
      return {a: derA, b: derB};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var stepGradConfig = {
    kernelName: Step,
    gradFunc: (dy) => {
      return {x: () => zerosLike(dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var subGradConfig = {
    kernelName: Sub,
    inputsToSave: ["a", "b"],
    gradFunc: (dy, saved) => {
      const [a, b] = saved;
      const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
      const derA = () => {
        let res = dy;
        const reduceAxes = getReductionAxes(a.shape, outShape);
        if (reduceAxes.length > 0) {
          res = sum2(res, reduceAxes);
        }
        return reshape(res, a.shape);
      };
      const derB = () => {
        let res = dy;
        const reduceAxes = getReductionAxes(b.shape, outShape);
        if (reduceAxes.length > 0) {
          res = sum2(res, reduceAxes);
        }
        return reshape(neg(res), b.shape);
      };
      return {a: derA, b: derB};
    }
  };
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var sumGradConfig = {
    kernelName: Sum,
    inputsToSave: ["x"],
    gradFunc: (dy, saved, attrs) => {
      const [x] = saved;
      const expandedDyShape = x.shape.slice();
      const {axis} = attrs;
      const axes = parseAxisParam(axis, x.shape);
      axes.forEach((axis2) => {
        expandedDyShape[axis2] = 1;
      });
      const expandedDy = reshape(dy, expandedDyShape);
      const derX = mul(expandedDy, ones2(x.shape, "float32"));
      return {x: () => derX};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var tanGradConfig = {
    kernelName: Tan,
    inputsToSave: ["x"],
    gradFunc: (dy, saved) => {
      const [x] = saved;
      return {x: () => div(dy, square(cos(x)))};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var tanhGradConfig = {
    kernelName: Tanh,
    outputsToSave: [true],
    gradFunc: (dy, saved) => {
      const [y] = saved;
      return {x: () => mul(sub(scalar(1), square(y)), dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var tileGradConfig = {
    kernelName: Tile,
    inputsToSave: ["x"],
    gradFunc: (dy, saved, attrs) => {
      const [x] = saved;
      const {reps} = attrs;
      const derX = () => {
        let xGrad = zerosLike(x);
        if (x.rank === 1) {
          for (let i = 0; i < reps[0]; ++i) {
            xGrad = add2(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));
          }
        } else if (x.rank === 2) {
          for (let i = 0; i < reps[0]; ++i) {
            for (let j = 0; j < reps[1]; ++j) {
              xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [
                x.shape[0],
                x.shape[1]
              ]));
            }
          }
        } else if (x.rank === 3) {
          for (let i = 0; i < reps[0]; ++i) {
            for (let j = 0; j < reps[1]; ++j) {
              for (let k = 0; k < reps[2]; ++k) {
                xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));
              }
            }
          }
        } else if (x.rank === 4) {
          for (let i = 0; i < reps[0]; ++i) {
            for (let j = 0; j < reps[1]; ++j) {
              for (let k = 0; k < reps[2]; ++k) {
                for (let l = 0; l < reps[3]; ++l) {
                  xGrad = add2(xGrad, slice(dy, [
                    i * x.shape[0],
                    j * x.shape[1],
                    k * x.shape[2],
                    l * x.shape[3]
                  ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));
                }
              }
            }
          }
        } else {
          throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);
        }
        return xGrad;
      };
      return {x: derX};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var transposeGradConfig = {
    kernelName: Transpose,
    gradFunc: (dy, saved, attrs) => {
      const transposeAttrs = attrs;
      const {perm} = transposeAttrs;
      const undoPerm = getUndoAxesPermutation(perm);
      return {x: () => transpose(dy, undoPerm)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var unpackGradConfig = {
    kernelName: Unpack,
    gradFunc: (dy, saved, attrs) => {
      const unpackAttrs = attrs;
      const {axis} = unpackAttrs;
      return {value: () => stack(dy, axis)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var unsortedSegmentSumGradConfig = {
    kernelName: UnsortedSegmentSum,
    inputsToSave: ["segmentIds"],
    gradFunc: (dy, saved) => {
      const [segmentIds] = saved;
      const derX = () => {
        return gatherDropNegatives(dy, segmentIds);
      };
      return {x: derX};
    }
  };
  function gatherDropNegatives(x, indices) {
    const zeroClippedIndices = maximum(indices, zerosLike(indices));
    const gathered = gather(x, zeroClippedIndices);
    let isPositive = greaterEqual(indices, scalar(0, "int32"));
    const numIters = gathered.rank - isPositive.rank;
    for (let i = 0; i < numIters; ++i) {
      isPositive = expandDims(isPositive, i + 1);
    }
    isPositive = logicalAnd(isPositive, ones2(gathered.shape, "bool"));
    const zeroSlice = zerosLike(gathered);
    return where(isPositive, gathered, zeroSlice);
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var zerosLikeGradConfig = {
    kernelName: ZerosLike,
    gradFunc: (dy) => {
      return {x: () => zerosLike(dy)};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var gradConfigs = [
    absGradConfig,
    acosGradConfig,
    acoshGradConfig,
    addGradConfig,
    addNGradConfig,
    argMaxGradConfig,
    argMinGradConfig,
    asinGradConfig,
    asinhGradConfig,
    atan2GradConfig,
    atanGradConfig,
    atanhGradConfig,
    avgPool3DGradConfig,
    avgPoolGradConfig,
    batchMatMulGradConfig,
    batchToSpaceNDGradConfig,
    broadcastToGradConfig,
    castGradConfig,
    ceilGradConfig,
    clipByValueGradConfig,
    complexAbsGradConfig,
    concatGradConfig,
    conv2DBackpropInputGradConfig,
    conv2DGradConfig,
    conv3DGradConfig,
    cosGradConfig,
    coshGradConfig,
    cumsumGradConfig,
    depthwiseConv2dNativeGradConfig,
    dilation2dGradConfig,
    divGradConfig,
    eluGradConfig,
    erfGradConfig,
    expGradConfig,
    expandDimsGradConfig,
    expm1GradConfig,
    floorDivGradConfig,
    floorGradConfig,
    fusedBatchNormGradConfig,
    gatherGradConfig,
    greaterEqualGradConfig,
    identityGradConfig,
    isFiniteGradConfig,
    isInfGradConfig,
    isNanGradConfig,
    leakyReluGradConfig,
    log1pGradConfig,
    logGradConfig,
    logSoftmaxGradConfig,
    lrnGradConfig,
    maxGradConfig,
    maxGradConfig,
    maximumGradConfig,
    maxPool3DGradConfig,
    maxPoolGradConfig,
    meanGradConfig,
    minGradConfig,
    minimumGradConfig,
    mirrorPadGradConfig,
    modGradConfig,
    multiplyGradConfig,
    negGradConfig,
    oneHotGradConfig,
    onesLikeGradConfig,
    packGradConfig,
    padV2GradConfig,
    padV2GradConfig,
    powGradConfig,
    preluGradConfig,
    reciprocalGradConfig,
    relu6GradConfig,
    reluGradConfig,
    reshapeGradConfig,
    resizeBilinearGradConfig,
    resizeNearestNeighborGradConfig,
    reverseGradConfig,
    roundGradConfig,
    rsqrtGradConfig,
    selectGradConfig,
    seluGradConfig,
    sigmoidGradConfig,
    signGradConfig,
    sinGradConfig,
    sinhGradConfig,
    sliceGradConfig,
    softmaxGradConfig,
    softplusGradConfig,
    spaceToBatchNDGradConfig,
    spaceToBatchNDGradConfig,
    splitVGradConfig,
    splitVGradConfig,
    sqrtGradConfig,
    squaredDifferenceGradConfig,
    squareGradConfig,
    stepGradConfig,
    subGradConfig,
    sumGradConfig,
    tanGradConfig,
    tanhGradConfig,
    tileGradConfig,
    transposeGradConfig,
    unpackGradConfig,
    unsortedSegmentSumGradConfig,
    zerosLikeGradConfig
  ];
  for (const gradientConfig of gradConfigs) {
    registerGradient(gradientConfig);
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.abs = function() {
    this.throwIfDisposed();
    return abs(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.acos = function() {
    this.throwIfDisposed();
    return acos(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.acosh = function() {
    this.throwIfDisposed();
    return acosh(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.add = function(b) {
    this.throwIfDisposed();
    return add2(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.all = function(axis, keepDims) {
    this.throwIfDisposed();
    return all(this, axis, keepDims);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.any = function(axis, keepDims) {
    this.throwIfDisposed();
    return any(this, axis, keepDims);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.argMax = function(axis) {
    this.throwIfDisposed();
    return argMax(this, axis);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.argMin = function(axis) {
    this.throwIfDisposed();
    return argMin(this, axis);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.asScalar = function() {
    this.throwIfDisposed();
    assert(this.size === 1, () => "The array must have only 1 element.");
    return reshape(this, []);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.asType = function(dtype) {
    this.throwIfDisposed();
    return cast(this, dtype);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.as1D = function() {
    this.throwIfDisposed();
    return reshape(this, [this.size]);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.as2D = function(rows, columns) {
    this.throwIfDisposed();
    return reshape(this, [rows, columns]);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.as3D = function(rows, columns, depth) {
    this.throwIfDisposed();
    return reshape(this, [rows, columns, depth]);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.as4D = function(rows, columns, depth, depth2) {
    this.throwIfDisposed();
    return reshape(this, [rows, columns, depth, depth2]);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.as5D = function(rows, columns, depth, depth2, depth3) {
    this.throwIfDisposed();
    return reshape(this, [rows, columns, depth, depth2, depth3]);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.asin = function() {
    this.throwIfDisposed();
    return asin(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.asinh = function() {
    this.throwIfDisposed();
    return asinh(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.atan = function() {
    this.throwIfDisposed();
    return atan(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.atan2 = function(b) {
    this.throwIfDisposed();
    return atan2(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.atanh = function() {
    this.throwIfDisposed();
    return atanh(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.avgPool = function(filterSize, strides, pad3, dimRoundingMode) {
    this.throwIfDisposed();
    return avgPool(this, filterSize, strides, pad3, dimRoundingMode);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.batchToSpaceND = function(blockShape, crops) {
    this.throwIfDisposed();
    return batchToSpaceND(this, blockShape, crops);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.batchNorm = function(mean4, variance, offset, scale22, varianceEpsilon) {
    this.throwIfDisposed();
    return batchNorm(this, mean4, variance, offset, scale22, varianceEpsilon);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.broadcastTo = function(shape) {
    this.throwIfDisposed();
    return broadcastTo(this, shape);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.cast = function(dtype) {
    this.throwIfDisposed();
    return cast(this, dtype);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.ceil = function() {
    this.throwIfDisposed();
    return ceil(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.clipByValue = function(min6, max6) {
    this.throwIfDisposed();
    return clipByValue(this, min6, max6);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.concat = function(x, axis) {
    this.throwIfDisposed();
    if (x instanceof Tensor) {
      x = [x];
    }
    return concat([this, ...x], axis);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.conv1d = function(filter, stride, pad3, dataFormat, dilation, dimRoundingMode) {
    this.throwIfDisposed();
    return conv1d(this, filter, stride, pad3, dataFormat, dilation, dimRoundingMode);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.conv2dTranspose = function(filter, outputShape, strides, pad3, dimRoundingMode) {
    this.throwIfDisposed();
    return conv2dTranspose(this, filter, outputShape, strides, pad3, dimRoundingMode);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.conv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) {
    this.throwIfDisposed();
    return conv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.cos = function() {
    this.throwIfDisposed();
    return cos(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.cosh = function() {
    this.throwIfDisposed();
    return cosh(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.cumsum = function(axis, exclusive, reverse5) {
    this.throwIfDisposed();
    return cumsum(this, axis, exclusive, reverse5);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.depthToSpace = function(blockSize, dataFormat) {
    this.throwIfDisposed();
    return depthToSpace(this, blockSize, dataFormat);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.depthwiseConv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) {
    this.throwIfDisposed();
    return depthwiseConv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.dilation2d = function(filter, strides, pad3, dilations, dataFormat) {
    this.throwIfDisposed();
    return dilation2d(this, filter, strides, pad3, dilations, dataFormat);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.divNoNan = function(b) {
    this.throwIfDisposed();
    return divNoNan(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.div = function(b) {
    this.throwIfDisposed();
    return div(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.dot = function(b) {
    this.throwIfDisposed();
    return dot(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.elu = function() {
    this.throwIfDisposed();
    return elu(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.equal = function(b) {
    this.throwIfDisposed();
    return equal(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.erf = function() {
    this.throwIfDisposed();
    return erf(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.exp = function() {
    this.throwIfDisposed();
    return exp(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.expandDims = function(axis) {
    this.throwIfDisposed();
    return expandDims(this, axis);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.expm1 = function() {
    this.throwIfDisposed();
    return expm1(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.fft = function() {
    this.throwIfDisposed();
    return fft(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.flatten = function() {
    this.throwIfDisposed();
    return reshape(this, [this.size]);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.floor = function() {
    this.throwIfDisposed();
    return floor(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.floorDiv = function(b) {
    this.throwIfDisposed();
    return floorDiv(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.gather = function(indices, axis) {
    this.throwIfDisposed();
    return gather(this, indices, axis);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.greaterEqual = function(b) {
    this.throwIfDisposed();
    return greaterEqual(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.greater = function(b) {
    this.throwIfDisposed();
    return greater(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.ifft = function() {
    this.throwIfDisposed();
    return ifft(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.irfft = function() {
    this.throwIfDisposed();
    return irfft(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.isFinite = function() {
    this.throwIfDisposed();
    return isFinite2(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.isInf = function() {
    this.throwIfDisposed();
    return isInf(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.isNaN = function() {
    this.throwIfDisposed();
    return isNaN2(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.leakyRelu = function(alpha) {
    this.throwIfDisposed();
    return leakyRelu(this, alpha);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.lessEqual = function(b) {
    this.throwIfDisposed();
    return lessEqual(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.less = function(b) {
    this.throwIfDisposed();
    return less(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.localResponseNormalization = function(depthRadius, bias, alpha, beta) {
    this.throwIfDisposed();
    return localResponseNormalization(this, depthRadius, bias, alpha, beta);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.logSigmoid = function() {
    this.throwIfDisposed();
    return logSigmoid(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.logSoftmax = function(axis) {
    this.throwIfDisposed();
    return logSoftmax(this, axis);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.logSumExp = function(axis, keepDims) {
    this.throwIfDisposed();
    return logSumExp(this, axis, keepDims);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.log = function() {
    this.throwIfDisposed();
    return log(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.log1p = function() {
    this.throwIfDisposed();
    return log1p(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.logicalAnd = function(b) {
    this.throwIfDisposed();
    return logicalAnd(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.logicalNot = function() {
    this.throwIfDisposed();
    return logicalNot(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.logicalOr = function(b) {
    this.throwIfDisposed();
    return logicalOr(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.logicalXor = function(b) {
    this.throwIfDisposed();
    return logicalXor(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.matMul = function(b, transposeA, transposeB) {
    this.throwIfDisposed();
    return matMul(this, b, transposeA, transposeB);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.maxPool = function(filterSize, strides, pad3, dimRoundingMode) {
    this.throwIfDisposed();
    return maxPool(this, filterSize, strides, pad3, dimRoundingMode);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.max = function(axis, keepDims) {
    this.throwIfDisposed();
    return max(this, axis, keepDims);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.maximum = function(b) {
    this.throwIfDisposed();
    return maximum(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.mean = function(axis, keepDims) {
    this.throwIfDisposed();
    return mean(this, axis, keepDims);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.min = function(axis, keepDims) {
    this.throwIfDisposed();
    return min(this, axis, keepDims);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.minimum = function(b) {
    this.throwIfDisposed();
    return minimum(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.mirrorPad = function(paddings, mode) {
    this.throwIfDisposed();
    return mirrorPad(this, paddings, mode);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.mod = function(b) {
    this.throwIfDisposed();
    return mod(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.mul = function(b) {
    this.throwIfDisposed();
    return mul(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.neg = function() {
    this.throwIfDisposed();
    return neg(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.norm = function(ord, axis, keepDims) {
    this.throwIfDisposed();
    return norm(this, ord, axis, keepDims);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.notEqual = function(b) {
    this.throwIfDisposed();
    return notEqual(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.oneHot = function(depth, onValue = 1, offValue = 0) {
    this.throwIfDisposed();
    return oneHot(this, depth, onValue, offValue);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.onesLike = function() {
    this.throwIfDisposed();
    return onesLike(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.pad = function(paddings, constantValue) {
    this.throwIfDisposed();
    return pad(this, paddings, constantValue);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.pool = function(windowShape, poolingType, padding, dilationRate, strides) {
    this.throwIfDisposed();
    return pool(this, windowShape, poolingType, padding, dilationRate, strides);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.pow = function(exp4) {
    this.throwIfDisposed();
    return pow(this, exp4);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.prelu = function(alpha) {
    this.throwIfDisposed();
    return prelu(this, alpha);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.prod = function(axis, keepDims) {
    this.throwIfDisposed();
    return prod(this, axis, keepDims);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.reciprocal = function() {
    this.throwIfDisposed();
    return reciprocal(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.relu = function() {
    this.throwIfDisposed();
    return relu(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.relu6 = function() {
    this.throwIfDisposed();
    return relu6(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.reshapeAs = function(x) {
    this.throwIfDisposed();
    return reshape(this, x.shape);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.reshape = function(shape) {
    this.throwIfDisposed();
    return reshape(this, shape);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.resizeBilinear = function(newShape2D, alignCorners, halfPixelCenters) {
    this.throwIfDisposed();
    return resizeBilinear(this, newShape2D, alignCorners, halfPixelCenters);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.resizeNearestNeighbor = function(newShape2D, alignCorners, halfFloatCenters) {
    this.throwIfDisposed();
    return resizeNearestNeighbor(this, newShape2D, alignCorners, halfFloatCenters);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.reverse = function(axis) {
    this.throwIfDisposed();
    return reverse(this, axis);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.rfft = function() {
    this.throwIfDisposed();
    return rfft(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.round = function() {
    this.throwIfDisposed();
    return round2(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.rsqrt = function() {
    this.throwIfDisposed();
    return rsqrt(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.selu = function() {
    this.throwIfDisposed();
    return selu(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.separableConv2d = function(depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat) {
    this.throwIfDisposed();
    return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.sigmoid = function() {
    this.throwIfDisposed();
    return sigmoid(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.sign = function() {
    this.throwIfDisposed();
    return sign(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.sin = function() {
    this.throwIfDisposed();
    return sin(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.sinh = function() {
    this.throwIfDisposed();
    return sinh(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.slice = function(begin, size) {
    this.throwIfDisposed();
    return slice(this, begin, size);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.softmax = function(dim) {
    this.throwIfDisposed();
    return softmax(this, dim);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.softplus = function() {
    this.throwIfDisposed();
    return softplus(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.spaceToBatchND = function(blockShape, paddings) {
    this.throwIfDisposed();
    return spaceToBatchND(this, blockShape, paddings);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.split = function(numOrSizeSplits, axis) {
    this.throwIfDisposed();
    return split(this, numOrSizeSplits, axis);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.sqrt = function() {
    this.throwIfDisposed();
    return sqrt(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.square = function() {
    this.throwIfDisposed();
    return square(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.squaredDifference = function(b) {
    this.throwIfDisposed();
    return squaredDifference(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.squeeze = function(axis) {
    this.throwIfDisposed();
    return squeeze(this, axis);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.stack = function(x, axis) {
    this.throwIfDisposed();
    const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];
    return stack(tensorsToBeStacked, axis);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.step = function(alpha) {
    this.throwIfDisposed();
    return step(this, alpha);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.stridedSlice = function(begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
    this.throwIfDisposed();
    return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.sub = function(b) {
    this.throwIfDisposed();
    return sub(this, b);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.sum = function(axis, keepDims) {
    this.throwIfDisposed();
    return sum2(this, axis, keepDims);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.tan = function() {
    this.throwIfDisposed();
    return tan(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.tanh = function() {
    this.throwIfDisposed();
    return tanh2(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.tile = function(reps) {
    this.throwIfDisposed();
    return tile(this, reps);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.toBool = function() {
    this.throwIfDisposed();
    return cast(this, "bool");
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.toFloat = function() {
    this.throwIfDisposed();
    return cast(this, "float32");
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.toInt = function() {
    this.throwIfDisposed();
    return cast(this, "int32");
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.topk = function(k, sorted) {
    this.throwIfDisposed();
    return topk(this, k, sorted);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.transpose = function(perm) {
    this.throwIfDisposed();
    return transpose(this, perm);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.unique = function(axis) {
    this.throwIfDisposed();
    return unique(this, axis);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.unsortedSegmentSum = function(segmentIds, numSegments) {
    this.throwIfDisposed();
    return unsortedSegmentSum(this, segmentIds, numSegments);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.unstack = function(axis) {
    this.throwIfDisposed();
    return unstack(this, axis);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.where = function(condition, x) {
    this.throwIfDisposed();
    return where(condition, this, x);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  getGlobalTensorClass().prototype.zerosLike = function() {
    this.throwIfDisposed();
    return zerosLike(this);
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var exports_constraints_exports = {};
  __export2(exports_constraints_exports, {
    maxNorm: () => maxNorm,
    minMaxNorm: () => minMaxNorm,
    nonNeg: () => nonNeg,
    unitNorm: () => unitNorm
  });
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var _epsilon;
  function epsilon() {
    if (_epsilon == null) {
      _epsilon = backend().epsilon();
    }
    return _epsilon;
  }
  function imageDataFormat() {
    return "channelsLast";
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var AttributeError = class extends Error {
    constructor(message) {
      super(message);
      Object.setPrototypeOf(this, AttributeError.prototype);
    }
  };
  var RuntimeError = class extends Error {
    constructor(message) {
      super(message);
      Object.setPrototypeOf(this, RuntimeError.prototype);
    }
  };
  var ValueError = class extends Error {
    constructor(message) {
      super(message);
      Object.setPrototypeOf(this, ValueError.prototype);
    }
  };
  var NotImplementedError = class extends Error {
    constructor(message) {
      super(message);
      Object.setPrototypeOf(this, NotImplementedError.prototype);
    }
  };
  var AssertionError = class extends Error {
    constructor(message) {
      super(message);
      Object.setPrototypeOf(this, AssertionError.prototype);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function pyListRepeat(value, numValues) {
    if (Array.isArray(value)) {
      let newArray = [];
      for (let i = 0; i < numValues; i++) {
        newArray = newArray.concat(value);
      }
      return newArray;
    } else {
      const newArray = new Array(numValues);
      newArray.fill(value);
      return newArray;
    }
  }
  function assert2(val, message) {
    if (!val) {
      throw new AssertionError(message);
    }
  }
  function count(array2, refernce) {
    let counter = 0;
    for (const item of array2) {
      if (item === refernce) {
        counter++;
      }
    }
    return counter;
  }
  function singletonOrArray(xs) {
    if (xs.length === 1) {
      return xs[0];
    }
    return xs;
  }
  function toList(x) {
    if (Array.isArray(x)) {
      return x;
    }
    return [x];
  }
  function toSnakeCase(name) {
    const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, "$1_$2");
    const insecure = intermediate.replace(/([a-z])([A-Z])/g, "$1_$2").toLowerCase();
    if (insecure[0] !== "_") {
      return insecure;
    }
    return "private" + insecure;
  }
  function toCamelCase(identifier) {
    if (identifier.length <= 1) {
      return identifier;
    }
    if (identifier.indexOf("_") === -1) {
      return identifier;
    }
    return identifier.replace(/[_]+(\w|$)/g, (m, p1) => p1.toUpperCase());
  }
  var _GLOBAL_CUSTOM_OBJECTS = {};
  function serializeKerasObject(instance) {
    if (instance === null || instance === void 0) {
      return null;
    }
    const dict = {};
    dict["className"] = instance.getClassName();
    dict["config"] = instance.getConfig();
    return dict;
  }
  function convertNDArrayScalarsInConfig(config) {
    if (config == null || typeof config !== "object") {
      return;
    } else if (Array.isArray(config)) {
      config.forEach((configItem) => convertNDArrayScalarsInConfig(configItem));
    } else {
      const fields = Object.keys(config);
      for (const field of fields) {
        const value = config[field];
        if (value != null && typeof value === "object") {
          if (!Array.isArray(value) && value["type"] === "ndarray" && typeof value["value"] === "number") {
            config[field] = value["value"];
          } else {
            convertNDArrayScalarsInConfig(value);
          }
        }
      }
    }
  }
  function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = "object", fastWeightInit = false) {
    if (typeof identifier === "string") {
      const functionName = identifier;
      let fn;
      if (functionName in customObjects) {
        fn = customObjects[functionName];
      } else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {
        fn = _GLOBAL_CUSTOM_OBJECTS[functionName];
      } else {
        fn = moduleObjects[functionName];
        if (fn == null) {
          throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons:
1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);
        }
      }
      return fn;
    } else {
      const config = identifier;
      if (config["className"] == null || config["config"] == null) {
        throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config)}.
'className' and 'config' must set.`);
      }
      const className = config["className"];
      let cls, fromConfig;
      if (className in customObjects) {
        [cls, fromConfig] = customObjects[className];
      } else if (className in _GLOBAL_CUSTOM_OBJECTS) {
        [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS["className"];
      } else if (className in moduleObjects) {
        [cls, fromConfig] = moduleObjects[className];
      }
      if (cls == null) {
        throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons:
1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);
      }
      if (fromConfig != null) {
        const customObjectsCombined = {};
        for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {
          customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];
        }
        for (const key of Object.keys(customObjects)) {
          customObjectsCombined[key] = customObjects[key];
        }
        const nestedConfig = config["config"];
        nestedConfig["customObjects"] = customObjectsCombined;
        const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);
        for (const key of Object.keys(customObjects)) {
          _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];
        }
        convertNDArrayScalarsInConfig(config["config"]);
        const returnObj = fromConfig(cls, config["config"], customObjects, fastWeightInit);
        _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);
        return returnObj;
      } else {
        const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);
        for (const key of Object.keys(customObjects)) {
          _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];
        }
        const returnObj = new cls(config["config"]);
        _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);
        return returnObj;
      }
    }
  }
  function numberCompare(a, b) {
    return a < b ? -1 : a > b ? 1 : 0;
  }
  function reverseNumberCompare(a, b) {
    return -1 * numberCompare(a, b);
  }
  function unique2(xs) {
    if (xs == null) {
      return xs;
    }
    const out = [];
    for (const x of xs) {
      if (out.indexOf(x) === -1) {
        out.push(x);
      }
    }
    return out;
  }
  function isObjectEmpty(obj) {
    if (obj == null) {
      throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);
    }
    for (const key in obj) {
      if (obj.hasOwnProperty(key)) {
        return false;
      }
    }
    return true;
  }
  function checkStringTypeUnionValue(values, label, value) {
    if (value == null) {
      return;
    }
    if (values.indexOf(value) < 0) {
      throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`);
    }
  }
  function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {
    assert2(minLength >= 0);
    assert2(maxLength >= minLength);
    return Array.isArray(x) && x.length >= minLength && x.length <= maxLength && x.every((e) => typeof e === expectedType);
  }
  function assertPositiveInteger(value, name) {
    if (Array.isArray(value)) {
      util_exports.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);
      value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));
    } else {
      util_exports.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`);
    }
  }
  function formatAsFriendlyString(value) {
    if (value === null) {
      return "null";
    } else if (Array.isArray(value)) {
      return "[" + value.map((v) => formatAsFriendlyString(v)).join(",") + "]";
    } else if (typeof value === "string") {
      return `"${value}"`;
    } else {
      return `${value}`;
    }
  }
  function debounce(f, waitMs) {
    let lastTime = util_exports.now();
    let lastResult;
    const f2 = (...args) => {
      const now2 = util_exports.now();
      if (now2 - lastTime < waitMs) {
        return lastResult;
      }
      lastTime = now2;
      lastResult = f(...args);
      return lastResult;
    };
    return f2;
  }
  function mapActivationToFusedKernel(activationName) {
    if (activationName === "relu") {
      return "relu";
    }
    if (activationName === "linear") {
      return "linear";
    }
    if (activationName === "elu") {
      return "elu";
    }
    return null;
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function calcL2Norms(w, axis) {
    return tidy(() => sqrt(sum2(mul(w, w), axis, true)));
  }
  var Constraint = class extends serialization_exports.Serializable {
    getConfig() {
      return {};
    }
  };
  var MaxNorm = class extends Constraint {
    constructor(args) {
      super();
      this.defaultMaxValue = 2;
      this.defaultAxis = 0;
      this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;
      this.axis = args.axis != null ? args.axis : this.defaultAxis;
    }
    apply(w) {
      return tidy(() => {
        const norms = calcL2Norms(w, this.axis);
        const desired = clipByValue(norms, 0, this.maxValue);
        return mul(w, div(desired, add2(epsilon(), norms)));
      });
    }
    getConfig() {
      return {maxValue: this.maxValue, axis: this.axis};
    }
  };
  MaxNorm.className = "MaxNorm";
  serialization_exports.registerClass(MaxNorm);
  var UnitNorm = class extends Constraint {
    constructor(args) {
      super();
      this.defaultAxis = 0;
      this.axis = args.axis != null ? args.axis : this.defaultAxis;
    }
    apply(w) {
      return tidy(() => div(w, add2(epsilon(), calcL2Norms(w, this.axis))));
    }
    getConfig() {
      return {axis: this.axis};
    }
  };
  UnitNorm.className = "UnitNorm";
  serialization_exports.registerClass(UnitNorm);
  var NonNeg = class extends Constraint {
    apply(w) {
      return relu(w);
    }
  };
  NonNeg.className = "NonNeg";
  serialization_exports.registerClass(NonNeg);
  var MinMaxNorm = class extends Constraint {
    constructor(args) {
      super();
      this.defaultMinValue = 0;
      this.defaultMaxValue = 1;
      this.defaultRate = 1;
      this.defaultAxis = 0;
      this.minValue = args.minValue != null ? args.minValue : this.defaultMinValue;
      this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;
      this.rate = args.rate != null ? args.rate : this.defaultRate;
      this.axis = args.axis != null ? args.axis : this.defaultAxis;
    }
    apply(w) {
      return tidy(() => {
        const norms = calcL2Norms(w, this.axis);
        const desired = add2(mul(this.rate, clipByValue(norms, this.minValue, this.maxValue)), mul(1 - this.rate, norms));
        return mul(w, div(desired, add2(epsilon(), norms)));
      });
    }
    getConfig() {
      return {
        minValue: this.minValue,
        maxValue: this.maxValue,
        rate: this.rate,
        axis: this.axis
      };
    }
  };
  MinMaxNorm.className = "MinMaxNorm";
  serialization_exports.registerClass(MinMaxNorm);
  var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
    maxNorm: "MaxNorm",
    minMaxNorm: "MinMaxNorm",
    nonNeg: "NonNeg",
    unitNorm: "UnitNorm"
  };
  function serializeConstraint(constraint) {
    return serializeKerasObject(constraint);
  }
  function deserializeConstraint(config, customObjects = {}) {
    return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "constraint");
  }
  function getConstraint(identifier) {
    if (identifier == null) {
      return null;
    }
    if (typeof identifier === "string") {
      const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ? CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;
      const config = {className, config: {}};
      return deserializeConstraint(config);
    } else if (identifier instanceof Constraint) {
      return identifier;
    } else {
      return deserializeConstraint(identifier);
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function maxNorm(args) {
    return new MaxNorm(args);
  }
  function unitNorm(args) {
    return new UnitNorm(args);
  }
  function nonNeg() {
    return new NonNeg();
  }
  function minMaxNorm(config) {
    return new MinMaxNorm(config);
  }
  var exports_initializers_exports = {};
  __export2(exports_initializers_exports, {
    constant: () => constant,
    glorotNormal: () => glorotNormal,
    glorotUniform: () => glorotUniform,
    heNormal: () => heNormal,
    heUniform: () => heUniform,
    identity: () => identity,
    leCunNormal: () => leCunNormal,
    leCunUniform: () => leCunUniform,
    ones: () => ones3,
    orthogonal: () => orthogonal,
    randomNormal: () => randomNormal3,
    randomUniform: () => randomUniform2,
    truncatedNormal: () => truncatedNormal2,
    varianceScaling: () => varianceScaling,
    zeros: () => zeros2
  });
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var VALID_DATA_FORMAT_VALUES = ["channelsFirst", "channelsLast"];
  var VALID_INTERPOLATION_FORMAT_VALUES = ["nearest", "bilinear"];
  var VALID_PADDING_MODE_VALUES = ["valid", "same", "causal"];
  var VALID_POOL_MODE_VALUES = ["max", "avg"];
  var VALID_BIDIRECTIONAL_MERGE_MODES = ["sum", "mul", "concat", "ave"];
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var nameMap = new Map();
  function checkDataFormat(value) {
    checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, "DataFormat", value);
  }
  function checkInterpolationFormat(value) {
    checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES, "InterpolationFormat", value);
  }
  function checkPaddingMode(value) {
    checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, "PaddingMode", value);
  }
  function checkPoolMode(value) {
    checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, "PoolMode", value);
  }
  var _nameScopeStack = [];
  var _nameScopeDivider = "/";
  function nameScope(name, fn) {
    _nameScopeStack.push(name);
    try {
      const val = fn();
      _nameScopeStack.pop();
      return val;
    } catch (e) {
      _nameScopeStack.pop();
      throw e;
    }
  }
  function currentNameScopePrefix() {
    if (_nameScopeStack.length === 0) {
      return "";
    } else {
      return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;
    }
  }
  function getScopedTensorName(tensorName) {
    if (!isValidTensorName(tensorName)) {
      throw new Error("Not a valid tensor name: '" + tensorName + "'");
    }
    return currentNameScopePrefix() + tensorName;
  }
  function getUniqueTensorName(scopedName) {
    if (!isValidTensorName(scopedName)) {
      throw new Error("Not a valid tensor name: '" + scopedName + "'");
    }
    if (!nameMap.has(scopedName)) {
      nameMap.set(scopedName, 0);
    }
    const index = nameMap.get(scopedName);
    nameMap.set(scopedName, nameMap.get(scopedName) + 1);
    if (index > 0) {
      const result = `${scopedName}_${index}`;
      nameMap.set(result, 1);
      return result;
    } else {
      return scopedName;
    }
  }
  var tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);
  function isValidTensorName(name) {
    return !!name.match(tensorNameRegex);
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function isInteger(x) {
    return x === parseInt(x.toString(), 10);
  }
  function arrayProd(array2, begin, end) {
    if (begin == null) {
      begin = 0;
    }
    if (end == null) {
      end = array2.length;
    }
    let prod5 = 1;
    for (let i = begin; i < end; ++i) {
      prod5 *= array2[i];
    }
    return prod5;
  }
  function toArray1D(array2) {
    array2 = Array.isArray(array2) ? new Float32Array(array2) : array2;
    return tensor1d(array2);
  }
  function min2(array2) {
    return min(toArray1D(array2)).dataSync()[0];
  }
  function max2(array2) {
    return max(toArray1D(array2)).dataSync()[0];
  }
  function range2(begin, end) {
    if (end < begin) {
      throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);
    }
    const out = [];
    for (let i = begin; i < end; ++i) {
      out.push(i);
    }
    return out;
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function cast2(x, dtype) {
    return x.asType(dtype);
  }
  function expandDims2(x, axis = -1) {
    const outShape = x.shape.slice();
    if (axis < 0) {
      axis = outShape.length + axis + 1;
    }
    outShape.splice(axis, 0, 1);
    return x.reshape(outShape);
  }
  function repeat(x, n) {
    return tidy(() => {
      if (x.shape.length !== 2) {
        throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);
      }
      const y = expandDims2(x, 1);
      return tile2(y, [1, n, 1]);
    });
  }
  function flatten2(x) {
    const newShape = [arrayProd(x.shape)];
    return x.reshape(newShape);
  }
  function batchFlatten(x) {
    if (x.rank <= 1) {
      throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);
    }
    const newShape = [x.shape[0], arrayProd(x.shape, 1)];
    return x.reshape(newShape);
  }
  function sliceAlongFirstAxis(array2, start, size) {
    return tidy(() => {
      switch (array2.rank) {
        case 1:
          return slice1d(array2, start, size);
        case 2:
          return slice2d(array2, [start, 0], [size, array2.shape[1]]);
        case 3:
          return slice3d(array2, [start, 0, 0], [size, array2.shape[1], array2.shape[2]]);
        case 4:
          return slice4d(array2, [start, 0, 0, 0], [size, array2.shape[1], array2.shape[2], array2.shape[3]]);
        case 5:
          return slice(array2, [start, 0, 0, 0, 0], [
            size,
            array2.shape[1],
            array2.shape[2],
            array2.shape[3],
            array2.shape[4]
          ]);
        case 6:
          return slice(array2, [start, 0, 0, 0, 0, 0], [
            size,
            array2.shape[1],
            array2.shape[2],
            array2.shape[3],
            array2.shape[4],
            array2.shape[5]
          ]);
        default:
          throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`);
      }
    });
  }
  function sliceAlongLastAxis(array2, start, size) {
    return tidy(() => {
      switch (array2.rank) {
        case 1:
          return slice1d(array2, start, size);
        case 2:
          return slice2d(array2, [0, start], [array2.shape[0], size]);
        case 3:
          return slice3d(array2, [0, 0, start], [array2.shape[0], array2.shape[1], size]);
        case 4:
          return slice4d(array2, [0, 0, 0, start], [array2.shape[0], array2.shape[1], array2.shape[2], size]);
        default:
          throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);
      }
    });
  }
  function sliceAlongAxis(array2, start, size, axis) {
    return tidy(() => {
      switch (array2.rank) {
        case 1:
          return slice1d(array2, start, size);
        case 2:
          switch (axis) {
            case 1:
              return sliceAlongFirstAxis(array2, start, size);
            case 2:
              return sliceAlongLastAxis(array2, start, size);
            default:
              throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);
          }
        case 3:
          switch (axis) {
            case 1:
              return sliceAlongFirstAxis(array2, start, size);
            case 2:
              return slice3d(array2, [0, start, 0], [array2.shape[0], size, array2.shape[2]]);
            case 3:
              return sliceAlongLastAxis(array2, start, size);
            default:
              throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);
          }
        case 4:
          switch (axis) {
            case 1:
              return sliceAlongFirstAxis(array2, start, size);
            case 2:
              return slice4d(array2, [0, start, 0, 0], [array2.shape[0], size, array2.shape[2], array2.shape[3]]);
            case 3:
              return slice4d(array2, [0, 0, start, 0], [array2.shape[0], array2.shape[1], size, array2.shape[3]]);
            case 4:
              return sliceAlongLastAxis(array2, start, size);
            default:
              throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);
          }
        default:
          throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);
      }
    });
  }
  function concatenate(tensors, axis = -1) {
    let rank;
    if (axis < 0) {
      rank = tensors[0].rank;
      if (rank !== 0) {
        axis = rank;
      } else {
        axis = 0;
      }
    }
    if (axis === tensors[0].rank) {
      axis = -1;
    }
    return concat(tensors, axis);
  }
  function concatAlongFirstAxis(a, b) {
    switch (a.rank) {
      case 1:
        return concat1d([a, b]);
      case 2:
        return concat2d([a, b], 0);
      case 3:
        return concat3d([a, b], 0);
      case 4:
        return concat4d([a, b], 0);
      default:
        throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`);
    }
  }
  function tile2(x, n) {
    if (!Array.isArray(n)) {
      n = [n];
    }
    if (x.rank !== n.length) {
      throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);
    }
    return tile(x, n);
  }
  function randomNormal2(shape, mean4 = 0, stddev = 1, dtype, seed) {
    return randomNormal(shape, mean4, stddev, dtype, seed);
  }
  function dot2(a, b, activation2, bias) {
    if (a.rank < 2 || b.rank < 2) {
      throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);
    }
    if (b.rank >= 3) {
      const xLastDim = a.shape.slice(-1)[0];
      const ySecondLastDim = b.shape.slice(-2)[0];
      if (xLastDim !== ySecondLastDim) {
        throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and  y shape = ${b.shape}`);
      }
    }
    if (a.rank === 2 && b.rank === 2) {
      const transposeA = false;
      const transposeB = false;
      return fused_ops_exports.matMul({
        a,
        b,
        transposeA,
        transposeB,
        bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,
        activation: activation2
      });
    } else {
      const aFirstDims = a.shape.slice();
      const aLastDim = aFirstDims.pop();
      a = a.reshape([-1, aLastDim]);
      const bShape = b.shape.slice();
      const bLastDim = bShape.pop();
      const ySecondLastDim = bShape.pop();
      const yOtherDims = [...bShape, bLastDim];
      const perm = Array.from({length: b.rank}, (_, i) => {
        if (i === 0) {
          return b.rank - 2;
        } else if (i <= b.rank - 2) {
          return i - 1;
        }
        return i;
      });
      b = b.transpose(perm).reshape([ySecondLastDim, -1]);
      const outputShape = [...aFirstDims, ...yOtherDims];
      const transposeA = false;
      const transposeB = false;
      return fused_ops_exports.matMul({
        a,
        b,
        transposeA,
        transposeB,
        bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,
        activation: activation2
      }).reshape(outputShape);
    }
  }
  function gather2(reference, indices, axis) {
    return tidy(() => {
      if (Array.isArray(indices)) {
        indices = tensor1d(indices, "int32");
      } else {
        indices = indices.toInt();
      }
      return gather(reference, indices, axis);
    });
  }
  function square2(x) {
    return mul(x, x);
  }
  function reshapeBias(xRank, bias, dataFormat) {
    const biasShape = bias.shape;
    if (bias.rank !== 1 && bias.rank !== xRank) {
      throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);
    }
    if (xRank === 5) {
      if (dataFormat === "channelsFirst") {
        if (biasShape.length === 1) {
          return bias.reshape([1, biasShape[0], 1, 1, 1]);
        } else {
          return bias.reshape([1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);
        }
      } else if (dataFormat === "channelsLast") {
        if (biasShape.length === 1) {
          return bias.reshape([1, 1, 1, 1, biasShape[0]]);
        } else {
          return bias.reshape([1].concat(biasShape));
        }
      }
    } else if (xRank === 4) {
      if (dataFormat === "channelsFirst") {
        if (biasShape.length === 1) {
          return bias.reshape([1, biasShape[0], 1, 1]);
        } else {
          return bias.reshape([1, biasShape[2], biasShape[0], biasShape[1]]);
        }
      } else if (dataFormat === "channelsLast") {
        if (biasShape.length === 1) {
          return bias.reshape([1, 1, 1, biasShape[0]]);
        } else {
          return bias.reshape([1].concat(biasShape));
        }
      }
    } else if (xRank === 3) {
      if (dataFormat === "channelsFirst") {
        if (biasShape.length === 1) {
          return bias.reshape([1, biasShape[0], 1]);
        } else {
          return bias.reshape([1, biasShape[1], biasShape[0]]);
        }
      } else if (dataFormat === "channelsLast") {
        if (biasShape.length === 1) {
          return bias.reshape([1, 1, biasShape[0]]);
        } else {
          return bias.reshape([1].concat(biasShape));
        }
      }
    } else if (xRank < 3) {
      return bias;
    }
    throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);
  }
  function biasAdd(x, bias, dataFormat) {
    return tidy(() => {
      if (dataFormat == null) {
        dataFormat = imageDataFormat();
      }
      checkDataFormat(dataFormat);
      return x.add(reshapeBias(x.rank, bias, dataFormat));
    });
  }
  function elu2(x, alpha = 1) {
    if (alpha !== 1) {
      throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);
    }
    return elu(x);
  }
  function softsign(x) {
    return tidy(() => div(x, abs(x).add(1)));
  }
  function dropout2(x, level, noiseShape, seed) {
    return tidy(() => dropout(x, level, noiseShape, seed));
  }
  function hardSigmoid(x) {
    return tidy(() => {
      const y = add2(0.5, mul(0.2, x));
      return clipByValue(y, 0, 1);
    });
  }
  function inTrainPhase(x, alt, training = false) {
    return training ? x() : alt();
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var VALID_FAN_MODE_VALUES = ["fanIn", "fanOut", "fanAvg"];
  var VALID_DISTRIBUTION_VALUES = ["normal", "uniform", "truncatedNormal"];
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function checkFanMode(value) {
    checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, "FanMode", value);
  }
  function checkDistribution(value) {
    checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, "Distribution", value);
  }
  var Initializer = class extends serialization_exports.Serializable {
    fromConfigUsesCustomObjects() {
      return false;
    }
    getConfig() {
      return {};
    }
  };
  var Zeros = class extends Initializer {
    apply(shape, dtype) {
      return zeros(shape, dtype);
    }
  };
  Zeros.className = "Zeros";
  serialization_exports.registerClass(Zeros);
  var Ones = class extends Initializer {
    apply(shape, dtype) {
      return ones2(shape, dtype);
    }
  };
  Ones.className = "Ones";
  serialization_exports.registerClass(Ones);
  var Constant = class extends Initializer {
    constructor(args) {
      super();
      if (typeof args !== "object") {
        throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);
      }
      if (args.value === void 0) {
        throw new ValueError(`config must have value set but got ${args}`);
      }
      this.value = args.value;
    }
    apply(shape, dtype) {
      return tidy(() => mul(scalar(this.value), ones2(shape, dtype)));
    }
    getConfig() {
      return {
        value: this.value
      };
    }
  };
  Constant.className = "Constant";
  serialization_exports.registerClass(Constant);
  var RandomUniform = class extends Initializer {
    constructor(args) {
      super();
      this.DEFAULT_MINVAL = -0.05;
      this.DEFAULT_MAXVAL = 0.05;
      this.minval = args.minval || this.DEFAULT_MINVAL;
      this.maxval = args.maxval || this.DEFAULT_MAXVAL;
      this.seed = args.seed;
    }
    apply(shape, dtype) {
      return randomUniform(shape, this.minval, this.maxval, dtype);
    }
    getConfig() {
      return {minval: this.minval, maxval: this.maxval, seed: this.seed};
    }
  };
  RandomUniform.className = "RandomUniform";
  serialization_exports.registerClass(RandomUniform);
  var RandomNormal = class extends Initializer {
    constructor(args) {
      super();
      this.DEFAULT_MEAN = 0;
      this.DEFAULT_STDDEV = 0.05;
      this.mean = args.mean || this.DEFAULT_MEAN;
      this.stddev = args.stddev || this.DEFAULT_STDDEV;
      this.seed = args.seed;
    }
    apply(shape, dtype) {
      dtype = dtype || "float32";
      if (dtype !== "float32" && dtype !== "int32") {
        throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);
      }
      return randomNormal2(shape, this.mean, this.stddev, dtype, this.seed);
    }
    getConfig() {
      return {mean: this.mean, stddev: this.stddev, seed: this.seed};
    }
  };
  RandomNormal.className = "RandomNormal";
  serialization_exports.registerClass(RandomNormal);
  var TruncatedNormal = class extends Initializer {
    constructor(args) {
      super();
      this.DEFAULT_MEAN = 0;
      this.DEFAULT_STDDEV = 0.05;
      this.mean = args.mean || this.DEFAULT_MEAN;
      this.stddev = args.stddev || this.DEFAULT_STDDEV;
      this.seed = args.seed;
    }
    apply(shape, dtype) {
      dtype = dtype || "float32";
      if (dtype !== "float32" && dtype !== "int32") {
        throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);
      }
      return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);
    }
    getConfig() {
      return {mean: this.mean, stddev: this.stddev, seed: this.seed};
    }
  };
  TruncatedNormal.className = "TruncatedNormal";
  serialization_exports.registerClass(TruncatedNormal);
  var Identity2 = class extends Initializer {
    constructor(args) {
      super();
      this.gain = args.gain != null ? args.gain : 1;
    }
    apply(shape, dtype) {
      return tidy(() => {
        if (shape.length !== 2 || shape[0] !== shape[1]) {
          throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");
        } else {
          return mul(this.gain, eye(shape[0]));
        }
      });
    }
    getConfig() {
      return {gain: this.gain};
    }
  };
  Identity2.className = "Identity";
  serialization_exports.registerClass(Identity2);
  function computeFans(shape, dataFormat = "channelsLast") {
    let fanIn;
    let fanOut;
    checkDataFormat(dataFormat);
    if (shape.length === 2) {
      fanIn = shape[0];
      fanOut = shape[1];
    } else if ([3, 4, 5].indexOf(shape.length) !== -1) {
      if (dataFormat === "channelsFirst") {
        const receptiveFieldSize = arrayProd(shape, 2);
        fanIn = shape[1] * receptiveFieldSize;
        fanOut = shape[0] * receptiveFieldSize;
      } else if (dataFormat === "channelsLast") {
        const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);
        fanIn = shape[shape.length - 2] * receptiveFieldSize;
        fanOut = shape[shape.length - 1] * receptiveFieldSize;
      }
    } else {
      const shapeProd = arrayProd(shape);
      fanIn = Math.sqrt(shapeProd);
      fanOut = Math.sqrt(shapeProd);
    }
    return [fanIn, fanOut];
  }
  var VarianceScaling = class extends Initializer {
    constructor(args) {
      super();
      if (args.scale < 0) {
        throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);
      }
      this.scale = args.scale == null ? 1 : args.scale;
      this.mode = args.mode == null ? "fanIn" : args.mode;
      checkFanMode(this.mode);
      this.distribution = args.distribution == null ? "normal" : args.distribution;
      checkDistribution(this.distribution);
      this.seed = args.seed;
    }
    apply(shape, dtype) {
      const fans = computeFans(shape);
      const fanIn = fans[0];
      const fanOut = fans[1];
      let scale22 = this.scale;
      if (this.mode === "fanIn") {
        scale22 /= Math.max(1, fanIn);
      } else if (this.mode === "fanOut") {
        scale22 /= Math.max(1, fanOut);
      } else {
        scale22 /= Math.max(1, (fanIn + fanOut) / 2);
      }
      if (this.distribution === "normal") {
        const stddev = Math.sqrt(scale22);
        dtype = dtype || "float32";
        if (dtype !== "float32" && dtype !== "int32") {
          throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);
        }
        return truncatedNormal(shape, 0, stddev, dtype, this.seed);
      } else {
        const limit = Math.sqrt(3 * scale22);
        return randomUniform(shape, -limit, limit, dtype);
      }
    }
    getConfig() {
      return {
        scale: this.scale,
        mode: this.mode,
        distribution: this.distribution,
        seed: this.seed
      };
    }
  };
  VarianceScaling.className = "VarianceScaling";
  serialization_exports.registerClass(VarianceScaling);
  var GlorotUniform = class extends VarianceScaling {
    constructor(args) {
      super({
        scale: 1,
        mode: "fanAvg",
        distribution: "uniform",
        seed: args == null ? null : args.seed
      });
    }
    getClassName() {
      return VarianceScaling.className;
    }
  };
  GlorotUniform.className = "GlorotUniform";
  serialization_exports.registerClass(GlorotUniform);
  var GlorotNormal = class extends VarianceScaling {
    constructor(args) {
      super({
        scale: 1,
        mode: "fanAvg",
        distribution: "normal",
        seed: args == null ? null : args.seed
      });
    }
    getClassName() {
      return VarianceScaling.className;
    }
  };
  GlorotNormal.className = "GlorotNormal";
  serialization_exports.registerClass(GlorotNormal);
  var HeNormal = class extends VarianceScaling {
    constructor(args) {
      super({
        scale: 2,
        mode: "fanIn",
        distribution: "normal",
        seed: args == null ? null : args.seed
      });
    }
    getClassName() {
      return VarianceScaling.className;
    }
  };
  HeNormal.className = "HeNormal";
  serialization_exports.registerClass(HeNormal);
  var HeUniform = class extends VarianceScaling {
    constructor(args) {
      super({
        scale: 2,
        mode: "fanIn",
        distribution: "uniform",
        seed: args == null ? null : args.seed
      });
    }
    getClassName() {
      return VarianceScaling.className;
    }
  };
  HeUniform.className = "HeUniform";
  serialization_exports.registerClass(HeUniform);
  var LeCunNormal = class extends VarianceScaling {
    constructor(args) {
      super({
        scale: 1,
        mode: "fanIn",
        distribution: "normal",
        seed: args == null ? null : args.seed
      });
    }
    getClassName() {
      return VarianceScaling.className;
    }
  };
  LeCunNormal.className = "LeCunNormal";
  serialization_exports.registerClass(LeCunNormal);
  var LeCunUniform = class extends VarianceScaling {
    constructor(args) {
      super({
        scale: 1,
        mode: "fanIn",
        distribution: "uniform",
        seed: args == null ? null : args.seed
      });
    }
    getClassName() {
      return VarianceScaling.className;
    }
  };
  LeCunUniform.className = "LeCunNormal";
  serialization_exports.registerClass(LeCunUniform);
  var Orthogonal = class extends Initializer {
    constructor(args) {
      super();
      this.DEFAULT_GAIN = 1;
      this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;
      this.seed = args.seed;
      if (this.seed != null) {
        throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.");
      }
    }
    apply(shape, dtype) {
      return tidy(() => {
        if (shape.length < 2) {
          throw new NotImplementedError("Shape must be at least 2D.");
        }
        if (shape[0] * shape[1] > 2e3) {
          console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0] * shape[1]}) elements: Slowness may result.`);
        }
        const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;
        const a = randomNormal2(normalizedShape, 0, 1, "float32");
        let q = linalg.gramSchmidt(a);
        if (shape[0] > shape[1]) {
          q = q.transpose();
        }
        return mul(this.gain, q);
      });
    }
    getConfig() {
      return {
        gain: this.gain,
        seed: this.seed
      };
    }
  };
  Orthogonal.className = "Orthogonal";
  serialization_exports.registerClass(Orthogonal);
  var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
    constant: "Constant",
    glorotNormal: "GlorotNormal",
    glorotUniform: "GlorotUniform",
    heNormal: "HeNormal",
    heUniform: "HeUniform",
    identity: "Identity",
    leCunNormal: "LeCunNormal",
    leCunUniform: "LeCunUniform",
    ones: "Ones",
    orthogonal: "Orthogonal",
    randomNormal: "RandomNormal",
    randomUniform: "RandomUniform",
    truncatedNormal: "TruncatedNormal",
    varianceScaling: "VarianceScaling",
    zeros: "Zeros"
  };
  function deserializeInitializer(config, customObjects = {}) {
    return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "initializer");
  }
  function serializeInitializer(initializer) {
    return serializeKerasObject(initializer);
  }
  function getInitializer(identifier) {
    if (typeof identifier === "string") {
      const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;
      if (className === "GlorotNormal") {
        return new GlorotNormal();
      } else if (className === "GlorotUniform") {
        return new GlorotUniform();
      } else if (className === "HeNormal") {
        return new HeNormal();
      } else if (className === "HeUniform") {
        return new HeUniform();
      } else if (className === "LeCunNormal") {
        return new LeCunNormal();
      } else if (className === "LeCunUniform") {
        return new LeCunUniform();
      } else {
        const config = {};
        config["className"] = className;
        config["config"] = {};
        return deserializeInitializer(config);
      }
    } else if (identifier instanceof Initializer) {
      return identifier;
    } else {
      return deserializeInitializer(identifier);
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function zeros2() {
    return new Zeros();
  }
  function ones3() {
    return new Ones();
  }
  function constant(args) {
    return new Constant(args);
  }
  function randomUniform2(args) {
    return new RandomUniform(args);
  }
  function randomNormal3(args) {
    return new RandomNormal(args);
  }
  function truncatedNormal2(args) {
    return new TruncatedNormal(args);
  }
  function identity(args) {
    return new Identity2(args);
  }
  function varianceScaling(config) {
    return new VarianceScaling(config);
  }
  function glorotUniform(args) {
    return new GlorotUniform(args);
  }
  function glorotNormal(args) {
    return new GlorotNormal(args);
  }
  function heNormal(args) {
    return new HeNormal(args);
  }
  function heUniform(args) {
    return new HeUniform(args);
  }
  function leCunNormal(args) {
    return new LeCunNormal(args);
  }
  function leCunUniform(args) {
    return new LeCunUniform(args);
  }
  function orthogonal(args) {
    return new Orthogonal(args);
  }
  var exports_layers_exports = {};
  __export2(exports_layers_exports, {
    Layer: () => Layer,
    RNN: () => RNN,
    RNNCell: () => RNNCell,
    activation: () => activation,
    add: () => add3,
    alphaDropout: () => alphaDropout,
    average: () => average,
    averagePooling1d: () => averagePooling1d,
    averagePooling2d: () => averagePooling2d,
    averagePooling3d: () => averagePooling3d,
    avgPool1d: () => avgPool1d,
    avgPool2d: () => avgPool2d,
    avgPool3d: () => avgPool3d2,
    avgPooling1d: () => avgPooling1d,
    avgPooling2d: () => avgPooling2d,
    avgPooling3d: () => avgPooling3d,
    batchNormalization: () => batchNormalization2,
    bidirectional: () => bidirectional,
    concatenate: () => concatenate2,
    conv1d: () => conv1d2,
    conv2d: () => conv2d3,
    conv2dTranspose: () => conv2dTranspose2,
    conv3d: () => conv3d2,
    convLstm2d: () => convLstm2d,
    convLstm2dCell: () => convLstm2dCell,
    cropping2D: () => cropping2D,
    dense: () => dense,
    depthwiseConv2d: () => depthwiseConv2d4,
    dot: () => dot3,
    dropout: () => dropout3,
    elu: () => elu3,
    embedding: () => embedding,
    flatten: () => flatten3,
    gaussianDropout: () => gaussianDropout,
    gaussianNoise: () => gaussianNoise,
    globalAveragePooling1d: () => globalAveragePooling1d,
    globalAveragePooling2d: () => globalAveragePooling2d,
    globalMaxPool1d: () => globalMaxPool1d,
    globalMaxPool2d: () => globalMaxPool2d,
    globalMaxPooling1d: () => globalMaxPooling1d,
    globalMaxPooling2d: () => globalMaxPooling2d,
    gru: () => gru,
    gruCell: () => gruCell,
    input: () => input,
    inputLayer: () => inputLayer,
    layerNormalization: () => layerNormalization,
    leakyReLU: () => leakyReLU,
    lstm: () => lstm,
    lstmCell: () => lstmCell,
    masking: () => masking,
    maxPool1d: () => maxPool1d,
    maxPool2d: () => maxPool2d,
    maxPooling1d: () => maxPooling1d,
    maxPooling2d: () => maxPooling2d,
    maxPooling3d: () => maxPooling3d,
    maximum: () => maximum2,
    minimum: () => minimum2,
    multiply: () => multiply,
    permute: () => permute,
    prelu: () => prelu2,
    reLU: () => reLU,
    repeatVector: () => repeatVector,
    reshape: () => reshape2,
    rnn: () => rnn2,
    separableConv2d: () => separableConv2d2,
    simpleRNN: () => simpleRNN,
    simpleRNNCell: () => simpleRNNCell,
    softmax: () => softmax2,
    spatialDropout1d: () => spatialDropout1d,
    stackedRNNCells: () => stackedRNNCells,
    thresholdedReLU: () => thresholdedReLU,
    timeDistributed: () => timeDistributed,
    upSampling2d: () => upSampling2d,
    zeroPadding2d: () => zeroPadding2d
  });
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var _nextUniqueTensorId = 0;
  function getNextUniqueTensorId() {
    return _nextUniqueTensorId++;
  }
  var _uidPrefixes = {};
  function getUid(prefix = "") {
    if (!(prefix in _uidPrefixes)) {
      _uidPrefixes[prefix] = 0;
    }
    _uidPrefixes[prefix] += 1;
    return prefix + _uidPrefixes[prefix].toString();
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function isArrayOfShapes(x) {
    return Array.isArray(x) && Array.isArray(x[0]);
  }
  function normalizeShapeList(x) {
    if (x.length === 0) {
      return [];
    }
    if (!Array.isArray(x[0])) {
      return [x];
    }
    return x;
  }
  function getExactlyOneTensor(xs) {
    let x;
    if (Array.isArray(xs)) {
      if (xs.length !== 1) {
        throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);
      }
      x = xs[0];
    } else {
      x = xs;
    }
    return x;
  }
  function getExactlyOneShape(shapes) {
    if (Array.isArray(shapes) && Array.isArray(shapes[0])) {
      if (shapes.length === 1) {
        shapes = shapes;
        return shapes[0];
      } else {
        throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);
      }
    } else {
      return shapes;
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function countParamsInWeights(weights) {
    let count2 = 0;
    for (const weight of weights) {
      if (weight.shape.length === 0) {
        count2 += 1;
      } else {
        count2 += weight.shape.reduce((a, b) => a * b);
      }
    }
    return count2;
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var DEFAULT_VARIABLE_NAME_PREFIX = "Variable";
  var LayerVariable = class {
    constructor(val, dtype = "float32", name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {
      this.dtype = dtype == null ? "float32" : dtype;
      this.shape = val.shape;
      this.id = getNextUniqueTensorId();
      name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;
      this.originalName = getScopedTensorName(name);
      this.name = getUniqueTensorName(this.originalName);
      this.trainable_ = trainable;
      this.constraint = constraint;
      this.val = variable(val, this.trainable_, this.name, this.dtype);
    }
    read() {
      this.assertNotDisposed();
      return this.val;
    }
    write(newVal) {
      this.assertNotDisposed();
      checkShapesMatch(this.val, newVal);
      if (this.val.id !== newVal.id) {
        this.val.assign(newVal);
        if (this.constraint != null) {
          this.val.assign(this.constraint.apply(this.val));
        }
      }
      return this;
    }
    dispose() {
      this.assertNotDisposed();
      this.val.dispose();
    }
    assertNotDisposed() {
      if (this.val.isDisposed) {
        throw new Error(`LayersVariable ${this.name} is already disposed.`);
      }
    }
    get trainable() {
      return this.trainable_;
    }
    set trainable(trainable) {
      this.trainable_ = trainable;
      this.val.trainable = trainable;
    }
  };
  function checkShapesMatch(x, y) {
    if (x.shape.toString() !== y.shape.toString()) {
      throw new Error("Shape mismatch: " + JSON.stringify(x.shape) + " vs. " + JSON.stringify(y.shape));
    }
  }
  function batchGetValue(xs) {
    return xs.map((x) => x.read());
  }
  function batchSetValue(variablesAndValues) {
    variablesAndValues.forEach((variableAndValue) => {
      const variable2 = variableAndValue[0];
      variable2.write(variableAndValue[1]);
    });
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var InputSpec = class {
    constructor(args) {
      this.dtype = args.dtype;
      this.shape = args.shape;
      if (args.shape != null) {
        this.ndim = args.shape.length;
      } else {
        this.ndim = args.ndim;
      }
      this.maxNDim = args.maxNDim;
      this.minNDim = args.minNDim;
      this.axes = args.axes || {};
    }
  };
  var SymbolicTensor = class {
    constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {
      this.dtype = dtype;
      this.shape = shape;
      this.sourceLayer = sourceLayer;
      this.inputs = inputs;
      this.callArgs = callArgs;
      this.outputTensorIndex = outputTensorIndex;
      this.id = getNextUniqueTensorId();
      if (name != null) {
        this.originalName = getScopedTensorName(name);
        this.name = getUniqueTensorName(this.originalName);
      }
      this.rank = shape.length;
    }
  };
  var _nextNodeID = 0;
  var Node = class {
    constructor(args, callArgs) {
      this.callArgs = callArgs;
      this.id = _nextNodeID++;
      this.outboundLayer = args.outboundLayer;
      this.inboundLayers = args.inboundLayers;
      this.nodeIndices = args.nodeIndices;
      this.tensorIndices = args.tensorIndices;
      this.inputTensors = args.inputTensors;
      this.outputTensors = args.outputTensors;
      this.inputMasks = args.inputMasks;
      this.outputMasks = args.outputMasks;
      this.inputShapes = args.inputShapes;
      this.outputShapes = args.outputShapes;
      for (const layer of args.inboundLayers) {
        if (layer != null) {
          layer.outboundNodes.push(this);
        }
      }
      args.outboundLayer.inboundNodes.push(this);
    }
    getConfig() {
      const inboundNames = [];
      for (const layer of this.inboundLayers) {
        if (layer != null) {
          inboundNames.push(layer.name);
        } else {
          inboundNames.push(null);
        }
      }
      return {
        outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,
        inboundLayers: inboundNames,
        nodeIndices: this.nodeIndices,
        tensorIndices: this.tensorIndices
      };
    }
  };
  var _nextLayerID = 0;
  var Layer = class extends serialization_exports.Serializable {
    constructor(args = {}) {
      super();
      this._callHook = null;
      this._addedWeightNames = [];
      this._stateful = false;
      this.id = _nextLayerID++;
      this.activityRegularizer = null;
      this.inputSpec = null;
      this.supportsMasking = false;
      this._trainableWeights = [];
      this._nonTrainableWeights = [];
      this._losses = [];
      this._updates = [];
      this._built = false;
      this.inboundNodes = [];
      this.outboundNodes = [];
      let name = args.name;
      if (!name) {
        const prefix = this.getClassName();
        name = toSnakeCase(prefix) + "_" + getUid(prefix);
      }
      this.name = name;
      this.trainable_ = args.trainable == null ? true : args.trainable;
      if (args.inputShape != null || args.batchInputShape != null) {
        let batchInputShape;
        if (args.batchInputShape != null) {
          batchInputShape = args.batchInputShape;
        } else if (args.inputShape != null) {
          let batchSize = null;
          if (args.batchSize != null) {
            batchSize = args.batchSize;
          }
          batchInputShape = [batchSize].concat(args.inputShape);
        }
        this.batchInputShape = batchInputShape;
        let dtype = args.dtype;
        if (dtype == null) {
          dtype = args.inputDType;
        }
        if (dtype == null) {
          dtype = "float32";
        }
        this.dtype = dtype;
      }
      if (args.weights != null) {
        this.initialWeights = args.weights;
      } else {
        this.initialWeights = null;
      }
      this._refCount = null;
      this.fastWeightInitDuringBuild = false;
    }
    static nodeKey(layer, nodeIndex) {
      return layer.name + "_ib-" + nodeIndex.toString();
    }
    getNodeAtIndex(nodeIndex, attrName) {
      if (this.inboundNodes.length === 0) {
        throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);
      }
      if (this.inboundNodes.length <= nodeIndex) {
        throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);
      }
      return this.inboundNodes[nodeIndex];
    }
    getInputAt(nodeIndex) {
      return singletonOrArray(this.getNodeAtIndex(nodeIndex, "input").inputTensors);
    }
    getOutputAt(nodeIndex) {
      return singletonOrArray(this.getNodeAtIndex(nodeIndex, "output").outputTensors);
    }
    get input() {
      if (this.inboundNodes.length > 1) {
        throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);
      } else if (this.inboundNodes.length === 0) {
        throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);
      }
      return singletonOrArray(this.getNodeAtIndex(0, "input").inputTensors);
    }
    get output() {
      if (this.inboundNodes.length === 0) {
        throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);
      }
      if (this.inboundNodes.length > 1) {
        throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);
      }
      return singletonOrArray(this.getNodeAtIndex(0, "output").outputTensors);
    }
    get losses() {
      return this._losses;
    }
    calculateLosses() {
      return this.losses.map((lossFn) => lossFn());
    }
    get updates() {
      return this._updates;
    }
    get built() {
      return this._built;
    }
    set built(built) {
      this._built = built;
    }
    get trainable() {
      return this.trainable_;
    }
    set trainable(trainable) {
      this._trainableWeights.forEach((w) => w.trainable = trainable);
      this.trainable_ = trainable;
    }
    get trainableWeights() {
      if (this.trainable_) {
        return this._trainableWeights.filter((w) => w.trainable);
      } else {
        return [];
      }
    }
    set trainableWeights(weights) {
      this._trainableWeights = weights;
    }
    get nonTrainableWeights() {
      if (this.trainable) {
        return this._trainableWeights.filter((w) => !w.trainable).concat(this._nonTrainableWeights);
      } else {
        return this._trainableWeights.concat(this._nonTrainableWeights);
      }
    }
    set nonTrainableWeights(weights) {
      this._nonTrainableWeights = weights;
    }
    get weights() {
      return this.trainableWeights.concat(this.nonTrainableWeights);
    }
    get stateful() {
      return this._stateful;
    }
    resetStates() {
      if (!this.stateful) {
        throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.");
      }
    }
    assertInputCompatibility(inputs) {
      inputs = toList(inputs);
      if (this.inputSpec == null || this.inputSpec.length === 0) {
        return;
      }
      const inputSpec = toList(this.inputSpec);
      if (inputs.length !== inputSpec.length) {
        throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);
      }
      for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {
        const x = inputs[inputIndex];
        const spec = inputSpec[inputIndex];
        if (spec == null) {
          continue;
        }
        const ndim = x.rank;
        if (spec.ndim != null) {
          if (ndim !== spec.ndim) {
            throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);
          }
        }
        if (spec.maxNDim != null) {
          if (ndim > spec.maxNDim) {
            throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);
          }
        }
        if (spec.minNDim != null) {
          if (ndim < spec.minNDim) {
            throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);
          }
        }
        if (spec.dtype != null) {
          if (x.dtype !== spec.dtype) {
            throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);
          }
        }
        if (spec.axes) {
          const xShape = x.shape;
          for (const key in spec.axes) {
            const axis = Number(key);
            const value = spec.axes[key];
            const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];
            if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {
              throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`);
            }
          }
        }
        if (spec.shape != null) {
          for (let i = 0; i < spec.shape.length; ++i) {
            const specDim = spec.shape[i];
            const dim = x.shape[i];
            if (specDim != null && dim != null) {
              if (specDim !== dim) {
                throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`);
              }
            }
          }
        }
      }
    }
    call(inputs, kwargs) {
      return inputs;
    }
    invokeCallHook(inputs, kwargs) {
      if (this._callHook != null) {
        this._callHook(inputs, kwargs);
      }
    }
    setCallHook(callHook) {
      this._callHook = callHook;
    }
    clearCallHook() {
      this._callHook = null;
    }
    apply(inputs, kwargs) {
      kwargs = kwargs || {};
      this.assertNotDisposed();
      const inputsList = toList(inputs);
      let allAreSymbolic = true;
      for (const input2 of inputsList) {
        if (!(input2 instanceof SymbolicTensor)) {
          allAreSymbolic = false;
          break;
        }
      }
      let noneAreSymbolic = true;
      for (const input2 of inputsList) {
        if (input2 instanceof SymbolicTensor) {
          noneAreSymbolic = false;
          break;
        }
      }
      if (allAreSymbolic === noneAreSymbolic) {
        throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");
      }
      return nameScope(this.name, () => {
        if (!this.built) {
          this.assertInputCompatibility(inputs);
          const inputShapes = [];
          for (const xElem of toList(inputs)) {
            inputShapes.push(xElem.shape);
          }
          this.build(singletonOrArray(inputShapes));
          this.built = true;
          if (this.initialWeights) {
            this.setWeights(this.initialWeights);
          }
          if (this._refCount === null && noneAreSymbolic) {
            this._refCount = 1;
          }
        }
        this.assertInputCompatibility(inputs);
        if (noneAreSymbolic) {
          let output = this.call(inputs, kwargs);
          const outputList = toList(output);
          const outputListCopy = [];
          for (let x of outputList) {
            if (inputsList.indexOf(x) !== -1) {
              x = x.clone();
            }
            outputListCopy.push(x);
          }
          output = singletonOrArray(outputListCopy);
          if (this.activityRegularizer != null) {
            throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
          }
          return output;
        } else {
          const inputShape = collectInputShape(inputs);
          const outputShape = this.computeOutputShape(inputShape);
          let output;
          const outputDType = guessOutputDType(inputs);
          this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] : inputShape);
          if (outputShape != null && outputShape.length > 0 && Array.isArray(outputShape[0])) {
            output = outputShape.map((shape, index) => new SymbolicTensor(outputDType, shape, this, toList(inputs), kwargs, this.name, index));
          } else {
            output = new SymbolicTensor(outputDType, outputShape, this, toList(inputs), kwargs, this.name);
          }
          this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);
          this._refCount++;
          if (this.activityRegularizer != null) {
            throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
          }
          return output;
        }
      });
    }
    warnOnIncompatibleInputShape(inputShape) {
      if (this.batchInputShape == null) {
        return;
      } else if (inputShape.length !== this.batchInputShape.length) {
        console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);
      } else {
        let dimMismatch = false;
        this.batchInputShape.forEach((dimension, i) => {
          if (dimension != null && inputShape[i] != null && inputShape[i] !== dimension) {
            dimMismatch = true;
          }
        });
        if (dimMismatch) {
          console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`);
        }
      }
    }
    get outputShape() {
      if (this.inboundNodes == null || this.inboundNodes.length === 0) {
        throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);
      }
      const allOutputShapes = [];
      for (const node2 of this.inboundNodes) {
        const shapeString = JSON.stringify(node2.outputShapes);
        if (allOutputShapes.indexOf(shapeString) === -1) {
          allOutputShapes.push(shapeString);
        }
      }
      if (allOutputShapes.length === 1) {
        const outputShapes = this.inboundNodes[0].outputShapes;
        if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) && outputShapes.length === 1) {
          return outputShapes[0];
        } else {
          return outputShapes;
        }
      } else {
        throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`);
      }
    }
    countParams() {
      if (!this.built) {
        throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);
      }
      return countParamsInWeights(this.weights);
    }
    build(inputShape) {
      this.built = true;
    }
    getWeights(trainableOnly = false) {
      return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);
    }
    setWeights(weights) {
      tidy(() => {
        const params = this.weights;
        if (params.length !== weights.length) {
          throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);
        }
        if (params.length === 0) {
          return;
        }
        const weightValueTuples = [];
        const paramValues = batchGetValue(params);
        for (let i = 0; i < paramValues.length; ++i) {
          const pv = paramValues[i];
          const p2 = params[i];
          const w = weights[i];
          if (!util_exports.arraysEqual(pv.shape, w.shape)) {
            throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);
          }
          weightValueTuples.push([p2, w]);
        }
        batchSetValue(weightValueTuples);
      });
    }
    addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint) {
      if (this._addedWeightNames.indexOf(name) !== -1) {
        throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);
      }
      this._addedWeightNames.push(name);
      if (dtype == null) {
        dtype = "float32";
      }
      if (this.fastWeightInitDuringBuild) {
        initializer = getInitializer("zeros");
      }
      const initValue = initializer.apply(shape, dtype);
      const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);
      initValue.dispose();
      if (regularizer != null) {
        this.addLoss(() => regularizer.apply(weight.read()));
      }
      if (trainable == null) {
        trainable = true;
      }
      if (trainable) {
        this._trainableWeights.push(weight);
      } else {
        this._nonTrainableWeights.push(weight);
      }
      return weight;
    }
    setFastWeightInitDuringBuild(value) {
      this.fastWeightInitDuringBuild = value;
    }
    addLoss(losses4) {
      if (losses4 == null || Array.isArray(losses4) && losses4.length === 0) {
        return;
      }
      losses4 = toList(losses4);
      if (this._losses !== void 0 && this._losses !== null) {
        this.losses.push(...losses4);
      }
    }
    computeOutputShape(inputShape) {
      return inputShape;
    }
    computeMask(inputs, mask) {
      if (!this.supportsMasking) {
        if (mask != null) {
          if (Array.isArray(mask)) {
            mask.forEach((maskElement) => {
              if (maskElement != null) {
                throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);
              }
            });
          } else {
            throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);
          }
        }
        return null;
      }
      return mask;
    }
    addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {
      const inputTensorList = toList(inputTensors);
      outputTensors = toList(outputTensors);
      inputMasks = toList(inputMasks);
      outputMasks = toList(outputMasks);
      inputShapes = normalizeShapeList(inputShapes);
      outputShapes = normalizeShapeList(outputShapes);
      const inboundLayers = [];
      const nodeIndices = [];
      const tensorIndices = [];
      for (const x of inputTensorList) {
        inboundLayers.push(x.sourceLayer);
        nodeIndices.push(x.nodeIndex);
        tensorIndices.push(x.tensorIndex);
      }
      new Node({
        outboundLayer: this,
        inboundLayers,
        nodeIndices,
        tensorIndices,
        inputTensors: inputTensorList,
        outputTensors,
        inputMasks,
        outputMasks,
        inputShapes,
        outputShapes
      }, kwargs);
      for (let i = 0; i < outputTensors.length; i++) {
        outputTensors[i].sourceLayer = this;
        outputTensors[i].nodeIndex = this.inboundNodes.length - 1;
        outputTensors[i].tensorIndex = i;
      }
    }
    getConfig() {
      const config = {name: this.name, trainable: this.trainable};
      if (this.batchInputShape != null) {
        config["batchInputShape"] = this.batchInputShape;
      }
      if (this.dtype != null) {
        config["dtype"] = this.dtype;
      }
      return config;
    }
    disposeWeights() {
      this.weights.forEach((weight) => weight.dispose());
      return this.weights.length;
    }
    assertNotDisposed() {
      if (this._refCount === 0) {
        throw new Error(`Layer '${this.name}' is already disposed.`);
      }
    }
    dispose() {
      if (!this.built) {
        throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);
      }
      if (this._refCount === null) {
        throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);
      }
      this.assertNotDisposed();
      let numDisposedVariables = 0;
      if (--this._refCount === 0) {
        numDisposedVariables = this.disposeWeights();
      }
      return {refCountAfterDispose: this._refCount, numDisposedVariables};
    }
  };
  function collectInputShape(inputTensors) {
    inputTensors = toList(inputTensors);
    const shapes = [];
    for (const x of inputTensors) {
      shapes.push(x.shape);
    }
    return singletonOrArray(shapes);
  }
  function guessOutputDType(inputTensors) {
    return "float32";
  }
  function getSourceInputs(tensor2, layer, nodeIndex) {
    if (layer == null || nodeIndex != null && nodeIndex > 0) {
      layer = tensor2.sourceLayer;
      nodeIndex = tensor2.nodeIndex;
    }
    if (layer.inboundNodes.length === 0) {
      return [tensor2];
    } else {
      const node2 = layer.inboundNodes[nodeIndex];
      if (node2.inboundLayers.length === 0) {
        return node2.inputTensors;
      } else {
        const sourceTensors = [];
        for (let i = 0; i < node2.inboundLayers.length; i++) {
          const x = node2.inputTensors[i];
          const layer2 = node2.inboundLayers[i];
          const nodeIndex2 = node2.nodeIndices[i];
          const previousSources = getSourceInputs(x, layer2, nodeIndex2);
          for (const x2 of previousSources) {
            if (sourceTensors.indexOf(x2) === -1) {
              sourceTensors.push(x2);
            }
          }
        }
        return sourceTensors;
      }
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var InputLayer = class extends Layer {
    constructor(args) {
      super({
        dtype: args.dtype,
        name: args.name != null ? args.name : getUid("input").toString()
      });
      if (args.batchSize == null) {
        args.batchSize = null;
      }
      if (args.sparse == null) {
        args.sparse = false;
      }
      this.trainable = false;
      this.built = true;
      this.sparse = args.sparse;
      if (args.inputShape != null && args.batchInputShape != null) {
        throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");
      }
      let batchInputShape = args.batchInputShape;
      if (batchInputShape == null) {
        if (args.inputShape == null) {
          throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");
        } else {
          batchInputShape = [args.batchSize].concat(args.inputShape);
        }
      } else {
        if (args.batchSize != null) {
          throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");
        }
      }
      const dtype = args.dtype || "float32";
      this.batchInputShape = batchInputShape;
      this.dtype = dtype;
      this.inputSpec = [{shape: batchInputShape}];
      const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);
      inputTensor.nodeIndex = 0;
      inputTensor.tensorIndex = 0;
      new Node({
        outboundLayer: this,
        inboundLayers: [],
        nodeIndices: [],
        tensorIndices: [],
        inputTensors: [inputTensor],
        outputTensors: [inputTensor],
        inputMasks: [null],
        outputMasks: [null],
        inputShapes: [batchInputShape],
        outputShapes: [batchInputShape]
      });
    }
    apply(inputs, kwargs) {
      throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`);
    }
    dispose() {
      return {refCountAfterDispose: this._refCount, numDisposedVariables: 0};
    }
    getConfig() {
      return {
        batchInputShape: this.batchInputShape,
        dtype: this.dtype,
        sparse: this.sparse,
        name: this.name
      };
    }
  };
  InputLayer.className = "InputLayer";
  serialization_exports.registerClass(InputLayer);
  function Input(config) {
    if (config.batchShape == null && config.shape == null) {
      throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");
    }
    if (config.batchShape != null && config.shape != null) {
      throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");
    }
    let batchShape = config.batchShape;
    if (config.shape != null && batchShape == null) {
      batchShape = [null].concat(config.shape);
    }
    let dtype = config.dtype;
    if (dtype == null) {
      dtype = "float32";
    }
    const inputLayer2 = new InputLayer({
      batchInputShape: batchShape,
      name: config.name,
      dtype,
      sparse: config.sparse
    });
    const outputs = inputLayer2.inboundNodes[0].outputTensors;
    return outputs[0];
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  async function resolveScalarsInLogs(logs) {
    if (logs == null) {
      return;
    }
    const promises = [];
    const keys = [];
    const scalarsToDispose = [];
    for (const key in logs) {
      const value = logs[key];
      if (typeof value !== "number") {
        const valueScalar = value;
        promises.push(valueScalar.data());
        keys.push(key);
        scalarsToDispose.push(valueScalar);
      }
    }
    if (promises.length > 0) {
      const values = await Promise.all(promises);
      for (let i = 0; i < values.length; ++i) {
        logs[keys[i]] = values[i][0];
      }
      dispose(scalarsToDispose);
    }
  }
  function disposeTensorsInLogs(logs) {
    if (logs == null) {
      return;
    }
    for (const key in logs) {
      const value = logs[key];
      if (typeof value !== "number") {
        value.dispose();
      }
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var ModelLoggingVerbosity;
  (function(ModelLoggingVerbosity2) {
    ModelLoggingVerbosity2[ModelLoggingVerbosity2["SILENT"] = 0] = "SILENT";
    ModelLoggingVerbosity2[ModelLoggingVerbosity2["VERBOSE"] = 1] = "VERBOSE";
  })(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));
  var DEFAULT_YIELD_EVERY_MS = 125;
  var BaseCallback = class {
    constructor() {
      this.validationData = null;
    }
    setParams(params) {
      this.params = params;
    }
    async onEpochBegin(epoch, logs) {
    }
    async onEpochEnd(epoch, logs) {
    }
    async onBatchBegin(batch, logs) {
    }
    async onBatchEnd(batch, logs) {
    }
    async onTrainBegin(logs) {
    }
    async onTrainEnd(logs) {
    }
    setModel(model2) {
    }
  };
  var CallbackList = class {
    constructor(callbacks2, queueLength = 10) {
      if (callbacks2 == null) {
        callbacks2 = [];
      }
      this.callbacks = callbacks2;
      this.queueLength = queueLength;
    }
    append(callback) {
      this.callbacks.push(callback);
    }
    setParams(params) {
      for (const callback of this.callbacks) {
        callback.setParams(params);
      }
    }
    setModel(model2) {
      for (const callback of this.callbacks) {
        callback.setModel(model2);
      }
    }
    async onEpochBegin(epoch, logs) {
      if (logs == null) {
        logs = {};
      }
      for (const callback of this.callbacks) {
        await callback.onEpochBegin(epoch, logs);
      }
    }
    async onEpochEnd(epoch, logs) {
      if (logs == null) {
        logs = {};
      }
      for (const callback of this.callbacks) {
        await callback.onEpochEnd(epoch, logs);
      }
    }
    async onBatchBegin(batch, logs) {
      if (logs == null) {
        logs = {};
      }
      for (const callback of this.callbacks) {
        await callback.onBatchBegin(batch, logs);
      }
    }
    async onBatchEnd(batch, logs) {
      if (logs == null) {
        logs = {};
      }
      for (const callback of this.callbacks) {
        await callback.onBatchEnd(batch, logs);
      }
    }
    async onTrainBegin(logs) {
      if (logs == null) {
        logs = {};
      }
      for (const callback of this.callbacks) {
        await callback.onTrainBegin(logs);
      }
    }
    async onTrainEnd(logs) {
      if (logs == null) {
        logs = {};
      }
      for (const callback of this.callbacks) {
        await callback.onTrainEnd(logs);
      }
    }
  };
  var BaseLogger = class extends BaseCallback {
    constructor() {
      super();
    }
    async onEpochBegin(epoch) {
      this.seen = 0;
      this.totals = {};
    }
    async onBatchEnd(batch, logs) {
      if (logs == null) {
        logs = {};
      }
      const batchSize = logs["size"] == null ? 0 : logs["size"];
      this.seen += batchSize;
      for (const key in logs) {
        const value = logs[key];
        if (typeof value === "number") {
          if (!this.totals.hasOwnProperty(key)) {
            this.totals[key] = 0;
          }
          this.totals[key] = this.totals[key] + value * batchSize;
        } else {
          let oldTotalsToDispose;
          if (key in this.totals) {
            oldTotalsToDispose = this.totals[key];
          } else {
            this.totals[key] = 0;
          }
          const total = tidy(() => add2(this.totals[key], mul(value, batchSize)));
          this.totals[key] = total;
          if (oldTotalsToDispose != null) {
            oldTotalsToDispose.dispose();
          }
        }
      }
    }
    async onEpochEnd(epoch, logs) {
      if (logs != null) {
        for (const key of this.params["metrics"]) {
          if (this.totals[key] == null) {
            continue;
          }
          if (typeof this.totals[key] === "number") {
            logs[key] = this.totals[key] / this.seen;
          } else {
            tidy(() => {
              const log5 = mul(div(1, this.seen), this.totals[key]);
              logs[key] = log5;
              this.totals[key].dispose();
              keep(logs[key]);
            });
          }
        }
      }
    }
  };
  var History = class extends BaseCallback {
    async onTrainBegin(logs) {
      this.epoch = [];
      this.history = {};
    }
    async onEpochEnd(epoch, logs) {
      if (logs == null) {
        logs = {};
      }
      this.epoch.push(epoch);
      for (const key in logs) {
        if (this.history[key] == null) {
          this.history[key] = [];
        }
        this.history[key].push(logs[key]);
      }
    }
    async syncData() {
      const promises = [];
      const keys = [];
      const indices = [];
      for (const key in this.history) {
        const valueArray = this.history[key];
        for (let i = 0; i < valueArray.length; ++i) {
          if (typeof valueArray[i] !== "number") {
            const valueScalar = valueArray[i];
            promises.push(valueScalar.data());
            keys.push(key);
            indices.push(i);
          }
        }
      }
      const values = await Promise.all(promises);
      for (let n = 0; n < values.length; ++n) {
        const tensorToDispose = this.history[keys[n]][indices[n]];
        tensorToDispose.dispose();
        this.history[keys[n]][indices[n]] = values[n][0];
      }
    }
  };
  var CustomCallback = class extends BaseCallback {
    constructor(args, yieldEvery) {
      super();
      this.currentEpoch = 0;
      this.yieldEvery = yieldEvery || "auto";
      if (this.yieldEvery === "auto") {
        this.yieldEvery = DEFAULT_YIELD_EVERY_MS;
      }
      if (this.yieldEvery === "never" && args.onYield != null) {
        throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");
      }
      if (util_exports.isNumber(this.yieldEvery)) {
        this.maybeWait = debounce(this.maybeWait.bind(this), this.yieldEvery);
      }
      this.trainBegin = args.onTrainBegin;
      this.trainEnd = args.onTrainEnd;
      this.epochBegin = args.onEpochBegin;
      this.epochEnd = args.onEpochEnd;
      this.batchBegin = args.onBatchBegin;
      this.batchEnd = args.onBatchEnd;
      this.yield = args.onYield;
    }
    async maybeWait(epoch, batch, logs) {
      const ps = [];
      if (this.yield != null) {
        await resolveScalarsInLogs(logs);
        ps.push(this.yield(epoch, batch, logs));
      }
      ps.push(nextFrame());
      await Promise.all(ps);
    }
    async onEpochBegin(epoch, logs) {
      this.currentEpoch = epoch;
      if (this.epochBegin != null) {
        await resolveScalarsInLogs(logs);
        await this.epochBegin(epoch, logs);
      }
    }
    async onEpochEnd(epoch, logs) {
      const ps = [];
      if (this.epochEnd != null) {
        await resolveScalarsInLogs(logs);
        ps.push(this.epochEnd(epoch, logs));
      }
      if (this.yieldEvery === "epoch") {
        ps.push(nextFrame());
      }
      await Promise.all(ps);
    }
    async onBatchBegin(batch, logs) {
      if (this.batchBegin != null) {
        await resolveScalarsInLogs(logs);
        await this.batchBegin(batch, logs);
      }
    }
    async onBatchEnd(batch, logs) {
      const ps = [];
      if (this.batchEnd != null) {
        await resolveScalarsInLogs(logs);
        ps.push(this.batchEnd(batch, logs));
      }
      if (this.yieldEvery === "batch") {
        ps.push(nextFrame());
      } else if (util_exports.isNumber(this.yieldEvery)) {
        ps.push(this.maybeWait(this.currentEpoch, batch, logs));
      }
      await Promise.all(ps);
    }
    async onTrainBegin(logs) {
      if (this.trainBegin != null) {
        await resolveScalarsInLogs(logs);
        await this.trainBegin(logs);
      }
    }
    async onTrainEnd(logs) {
      if (this.trainEnd != null) {
        await resolveScalarsInLogs(logs);
        await this.trainEnd(logs);
      }
    }
  };
  function standardizeCallbacks(callbacks2, yieldEvery) {
    if (callbacks2 == null) {
      callbacks2 = {};
    }
    if (callbacks2 instanceof BaseCallback) {
      return [callbacks2];
    }
    if (Array.isArray(callbacks2) && callbacks2[0] instanceof BaseCallback) {
      return callbacks2;
    }
    const callbackConfigs = toList(callbacks2);
    return callbackConfigs.map((callbackConfig) => new CustomCallback(callbackConfig, yieldEvery));
  }
  var CallbackConstructorRegistry = class {
    constructor() {
    }
    static registerCallbackConstructor(verbosityLevel, callbackConstructor) {
      util_exports.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`);
      CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);
      if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {
        CallbackConstructorRegistry.constructors[verbosityLevel] = [];
      }
      CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);
    }
    static checkForDuplicate(callbackConstructor) {
      for (const levelName in CallbackConstructorRegistry.constructors) {
        const constructors = CallbackConstructorRegistry.constructors[+levelName];
        constructors.forEach((ctor) => {
          if (ctor === callbackConstructor) {
            throw new ValueError("Duplicate callback constructor.");
          }
        });
      }
    }
    static clear() {
      CallbackConstructorRegistry.constructors = {};
    }
    static createCallbacks(verbosityLevel) {
      const constructors = [];
      for (const levelName in CallbackConstructorRegistry.constructors) {
        const level = +levelName;
        if (verbosityLevel >= level) {
          constructors.push(...CallbackConstructorRegistry.constructors[level]);
        }
      }
      return constructors.map((ctor) => new ctor());
    }
  };
  CallbackConstructorRegistry.constructors = {};
  function configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {
    const history = new History();
    const actualCallbacks = [
      new BaseLogger(),
      ...CallbackConstructorRegistry.createCallbacks(verbose)
    ];
    if (callbacks2 != null) {
      actualCallbacks.push(...callbacks2);
    }
    actualCallbacks.push(history);
    const callbackList = new CallbackList(actualCallbacks);
    callbackList.setParams({
      epochs,
      initialEpoch,
      samples: numTrainSamples,
      steps: stepsPerEpoch,
      batchSize,
      verbose,
      doValidation,
      metrics: callbackMetrics
    });
    return {callbackList, history};
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function deserialize(config, customObjects = {}, fastWeightInit = false) {
    return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "layer", fastWeightInit);
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function l2Normalize(x, axis) {
    return tidy(() => {
      if (x.dtype !== "float32") {
        x = x.asType("float32");
      }
      const squareSum = sum2(square2(x), axis, true);
      const epsilonTensor = fill(squareSum.shape, epsilon());
      const norm2 = sqrt(maximum(squareSum, epsilonTensor));
      return div(x, norm2);
    });
  }
  function meanSquaredError2(yTrue, yPred) {
    return tidy(() => mean(square2(sub(yPred, yTrue)), -1));
  }
  function meanAbsoluteError(yTrue, yPred) {
    return tidy(() => mean(abs(sub(yPred, yTrue)), -1));
  }
  function meanAbsolutePercentageError(yTrue, yPred) {
    return tidy(() => {
      const diff = sub(yTrue, yPred);
      const clippedTrue = clipByValue(abs(yTrue), epsilon(), Number.MAX_VALUE);
      const absResult = abs(div(diff, clippedTrue));
      return mul(100, mean(absResult, -1));
    });
  }
  function meanSquaredLogarithmicError(yTrue, yPred) {
    return tidy(() => {
      const clippedPred = clipByValue(yPred, epsilon(), Number.MAX_VALUE);
      const firstLog = log(add2(1, clippedPred));
      const clippedTrue = clipByValue(yTrue, epsilon(), Number.MAX_VALUE);
      const secondLog = log(add2(1, clippedTrue));
      return mean(square2(sub(firstLog, secondLog)), -1);
    });
  }
  function squaredHinge(yTrue, yPred) {
    return tidy(() => {
      const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));
      return mean(square2(maxResult), -1);
    });
  }
  function hinge(yTrue, yPred) {
    return tidy(() => {
      const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));
      return mean(maxResult, -1);
    });
  }
  function categoricalHinge(yTrue, yPred) {
    return tidy(() => {
      const pos = sum2(mul(yTrue, yPred), -1);
      const neg4 = max(mul(sub(1, yTrue), yPred), -1);
      return maximum(0, add2(1, sub(neg4, pos)));
    });
  }
  function logcosh(yTrue, yPred) {
    return tidy(() => {
      const log22 = Math.log(2);
      const predictionDiff = sub(yPred, yTrue);
      const logcoshResult = sub(add2(predictionDiff, softplus(mul(-2, predictionDiff))), log22);
      return mean(logcoshResult, -1);
    });
  }
  function categoricalCrossentropy(target, output, fromLogits = false) {
    return tidy(() => {
      if (fromLogits) {
        output = softmax(output);
      } else {
        const outputSum = sum2(output, output.shape.length - 1, true);
        output = div(output, outputSum);
      }
      output = clipByValue(output, epsilon(), 1 - epsilon());
      return neg(sum2(mul(target.toFloat(), log(output)), output.shape.length - 1));
    });
  }
  function sparseCategoricalCrossentropy(target, output, fromLogits = false) {
    return tidy(() => {
      const flatTarget = floor(flatten2(target)).toInt();
      output = clipByValue(output, epsilon(), 1 - epsilon());
      const outputShape = output.shape;
      const oneHotTarget = oneHot(flatTarget, outputShape[outputShape.length - 1]).reshape(outputShape);
      return categoricalCrossentropy(oneHotTarget, output, fromLogits);
    });
  }
  function sigmoidCrossEntropyWithLogits(labels, logits) {
    if (!util_exports.arraysEqual(labels.shape, logits.shape)) {
      throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);
    }
    return tidy(() => {
      const reluLogits = logits.relu();
      const negAbsLogits = logits.abs().neg();
      return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p());
    });
  }
  function binaryCrossentropy(yTrue, yPred) {
    return tidy(() => {
      let y;
      y = clipByValue(yPred, epsilon(), 1 - epsilon());
      y = log(div(y, sub(1, y)));
      return mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);
    });
  }
  function kullbackLeiblerDivergence(yTrue, yPred) {
    return tidy(() => {
      const clippedTrue = clipByValue(yTrue, epsilon(), 1);
      const clippedPred = clipByValue(yPred, epsilon(), 1);
      return sum2(mul(yTrue, log(div(clippedTrue, clippedPred))), -1);
    });
  }
  function poisson(yTrue, yPred) {
    return tidy(() => {
      const logPred = log(add2(epsilon(), yPred));
      return mean(sub(yPred, mul(yTrue, logPred)), -1);
    });
  }
  function cosineProximity(yTrue, yPred) {
    return tidy(() => {
      const trueNormalized = l2Normalize(yTrue, -1);
      const predNormalized = l2Normalize(yPred, -1);
      const trueXPred = mul(trueNormalized, predNormalized);
      return neg(sum2(trueXPred, -1));
    });
  }
  var lossesMap = {
    meanSquaredError: meanSquaredError2,
    meanAbsoluteError,
    meanAbsolutePercentageError,
    meanSquaredLogarithmicError,
    squaredHinge,
    hinge,
    categoricalHinge,
    logcosh,
    categoricalCrossentropy,
    sparseCategoricalCrossentropy,
    binaryCrossentropy,
    kullbackLeiblerDivergence,
    poisson,
    cosineProximity
  };
  function get(identifierOrFn) {
    if (typeof identifierOrFn === "string") {
      if (identifierOrFn in lossesMap) {
        return lossesMap[identifierOrFn];
      }
      let errMsg = `Unknown loss ${identifierOrFn}`;
      if (identifierOrFn.toLowerCase().includes("softmaxcrossentropy")) {
        errMsg = `Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`;
      }
      throw new ValueError(errMsg);
    } else {
      return identifierOrFn;
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function binaryAccuracy(yTrue, yPred) {
    return tidy(() => {
      const threshold2 = mul(0.5, onesLike(yPred));
      const yPredThresholded = cast2(greater(yPred, threshold2), yTrue.dtype);
      return mean(equal(yTrue, yPredThresholded), -1);
    });
  }
  function categoricalAccuracy(yTrue, yPred) {
    return tidy(() => cast2(equal(argMax(yTrue, -1), argMax(yPred, -1)), "float32"));
  }
  function truePositives(yTrue, yPred) {
    return tidy(() => {
      return logicalAnd(yTrue.equal(1), yPred.equal(1)).sum().cast("float32");
    });
  }
  function falseNegatives(yTrue, yPred) {
    return tidy(() => {
      return logicalAnd(yTrue.equal(1), yPred.equal(0)).sum().cast("float32");
    });
  }
  function falsePositives(yTrue, yPred) {
    return tidy(() => {
      return logicalAnd(yTrue.equal(0), yPred.equal(1)).sum().cast("float32");
    });
  }
  function precision(yTrue, yPred) {
    return tidy(() => {
      const tp = truePositives(yTrue, yPred);
      const fp = falsePositives(yTrue, yPred);
      const denominator = tp.add(fp);
      return where(greater(denominator, 0), tp.div(denominator), 0).cast("float32");
    });
  }
  function recall(yTrue, yPred) {
    return tidy(() => {
      const tp = truePositives(yTrue, yPred);
      const fn = falseNegatives(yTrue, yPred);
      const denominator = tp.add(fn);
      return where(greater(denominator, 0), tp.div(denominator), 0).cast("float32");
    });
  }
  function binaryCrossentropy2(yTrue, yPred) {
    return binaryCrossentropy(yTrue, yPred);
  }
  function sparseCategoricalAccuracy(yTrue, yPred) {
    if (yTrue.rank === yPred.rank) {
      yTrue = yTrue.squeeze([yTrue.rank - 1]);
    }
    yPred = yPred.argMax(-1);
    if (yPred.dtype !== yTrue.dtype) {
      yPred = yPred.asType(yTrue.dtype);
    }
    return equal(yTrue, yPred).asType("float32");
  }
  var mse = meanSquaredError2;
  var MSE = meanSquaredError2;
  var mae = meanAbsoluteError;
  var MAE = meanAbsoluteError;
  var mape = meanAbsolutePercentageError;
  var MAPE = meanAbsolutePercentageError;
  var categoricalCrossentropy2 = categoricalCrossentropy;
  var cosine = cosineProximity;
  var sparseCategoricalCrossentropy2 = sparseCategoricalCrossentropy;
  var metricsMap = {
    binaryAccuracy,
    categoricalAccuracy,
    precision,
    categoricalCrossentropy: categoricalCrossentropy2,
    sparseCategoricalCrossentropy: sparseCategoricalCrossentropy2,
    mse,
    MSE,
    mae,
    MAE,
    mape,
    MAPE,
    cosine
  };
  function get2(identifier) {
    if (typeof identifier === "string" && identifier in metricsMap) {
      return metricsMap[identifier];
    } else if (typeof identifier !== "string" && identifier != null) {
      return identifier;
    } else {
      throw new ValueError(`Unknown metric ${identifier}`);
    }
  }
  function getLossOrMetricName(fn) {
    assert2(fn !== null, `Unknown LossOrMetricFn ${fn}`);
    if (typeof fn === "string") {
      return fn;
    } else {
      let fnName;
      for (const key of Object.keys(lossesMap)) {
        if (lossesMap[key] === fn) {
          fnName = key;
          break;
        }
      }
      if (fnName !== void 0) {
        return fnName;
      }
      for (const key of Object.keys(metricsMap)) {
        if (metricsMap[key] === fn) {
          fnName = key;
          break;
        }
      }
      if (fnName !== void 0) {
        return fnName;
      }
      return fn.name;
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function getOptimizer(identifier) {
    const optimizerMap = {
      Adagrad: () => train.adagrad(0.01),
      Adadelta: () => train.adadelta(1, 0.95, epsilon()),
      Adam: () => train.adam(1e-3, 0.9, 0.999, epsilon()),
      Adamax: () => train.adamax(2e-3, 0.9, 0.999, epsilon(), 0),
      RMSProp: () => train.rmsprop(1e-3, 0.9, 0, epsilon()),
      SGD: () => train.sgd(0.01)
    };
    optimizerMap["adagrad"] = optimizerMap["Adagrad"];
    optimizerMap["adadelta"] = optimizerMap["Adadelta"];
    optimizerMap["adam"] = optimizerMap["Adam"];
    optimizerMap["adamax"] = optimizerMap["Adamax"];
    optimizerMap["rmsprop"] = optimizerMap["RMSProp"];
    optimizerMap["sgd"] = optimizerMap["SGD"];
    if (identifier in optimizerMap) {
      return optimizerMap[identifier]();
    }
    throw new ValueError(`Unknown Optimizer ${identifier}`);
  }
  /**
   * @license
   * Copyright 2019 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;
  function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {
    if (userDefinedMetadata == null || typeof userDefinedMetadata !== "object" || Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype || !plainObjectCheck(userDefinedMetadata)) {
      throw new Error("User-defined metadata is expected to be a JSON object, but is not.");
    }
    if (checkSize) {
      const out = JSON.stringify(userDefinedMetadata);
      if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {
        console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);
      }
    }
  }
  function plainObjectCheck(x) {
    if (x === null) {
      return true;
    } else if (typeof x === "object") {
      if (Object.getPrototypeOf(x) === Object.prototype) {
        const keys = Object.keys(x);
        for (const key of keys) {
          if (typeof key !== "string") {
            return false;
          }
          if (!plainObjectCheck(x[key])) {
            return false;
          }
        }
        return true;
      } else {
        if (Array.isArray(x)) {
          for (const item of x) {
            if (!plainObjectCheck(item)) {
              return false;
            }
          }
          return true;
        } else {
          return false;
        }
      }
    } else {
      const xType = typeof x;
      return xType === "string" || xType === "number" || xType === "boolean";
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function printSummary(model2, lineLength, positions, printFn = console.log) {
    const sequentialLike = isModelSequentialLike(model2);
    const toDisplay = ["Layer (type)", "Output shape", "Param #"];
    if (sequentialLike) {
      lineLength = lineLength || 65;
      positions = positions || [0.45, 0.85, 1];
    } else {
      lineLength = lineLength || 98;
      positions = positions || [0.33, 0.55, 0.67, 1];
    }
    if (positions[positions.length - 1] <= 1) {
      positions = positions.map((p2) => Math.floor(lineLength * p2));
    }
    let relevantNodes;
    if (!sequentialLike) {
      toDisplay.push("Receives inputs");
      relevantNodes = [];
      for (const depth in model2.nodesByDepth) {
        relevantNodes.push(...model2.nodesByDepth[depth]);
      }
    }
    printFn("_".repeat(lineLength));
    printRow(toDisplay, positions, printFn);
    printFn("=".repeat(lineLength));
    const layers = model2.layers;
    for (let i = 0; i < layers.length; ++i) {
      if (sequentialLike) {
        printLayerSummary(layers[i], positions, printFn);
      } else {
        printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn);
      }
      printFn((i === layers.length - 1 ? "=" : "_").repeat(lineLength));
    }
    model2.checkTrainableWeightsConsistency();
    const trainableCount = countTrainableParams(model2);
    const nonTrainableCount = countParamsInWeights(model2.nonTrainableWeights);
    printFn(`Total params: ${trainableCount + nonTrainableCount}`);
    printFn(`Trainable params: ${trainableCount}`);
    printFn(`Non-trainable params: ${nonTrainableCount}`);
    printFn("_".repeat(lineLength));
  }
  function countTrainableParams(model2) {
    let trainableCount;
    if (model2.collectedTrainableWeights != null) {
      trainableCount = countParamsInWeights(model2.collectedTrainableWeights);
    } else {
      trainableCount = countParamsInWeights(model2.trainableWeights);
    }
    return trainableCount;
  }
  function isModelSequentialLike(model2) {
    let sequentialLike = true;
    const nodesByDepth = [];
    const nodes = [];
    for (const depth in model2.nodesByDepth) {
      nodesByDepth.push(model2.nodesByDepth[depth]);
    }
    for (const depthNodes of nodesByDepth) {
      if (depthNodes.length > 1 || depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {
        sequentialLike = false;
        break;
      }
      nodes.push(...depthNodes);
    }
    if (sequentialLike) {
      for (const layer of model2.layers) {
        let flag = false;
        for (const node2 of layer.inboundNodes) {
          if (nodes.indexOf(node2) !== -1) {
            if (flag) {
              sequentialLike = false;
              break;
            } else {
              flag = true;
            }
          }
        }
        if (!sequentialLike) {
          break;
        }
      }
    }
    return sequentialLike;
  }
  function printRow(fields, positions, printFn = console.log) {
    let line = "";
    for (let i = 0; i < fields.length; ++i) {
      if (i > 0) {
        line = line.slice(0, line.length - 1) + " ";
      }
      line += fields[i];
      line = line.slice(0, positions[i]);
      line += " ".repeat(positions[i] - line.length);
    }
    printFn(line);
  }
  function printLayerSummary(layer, positions, printFn) {
    let outputShape;
    try {
      outputShape = JSON.stringify(layer.outputShape);
    } catch (err) {
      outputShape = "multiple";
    }
    const name = layer.name;
    const className = layer.getClassName();
    const fields = [`${name} (${className})`, outputShape, layer.countParams().toString()];
    printRow(fields, positions, printFn);
  }
  function printLayerSummaryWithConnections(layer, positions, relevantNodes, printFn) {
    let outputShape;
    try {
      outputShape = JSON.stringify(layer.outputShape);
    } catch (err) {
      outputShape = "multiple";
    }
    const connections = [];
    for (const node2 of layer.inboundNodes) {
      if (relevantNodes != null && relevantNodes.length > 0 && relevantNodes.indexOf(node2) === -1) {
        continue;
      }
      for (let i = 0; i < node2.inboundLayers.length; ++i) {
        const inboundLayer = node2.inboundLayers[i].name;
        const inboundLayerIndex = node2.nodeIndices[i];
        const inboundTensorIndex = node2.tensorIndices[i];
        connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);
      }
    }
    const name = layer.name;
    const className = layer.getClassName();
    const firstConnection = connections.length === 0 ? "" : connections[0];
    const fields = [
      `${name} (${className})`,
      outputShape,
      layer.countParams().toString(),
      firstConnection
    ];
    printRow(fields, positions, printFn);
    for (let i = 1; i < connections.length; ++i) {
      printRow(["", "", "", connections[i]], positions, printFn);
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function isArrayItemInputOrOutputName(key, index, value) {
    return (key === "inboundNodes" || key === "outputLayers" || key === "inputLayers") && index === 0 && typeof value === "string";
  }
  function convertPythonicToTs(pythonicConfig, key) {
    if (pythonicConfig === null) {
      return null;
    } else if (typeof pythonicConfig === "string") {
      return toCamelCase(pythonicConfig);
    } else if (typeof pythonicConfig === "number" || typeof pythonicConfig === "boolean") {
      return pythonicConfig;
    } else if (pythonicConfig instanceof Array) {
      const tsArray = [];
      const arrayLength = pythonicConfig.length;
      for (let i = 0; i < arrayLength; ++i) {
        const item = pythonicConfig[i];
        if (isArrayItemInputOrOutputName(key, i, item)) {
          tsArray.push(item);
        } else {
          tsArray.push(convertPythonicToTs(item, key));
        }
      }
      return tsArray;
    } else {
      const tsDict = {};
      for (const pythonicKey of Object.keys(pythonicConfig)) {
        const pythonicValue = pythonicConfig[pythonicKey];
        if (pythonicKey === "name" && typeof pythonicValue === "string") {
          tsDict[pythonicKey] = pythonicValue;
        } else {
          const tsKey = toCamelCase(pythonicKey);
          tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);
        }
      }
      return tsDict;
    }
  }
  function convertTsToPythonic(tsConfig, key) {
    if (tsConfig === null || tsConfig === void 0) {
      return null;
    } else if (typeof tsConfig === "string") {
      return toSnakeCase(tsConfig);
    } else if (typeof tsConfig === "number" || typeof tsConfig === "boolean") {
      return tsConfig;
    } else if (tsConfig instanceof Array) {
      const pyArray = [];
      const arrayLength = tsConfig.length;
      for (let i = 0; i < arrayLength; ++i) {
        const item = tsConfig[i];
        if (isArrayItemInputOrOutputName(key, i, item)) {
          pyArray.push(item);
        } else {
          pyArray.push(convertTsToPythonic(item, key));
        }
      }
      return pyArray;
    } else {
      const pyDict = {};
      for (const tsKey of Object.keys(tsConfig)) {
        const tsValue = tsConfig[tsKey];
        const pyKey = toSnakeCase(tsKey);
        if ((tsKey === "name" || tsKey === "className") && typeof tsValue === "string") {
          pyDict[pyKey] = tsValue;
        } else {
          pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);
        }
      }
      return pyDict;
    }
  }
  /** @license See the LICENSE file. */
  var version2 = "3.3.0";
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function assertFeedCompatibility(key, val) {
    if (key.dtype == null || key.dtype === val.dtype) {
      return val;
    }
    try {
      return cast(val, key.dtype);
    } catch (err) {
      throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`);
    }
  }
  var FeedDict = class {
    constructor(feeds) {
      this.id2Value = {};
      this.id2Mask = {};
      this.name2Id = {};
      if (feeds instanceof FeedDict) {
        for (const id in feeds.id2Value) {
          this.id2Value[id] = feeds.id2Value[id];
          if (id in feeds.id2Mask) {
            this.id2Mask[id] = feeds.id2Mask[id];
          }
        }
      } else {
        if (feeds == null) {
          return;
        }
        for (const feed of feeds) {
          this.add(feed.key, feed.value);
        }
      }
    }
    add(key, value, mask) {
      if (this.id2Value[key.id] == null) {
        this.id2Value[key.id] = assertFeedCompatibility(key, value);
        this.name2Id[key.name] = key.id;
        if (mask != null) {
          this.id2Mask[key.id] = mask;
        }
      } else {
        throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);
      }
      return this;
    }
    addFeed(feed) {
      this.add(feed.key, feed.value);
    }
    hasKey(key) {
      return this.id2Value[key.id] != null;
    }
    names() {
      return Object.keys(this.name2Id);
    }
    getValue(key) {
      if (key instanceof SymbolicTensor) {
        if (this.id2Value[key.id] == null) {
          throw new ValueError(`Nonexistent key: ${key.name}`);
        } else {
          return this.id2Value[key.id];
        }
      } else {
        const id = this.name2Id[key];
        if (id == null) {
          throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);
        }
        return this.id2Value[id];
      }
    }
    getMask(key) {
      if (key instanceof SymbolicTensor) {
        if (this.id2Value[key.id] == null) {
          throw new ValueError(`Nonexistent key: ${key.name}`);
        } else {
          return this.id2Mask[key.id];
        }
      } else {
        const id = this.name2Id[key];
        if (id == null) {
          throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);
        }
        return this.id2Mask[id];
      }
    }
    disposeMasks() {
      if (this.id2Mask != null) {
        dispose(this.id2Mask);
      }
    }
  };
  var cachedSorted = {};
  var cachedRecipientCounts = {};
  function execute(fetches, feedDict, kwargs, probe) {
    const training = kwargs == null ? false : kwargs["training"];
    const arrayFetches = Array.isArray(fetches);
    const fetchArray = arrayFetches ? fetches : [fetches];
    const outputNames = fetchArray.map((t) => t.name);
    const finalOutputs = [];
    const feedNames = feedDict.names();
    for (const outputName of outputNames) {
      if (feedNames.indexOf(outputName) !== -1) {
        finalOutputs.push(feedDict.getValue(outputName));
      } else {
        finalOutputs.push(null);
      }
    }
    if (probe != null) {
      probe.maxNumTensors = -Infinity;
      probe.minNumTensors = Infinity;
    }
    const fetchAndFeedKey = outputNames.join(",") + "|" + feedDict.names().join(",");
    let sorted;
    let recipientCounts;
    if (cachedSorted[fetchAndFeedKey] == null) {
      const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);
      sorted = out.sorted;
      recipientCounts = out.recipientCounts;
      cachedSorted[fetchAndFeedKey] = sorted;
      cachedRecipientCounts[fetchAndFeedKey] = recipientCounts;
    }
    sorted = cachedSorted[fetchAndFeedKey];
    recipientCounts = {};
    if (!training) {
      Object.assign(recipientCounts, cachedRecipientCounts[fetchAndFeedKey]);
    }
    const internalFeedDict = new FeedDict(feedDict);
    for (let i = 0; i < sorted.length; ++i) {
      if (probe != null) {
        const numTensors = memory().numTensors;
        if (numTensors > probe.maxNumTensors) {
          probe.maxNumTensors = numTensors;
        }
        if (numTensors < probe.minNumTensors) {
          probe.minNumTensors = numTensors;
        }
      }
      const symbolic = sorted[i];
      const srcLayer = symbolic.sourceLayer;
      if (srcLayer instanceof InputLayer) {
        continue;
      }
      const inputValues = [];
      const inputMasks = [];
      const tensorsToDispose = [];
      let maskExists = false;
      for (const input2 of symbolic.inputs) {
        const value = internalFeedDict.getValue(input2);
        const mask = internalFeedDict.getMask(input2);
        inputValues.push(value);
        inputMasks.push(mask);
        if (mask != null) {
          maskExists = true;
        }
        if (!training) {
          recipientCounts[input2.name]--;
          if (recipientCounts[input2.name] === 0 && !feedDict.hasKey(input2) && outputNames.indexOf(input2.name) === -1 && !value.isDisposed && input2.sourceLayer.stateful !== true) {
            tensorsToDispose.push(value);
          }
        }
      }
      if (maskExists) {
        kwargs = kwargs || {};
        kwargs["mask"] = inputMasks[0];
      }
      const outputTensors = toList(srcLayer.apply(inputValues, kwargs));
      let outputMask = null;
      if (srcLayer.supportsMasking) {
        outputMask = srcLayer.computeMask(inputValues, inputMasks);
      }
      const layerOutputs = getNodeOutputs(symbolic);
      const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];
      for (let i2 = 0; i2 < outputSymbolicTensors.length; ++i2) {
        if (!internalFeedDict.hasKey(outputSymbolicTensors[i2])) {
          internalFeedDict.add(outputSymbolicTensors[i2], outputTensors[i2], Array.isArray(outputMask) ? outputMask[0] : outputMask);
        }
        const index = outputNames.indexOf(outputSymbolicTensors[i2].name);
        if (index !== -1) {
          finalOutputs[index] = outputTensors[i2];
        }
      }
      if (!training) {
        dispose(tensorsToDispose);
      }
    }
    internalFeedDict.disposeMasks();
    return arrayFetches ? finalOutputs : finalOutputs[0];
  }
  function getTopologicalSortAndRecipientCounts(fetches, feedDict) {
    util_exports.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);
    let finalSorted = [];
    let finalRecipientMap = {};
    if (fetches.length === 1) {
      const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);
      finalSorted = out.sorted;
      finalRecipientMap = out.recipientMap;
    } else {
      const visited = new Set();
      for (const fetch3 of fetches) {
        const {sorted, recipientMap} = getTopologicalSortAndRecipientCountsForOneFetch(fetch3, feedDict);
        for (const symbolicTensor of sorted) {
          if (!visited.has(symbolicTensor.name)) {
            finalSorted.push(symbolicTensor);
            visited.add(symbolicTensor.name);
          }
        }
        for (const name in recipientMap) {
          if (finalRecipientMap[name] == null) {
            finalRecipientMap[name] = new Set();
          }
          recipientMap[name].forEach((recipient) => finalRecipientMap[name].add(recipient));
        }
      }
    }
    return {
      sorted: finalSorted,
      recipientCounts: recipientMap2Counts(finalRecipientMap)
    };
  }
  function recipientMap2Counts(recipientMap) {
    const recipientCounts = {};
    for (const name in recipientMap) {
      recipientCounts[name] = recipientMap[name].size;
    }
    return recipientCounts;
  }
  function getTopologicalSortAndRecipientCountsForOneFetch(fetch3, feedDict) {
    const visited = new Set();
    const sorted = [];
    const recipientMap = {};
    for (const key of feedDict.names()) {
      visited.add(key);
    }
    const stack2 = [];
    const marks = [];
    stack2.push(fetch3);
    while (stack2.length > 0) {
      const top = stack2[stack2.length - 1];
      if (visited.has(top.name)) {
        stack2.pop();
        continue;
      }
      const topIsMarked = marks[marks.length - 1] === stack2.length - 1;
      if (top.inputs.length === 0 || topIsMarked) {
        stack2.pop();
        sorted.push(top);
        visited.add(top.name);
        if (topIsMarked) {
          marks.pop();
        }
      } else {
        marks.push(stack2.length - 1);
        for (const input2 of top.inputs) {
          if (recipientMap[input2.name] == null) {
            recipientMap[input2.name] = new Set();
          }
          recipientMap[input2.name].add(top.name);
          if (visited.has(input2.name)) {
            continue;
          }
          stack2.push(input2);
        }
      }
    }
    return {sorted, recipientMap};
  }
  function getNodeOutputs(fetch3) {
    let layerOutputs;
    if (fetch3.sourceLayer.inboundNodes.length === 1) {
      layerOutputs = fetch3.sourceLayer.output;
    } else {
      let nodeIndex = null;
      for (let i = 0; i < fetch3.sourceLayer.inboundNodes.length; ++i) {
        for (const outputTensor of fetch3.sourceLayer.inboundNodes[i].outputTensors) {
          if (outputTensor.id === fetch3.id) {
            nodeIndex = i;
            break;
          }
        }
      }
      layerOutputs = fetch3.sourceLayer.getOutputAt(nodeIndex);
    }
    return layerOutputs;
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var Container = class extends Layer {
    constructor(args) {
      super({});
      this.containerNodes = new Set();
      this.name = args.name;
      if (this.name == null) {
        const prefix = this.getClassName().toLowerCase();
        this.name = getUid(prefix);
      }
      this.supportsMasking = false;
      this.trainable_ = true;
      if (Array.isArray(args.inputs)) {
        this.inputs = args.inputs.slice();
      } else {
        this.inputs = [args.inputs];
      }
      if (Array.isArray(args.outputs)) {
        this.outputs = args.outputs.slice();
      } else {
        this.outputs = [args.outputs];
      }
      if (unique2(this.inputs).length !== this.inputs.length) {
        throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((x) => x.name)}`);
      }
      if (unique2(this.outputs).length !== this.outputs.length) {
        console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((x) => x.name)}`);
      }
      this.inputLayers = [];
      this.inputLayersNodeIndices = [];
      this.inputLayersTensorIndices = [];
      this.outputLayers = [];
      this.outputLayersNodeIndices = [];
      this.outputLayersTensorIndices = [];
      this.layers = [];
      this.internalContainerRefs = [];
      for (const x of this.outputs) {
        const layer = x.sourceLayer;
        const nodeIndex = x.nodeIndex;
        const tensorIndex = x.tensorIndex;
        this.outputLayers.push(layer);
        this.outputLayersNodeIndices.push(nodeIndex);
        this.outputLayersTensorIndices.push(tensorIndex);
      }
      for (const x of this.inputs) {
        const layer = x.sourceLayer;
        const nodeIndex = x.nodeIndex;
        const tensorIndex = x.tensorIndex;
        assert2(nodeIndex === 0, "input layer has >1 nodes");
        assert2(tensorIndex === 0, "input layer has >1 tensors");
        this.inputLayers.push(layer);
        this.inputLayersNodeIndices.push(nodeIndex);
        this.inputLayersTensorIndices.push(tensorIndex);
      }
      this.inputNames = [];
      this.outputNames = [];
      this.feedInputShapes = [];
      this.feedInputNames = [];
      this.feedOutputNames = [];
      for (let i = 0; i < this.inputLayers.length; i++) {
        const layer = this.inputLayers[i];
        if (!(layer instanceof InputLayer)) {
          throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);
        }
        this.inputNames.push(layer.name);
        this.feedInputShapes.push(layer.batchInputShape);
        this.feedInputNames.push(layer.name);
      }
      for (const layer of this.outputLayers) {
        this.outputNames.push(layer.name);
      }
      this.internalInputShapes = this.inputs.map((x) => x.shape);
      this.internalOutputShapes = this.outputs.map((x) => x.shape);
      const nodesDepths = {};
      const nodeIDToNode = {};
      const layersDepths = {};
      const layerIDToLayer = {};
      const layerIndices = {};
      const nodesInDecreasingDepth = [];
      const buildMapOfGraph = (tensor2, finishedNodes2, nodesInProgress2, layer, nodeIndex, tensorIndex) => {
        if (layer == null || nodeIndex == null || tensorIndex == null) {
          layer = tensor2.sourceLayer;
          nodeIndex = tensor2.nodeIndex;
          tensorIndex = tensor2.tensorIndex;
        }
        const node2 = layer.inboundNodes[nodeIndex];
        if (nodesInProgress2.indexOf(node2) !== -1) {
          throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`);
        }
        if (finishedNodes2.indexOf(node2) !== -1) {
          return;
        }
        this.containerNodes.add(Container.nodeKey(layer, nodeIndex));
        if (!(layer.id in layerIndices)) {
          layerIndices[layer.id] = Object.keys(layerIndices).length;
        }
        if (nodesInProgress2.indexOf(node2) === -1) {
          nodesInProgress2.push(node2);
        }
        const numInboundLayers = node2.inboundLayers.length;
        for (let i = 0; i < numInboundLayers; i++) {
          const x = node2.inputTensors[i];
          const layer2 = node2.inboundLayers[i];
          const nodeIndex2 = node2.nodeIndices[i];
          const tensorIndex2 = node2.tensorIndices[i];
          buildMapOfGraph(x, finishedNodes2, nodesInProgress2, layer2, nodeIndex2, tensorIndex2);
        }
        finishedNodes2.push(node2);
        while (nodesInProgress2.indexOf(node2) >= 0) {
          nodesInProgress2.splice(nodesInProgress2.indexOf(node2), 1);
        }
        nodesInDecreasingDepth.push(node2);
      };
      const finishedNodes = [];
      const nodesInProgress = [];
      for (const x of this.outputs) {
        buildMapOfGraph(x, finishedNodes, nodesInProgress);
      }
      const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();
      for (const node2 of reversedNodesInDecreasingDepth) {
        nodeIDToNode[node2.id] = node2;
        if (!(node2.id in nodesDepths)) {
          nodesDepths[node2.id] = 0;
        }
        let depth = nodesDepths[node2.id];
        const previousDepth = layersDepths[node2.outboundLayer.id] == null ? 0 : layersDepths[node2.outboundLayer.id];
        depth = Math.max(depth, previousDepth);
        layersDepths[node2.outboundLayer.id] = depth;
        layerIDToLayer[node2.outboundLayer.id] = node2.outboundLayer;
        nodesDepths[node2.id] = depth;
        for (let i = 0; i < node2.inboundLayers.length; i++) {
          const inboundLayer = node2.inboundLayers[i];
          const nodeIndex = node2.nodeIndices[i];
          const inboundNode = inboundLayer.inboundNodes[nodeIndex];
          const previousDepth2 = nodesDepths[inboundNode.id] == null ? 0 : nodesDepths[inboundNode.id];
          nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth2);
          nodeIDToNode[inboundNode.id] = inboundNode;
        }
      }
      const nodesByDepth = {};
      for (const nodeID in nodesDepths) {
        const depth = nodesDepths[nodeID];
        if (!(depth in nodesByDepth)) {
          nodesByDepth[depth] = [];
        }
        nodesByDepth[depth].push(nodeIDToNode[nodeID]);
      }
      const layersByDepth = {};
      for (const layerID in layersDepths) {
        const depth = layersDepths[layerID];
        if (!(depth in layersByDepth)) {
          layersByDepth[depth] = [];
        }
        layersByDepth[depth].push(layerIDToLayer[layerID]);
      }
      let depthKeys = Object.keys(layersByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
      this.layers = [];
      for (const depth of depthKeys) {
        const layersForDepth = layersByDepth[depth];
        layersForDepth.sort((a, b) => {
          const aIndex = layerIndices[a.id];
          const bIndex = layerIndices[b.id];
          if (aIndex < bIndex) {
            return -1;
          }
          if (aIndex > bIndex) {
            return 1;
          }
          return 0;
        });
        for (const layer of layersForDepth) {
          if (layer instanceof Container) {
            this.internalContainerRefs.push(layer);
          }
          this.layers.push(layer);
        }
      }
      this.layersByDepth = layersByDepth;
      depthKeys = Object.keys(nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
      const computableTensors = this.inputs.slice();
      const layersWithCompleteInput = [];
      for (const depth of depthKeys) {
        for (const node2 of nodesByDepth[depth]) {
          const layer = node2.outboundLayer;
          if (layer != null) {
            for (const x of node2.inputTensors) {
              if (computableTensors.indexOf(x) === -1) {
                throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);
              }
            }
            for (const x of node2.outputTensors) {
              computableTensors.push(x);
            }
            layersWithCompleteInput.push(layer.name);
          }
        }
      }
      this.nodesByDepth = nodesByDepth;
      const allNames = this.layers.map((x) => x.name);
      for (const name of allNames) {
        const numOccurrences = allNames.filter((x) => x === name).length;
        if (numOccurrences !== 1) {
          throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: ` + JSON.stringify(allNames));
        }
      }
      this.outboundNodes = [];
      this.inboundNodes = [];
      new Node({
        outboundLayer: this,
        inboundLayers: [],
        nodeIndices: [],
        tensorIndices: [],
        inputTensors: this.inputs,
        outputTensors: this.outputs,
        inputMasks: this.inputs.map((x) => null),
        outputMasks: this.outputs.map((x) => null),
        inputShapes: this.inputs.map((x) => x.shape),
        outputShapes: this.outputs.map((x) => x.shape)
      });
      this.built = true;
      this._refCount = 1;
    }
    assertNotDisposed() {
      if (this._refCount === 0) {
        throw new Error(`Container '${this.name}' is already disposed.`);
      }
    }
    dispose() {
      this.assertNotDisposed();
      const result = {refCountAfterDispose: null, numDisposedVariables: 0};
      if (--this._refCount === 0) {
        for (const layer of this.layers) {
          result.numDisposedVariables += layer.dispose().numDisposedVariables;
        }
        for (const container of this.internalContainerRefs) {
          result.numDisposedVariables += container.dispose().numDisposedVariables;
        }
      }
      result.refCountAfterDispose = this._refCount;
      return result;
    }
    get trainable() {
      return this.trainable_;
    }
    set trainable(trainable) {
      this.layers.forEach((layer) => {
        layer._trainableWeights.forEach((w) => w.trainable = trainable);
      });
      this.trainable_ = trainable;
    }
    get trainableWeights() {
      if (this._trainableWeights.length > 0) {
        throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");
      }
      if (!this.trainable) {
        return [];
      }
      let weights = [];
      for (const layer of this.layers) {
        weights = weights.concat(layer.trainableWeights);
      }
      return weights;
    }
    get nonTrainableWeights() {
      const weights = [];
      for (const layer of this.layers) {
        weights.push(...layer.nonTrainableWeights);
      }
      if (!this.trainable) {
        const trainableWeights = [];
        for (const layer of this.layers) {
          trainableWeights.push(...layer.trainableWeights);
        }
        return trainableWeights.concat(weights);
      }
      return weights;
    }
    get weights() {
      return this.trainableWeights.concat(this.nonTrainableWeights);
    }
    loadWeights(weights, strict = true) {
      const nameToWeight = {};
      let totalWeightsCount = 0;
      for (const layer of this.layers) {
        for (const weight of layer.weights) {
          if (nameToWeight[weight.originalName] != null) {
            throw new ValueError(`Duplicate weight name: ${weight.originalName}`);
          }
          nameToWeight[weight.originalName] = weight;
          totalWeightsCount++;
        }
      }
      const weightValueTuples = [];
      for (const name in weights) {
        let validatedName = name;
        if (nameToWeight[name] == null) {
          const tokens = name.split("/");
          const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);
          validatedName = shortenNameArray.join("/");
        }
        if (nameToWeight[validatedName] != null) {
          weightValueTuples.push([nameToWeight[validatedName], weights[name]]);
        } else if (strict) {
          throw new ValueError(`Provided weight data has no target variable: ${name}`);
        }
        delete nameToWeight[validatedName];
      }
      if (strict) {
        const unsetNames = [];
        for (const name in nameToWeight) {
          unsetNames.push(name);
        }
        if (unsetNames.length > 0) {
          throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`);
        }
      }
      batchSetValue(weightValueTuples);
    }
    updatedConfig() {
      const theConfig = this.getConfig();
      const modelConfig = {};
      modelConfig["className"] = this.getClassName();
      modelConfig["config"] = theConfig;
      modelConfig["kerasVersion"] = `tfjs-layers ${version2}`;
      modelConfig["backend"] = "TensorFlow.js";
      return modelConfig;
    }
    toJSON(unused, returnString = true) {
      const modelConfig = convertTsToPythonic(this.updatedConfig());
      return returnString ? JSON.stringify(modelConfig) : modelConfig;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        inputs = toList(inputs);
        const feedDict = new FeedDict();
        for (let i = 0; i < this.inputs.length; ++i) {
          feedDict.add(this.inputs[i], inputs[i]);
        }
        return execute(this.outputs, feedDict, kwargs);
      });
    }
    computeMask(inputs, mask) {
      return tidy(() => {
        inputs = toList(inputs);
        let masks;
        if (mask == null) {
          masks = pyListRepeat(null, inputs.length);
        } else {
          masks = toList(mask);
        }
        return this.runInternalGraph(inputs, masks)[1];
      });
    }
    computeOutputShape(inputShape) {
      const inputShapes = normalizeShapeList(inputShape);
      if (inputShapes.length !== this.inputLayers.length) {
        throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);
      }
      const layersToOutputShapes = {};
      for (let i = 0; i < inputShapes.length; i++) {
        const layer = this.inputLayers[i];
        const inputShape2 = inputShapes[i];
        const shapeKey = layer.name + "_0_0";
        layersToOutputShapes[shapeKey] = inputShape2;
      }
      const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
      if (depthKeys.length > 1) {
        for (const depth of depthKeys) {
          const nodes = this.nodesByDepth[depth];
          for (const node2 of nodes) {
            const layer = node2.outboundLayer;
            if (this.inputLayers.map((x) => x.id).indexOf(layer.id) !== -1) {
              continue;
            }
            const inputShapes2 = [];
            for (let j = 0; j < node2.inboundLayers.length; j++) {
              const inboundLayer = node2.inboundLayers[j];
              const nodeIndex2 = node2.nodeIndices[j];
              const tensorIndex = node2.tensorIndices[j];
              const shapeKey = `${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`;
              const inputShape2 = layersToOutputShapes[shapeKey];
              inputShapes2.push(inputShape2);
            }
            const outputShape = layer.computeOutputShape(singletonOrArray(inputShapes2));
            const outputShapes2 = normalizeShapeList(outputShape);
            const nodeIndex = layer.inboundNodes.indexOf(node2);
            for (let j = 0; j < outputShapes2.length; j++) {
              const shapeKey = `${layer.name}_${nodeIndex}_${j}`;
              layersToOutputShapes[shapeKey] = outputShapes2[j];
            }
          }
        }
      }
      const outputShapes = [];
      const outputShapeKeys = [];
      for (let i = 0; i < this.outputLayers.length; i++) {
        const layer = this.outputLayers[i];
        const nodeIndex = this.outputLayersNodeIndices[i];
        const tensorIndex = this.outputLayersTensorIndices[i];
        const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;
        outputShapeKeys.push(shapeKey);
      }
      for (let i = 0; i < outputShapeKeys.length; i++) {
        const key = outputShapeKeys[i];
        assert2(key in layersToOutputShapes);
        outputShapes.push(layersToOutputShapes[key]);
      }
      return singletonOrArray(outputShapes);
    }
    runInternalGraph(inputs, masks) {
      if (masks == null) {
        masks = pyListRepeat(null, inputs.length);
      }
      const tensorMap = {};
      for (let i = 0; i < this.inputs.length; ++i) {
        const x = this.inputs[i];
        const y = inputs[i];
        const mask = masks[i];
        tensorMap[x.id] = [y, mask];
      }
      const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
      for (const depth of depthKeys) {
        const nodes = this.nodesByDepth[depth];
        for (const node2 of nodes) {
          const layer = node2.outboundLayer;
          const referenceInputTensors = node2.inputTensors;
          const referenceOutputTensors = node2.outputTensors;
          const computedData = new Array();
          for (const x of referenceInputTensors) {
            if (x.id in tensorMap) {
              computedData.push(tensorMap[x.id]);
            }
          }
          if (computedData.length === referenceInputTensors.length) {
            let kwargs = {};
            let computedTensors;
            let computedMasks;
            let outputTensors2;
            let outputMasks2;
            if (node2.callArgs != null) {
              kwargs = node2.callArgs;
            }
            if (computedData.length === 1) {
              const [computedTensor, computedMask] = computedData[0];
              if (kwargs["mask"] == null) {
                kwargs["mask"] = computedMask;
              }
              outputTensors2 = toList(layer.call(computedTensor, kwargs));
              outputMasks2 = toList(layer.computeMask(computedTensor, computedMask));
              computedTensors = [computedTensor];
              computedMasks = [computedMask];
            } else {
              computedTensors = computedData.map((x) => x[0]);
              computedMasks = computedData.map((x) => x[1]);
              if (kwargs["mask"] == null) {
                kwargs["mask"] = computedMasks;
              }
              outputTensors2 = toList(layer.call(computedTensors, kwargs));
              outputMasks2 = toList(layer.computeMask(computedTensors, computedMasks));
            }
            if (layer.activityRegularizer) {
              throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");
            }
            for (let i = 0; i < referenceOutputTensors.length; ++i) {
              const x = referenceOutputTensors[i];
              const y = outputTensors2[i];
              const mask = outputMasks2[i];
              tensorMap[x.id] = [y, mask];
            }
          }
        }
      }
      const outputTensors = [];
      const outputMasks = [];
      const outputShapes = [];
      for (const x of this.outputs) {
        assert2(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);
        const [tensor2, mask] = tensorMap[x.id];
        outputShapes.push(tensor2.shape);
        outputTensors.push(tensor2);
        outputMasks.push(mask);
      }
      return [outputTensors, outputMasks, outputShapes];
    }
    buildNodeConversionMap(layers) {
      const nodeConversionMap = {};
      let keptNodes;
      for (const layer of this.layers) {
        keptNodes = layer instanceof Container ? 1 : 0;
        for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {
          const nodeKey = Container.nodeKey(layer, originalNodeIndex);
          if (this.containerNodes.has(nodeKey)) {
            nodeConversionMap[nodeKey] = keptNodes;
            keptNodes += 1;
          }
        }
      }
      return nodeConversionMap;
    }
    getLayer(name, index) {
      if (index != null) {
        if (this.layers.length <= index) {
          throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);
        } else {
          return this.layers[index];
        }
      } else {
        if (name == null) {
          throw new ValueError("Provide either a layer name or layer index");
        }
      }
      for (const layer of this.layers) {
        if (layer.name === name) {
          return layer;
        }
      }
      throw new ValueError(`No such layer: ${name}`);
    }
    calculateLosses() {
      return tidy(() => {
        const losses4 = [];
        for (const layer of this.layers) {
          for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {
            const nodeKey = Container.nodeKey(layer, nodeIndex);
            if (this.containerNodes.has(nodeKey)) {
              losses4.push(...layer.calculateLosses());
            }
          }
        }
        return losses4;
      });
    }
    getConfig() {
      const config = {name: this.name};
      const nodeConversionMap = this.buildNodeConversionMap(this.layers);
      const layerConfigs = [];
      for (const layer of this.layers) {
        const layerClassName = layer.getClassName();
        const layerConfig = layer.getConfig();
        const filteredInboundNodes = [];
        for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {
          const node2 = layer.inboundNodes[originalNodeIndex];
          const nodeKey = Container.nodeKey(layer, originalNodeIndex);
          let kwargs = {};
          if (this.containerNodes.has(nodeKey)) {
            if (node2.callArgs) {
              try {
                JSON.stringify(node2.callArgs);
                kwargs = node2.callArgs;
              } catch (err) {
                console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node2.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`);
                kwargs = {};
              }
            }
            if (node2.inboundLayers.length > 0) {
              const nodeData = [];
              for (let i = 0; i < node2.inboundLayers.length; i++) {
                const inboundLayer = node2.inboundLayers[i];
                const nodeIndex = node2.nodeIndices[i];
                const tensorIndex = node2.tensorIndices[i];
                const nodeKey2 = Container.nodeKey(inboundLayer, nodeIndex);
                let newNodeIndex = nodeConversionMap[nodeKey2];
                if (newNodeIndex == null) {
                  newNodeIndex = 0;
                }
                nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);
              }
              filteredInboundNodes.push(nodeData);
            }
          }
        }
        const dict = {};
        dict["name"] = layer.name;
        dict["className"] = layerClassName;
        dict["config"] = layerConfig;
        dict["inboundNodes"] = filteredInboundNodes;
        layerConfigs.push(dict);
      }
      config["layers"] = layerConfigs;
      const modelInputs = [];
      for (let i = 0; i < this.inputLayers.length; i++) {
        const layer = this.inputLayers[i];
        const nodeIndex = this.inputLayersNodeIndices[i];
        const nodeKey = Container.nodeKey(layer, nodeIndex);
        if (!this.containerNodes.has(nodeKey)) {
          continue;
        }
        let newNodeIndex = nodeConversionMap[nodeKey];
        if (newNodeIndex === null || newNodeIndex === void 0) {
          newNodeIndex = 0;
        }
        const tensorIndex = this.inputLayersTensorIndices[i];
        modelInputs.push([layer.name, newNodeIndex, tensorIndex]);
      }
      config["inputLayers"] = modelInputs;
      const modelOutputs = [];
      for (let i = 0; i < this.outputLayers.length; i++) {
        const layer = this.outputLayers[i];
        const nodeIndex = this.outputLayersNodeIndices[i];
        const nodeKey = Container.nodeKey(layer, nodeIndex);
        if (!this.containerNodes.has(nodeKey)) {
          continue;
        }
        let newNodeIndex = nodeConversionMap[nodeKey];
        if (newNodeIndex === null || newNodeIndex === void 0) {
          newNodeIndex = 0;
        }
        const tensorIndex = this.outputLayersTensorIndices[i];
        modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);
      }
      config["outputLayers"] = modelOutputs;
      return config;
    }
    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {
      const createdLayers = {};
      const unprocessedNodes = {};
      function addUnprocessedNode(layer, nodeData) {
        if (!(layer.name in unprocessedNodes)) {
          unprocessedNodes[layer.name] = [nodeData];
        } else {
          unprocessedNodes[layer.name].push(nodeData);
        }
      }
      function processNode(layer, nodeData) {
        const inputTensors2 = [];
        let kwargs;
        for (const inputData of nodeData) {
          const inboundLayerName = inputData[0];
          const inboundNodeIndex = inputData[1];
          const inboundTensorIndex = inputData[2];
          kwargs = inputData[3] == null ? {} : inputData[3];
          if (!(inboundLayerName in createdLayers)) {
            addUnprocessedNode(layer, nodeData);
            return;
          }
          const inboundLayer = createdLayers[inboundLayerName];
          if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {
            addUnprocessedNode(layer, nodeData);
            return;
          }
          const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];
          inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex]);
        }
        if (inputTensors2.length > 0) {
          layer.apply(singletonOrArray(inputTensors2), kwargs);
        }
      }
      function processLayer(layerData) {
        const layerName = layerData["name"];
        const layer = deserialize(layerData, config["customObjects"] != null ? config["customObjects"] : {});
        layer.setFastWeightInitDuringBuild(fastWeightInit);
        createdLayers[layerName] = layer;
        const inboundNodesData = layerData["inboundNodes"];
        inboundNodesData.forEach((nodeData) => {
          if (!(nodeData instanceof Array)) {
            throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);
          }
          addUnprocessedNode(layer, nodeData);
        });
      }
      const name = config["name"];
      const layersFromConfig = config["layers"];
      for (const layerData of layersFromConfig) {
        processLayer(layerData);
      }
      while (!isObjectEmpty(unprocessedNodes)) {
        for (const layerData of layersFromConfig) {
          const layer = createdLayers[layerData["name"]];
          if (layer.name in unprocessedNodes) {
            const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];
            delete unprocessedNodes[layer.name];
            for (const nodeData of currentUnprocessedNodesForLayer) {
              processNode(layer, nodeData);
            }
          }
        }
      }
      const inputTensors = [];
      const outputTensors = [];
      const inputLayersFromConfig = config["inputLayers"];
      for (const layerData of inputLayersFromConfig) {
        const layerName = layerData[0];
        const nodeIndex = layerData[1];
        const tensorIndex = layerData[2];
        assert2(layerName in createdLayers);
        const layer = createdLayers[layerName];
        const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;
        inputTensors.push(layerOutputTensors[tensorIndex]);
      }
      const outputLayersFromConfig = config["outputLayers"];
      for (const layerData of outputLayersFromConfig) {
        const layerName = layerData[0];
        const nodeIndex = layerData[1];
        const tensorIndex = layerData[2];
        assert2(layerName in createdLayers);
        const layer = createdLayers[layerName];
        const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;
        outputTensors.push(layerOutputTensors[tensorIndex]);
      }
      return new cls({inputs: inputTensors, outputs: outputTensors, name});
    }
    get stateful() {
      if (this._stateful) {
        throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");
      }
      for (const layer of this.layers) {
        if (layer.stateful) {
          return true;
        }
      }
      return false;
    }
    resetStates() {
      tidy(() => {
        this.layers.forEach((layer) => {
          if (layer.stateful) {
            layer.resetStates();
          }
        });
      });
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {
    const numOutputs = outputNames.length;
    if (xWeight == null || Array.isArray(xWeight) && xWeight.length === 0) {
      return outputNames.map((name) => null);
    }
    if (numOutputs === 1) {
      if (Array.isArray(xWeight) && xWeight.length === 1) {
        return xWeight;
      } else if (typeof xWeight === "object" && outputNames[0] in xWeight) {
        return [xWeight[outputNames[0]]];
      } else {
        return [xWeight];
      }
    }
    if (Array.isArray(xWeight)) {
      if (xWeight.length !== numOutputs) {
        throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);
      }
      return xWeight;
    } else if (typeof xWeight === "object" && Object.keys(xWeight).length > 0 && typeof xWeight[Object.keys(xWeight)[0]] === "object") {
      const output = [];
      outputNames.forEach((outputName) => {
        if (outputName in xWeight) {
          output.push(xWeight[outputName]);
        } else {
          output.push(null);
        }
      });
      return output;
    } else {
      throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);
    }
  }
  function standardizeClassWeights(classWeight, outputNames) {
    return standardizeSampleOrClassWeights(classWeight, outputNames, "classWeight");
  }
  async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {
    if (sampleWeight != null || sampleWeightMode != null) {
      throw new Error("Support sampleWeight is not implemented yet");
    }
    if (classWeight != null) {
      const yClasses = tidy(() => {
        if (y.shape.length === 1) {
          return y.clone();
        } else if (y.shape.length === 2) {
          if (y.shape[1] > 1) {
            const axis = 1;
            return y.argMax(axis);
          } else if (y.shape[1] === 1) {
            return y.reshape([y.shape[0]]);
          } else {
            throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`);
          }
        } else {
          throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`);
        }
      });
      const yClassIndices = Array.from(await yClasses.data());
      dispose(yClasses);
      const classSampleWeight = [];
      yClassIndices.forEach((classIndex) => {
        if (classWeight[classIndex] == null) {
          throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);
        } else {
          classSampleWeight.push(classWeight[classIndex]);
        }
      });
      return tensor1d(classSampleWeight, "float32");
    } else {
      return null;
    }
  }
  function computeWeightedLoss2(losses4, sampleWeights) {
    return mul(losses4, sampleWeights);
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var DEFAULT_VALIDATION_BATCH_SIZE = 32;
  function standardizeDataIteratorOutput(model2, iteratorOut) {
    let xs;
    let ys;
    const iteratorOutObj = iteratorOut;
    xs = iteratorOutObj["xs"];
    ys = iteratorOutObj["ys"];
    util_exports.assert(xs != null && ys != null, () => `A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${iteratorOut}`);
    const flattenedXs = flattenTensorOrArrayOrMap("input", model2.inputNames, xs);
    const flattenedYs = flattenTensorOrArrayOrMap("output", model2.outputNames, ys);
    const batchSize = flattenedXs[0].shape[0];
    util_exports.assert(flattenedXs.length === model2.inputs.length, () => `LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model2.inputNames)})`);
    util_exports.assert(flattenedYs.length === model2.outputs.length, () => `LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model2.outputNames)})`);
    for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {
      util_exports.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);
    }
    for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {
      util_exports.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);
    }
    return {xs: flattenedXs, ys: flattenedYs};
  }
  function flattenTensorOrArrayOrMap(inputOrOutput, names, values) {
    if (values instanceof Tensor) {
      return [values];
    } else if (Array.isArray(values)) {
      util_exports.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);
      return values;
    } else {
      const result = [];
      for (const name of names) {
        if (values[name] == null) {
          throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);
        }
        result.push(values[name]);
      }
      return result;
    }
  }
  function standardizeTensorValidationData(data) {
    if (data.length === 3) {
      throw new NotImplementedError("Validation with sample weights is not implemented yet.");
    }
    return {xs: data[0], ys: data[1]};
  }
  async function fitDataset(model2, dataset, args) {
    const hasBatchesPerEpoch = args.batchesPerEpoch != null;
    util_exports.assert(model2.optimizer != null, () => "You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).");
    util_exports.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.`);
    util_exports.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`);
    util_exports.assert(!hasBatchesPerEpoch || args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch), () => `For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`);
    util_exports.assert(args["validationSplit"] == null, () => "`validationSplit` is not supported by `fitDataset()`. Use validationData instead.");
    if (model2.isTraining) {
      throw new Error("Cannot start training because another fit() call is ongoing.");
    }
    model2.isTraining = true;
    try {
      const doValidation = args.validationData != null;
      let valXs;
      let valYs;
      if (doValidation) {
        if (isDatasetObject(args.validationData)) {
          util_exports.assert(args.validationBatches == null || args.validationBatches > 0 && Number.isInteger(args.validationBatches), () => `For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);
        } else {
          const validationData = standardizeTensorValidationData(args.validationData);
          valXs = validationData.xs;
          valYs = validationData.ys;
        }
      }
      const trainFunction = model2.makeTrainFunction();
      const outLabels = model2.getDedupedMetricsNames();
      let callbackMetrics;
      if (doValidation) {
        callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n));
      } else {
        callbackMetrics = outLabels.slice();
      }
      const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);
      const verbose = args.verbose == null ? 1 : args.verbose;
      const {callbackList, history} = configureCallbacks(callbacks2, verbose, args.epochs, null, null, getStepsPerEpoch(dataset, args), null, doValidation, callbackMetrics);
      callbackList.setModel(model2);
      model2.history = history;
      await callbackList.onTrainBegin();
      model2.stopTraining_ = false;
      let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;
      let dataIterator = await dataset.iterator();
      while (epoch < args.epochs) {
        const epochLogs = {};
        await callbackList.onEpochBegin(epoch);
        let stepsDone = 0;
        let batchIndex = 0;
        if (!hasBatchesPerEpoch) {
          dataIterator = await dataset.iterator();
        }
        while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {
          const iteratorOut = await dataIterator.next();
          if (hasBatchesPerEpoch && iteratorOut.done) {
            console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch * args.epochs} batches). You may need to use the repeat() function when building your dataset.`);
            break;
          }
          if (iteratorOut.value != null) {
            const {xs, ys} = standardizeDataIteratorOutput(model2, iteratorOut.value);
            const batchLogs = {};
            batchLogs["batch"] = batchIndex;
            batchLogs["size"] = xs[0].shape[0];
            await callbackList.onBatchBegin(batchIndex, batchLogs);
            const sampleWeights = [];
            if (args.classWeight != null) {
              const standardClassWeights = standardizeClassWeights(args.classWeight, model2.outputNames);
              for (let i = 0; i < standardClassWeights.length; ++i) {
                sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));
              }
            }
            const ins = xs.concat(ys).concat(sampleWeights);
            const outs = trainFunction(ins);
            dispose(ins);
            for (let i = 0; i < outLabels.length; ++i) {
              const label = outLabels[i];
              const out = outs[i];
              batchLogs[label] = out;
              keep(out);
            }
            await callbackList.onBatchEnd(batchIndex, batchLogs);
            disposeTensorsInLogs(batchLogs);
            batchIndex++;
            stepsDone++;
          }
          if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch : iteratorOut.done) {
            if (doValidation) {
              let valOuts;
              if (isDatasetObject(args.validationData)) {
                valOuts = toList(await model2.evaluateDataset(args.validationData, {batches: args.validationBatches}));
              } else {
                valOuts = toList(model2.evaluate(valXs, valYs, {
                  batchSize: args.validationBatchSize == null ? DEFAULT_VALIDATION_BATCH_SIZE : args.validationBatchSize,
                  verbose: 0
                }));
              }
              for (let i = 0; i < model2.metricsNames.length; ++i) {
                epochLogs[`val_${model2.metricsNames[i]}`] = valOuts[i];
              }
            }
            break;
          }
          if (model2.stopTraining_) {
            break;
          }
        }
        await callbackList.onEpochEnd(epoch, epochLogs);
        epoch++;
        if (model2.stopTraining_) {
          break;
        }
      }
      await callbackList.onTrainEnd();
      await model2.history.syncData();
      return model2.history;
    } finally {
      model2.isTraining = false;
    }
  }
  function getStepsPerEpoch(dataset, args) {
    let stepsPerEpoch = null;
    if (args.batchesPerEpoch != null) {
      stepsPerEpoch = args.batchesPerEpoch;
    } else if (Number.isFinite(dataset.size)) {
      stepsPerEpoch = dataset.size;
    }
    return stepsPerEpoch;
  }
  function isDatasetObject(dataset) {
    return typeof dataset.iterator === "function";
  }
  function isLazyIteratorObject(iterator) {
    return typeof iterator.next === "function";
  }
  async function evaluateDataset(model2, dataset, args) {
    args = args || {};
    const hasBatches = args.batches != null;
    const f = model2.testFunction;
    let outs = [];
    if (args.verbose > 0) {
      throw new NotImplementedError("Verbose mode is not implemented yet.");
    }
    util_exports.assert(!hasBatches || args.batches > 0 && Number.isInteger(args.batches), () => `Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);
    const dataIterator = isLazyIteratorObject(dataset) ? dataset : await dataset.iterator();
    let numExamples = 0;
    let batch = 0;
    while (hasBatches ? batch < args.batches : true) {
      const iteratorOut = await dataIterator.next();
      outs = tidy(() => {
        if (iteratorOut.value) {
          const {xs, ys} = standardizeDataIteratorOutput(model2, iteratorOut.value);
          const xsAndYs = xs.concat(ys);
          const batchOuts = tidy(() => f(xsAndYs));
          dispose(xsAndYs);
          if (batch === 0) {
            for (let i = 0; i < batchOuts.length; ++i) {
              outs.push(scalar(0));
            }
          }
          const batchSize = xsAndYs[0].shape[0];
          for (let i = 0; i < batchOuts.length; ++i) {
            const batchOut = batchOuts[i];
            const oldScalar = outs[i];
            outs[i] = tidy(() => add2(outs[i], mul(batchSize, batchOut)));
            if (batch > 0) {
              dispose(oldScalar);
            }
          }
          dispose(batchOuts);
          numExamples += batchSize;
          ++batch;
        }
        return outs;
      });
      if (iteratorOut.done) {
        if (hasBatches) {
          console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);
        }
        break;
      }
    }
    for (let i = 0; i < outs.length; ++i) {
      const oldScalar = outs[i];
      outs[i] = div(outs[i], numExamples);
      dispose(oldScalar);
    }
    return singletonOrArray(outs);
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function checkBatchSize(batchSize) {
    util_exports.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);
  }
  function sliceArrays(arrays, start, stop) {
    if (arrays == null) {
      return [null];
    } else if (Array.isArray(arrays)) {
      return arrays.map((array2) => sliceAlongFirstAxis(array2, start, stop - start));
    } else {
      return sliceAlongFirstAxis(arrays, start, stop - start);
    }
  }
  function sliceArraysByIndices(arrays, indices) {
    return tidy(() => {
      if (arrays == null) {
        return null;
      } else if (Array.isArray(arrays)) {
        return arrays.map((array2) => sliceArraysByIndices(array2, indices));
      } else {
        return gather2(arrays, indices.dtype === "int32" ? indices : indices.toInt());
      }
    });
  }
  function makeBatches(size, batchSize) {
    const output = [];
    let batchStart = 0;
    let batchEnd = null;
    while (batchStart < size) {
      batchEnd = batchStart + batchSize;
      if (batchEnd >= size) {
        batchEnd = size;
      }
      output.push([batchStart, batchEnd]);
      batchStart = batchEnd;
    }
    return output;
  }
  async function fitLoop(model2, f, ins, outLabels, batchSize, epochs, verbose, callbacks2, valF, valIns, shuffle2, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {
    if (batchSize == null) {
      batchSize = 32;
    }
    if (epochs == null) {
      epochs = 1;
    }
    if (shuffle2 == null) {
      shuffle2 = true;
    }
    if (initialEpoch == null) {
      initialEpoch = 0;
    }
    let doValidation = false;
    if (valF != null && valIns != null) {
      doValidation = true;
    }
    if (validationSteps != null) {
      doValidation = true;
      if (stepsPerEpoch == null) {
        throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");
      }
    }
    const numTrainSamples = model2.checkNumSamples(ins, batchSize, stepsPerEpoch, "steps_per_epoch");
    let indexArray;
    if (numTrainSamples != null) {
      indexArray = range2(0, numTrainSamples);
    }
    if (verbose == null) {
      verbose = 1;
    }
    const {callbackList, history} = configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);
    callbackList.setModel(model2);
    model2.history = history;
    await callbackList.onTrainBegin();
    model2.stopTraining_ = false;
    for (let epoch = initialEpoch; epoch < epochs; ++epoch) {
      await callbackList.onEpochBegin(epoch);
      const epochLogs = {};
      if (stepsPerEpoch != null) {
        throw new NotImplementedError("stepsPerEpoch mode is not implemented yet.");
      } else {
        if (shuffle2 === "batch") {
          throw new NotImplementedError("batch shuffling is not implemneted yet");
        } else if (shuffle2) {
          util_exports.shuffle(indexArray);
        }
        const epochIndexArray1D = tensor1d(indexArray);
        const batches = makeBatches(numTrainSamples, batchSize);
        for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
          const batchLogs = {};
          await callbackList.onBatchBegin(batchIndex, batchLogs);
          tidy(() => {
            const batchStart = batches[batchIndex][0];
            const batchEnd = batches[batchIndex][1];
            const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);
            batchLogs["batch"] = batchIndex;
            batchLogs["size"] = batchEnd - batchStart;
            const insBatch = sliceArraysByIndices(ins, batchIds);
            const outs = f(insBatch);
            for (let i = 0; i < outLabels.length; ++i) {
              const label = outLabels[i];
              const out = outs[i];
              batchLogs[label] = out;
              keep(out);
            }
            if (batchIndex === batches.length - 1) {
              if (doValidation) {
                const valOuts = model2.testLoop(valF, valIns, batchSize);
                for (let i = 0; i < outLabels.length; ++i) {
                  const label = outLabels[i];
                  const out = valOuts[i];
                  keep(out);
                  epochLogs["val_" + label] = out;
                }
              }
            }
          });
          await callbackList.onBatchEnd(batchIndex, batchLogs);
          disposeTensorsInLogs(batchLogs);
          if (model2.stopTraining_) {
            break;
          }
        }
        epochIndexArray1D.dispose();
      }
      await callbackList.onEpochEnd(epoch, epochLogs);
      if (model2.stopTraining_) {
        break;
      }
    }
    await callbackList.onTrainEnd();
    await model2.history.syncData();
    return model2.history;
  }
  async function fitTensors(model2, x, y, args = {}) {
    if (model2.isTraining) {
      throw new Error("Cannot start training because another fit() call is ongoing.");
    }
    model2.isTraining = true;
    let inputs;
    let targets;
    let inputValX;
    let inputValY;
    let valX;
    let valY;
    let sampleWeights;
    try {
      const batchSize = args.batchSize == null ? 32 : args.batchSize;
      checkBatchSize(batchSize);
      const checkBatchAxis = false;
      const standardizedOuts = await model2.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);
      inputs = standardizedOuts[0];
      targets = standardizedOuts[1];
      sampleWeights = standardizedOuts[2];
      let doValidation = false;
      let valIns;
      if (args.validationData != null && args.validationData.length > 0) {
        doValidation = true;
        if (args.validationData.length === 2) {
          inputValX = args.validationData[0];
          inputValY = args.validationData[1];
        } else if (args.validationData.length === 3) {
          throw new NotImplementedError("validationData including sample weights is not supported yet.");
        } else {
          throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);
        }
        const checkBatchAxis2 = true;
        const valStandardized = await model2.standardizeUserData(inputValX, inputValY, null, null, checkBatchAxis2, batchSize);
        valX = valStandardized[0];
        valY = valStandardized[1];
        valIns = valX.concat(valY);
      } else if (args.validationSplit != null && args.validationSplit > 0 && args.validationSplit < 1) {
        doValidation = true;
        const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));
        const originalBatchSize = inputs[0].shape[0];
        valX = sliceArrays(inputs, splitAt, originalBatchSize);
        inputs = sliceArrays(inputs, 0, splitAt);
        valY = sliceArrays(targets, splitAt, originalBatchSize);
        targets = sliceArrays(targets, 0, splitAt);
        valIns = valX.concat(valY);
      } else if (args.validationSteps != null) {
        doValidation = true;
      }
      const ins = inputs.concat(targets).concat(sampleWeights);
      model2.checkTrainableWeightsConsistency();
      const trainFunction = model2.makeTrainFunction();
      const outLabels = model2.getDedupedMetricsNames();
      let valFunction;
      let callbackMetrics;
      if (doValidation) {
        model2.makeTestFunction();
        valFunction = model2.testFunction;
        callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n));
      } else {
        valFunction = null;
        valIns = [];
        callbackMetrics = outLabels.slice();
      }
      const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);
      const out = await fitLoop(model2, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks2, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);
      return out;
    } finally {
      model2.isTraining = false;
      disposeNewTensors(inputs, x);
      disposeNewTensors(targets, y);
      disposeNewTensors(valX, inputValX);
      disposeNewTensors(valY, inputValY);
      if (sampleWeights != null) {
        dispose(sampleWeights);
      }
    }
  }
  function ensureTensorsRank2OrHigher(tensors) {
    const outs = [];
    if (tensors instanceof Tensor) {
      tensors = [tensors];
    }
    for (let i = 0; i < tensors.length; ++i) {
      const tensor2 = tensors[i];
      if (tensor2.rank === 1) {
        outs.push(expandDims2(tensor2, 1));
      } else if (tensor2.rank === 0) {
        throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");
      } else {
        outs.push(tensor2);
      }
    }
    return outs;
  }
  function disposeNewTensors(tensors, refTensors) {
    if (tensors == null) {
      return;
    }
    const oldTensorIds = [];
    if (refTensors instanceof Tensor) {
      oldTensorIds.push(refTensors.id);
    } else if (Array.isArray(refTensors)) {
      refTensors.forEach((t) => oldTensorIds.push(t.id));
    } else if (refTensors != null) {
      for (const name in refTensors) {
        const oldTensor = refTensors[name];
        oldTensorIds.push(oldTensor.id);
      }
    }
    const tensorsToDispose = [];
    if (tensors instanceof Tensor) {
      if (oldTensorIds.indexOf(tensors.id) === -1) {
        tensorsToDispose.push(tensors);
      }
    } else if (Array.isArray(tensors)) {
      tensors.forEach((t) => {
        if (oldTensorIds.indexOf(t.id) === -1) {
          tensorsToDispose.push(t);
        }
      });
    } else if (tensors != null) {
      for (const name in tensors) {
        const tensor2 = tensors[name];
        if (oldTensorIds.indexOf(tensor2.id) === -1) {
          tensorsToDispose.push(tensor2);
        }
      }
    }
    tensorsToDispose.forEach((t) => {
      if (!t.isDisposed) {
        t.dispose();
      }
    });
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function isDataTensor(x) {
    return x instanceof Tensor;
  }
  function isDataArray(x) {
    return Array.isArray(x);
  }
  function isDataDict(x) {
    return !isDataTensor(x) && !isDataArray(x);
  }
  function standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = "") {
    if (names == null || names.length === 0) {
      if (data != null) {
        let gotUnexpectedData = false;
        if (isDataArray(data) && data.length > 0) {
          gotUnexpectedData = true;
        } else if (isDataDict(data)) {
          for (const key in data) {
            if (data.hasOwnProperty(key)) {
              gotUnexpectedData = true;
              break;
            }
          }
        } else {
          gotUnexpectedData = true;
        }
        if (gotUnexpectedData) {
          throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`);
        }
      }
      return [];
    }
    if (data == null) {
      return names.map((name) => null);
    }
    let arrays;
    if (isDataDict(data)) {
      data = data;
      arrays = [];
      for (const name of names) {
        if (data[name] == null) {
          throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);
        }
        arrays.push(data[name]);
      }
    } else if (isDataArray(data)) {
      data = data;
      if (data.length !== names.length) {
        throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);
      }
      arrays = data;
    } else {
      data = data;
      if (names.length > 1) {
        throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);
      }
      arrays = [data];
    }
    arrays = ensureTensorsRank2OrHigher(arrays);
    if (shapes != null) {
      for (let i = 0; i < names.length; ++i) {
        if (shapes[i] == null) {
          continue;
        }
        const array2 = arrays[i];
        if (array2.shape.length !== shapes[i].length) {
          throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);
        }
        for (let j = 0; j < shapes[i].length; ++j) {
          if (j === 0 && !checkBatchAxis) {
            continue;
          }
          const dim = array2.shape[j];
          const refDim = shapes[i][j];
          if (refDim != null && refDim >= 0 && dim !== refDim) {
            throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`);
          }
        }
      }
    }
    return arrays;
  }
  function checkArrayLengths(inputs, targets, weights) {
    const setX = unique2(inputs.map((input2) => input2.shape[0]));
    setX.sort();
    const setY = unique2(targets.map((target) => target.shape[0]));
    setY.sort();
    if (setX.length > 1) {
      throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map((input2) => input2.shape))}`);
    }
    if (setY.length > 1) {
      throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map((target) => target.shape))}`);
    }
    if (setX.length > 0 && setY.length > 0 && !util_exports.arraysEqual(setX, setY)) {
      throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`);
    }
  }
  function checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {
    const keyLosses = [
      meanSquaredError2,
      binaryCrossentropy,
      categoricalCrossentropy
    ];
    for (let i = 0; i < targets.length; ++i) {
      const y = targets[i];
      const loss = lossFns[i];
      const shape = outputShapes[i];
      if (loss == null) {
        continue;
      }
      if (loss === categoricalCrossentropy) {
        if (y.shape[y.shape.length - 1] === 1) {
          throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);
        }
      }
      if (keyLosses.indexOf(loss) !== -1) {
        const slicedYShape = y.shape.slice(1);
        const slicedShape = shape.slice(1);
        for (let j = 0; j < slicedYShape.length; ++j) {
          const targetDim = slicedYShape[j];
          const outDim = slicedShape[j];
          if (outDim != null && targetDim !== outDim) {
            throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`);
          }
        }
      }
    }
  }
  function checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = "") {
    let arrays;
    if (Array.isArray(data)) {
      if (data.length !== names.length) {
        throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);
      }
      arrays = data;
    } else {
      if (names.length > 1) {
        throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);
      }
      arrays = [data];
    }
    if (shapes != null) {
      for (let i = 0; i < names.length; ++i) {
        if (shapes[i] == null) {
          continue;
        }
        const array2 = arrays[i];
        if (array2.shape.length !== shapes[i].length) {
          throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);
        }
        for (let j = 0; j < shapes[i].length; ++j) {
          if (j === 0 && !checkBatchAxis) {
            continue;
          }
          const dim = array2.shape[j];
          const refDim = shapes[i][j];
          if (refDim != null) {
            if (refDim !== dim) {
              throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`);
            }
          }
        }
      }
    }
  }
  function collectMetrics(metrics2, outputNames) {
    if (metrics2 == null || Array.isArray(metrics2) && metrics2.length === 0) {
      return outputNames.map((name) => []);
    }
    let wrappedMetrics;
    if (typeof metrics2 === "string" || typeof metrics2 === "function") {
      wrappedMetrics = [metrics2];
    } else if (Array.isArray(metrics2) || typeof metrics2 === "object") {
      wrappedMetrics = metrics2;
    } else {
      throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);
    }
    if (Array.isArray(wrappedMetrics)) {
      return outputNames.map((name) => wrappedMetrics);
    } else {
      const nestedMetrics = [];
      for (const name of outputNames) {
        let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];
        if (!Array.isArray(outputMetrics)) {
          outputMetrics = [outputMetrics];
        }
        nestedMetrics.push(outputMetrics);
      }
      return nestedMetrics;
    }
  }
  var LAYERS_MODEL_FORMAT_NAME = "layers-model";
  var LayersModel = class extends Container {
    constructor(args) {
      super(args);
      this.isTraining = false;
    }
    summary(lineLength, positions, printFn = console.log) {
      if (!this.built) {
        throw new ValueError(`This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).`);
      }
      printSummary(this, lineLength, positions, printFn);
    }
    compile(args) {
      if (args.loss == null) {
        args.loss = [];
      }
      this.loss = args.loss;
      if (typeof args.optimizer === "string") {
        this.optimizer_ = getOptimizer(args.optimizer);
        this.isOptimizerOwned = true;
      } else {
        if (!(args.optimizer instanceof Optimizer)) {
          throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);
        }
        this.optimizer_ = args.optimizer;
        this.isOptimizerOwned = false;
      }
      let lossFunctions = [];
      if (!Array.isArray(args.loss) && typeof args.loss !== "string" && typeof args.loss !== "function") {
        args.loss = args.loss;
        for (const name in args.loss) {
          if (this.outputNames.indexOf(name) === -1) {
            throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);
          }
        }
        for (const name of this.outputNames) {
          if (args.loss[name] == null) {
            console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`);
          }
          lossFunctions.push(get(args.loss[name]));
        }
      } else if (Array.isArray(args.loss)) {
        if (args.loss.length !== this.outputs.length) {
          throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);
        }
        const theLosses = args.loss;
        lossFunctions = theLosses.map((l) => get(l));
      } else {
        const lossFunction = get(args.loss);
        this.outputs.forEach((_) => {
          lossFunctions.push(lossFunction);
        });
      }
      this.lossFunctions = lossFunctions;
      this.feedOutputNames = [];
      this.feedOutputShapes = [];
      this.feedLossFns = [];
      for (let i = 0; i < this.outputs.length; ++i) {
        const shape = this.internalOutputShapes[i];
        const name = this.outputNames[i];
        this.feedOutputNames.push(name);
        this.feedOutputShapes.push(shape);
        this.feedLossFns.push(this.lossFunctions[i]);
      }
      const skipTargetIndices = [];
      this.metrics = args.metrics;
      this.metricsNames = ["loss"];
      this.metricsTensors = [];
      nameScope("loss", () => {
        for (let i = 0; i < this.outputs.length; ++i) {
          if (skipTargetIndices.indexOf(i) !== -1) {
            continue;
          }
          const weightedLoss = this.lossFunctions[i];
          if (this.outputs.length > 1) {
            this.metricsTensors.push([weightedLoss, i]);
            this.metricsNames.push(this.outputNames[i] + "_loss");
          }
        }
      });
      const nestedMetrics = collectMetrics(args.metrics, this.outputNames);
      const appendMetric = (outputIndex, metricName, metricTensor) => {
        if (this.outputNames.length > 1) {
          metricName = this.outputNames[outputIndex] + "_" + metricName;
        }
        this.metricsNames.push(metricName);
        this.metricsTensors.push([metricTensor, outputIndex]);
      };
      nameScope("metric", () => {
        for (let i = 0; i < this.outputs.length; ++i) {
          if (skipTargetIndices.indexOf(i) !== -1) {
            continue;
          }
          const outputMetrics = nestedMetrics[i];
          const handleMetrics = (metrics2) => {
            const metricNamePrefix = "";
            let metricName;
            let accFn;
            let weightedMetricFn;
            for (const metric of metrics2) {
              if (typeof metric === "string" && ["accuracy", "acc", "crossentropy", "ce"].indexOf(metric) !== -1) {
                const outputShape = this.internalOutputShapes[i];
                if (outputShape[outputShape.length - 1] === 1 || this.lossFunctions[i] === binaryCrossentropy) {
                  if (["accuracy", "acc"].indexOf(metric) !== -1) {
                    accFn = binaryAccuracy;
                  } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
                    accFn = binaryCrossentropy2;
                  }
                } else if (this.lossFunctions[i] === sparseCategoricalCrossentropy) {
                  if (["accuracy", "acc"].indexOf(metric) !== -1) {
                    accFn = sparseCategoricalAccuracy;
                  } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
                    accFn = sparseCategoricalCrossentropy2;
                  }
                } else {
                  if (["accuracy", "acc"].indexOf(metric) !== -1) {
                    accFn = categoricalAccuracy;
                  } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
                    accFn = categoricalCrossentropy2;
                  }
                }
                let suffix;
                if (["accuracy", "acc"].indexOf(metric) !== -1) {
                  suffix = "acc";
                } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
                  suffix = "ce";
                }
                weightedMetricFn = accFn;
                metricName = metricNamePrefix + suffix;
              } else {
                const metricFn = get2(metric);
                weightedMetricFn = metricFn;
                metricName = metricNamePrefix + getLossOrMetricName(metric);
              }
              let metricResult;
              nameScope(metricName, () => {
                metricResult = weightedMetricFn;
              });
              appendMetric(i, metricName, metricResult);
            }
          };
          handleMetrics(outputMetrics);
        }
      });
      this.collectedTrainableWeights = this.trainableWeights;
    }
    checkTrainableWeightsConsistency() {
      if (this.collectedTrainableWeights == null) {
        return;
      }
      if (this.trainableWeights.length !== this.collectedTrainableWeights.length) {
        console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?");
      }
    }
    evaluate(x, y, args = {}) {
      const batchSize = args.batchSize == null ? 32 : args.batchSize;
      checkBatchSize(batchSize);
      const checkBatchAxis = true;
      const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);
      try {
        const ins = standardizedOuts[0].concat(standardizedOuts[1]);
        this.makeTestFunction();
        const f = this.testFunction;
        const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);
        return singletonOrArray(testOuts);
      } finally {
        disposeNewTensors(standardizedOuts[0], x);
        disposeNewTensors(standardizedOuts[1], y);
      }
    }
    async evaluateDataset(dataset, args) {
      this.makeTestFunction();
      return evaluateDataset(this, dataset, args);
    }
    checkNumSamples(ins, batchSize, steps, stepsName = "steps") {
      let numSamples;
      if (steps != null) {
        numSamples = null;
        if (batchSize != null) {
          throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`);
        }
      } else if (ins != null) {
        if (Array.isArray(ins)) {
          numSamples = ins[0].shape[0];
        } else {
          numSamples = ins.shape[0];
        }
      } else {
        throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);
      }
      return numSamples;
    }
    execute(inputs, outputs) {
      if (Array.isArray(outputs) && outputs.length === 0) {
        throw new ValueError("`outputs` is an empty Array, which is not allowed.");
      }
      const outputsIsArray = Array.isArray(outputs);
      const outputNames = outputsIsArray ? outputs : [outputs];
      const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);
      const feedDict = new FeedDict();
      if (inputs instanceof Tensor) {
        inputs = [inputs];
      }
      if (Array.isArray(inputs)) {
        if (inputs.length !== this.inputs.length) {
          throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);
        }
        for (let i = 0; i < this.inputs.length; ++i) {
          feedDict.add(this.inputs[i], inputs[i]);
        }
      } else {
        for (const input2 of this.inputs) {
          const tensorValue = inputs[input2.name];
          if (tensorValue == null) {
            throw new ValueError(`No value is provided for the model's input ${input2.name}`);
          }
          feedDict.add(input2, tensorValue);
        }
      }
      const executeOutputs = execute(outputSymbolicTensors, feedDict);
      return outputsIsArray ? executeOutputs : executeOutputs[0];
    }
    retrieveSymbolicTensors(symbolicTensorNames) {
      const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);
      let outputsRemaining = symbolicTensorNames.length;
      for (const layer of this.layers) {
        const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];
        const layerOutputNames = layerOutputs.map((output) => output.name);
        for (let i = 0; i < symbolicTensorNames.length; ++i) {
          const index = layerOutputNames.indexOf(symbolicTensorNames[i]);
          if (index !== -1) {
            outputSymbolicTensors[i] = layerOutputs[index];
            outputsRemaining--;
          }
          if (outputsRemaining === 0) {
            break;
          }
        }
        if (outputsRemaining === 0) {
          break;
        }
      }
      if (outputsRemaining > 0) {
        const remainingNames = [];
        outputSymbolicTensors.forEach((tensor2, i) => {
          if (tensor2 == null) {
            remainingNames.push(symbolicTensorNames[i]);
          }
        });
        throw new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`);
      }
      return outputSymbolicTensors;
    }
    predictLoop(ins, batchSize = 32, verbose = false) {
      return tidy(() => {
        const numSamples = this.checkNumSamples(ins);
        if (verbose) {
          throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");
        }
        const batches = makeBatches(numSamples, batchSize);
        const outsBatches = this.outputs.map((output) => []);
        for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
          const batchOuts = tidy(() => {
            const batchStart = batches[batchIndex][0];
            const batchEnd = batches[batchIndex][1];
            const insBatch = sliceArrays(ins, batchStart, batchEnd);
            const feeds = [];
            if (Array.isArray(insBatch)) {
              for (let i = 0; i < insBatch.length; ++i) {
                feeds.push({key: this.inputs[i], value: insBatch[i]});
              }
            } else {
              feeds.push({key: this.inputs[0], value: insBatch});
            }
            const feedDict = new FeedDict(feeds);
            return execute(this.outputs, feedDict);
          });
          batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));
        }
        return singletonOrArray(outsBatches.map((batches2) => concat(batches2, 0)));
      });
    }
    predict(x, args = {}) {
      const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);
      checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);
      try {
        const batchSize = args.batchSize == null ? 32 : args.batchSize;
        checkBatchSize(batchSize);
        return this.predictLoop(xsRank2OrHigher, batchSize);
      } finally {
        disposeNewTensors(xsRank2OrHigher, x);
      }
    }
    predictOnBatch(x) {
      checkInputData(x, this.inputNames, this.feedInputShapes, true);
      const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];
      return this.predictLoop(x, batchSize);
    }
    standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {
      if (this.optimizer_ == null) {
        throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");
      }
      const outputShapes = [];
      for (let i = 0; i < this.feedOutputShapes.length; ++i) {
        const outputShape = this.feedOutputShapes[i];
        const lossFn = this.feedLossFns[i];
        if (lossFn === sparseCategoricalCrossentropy) {
          outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));
        } else {
          outputShapes.push(outputShape);
        }
      }
      x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, "input");
      y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, "target");
      checkArrayLengths(x, y, null);
      checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);
      if (this.stateful && batchSize != null && batchSize > 0) {
        if (x[0].shape[0] % batchSize !== 0) {
          throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);
        }
      }
      return [x, y];
    }
    async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {
      const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);
      if (sampleWeight != null) {
        throw new Error("sample weight is not supported yet.");
      }
      let standardSampleWeights = null;
      if (classWeight != null) {
        const classWeights = standardizeClassWeights(classWeight, this.outputNames);
        standardSampleWeights = [];
        for (let i = 0; i < classWeights.length; ++i) {
          standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i]));
        }
      }
      return [standardXs, standardYs, standardSampleWeights];
    }
    testLoop(f, ins, batchSize, verbose = 0, steps) {
      return tidy(() => {
        const numSamples = this.checkNumSamples(ins, batchSize, steps, "steps");
        const outs = [];
        if (verbose > 0) {
          throw new NotImplementedError("Verbose mode is not implemented yet.");
        }
        if (steps != null) {
          throw new NotImplementedError("steps mode in testLoop() is not implemented yet");
        } else {
          const batches = makeBatches(numSamples, batchSize);
          const indexArray = tensor1d(range2(0, numSamples));
          for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
            const batchStart = batches[batchIndex][0];
            const batchEnd = batches[batchIndex][1];
            const batchIds = sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);
            const insBatch = sliceArraysByIndices(ins, batchIds);
            const batchOuts = f(insBatch);
            if (batchIndex === 0) {
              for (let i = 0; i < batchOuts.length; ++i) {
                outs.push(scalar(0));
              }
            }
            for (let i = 0; i < batchOuts.length; ++i) {
              const batchOut = batchOuts[i];
              outs[i] = add2(outs[i], mul(batchEnd - batchStart, batchOut));
            }
          }
          for (let i = 0; i < outs.length; ++i) {
            outs[i] = div(outs[i], numSamples);
          }
        }
        return outs;
      });
    }
    getDedupedMetricsNames() {
      const outLabels = this.metricsNames;
      const dedupedOutLabels = [];
      for (let i = 0; i < outLabels.length; ++i) {
        const label = outLabels[i];
        let newLabel = label;
        if (count(outLabels, label) > 1) {
          const dupIndex = count(outLabels.slice(0, i), label);
          newLabel += `_${dupIndex}`;
        }
        dedupedOutLabels.push(newLabel);
      }
      return dedupedOutLabels;
    }
    makeTrainFunction() {
      return (data) => {
        const lossValues = [];
        const inputs = data.slice(0, this.inputs.length);
        const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);
        const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);
        const metricsValues = [];
        const totalLossFunction = () => {
          const feeds = [];
          for (let i = 0; i < this.inputs.length; ++i) {
            feeds.push({key: this.inputs[i], value: inputs[i]});
          }
          const feedDict = new FeedDict(feeds);
          const outputs = execute(this.outputs, feedDict, {training: true});
          let totalLoss;
          for (let i = 0; i < this.lossFunctions.length; ++i) {
            const lossFunction = this.lossFunctions[i];
            let loss = lossFunction(targets[i], outputs[i]);
            if (sampleWeights[i] != null) {
              loss = computeWeightedLoss2(loss, sampleWeights[i]);
            }
            const meanLoss = mean(loss);
            lossValues.push(meanLoss);
            if (i === 0) {
              totalLoss = loss;
            } else {
              totalLoss = add2(totalLoss, loss);
            }
          }
          for (let i = 0; i < this.metricsTensors.length; ++i) {
            let weightedMetric;
            if (this.outputs.length > 1 && i < this.outputs.length) {
              weightedMetric = lossValues[i];
            } else {
              const metric = this.metricsTensors[i][0];
              const outputIndex = this.metricsTensors[i][1];
              weightedMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));
            }
            keep(weightedMetric);
            metricsValues.push(weightedMetric);
          }
          totalLoss = mean(totalLoss);
          this.calculateLosses().forEach((regularizerLoss) => {
            totalLoss = add2(totalLoss, regularizerLoss);
          });
          return totalLoss;
        };
        const variables = this.collectedTrainableWeights.map((param) => param.read());
        const returnCost = true;
        const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);
        return [totalLossValue].concat(metricsValues);
      };
    }
    makeTestFunction() {
      this.testFunction = (data) => {
        return tidy(() => {
          const valOutputs = [];
          let totalLoss;
          const inputs = data.slice(0, this.inputs.length);
          const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);
          const feeds = [];
          for (let i = 0; i < this.inputs.length; ++i) {
            feeds.push({key: this.inputs[i], value: inputs[i]});
          }
          const feedDict = new FeedDict(feeds);
          const outputs = execute(this.outputs, feedDict);
          for (let i = 0; i < this.lossFunctions.length; ++i) {
            const lossFunction = this.lossFunctions[i];
            const loss = mean(lossFunction(targets[i], outputs[i]));
            if (i === 0) {
              totalLoss = loss;
            } else {
              totalLoss = add2(totalLoss, loss);
            }
            valOutputs.push(totalLoss);
          }
          for (let i = 0; i < this.metricsTensors.length; ++i) {
            const metric = this.metricsTensors[i][0];
            const outputIndex = this.metricsTensors[i][1];
            const meanMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));
            valOutputs.push(meanMetric);
          }
          return valOutputs;
        });
      };
    }
    async fit(x, y, args = {}) {
      return fitTensors(this, x, y, args);
    }
    async fitDataset(dataset, args) {
      return fitDataset(this, dataset, args);
    }
    async trainOnBatch(x, y) {
      const standardizeOut = await this.standardizeUserData(x, y);
      const inputs = standardizeOut[0];
      const targets = standardizeOut[1];
      const trainFunction = this.makeTrainFunction();
      const losses4 = trainFunction(inputs.concat(targets));
      const lossValues = [];
      for (const loss of losses4) {
        const v = await loss.data();
        lossValues.push(v[0]);
      }
      dispose(losses4);
      return singletonOrArray(lossValues);
    }
    getNamedWeights(config) {
      const namedWeights = [];
      const trainableOnly = config != null && config.trainableOnly;
      const weights = trainableOnly ? this.trainableWeights : this.weights;
      const weightValues = this.getWeights(trainableOnly);
      for (let i = 0; i < weights.length; ++i) {
        if (trainableOnly && !weights[i].trainable) {
          continue;
        }
        namedWeights.push({name: weights[i].originalName, tensor: weightValues[i]});
      }
      return namedWeights;
    }
    set stopTraining(stop) {
      this.stopTraining_ = stop;
    }
    get stopTraining() {
      return this.stopTraining_;
    }
    get optimizer() {
      return this.optimizer_;
    }
    set optimizer(optimizer) {
      if (this.optimizer_ !== optimizer) {
        this.optimizer_ = optimizer;
        this.isOptimizerOwned = false;
      }
    }
    dispose() {
      const result = super.dispose();
      if (result.refCountAfterDispose === 0 && this.optimizer != null && this.isOptimizerOwned) {
        const numTensorsBeforeOptmizerDisposal = memory().numTensors;
        this.optimizer_.dispose();
        result.numDisposedVariables += numTensorsBeforeOptmizerDisposal - memory().numTensors;
      }
      return result;
    }
    getLossIdentifiers() {
      let lossNames;
      if (typeof this.loss === "string") {
        lossNames = toSnakeCase(this.loss);
      } else if (Array.isArray(this.loss)) {
        for (const loss of this.loss) {
          if (typeof loss !== "string") {
            throw new Error("Serialization of non-string loss is not supported.");
          }
        }
        lossNames = this.loss.map((name) => toSnakeCase(name));
      } else {
        const outputNames = Object.keys(this.loss);
        lossNames = {};
        const losses4 = this.loss;
        for (const outputName of outputNames) {
          if (typeof losses4[outputName] === "string") {
            lossNames[outputName] = toSnakeCase(losses4[outputName]);
          } else {
            throw new Error("Serialization of non-string loss is not supported.");
          }
        }
      }
      return lossNames;
    }
    getMetricIdentifiers() {
      if (typeof this.metrics === "string" || typeof this.metrics === "function") {
        return [toSnakeCase(getLossOrMetricName(this.metrics))];
      } else if (Array.isArray(this.metrics)) {
        return this.metrics.map((metric) => toSnakeCase(getLossOrMetricName(metric)));
      } else {
        const metricsIdentifiers = {};
        for (const key in this.metrics) {
          metricsIdentifiers[key] = toSnakeCase(getLossOrMetricName(this.metrics[key]));
        }
        return metricsIdentifiers;
      }
    }
    getTrainingConfig() {
      return {
        loss: this.getLossIdentifiers(),
        metrics: this.getMetricIdentifiers(),
        optimizer_config: {
          class_name: this.optimizer.getClassName(),
          config: this.optimizer.getConfig()
        }
      };
    }
    loadTrainingConfig(trainingConfig) {
      if (trainingConfig.weighted_metrics != null) {
        throw new Error("Loading weight_metrics is not supported yet.");
      }
      if (trainingConfig.loss_weights != null) {
        throw new Error("Loading loss_weights is not supported yet.");
      }
      if (trainingConfig.sample_weight_mode != null) {
        throw new Error("Loading sample_weight_mode is not supported yet.");
      }
      const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);
      const optimizer = deserialize(tsConfig);
      let loss;
      if (typeof trainingConfig.loss === "string") {
        loss = toCamelCase(trainingConfig.loss);
      } else if (Array.isArray(trainingConfig.loss)) {
        loss = trainingConfig.loss.map((lossEntry) => toCamelCase(lossEntry));
      } else if (trainingConfig.loss != null) {
        loss = {};
        for (const key in trainingConfig.loss) {
          loss[key] = toCamelCase(trainingConfig.loss[key]);
        }
      }
      let metrics2;
      if (Array.isArray(trainingConfig.metrics)) {
        metrics2 = trainingConfig.metrics.map((metric) => toCamelCase(metric));
      } else if (trainingConfig.metrics != null) {
        metrics2 = {};
        for (const key in trainingConfig.metrics) {
          metrics2[key] = toCamelCase(trainingConfig.metrics[key]);
        }
      }
      this.compile({loss, metrics: metrics2, optimizer});
    }
    async save(handlerOrURL, config) {
      if (typeof handlerOrURL === "string") {
        const handlers = io_exports.getSaveHandlers(handlerOrURL);
        if (handlers.length === 0) {
          throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);
        } else if (handlers.length > 1) {
          throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);
        }
        handlerOrURL = handlers[0];
      }
      if (handlerOrURL.save == null) {
        throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
      }
      const weightDataAndSpecs = await io_exports.encodeWeights(this.getNamedWeights(config));
      const returnString = false;
      const unusedArg = null;
      const modelConfig = this.toJSON(unusedArg, returnString);
      const modelArtifacts = {
        modelTopology: modelConfig,
        format: LAYERS_MODEL_FORMAT_NAME,
        generatedBy: `TensorFlow.js tfjs-layers v${version2}`,
        convertedBy: null
      };
      const includeOptimizer = config == null ? false : config.includeOptimizer;
      if (includeOptimizer && this.optimizer != null) {
        modelArtifacts.trainingConfig = this.getTrainingConfig();
        const weightType = "optimizer";
        const {data: optimizerWeightData, specs: optimizerWeightSpecs} = await io_exports.encodeWeights(await this.optimizer.getWeights(), weightType);
        weightDataAndSpecs.specs.push(...optimizerWeightSpecs);
        weightDataAndSpecs.data = io_exports.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);
      }
      if (this.userDefinedMetadata != null) {
        const checkSize = true;
        checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);
        modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;
      }
      modelArtifacts.weightData = weightDataAndSpecs.data;
      modelArtifacts.weightSpecs = weightDataAndSpecs.specs;
      return handlerOrURL.save(modelArtifacts);
    }
    setUserDefinedMetadata(userDefinedMetadata) {
      checkUserDefinedMetadata(userDefinedMetadata, this.name);
      this.userDefinedMetadata = userDefinedMetadata;
    }
    getUserDefinedMetadata() {
      return this.userDefinedMetadata;
    }
  };
  LayersModel.className = "Model";
  serialization_exports.registerClass(LayersModel);
  var Functional = class extends LayersModel {
  };
  Functional.className = "Functional";
  serialization_exports.registerClass(Functional);
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  async function modelFromJSON(modelAndWeightsConfig, customObjects) {
    if (!("modelTopology" in modelAndWeightsConfig)) {
      modelAndWeightsConfig = {modelTopology: modelAndWeightsConfig};
    }
    modelAndWeightsConfig = modelAndWeightsConfig;
    let modelTopology = modelAndWeightsConfig.modelTopology;
    if (modelTopology["model_config"] != null) {
      modelTopology = modelTopology["model_config"];
    }
    const tsConfig = convertPythonicToTs(modelTopology);
    const model2 = deserialize(tsConfig, customObjects);
    if (modelAndWeightsConfig.weightsManifest != null) {
      const weightValues = await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model2.weights.map((weight) => weight.originalName));
      const uniqueWeightValues = {};
      for (const weight of model2.weights) {
        uniqueWeightValues[weight.originalName] = weightValues[weight.originalName];
      }
      model2.loadWeights(uniqueWeightValues);
      dispose(weightValues);
    }
    return model2;
  }
  async function loadLayersModelInternal(pathOrIOHandler, options) {
    if (options == null) {
      options = {};
    }
    if (typeof pathOrIOHandler === "string") {
      const handlers = io_exports.getLoadHandlers(pathOrIOHandler, options);
      if (handlers.length === 0) {
        handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler, options));
      } else if (handlers.length > 1) {
        throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);
      }
      pathOrIOHandler = handlers[0];
    }
    return loadLayersModelFromIOHandler(pathOrIOHandler, void 0, options);
  }
  async function loadLayersModelFromIOHandler(handler, customObjects, options) {
    if (options == null) {
      options = {};
    }
    if (handler.load == null) {
      throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
    }
    const artifacts = await handler.load();
    let modelTopology = artifacts.modelTopology;
    if (modelTopology["model_config"] != null) {
      modelTopology = modelTopology["model_config"];
    }
    const strict = options.strict == null ? true : options.strict;
    const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;
    const model2 = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);
    const trainingConfig = artifacts.trainingConfig;
    if (trainingConfig != null) {
      model2.loadTrainingConfig(trainingConfig);
    }
    if (artifacts.userDefinedMetadata != null) {
      model2.setUserDefinedMetadata(artifacts.userDefinedMetadata);
    }
    if (artifacts.weightData != null) {
      if (artifacts.weightSpecs == null) {
        throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");
      }
      const {modelWeights, optimizerWeights} = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);
      model2.loadWeights(modelWeights, strict);
      if (model2.optimizer != null && optimizerWeights.length > 0) {
        await model2.optimizer.setWeights(optimizerWeights);
      }
      dispose(modelWeights);
      dispose(optimizerWeights.map((w) => w.tensor));
    }
    return model2;
  }
  function decodeModelAndOptimizerWeights(buffer2, specs) {
    const name2Tensor = io_exports.decodeWeights(buffer2, specs);
    const modelWeights = {};
    const optimizerWeights = [];
    specs.forEach((spec) => {
      if (spec.group === "optimizer") {
        optimizerWeights.push({name: spec.name, tensor: name2Tensor[spec.name]});
      } else {
        modelWeights[spec.name] = name2Tensor[spec.name];
      }
    });
    return {modelWeights, optimizerWeights};
  }
  var Sequential = class extends LayersModel {
    constructor(args) {
      super({inputs: [], outputs: []});
      args = args || {};
      this.trainable = true;
      this.built = false;
      this.name = args.name != null ? args.name : getUid("sequential_");
      if (args.layers != null) {
        for (const layer of args.layers) {
          this.add(layer);
        }
      }
    }
    checkShape(layer) {
      const shape = layer.inboundNodes[0].outputTensors[0].shape;
      if (shape.some((x) => x < 0)) {
        throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`);
      }
    }
    add(layer) {
      const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;
      let modelLayer;
      if (isLayerModelInstance) {
        modelLayer = layer;
        if (modelLayer.outputs.length !== 1) {
          throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
        }
        if (modelLayer.inputs.length !== 1) {
          throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.");
        }
      }
      if (this.outputs.length === 0) {
        if (layer.inboundNodes.length === 0) {
          if (layer.batchInputShape == null) {
            throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");
          }
          const x = Input({
            batchShape: layer.batchInputShape,
            dtype: layer.dtype,
            name: layer.name + "_input"
          });
          layer.apply(x);
        }
        if (isLayerModelInstance) {
          this.outputs = modelLayer.outputs;
          this.inputs = modelLayer.inputs;
        } else {
          if (layer.inboundNodes.length !== 1) {
            throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);
          }
          if (layer.inboundNodes[0].outputTensors.length !== 1) {
            throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
          }
          this.checkShape(layer);
          this.outputs = [layer.inboundNodes[0].outputTensors[0]];
          this.inputs = getSourceInputs(this.outputs[0]);
        }
        this.inboundNodes = [];
        new Node({
          outboundLayer: this,
          inboundLayers: [],
          nodeIndices: [],
          tensorIndices: [],
          inputTensors: this.inputs,
          outputTensors: this.outputs,
          inputMasks: pyListRepeat(null, this.inputs.length),
          outputMasks: [null],
          inputShapes: this.inputs.map((x) => x.shape),
          outputShapes: this.outputs[0].shape
        });
      } else {
        const outputTensor = layer.apply(this.outputs[0]);
        if (Array.isArray(outputTensor)) {
          throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
        }
        this.checkShape(layer);
        this.outputs = [outputTensor];
        this.inboundNodes[0].outputTensors = this.outputs;
        this.inboundNodes[0].outputShapes = [this.outputs[0].shape];
      }
      this.layers.push(layer);
      this.built = false;
    }
    pop() {
      if (this.layers.length === 0) {
        throw new TypeError("There are no layers in the model.");
      }
      this.layers.pop();
      if (this.layers.length === 0) {
        this.outputs = [];
        this.inboundNodes = [];
        this.outboundNodes = [];
      } else {
        const lastLayerIndex = this.layers.length - 1;
        this.layers[lastLayerIndex].outboundNodes = [];
        this.outputs = [this.layers[lastLayerIndex].output];
        this.inboundNodes[0].outputTensors = this.outputs;
        this.inboundNodes[0].outputShapes = [this.outputs[0].shape];
      }
    }
    call(inputs, kwargs) {
      if (this.model == null) {
        this.build();
      }
      return this.model.call(inputs, kwargs);
    }
    build(inputShape) {
      getExactlyOneShape(inputShape);
      if (this.inputs.length === 0 || this.outputs.length === 0) {
        throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");
      }
      this.model = new LayersModel({
        inputs: this.inputs,
        outputs: this.outputs[0],
        name: this.name + "_model"
      });
      this.model.trainable = this.trainable;
      this.supportsMasking = this.model.supportsMasking;
      this.inputLayers = this.model.inputLayers;
      this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;
      this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;
      this.outputLayers = this.model.outputLayers;
      this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;
      this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;
      this.nodesByDepth = this.model.nodesByDepth;
      this.containerNodes = this.model.containerNodes;
      this.outputNames = this.model.outputNames;
      this.inputNames = this.model.inputNames;
      this.built = true;
    }
    countParams() {
      if (!this.built) {
        this.build();
      }
      return super.countParams();
    }
    summary(lineLength, positions, printFn = console.log) {
      if (!this.built) {
        this.build();
      }
      super.summary(lineLength, positions, printFn);
    }
    setWeights(weights) {
      if (this.model == null) {
        this.build();
      }
      this.model.setWeights(weights);
    }
    evaluate(x, y, args = {}) {
      if (!this.built) {
        throw new RuntimeError("The model needs to be compiled before being used.");
      }
      return this.model.evaluate(x, y, args);
    }
    async evaluateDataset(dataset, args) {
      if (!this.built) {
        throw new RuntimeError("The model needs to be compiled before being used.");
      }
      return this.model.evaluateDataset(dataset, args);
    }
    predict(x, args = {}) {
      if (this.model == null) {
        this.build();
      }
      return this.model.predict(x, args);
    }
    predictOnBatch(x) {
      if (this.model == null) {
        this.build();
      }
      return this.model.predictOnBatch(x);
    }
    compile(args) {
      this.build();
      this.model.compile(args);
      this.optimizer_ = this.model.optimizer;
      this.isOptimizerOwned = this.model.isOptimizerOwned;
      this.loss = this.model.loss;
      this.metrics = this.model.metrics;
      this.metricsTensors = this.model.metricsTensors;
      this.metricsNames = this.model.metricsNames;
    }
    get optimizer() {
      return this.model == null ? void 0 : this.model.optimizer;
    }
    set optimizer(optimizer) {
      this.model.optimizer = optimizer;
    }
    async fit(x, y, args = {}) {
      if (!this.built) {
        throw new RuntimeError("The model needs to be compiled before being used.");
      }
      return this.model.fit(x, y, args);
    }
    async fitDataset(dataset, args) {
      if (!this.built) {
        throw new RuntimeError("The model needs to be compiled before being used.");
      }
      return this.model.fitDataset(dataset, args);
    }
    async trainOnBatch(x, y) {
      return this.model.trainOnBatch(x, y);
    }
    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {
      let configArray;
      let extraModelConfig = {};
      if (config instanceof Array) {
        if (!(config[0].className != null) || config[0]["className"] === "Merge") {
          throw new ValueError("Legacy serialization format not supported yet.");
        }
        configArray = config;
      } else {
        util_exports.assert(config["layers"] != null, () => `When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.`);
        configArray = config["layers"];
        delete config["layers"];
        extraModelConfig = config;
      }
      const model2 = new cls(extraModelConfig);
      if (!(model2 instanceof Sequential)) {
        throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);
      }
      for (const conf of configArray) {
        const customObjects2 = void 0;
        const layer = deserialize(conf, customObjects2, fastWeightInit);
        if (fastWeightInit) {
          layer.setFastWeightInitDuringBuild(true);
        }
        model2.add(layer);
      }
      return model2;
    }
    set stopTraining(stop) {
      if (this.model == null) {
        throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");
      }
      this.model.stopTraining = stop;
    }
    get stopTraining() {
      if (this.model == null) {
        throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");
      }
      return this.model.stopTraining;
    }
    getConfig() {
      const layers = [];
      for (const layer of this.layers) {
        const dict = {};
        dict["className"] = layer.getClassName();
        dict["config"] = layer.getConfig();
        layers.push(dict);
      }
      return {name: this.name, layers};
    }
  };
  Sequential.className = "Sequential";
  serialization_exports.registerClass(Sequential);
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function model(args) {
    return new LayersModel(args);
  }
  function sequential(config) {
    return new Sequential(config);
  }
  function loadLayersModel(pathOrIOHandler, options) {
    if (options == null) {
      options = {};
    }
    return loadLayersModelInternal(pathOrIOHandler, options);
  }
  function input(config) {
    return Input(config);
  }
  function registerCallbackConstructor(verbosityLevel, callbackConstructor) {
    CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var Activation = class extends serialization_exports.Serializable {
    getConfig() {
      return {};
    }
  };
  var Elu2 = class extends Activation {
    apply(x, alpha = 1) {
      return elu2(x, alpha);
    }
  };
  Elu2.className = "elu";
  serialization_exports.registerClass(Elu2);
  var Selu2 = class extends Activation {
    apply(x) {
      return selu(x);
    }
  };
  Selu2.className = "selu";
  serialization_exports.registerClass(Selu2);
  var Relu2 = class extends Activation {
    apply(x) {
      return relu(x);
    }
  };
  Relu2.className = "relu";
  serialization_exports.registerClass(Relu2);
  var Relu62 = class extends Activation {
    apply(x) {
      return tidy(() => minimum(6, relu(x)));
    }
  };
  Relu62.className = "relu6";
  serialization_exports.registerClass(Relu62);
  var Linear = class extends Activation {
    apply(x) {
      return x;
    }
  };
  Linear.className = "linear";
  serialization_exports.registerClass(Linear);
  var Sigmoid2 = class extends Activation {
    apply(x) {
      return sigmoid(x);
    }
  };
  Sigmoid2.className = "sigmoid";
  serialization_exports.registerClass(Sigmoid2);
  var HardSigmoid = class extends Activation {
    apply(x) {
      return hardSigmoid(x);
    }
  };
  HardSigmoid.className = "hardSigmoid";
  serialization_exports.registerClass(HardSigmoid);
  var Softplus2 = class extends Activation {
    apply(x) {
      return softplus(x);
    }
  };
  Softplus2.className = "softplus";
  serialization_exports.registerClass(Softplus2);
  var Softsign = class extends Activation {
    apply(x) {
      return softsign(x);
    }
  };
  Softsign.className = "softsign";
  serialization_exports.registerClass(Softsign);
  var Tanh2 = class extends Activation {
    apply(x) {
      return tanh2(x);
    }
  };
  Tanh2.className = "tanh";
  serialization_exports.registerClass(Tanh2);
  var Softmax2 = class extends Activation {
    apply(x, axis = -1) {
      return softmax(x, axis);
    }
  };
  Softmax2.className = "softmax";
  serialization_exports.registerClass(Softmax2);
  var LogSoftmax2 = class extends Activation {
    apply(x, axis = -1) {
      return logSoftmax(x, axis);
    }
  };
  LogSoftmax2.className = "logSoftmax";
  serialization_exports.registerClass(LogSoftmax2);
  var Swish = class extends Activation {
    apply(x, alpha = 1) {
      return tidy(() => sigmoid(x.mul(alpha)).mul(x));
    }
  };
  Swish.className = "swish";
  serialization_exports.registerClass(Swish);
  function serializeActivation(activation2) {
    return activation2.getClassName();
  }
  function deserializeActivation(config, customObjects = {}) {
    return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "activation");
  }
  function getActivation(identifier) {
    if (identifier == null) {
      const config = {};
      config["className"] = "linear";
      config["config"] = {};
      return deserializeActivation(config);
    }
    if (typeof identifier === "string") {
      const config = {};
      config["className"] = identifier;
      config["config"] = {};
      return deserializeActivation(config);
    } else if (identifier instanceof Activation) {
      return identifier;
    } else {
      return deserializeActivation(identifier);
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function assertObjectArgs(args) {
    if (args != null && typeof args !== "object") {
      throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`);
    }
  }
  var Regularizer = class extends serialization_exports.Serializable {
  };
  var L1L2 = class extends Regularizer {
    constructor(args) {
      super();
      assertObjectArgs(args);
      this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;
      this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;
      this.hasL1 = this.l1 !== 0;
      this.hasL2 = this.l2 !== 0;
    }
    apply(x) {
      return tidy(() => {
        let regularization = zeros([1]);
        if (this.hasL1) {
          regularization = add2(regularization, sum2(mul(this.l1, abs(x))));
        }
        if (this.hasL2) {
          regularization = add2(regularization, sum2(mul(this.l2, square2(x))));
        }
        return regularization.asScalar();
      });
    }
    getConfig() {
      return {l1: this.l1, l2: this.l2};
    }
    static fromConfig(cls, config) {
      return new cls({l1: config["l1"], l2: config["l2"]});
    }
  };
  L1L2.className = "L1L2";
  serialization_exports.registerClass(L1L2);
  function l1(args) {
    assertObjectArgs(args);
    return new L1L2({l1: args != null ? args.l1 : null, l2: 0});
  }
  function l2(args) {
    assertObjectArgs(args);
    return new L1L2({l2: args != null ? args.l2 : null, l1: 0});
  }
  var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
    l1l2: "L1L2"
  };
  function serializeRegularizer(constraint) {
    return serializeKerasObject(constraint);
  }
  function deserializeRegularizer(config, customObjects = {}) {
    return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "regularizer");
  }
  function getRegularizer(identifier) {
    if (identifier == null) {
      return null;
    }
    if (typeof identifier === "string") {
      const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;
      const config = {className, config: {}};
      return deserializeRegularizer(config);
    } else if (identifier instanceof Regularizer) {
      return identifier;
    } else {
      return deserializeRegularizer(identifier);
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var ReLU = class extends Layer {
    constructor(args) {
      super(args == null ? {} : args);
      this.supportsMasking = true;
      if (args != null) {
        this.maxValue = args.maxValue;
      }
    }
    call(inputs, kwargs) {
      inputs = getExactlyOneTensor(inputs);
      let output = relu(inputs);
      if (this.maxValue != null) {
        output = clipByValue(output, 0, this.maxValue);
      }
      return output;
    }
    computeOutputShape(inputShape) {
      return inputShape;
    }
    getConfig() {
      const config = {maxValue: this.maxValue};
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  ReLU.className = "ReLU";
  serialization_exports.registerClass(ReLU);
  var LeakyReLU = class extends Layer {
    constructor(args) {
      super(args == null ? {} : args);
      this.DEFAULT_ALPHA = 0.3;
      if (args == null) {
        args = {};
      }
      this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;
    }
    call(inputs, kwargs) {
      const x = getExactlyOneTensor(inputs);
      return leakyRelu(x, this.alpha);
    }
    computeOutputShape(inputShape) {
      return inputShape;
    }
    getConfig() {
      const config = {alpha: this.alpha};
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  LeakyReLU.className = "LeakyReLU";
  serialization_exports.registerClass(LeakyReLU);
  var PReLU = class extends Layer {
    constructor(args) {
      super(args == null ? {} : args);
      this.DEFAULT_ALPHA_INITIALIZER = "zeros";
      if (args == null) {
        args = {};
      }
      this.supportsMasking = true;
      this.alphaInitializer = getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);
      this.alphaRegularizer = getRegularizer(args.alphaRegularizer);
      this.alphaConstraint = getConstraint(args.alphaConstraint);
      if (args.sharedAxes == null) {
        this.sharedAxes = null;
      } else if (Array.isArray(args.sharedAxes)) {
        this.sharedAxes = args.sharedAxes;
      } else if (typeof args.sharedAxes === "number") {
        this.sharedAxes = [args.sharedAxes];
      } else {
        throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`);
      }
    }
    build(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      const paramShape = inputShape.slice(1);
      if (this.sharedAxes != null) {
        for (const i of this.sharedAxes) {
          paramShape[i - 1] = 1;
        }
      }
      this.alpha = this.addWeight("alpha", paramShape, "float32", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);
      const axes = {};
      if (this.sharedAxes != null) {
        for (let i = 1; i < inputShape.length; ++i) {
          axes[i] = inputShape[i];
        }
      }
      this.inputSpec = [new InputSpec({
        ndim: inputShape.length,
        axes
      })];
      this.built = true;
    }
    call(inputs, kwargs) {
      inputs = getExactlyOneTensor(inputs);
      return prelu(inputs, this.alpha.read());
    }
    getConfig() {
      const config = {
        alphaInitializer: serializeInitializer(this.alphaInitializer),
        alphaRegularizer: serializeRegularizer(this.alphaRegularizer),
        alphaConstraint: serializeConstraint(this.alphaConstraint),
        sharedAxes: this.sharedAxes
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  PReLU.className = "PReLU";
  serialization_exports.registerClass(PReLU);
  var ELU = class extends Layer {
    constructor(args) {
      super(args == null ? {} : args);
      this.DEFAULT_ALPHA = 1;
      if (args == null) {
        args = {};
      }
      if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {
        throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);
      }
      this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;
    }
    call(inputs, kwargs) {
      const x = getExactlyOneTensor(inputs);
      return elu(x);
    }
    computeOutputShape(inputShape) {
      return inputShape;
    }
    getConfig() {
      const config = {alpha: this.alpha};
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  ELU.className = "ELU";
  serialization_exports.registerClass(ELU);
  var ThresholdedReLU = class extends Layer {
    constructor(args) {
      super(args == null ? {} : args);
      this.DEFAULT_THETA = 1;
      if (args == null) {
        args = {};
      }
      this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;
    }
    call(inputs, kwargs) {
      const x = getExactlyOneTensor(inputs);
      return x.mul(cast2(x.greater(this.theta), "float32"));
    }
    computeOutputShape(inputShape) {
      return inputShape;
    }
    getConfig() {
      const config = {theta: this.theta};
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  ThresholdedReLU.className = "ThresholdedReLU";
  serialization_exports.registerClass(ThresholdedReLU);
  var Softmax3 = class extends Layer {
    constructor(args) {
      super(args == null ? {} : args);
      this.DEFAULT_AXIS = 1;
      if (args == null) {
        args = {};
      }
      this.softmax = new Softmax2().apply;
      this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;
    }
    call(inputs, kwargs) {
      const x = getExactlyOneTensor(inputs);
      return this.softmax(x, this.axis);
    }
    computeOutputShape(inputShape) {
      return inputShape;
    }
    getConfig() {
      const config = {axis: this.axis};
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  Softmax3.className = "Softmax";
  serialization_exports.registerClass(Softmax3);
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function normalizeArray(value, n, name) {
    if (typeof value === "number") {
      return pyListRepeat(value, n);
    } else {
      if (value.length !== n) {
        throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);
      }
      for (let i = 0; i < n; ++i) {
        const singleValue = value[i];
        if (!isInteger(singleValue)) {
          throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`);
        }
      }
      return value;
    }
  }
  function convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) {
    if (inputLength == null) {
      return inputLength;
    }
    const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);
    let outputLength;
    if (padding === "same") {
      outputLength = inputLength;
    } else {
      outputLength = inputLength - dilatedFilterSize + 1;
    }
    return Math.floor((outputLength + stride - 1) / stride);
  }
  function deconvLength(dimSize, strideSize, kernelSize, padding) {
    if (dimSize == null) {
      return null;
    }
    if (padding === "valid") {
      dimSize = dimSize * strideSize + max2([kernelSize - strideSize, 0]);
    } else if (padding === "same") {
      dimSize = dimSize * strideSize;
    } else {
      throw new ValueError(`Unsupport padding mode: ${padding}.`);
    }
    return dimSize;
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function preprocessConv2DInput(x, dataFormat) {
    return tidy(() => {
      checkDataFormat(dataFormat);
      if (dataFormat === "channelsFirst") {
        return transpose(x, [0, 2, 3, 1]);
      } else {
        return x;
      }
    });
  }
  function preprocessConv3DInput(x, dataFormat) {
    return tidy(() => {
      checkDataFormat(dataFormat);
      if (dataFormat === "channelsFirst") {
        return transpose(x, [0, 2, 3, 4, 1]);
      } else {
        return x;
      }
    });
  }
  function conv1dWithBias(x, kernel, bias, strides = 1, padding = "valid", dataFormat, dilationRate = 1) {
    return tidy(() => {
      if (dataFormat == null) {
        dataFormat = imageDataFormat();
      }
      checkDataFormat(dataFormat);
      if (x.shape.length !== 3) {
        throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);
      }
      if (kernel.shape.length !== 3) {
        throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);
      }
      if (bias != null && bias.shape.length !== 1) {
        throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);
      }
      if (dataFormat === "channelsFirst") {
        x = transpose(x, [0, 2, 1]);
      }
      if (padding === "causal") {
        throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
      }
      let y = conv1d(x, kernel, strides, padding === "same" ? "same" : "valid", "NWC", dilationRate);
      if (bias != null) {
        y = biasAdd(y, bias);
      }
      return y;
    });
  }
  function conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = "valid", dataFormat, dilationRate, activation2 = null) {
    return tidy(() => {
      if (dataFormat == null) {
        dataFormat = imageDataFormat();
      }
      checkDataFormat(dataFormat);
      if (x.rank !== 3 && x.rank !== 4) {
        throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);
      }
      if (kernel.rank !== 3 && kernel.rank !== 4) {
        throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);
      }
      let y = preprocessConv2DInput(x, dataFormat);
      if (padding === "causal") {
        throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
      }
      y = fused_ops_exports.conv2d({
        x: y,
        filter: kernel,
        strides,
        pad: padding === "same" ? "same" : "valid",
        dilations: dilationRate,
        dataFormat: "NHWC",
        bias,
        activation: activation2
      });
      if (dataFormat === "channelsFirst") {
        y = transpose(y, [0, 3, 1, 2]);
      }
      return y;
    });
  }
  function conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = "valid", dataFormat, dilationRate) {
    return tidy(() => {
      if (dataFormat == null) {
        dataFormat = imageDataFormat();
      }
      checkDataFormat(dataFormat);
      if (x.rank !== 4 && x.rank !== 5) {
        throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);
      }
      if (kernel.rank !== 4 && kernel.rank !== 5) {
        throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);
      }
      let y = preprocessConv3DInput(x, dataFormat);
      if (padding === "causal") {
        throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");
      }
      y = conv3d(y, kernel, strides, padding === "same" ? "same" : "valid", "NDHWC", dilationRate);
      if (bias != null) {
        y = biasAdd(y, bias);
      }
      if (dataFormat === "channelsFirst") {
        y = transpose(y, [0, 4, 1, 2, 3]);
      }
      return y;
    });
  }
  var BaseConv = class extends Layer {
    constructor(rank, args) {
      super(args);
      this.bias = null;
      this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
      this.DEFAULT_BIAS_INITIALIZER = "zeros";
      BaseConv.verifyArgs(args);
      this.rank = rank;
      assertPositiveInteger(this.rank, "rank");
      if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {
        throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);
      }
      this.kernelSize = normalizeArray(args.kernelSize, rank, "kernelSize");
      this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, "strides");
      this.padding = args.padding == null ? "valid" : args.padding;
      checkPaddingMode(this.padding);
      this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
      checkDataFormat(this.dataFormat);
      this.activation = getActivation(args.activation);
      this.useBias = args.useBias == null ? true : args.useBias;
      this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
      this.biasConstraint = getConstraint(args.biasConstraint);
      this.biasRegularizer = getRegularizer(args.biasRegularizer);
      this.activityRegularizer = getRegularizer(args.activityRegularizer);
      this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, "dilationRate");
      if (this.rank === 1 && (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {
        throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);
      } else if (this.rank === 2) {
        if (typeof this.dilationRate === "number") {
          this.dilationRate = [this.dilationRate, this.dilationRate];
        } else if (this.dilationRate.length !== 2) {
          throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`);
        }
      } else if (this.rank === 3) {
        if (typeof this.dilationRate === "number") {
          this.dilationRate = [this.dilationRate, this.dilationRate, this.dilationRate];
        } else if (this.dilationRate.length !== 3) {
          throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`);
        }
      }
    }
    static verifyArgs(args) {
      assert2("kernelSize" in args, `required key 'kernelSize' not in config`);
      if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 3)) {
        throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);
      }
    }
    getConfig() {
      const config = {
        kernelSize: this.kernelSize,
        strides: this.strides,
        padding: this.padding,
        dataFormat: this.dataFormat,
        dilationRate: this.dilationRate,
        activation: serializeActivation(this.activation),
        useBias: this.useBias,
        biasInitializer: serializeInitializer(this.biasInitializer),
        biasRegularizer: serializeRegularizer(this.biasRegularizer),
        activityRegularizer: serializeRegularizer(this.activityRegularizer),
        biasConstraint: serializeConstraint(this.biasConstraint)
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  var Conv = class extends BaseConv {
    constructor(rank, args) {
      super(rank, args);
      this.kernel = null;
      Conv.verifyArgs(args);
      this.filters = args.filters;
      assertPositiveInteger(this.filters, "filters");
      this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
      this.kernelConstraint = getConstraint(args.kernelConstraint);
      this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
    }
    build(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
      if (inputShape[channelAxis] == null) {
        throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);
      }
      const inputDim = inputShape[channelAxis];
      const kernelShape = this.kernelSize.concat([inputDim, this.filters]);
      this.kernel = this.addWeight("kernel", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
      if (this.useBias) {
        this.bias = this.addWeight("bias", [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
      }
      this.inputSpec = [{ndim: this.rank + 2, axes: {[channelAxis]: inputDim}}];
      this.built = true;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        inputs = getExactlyOneTensor(inputs);
        let outputs;
        const biasValue = this.bias == null ? null : this.bias.read();
        const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());
        if (fusedActivationName != null && this.rank === 2) {
          outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);
        } else {
          if (this.rank === 1) {
            outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);
          } else if (this.rank === 2) {
            outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);
          } else if (this.rank === 3) {
            outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);
          } else {
            throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");
          }
          if (this.activation != null) {
            outputs = this.activation.apply(outputs);
          }
        }
        return outputs;
      });
    }
    computeOutputShape(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      const newSpace = [];
      const space = this.dataFormat === "channelsLast" ? inputShape.slice(1, inputShape.length - 1) : inputShape.slice(2);
      for (let i = 0; i < space.length; ++i) {
        const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === "number" ? this.dilationRate : this.dilationRate[i]);
        newSpace.push(newDim);
      }
      let outputShape = [inputShape[0]];
      if (this.dataFormat === "channelsLast") {
        outputShape = outputShape.concat(newSpace);
        outputShape.push(this.filters);
      } else {
        outputShape.push(this.filters);
        outputShape = outputShape.concat(newSpace);
      }
      return outputShape;
    }
    getConfig() {
      const config = {
        filters: this.filters,
        kernelInitializer: serializeInitializer(this.kernelInitializer),
        kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
        kernelConstraint: serializeConstraint(this.kernelConstraint)
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
    static verifyArgs(args) {
      if (!("filters" in args) || typeof args.filters !== "number" || args.filters < 1) {
        throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`);
      }
    }
  };
  var Conv2D2 = class extends Conv {
    constructor(args) {
      super(2, args);
      Conv2D2.verifyArgs(args);
    }
    getConfig() {
      const config = super.getConfig();
      delete config["rank"];
      return config;
    }
    static verifyArgs(args) {
      if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 2)) {
        throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);
      }
    }
  };
  Conv2D2.className = "Conv2D";
  serialization_exports.registerClass(Conv2D2);
  var Conv3D2 = class extends Conv {
    constructor(args) {
      super(3, args);
      Conv3D2.verifyArgs(args);
    }
    getConfig() {
      const config = super.getConfig();
      delete config["rank"];
      return config;
    }
    static verifyArgs(args) {
      if (typeof args.kernelSize !== "number") {
        if (!(Array.isArray(args.kernelSize) && (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {
          throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);
        }
      }
    }
  };
  Conv3D2.className = "Conv3D";
  serialization_exports.registerClass(Conv3D2);
  var Conv2DTranspose = class extends Conv2D2 {
    constructor(args) {
      super(args);
      this.inputSpec = [new InputSpec({ndim: 4})];
      if (this.padding !== "same" && this.padding !== "valid") {
        throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
      }
    }
    build(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      if (inputShape.length !== 4) {
        throw new ValueError("Input should have rank 4; Received input shape: " + JSON.stringify(inputShape));
      }
      const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
      if (inputShape[channelAxis] == null) {
        throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");
      }
      const inputDim = inputShape[channelAxis];
      const kernelShape = this.kernelSize.concat([this.filters, inputDim]);
      this.kernel = this.addWeight("kernel", kernelShape, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
      if (this.useBias) {
        this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
      }
      this.inputSpec = [new InputSpec({ndim: 4, axes: {[channelAxis]: inputDim}})];
      this.built = true;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        let input2 = getExactlyOneTensor(inputs);
        if (input2.shape.length !== 4) {
          throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);
        }
        const inputShape = input2.shape;
        const batchSize = inputShape[0];
        let hAxis;
        let wAxis;
        if (this.dataFormat === "channelsFirst") {
          hAxis = 2;
          wAxis = 3;
        } else {
          hAxis = 1;
          wAxis = 2;
        }
        const height = inputShape[hAxis];
        const width = inputShape[wAxis];
        const kernelH = this.kernelSize[0];
        const kernelW = this.kernelSize[1];
        const strideH = this.strides[0];
        const strideW = this.strides[1];
        const outHeight = deconvLength(height, strideH, kernelH, this.padding);
        const outWidth = deconvLength(width, strideW, kernelW, this.padding);
        const outputShape = [batchSize, outHeight, outWidth, this.filters];
        if (this.dataFormat !== "channelsLast") {
          input2 = transpose(input2, [0, 2, 3, 1]);
        }
        let outputs = conv2dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding);
        if (this.dataFormat !== "channelsLast") {
          outputs = transpose(outputs, [0, 3, 1, 2]);
        }
        if (this.bias != null) {
          outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);
        }
        if (this.activation != null) {
          outputs = this.activation.apply(outputs);
        }
        return outputs;
      });
    }
    computeOutputShape(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      const outputShape = inputShape.slice();
      let channelAxis;
      let heightAxis;
      let widthAxis;
      if (this.dataFormat === "channelsFirst") {
        channelAxis = 1;
        heightAxis = 2;
        widthAxis = 3;
      } else {
        channelAxis = 3;
        heightAxis = 1;
        widthAxis = 2;
      }
      const kernelH = this.kernelSize[0];
      const kernelW = this.kernelSize[1];
      const strideH = this.strides[0];
      const strideW = this.strides[1];
      outputShape[channelAxis] = this.filters;
      outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);
      outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);
      return outputShape;
    }
    getConfig() {
      const config = super.getConfig();
      delete config["dilationRate"];
      return config;
    }
  };
  Conv2DTranspose.className = "Conv2DTranspose";
  serialization_exports.registerClass(Conv2DTranspose);
  var SeparableConv = class extends Conv {
    constructor(rank, config) {
      super(rank, config);
      this.DEFAULT_DEPTHWISE_INITIALIZER = "glorotUniform";
      this.DEFAULT_POINTWISE_INITIALIZER = "glorotUniform";
      this.depthwiseKernel = null;
      this.pointwiseKernel = null;
      if (config.filters == null) {
        throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");
      }
      if (config.kernelInitializer != null || config.kernelRegularizer != null || config.kernelConstraint != null) {
        throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");
      }
      if (config.padding != null && config.padding !== "same" && config.padding !== "valid") {
        throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`);
      }
      this.depthMultiplier = config.depthMultiplier == null ? 1 : config.depthMultiplier;
      this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);
      this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);
      this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);
      this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);
      this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);
      this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);
    }
    build(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      if (inputShape.length < this.rank + 2) {
        throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank + 2}, but received input shape: ${JSON.stringify(inputShape)}`);
      }
      const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
      if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {
        throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);
      }
      const inputDim = inputShape[channelAxis];
      const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);
      const pointwiseKernelShape = [];
      for (let i = 0; i < this.rank; ++i) {
        pointwiseKernelShape.push(1);
      }
      pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);
      const trainable = true;
      this.depthwiseKernel = this.addWeight("depthwise_kernel", depthwiseKernelShape, "float32", this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);
      this.pointwiseKernel = this.addWeight("pointwise_kernel", pointwiseKernelShape, "float32", this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);
      if (this.useBias) {
        this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);
      } else {
        this.bias = null;
      }
      this.inputSpec = [new InputSpec({ndim: this.rank + 2, axes: {[channelAxis]: inputDim}})];
      this.built = true;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        inputs = getExactlyOneTensor(inputs);
        let output;
        if (this.rank === 1) {
          throw new NotImplementedError("1D separable convolution is not implemented yet.");
        } else if (this.rank === 2) {
          if (this.dataFormat === "channelsFirst") {
            inputs = transpose(inputs, [0, 2, 3, 1]);
          }
          output = separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, "NHWC");
        }
        if (this.useBias) {
          output = biasAdd(output, this.bias.read(), this.dataFormat);
        }
        if (this.activation != null) {
          output = this.activation.apply(output);
        }
        if (this.dataFormat === "channelsFirst") {
          output = transpose(output, [0, 3, 1, 2]);
        }
        return output;
      });
    }
    getConfig() {
      const config = super.getConfig();
      delete config["rank"];
      delete config["kernelInitializer"];
      delete config["kernelRegularizer"];
      delete config["kernelConstraint"];
      config["depthwiseInitializer"] = serializeInitializer(this.depthwiseInitializer);
      config["pointwiseInitializer"] = serializeInitializer(this.pointwiseInitializer);
      config["depthwiseRegularizer"] = serializeRegularizer(this.depthwiseRegularizer);
      config["pointwiseRegularizer"] = serializeRegularizer(this.pointwiseRegularizer);
      config["depthwiseConstraint"] = serializeConstraint(this.depthwiseConstraint);
      config["pointwiseConstraint"] = serializeConstraint(this.pointwiseConstraint);
      return config;
    }
  };
  SeparableConv.className = "SeparableConv";
  var SeparableConv2D = class extends SeparableConv {
    constructor(args) {
      super(2, args);
    }
  };
  SeparableConv2D.className = "SeparableConv2D";
  serialization_exports.registerClass(SeparableConv2D);
  var Conv1D = class extends Conv {
    constructor(args) {
      super(1, args);
      Conv1D.verifyArgs(args);
      this.inputSpec = [{ndim: 3}];
    }
    getConfig() {
      const config = super.getConfig();
      delete config["rank"];
      delete config["dataFormat"];
      return config;
    }
    static verifyArgs(args) {
      if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 1)) {
        throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`);
      }
    }
  };
  Conv1D.className = "Conv1D";
  serialization_exports.registerClass(Conv1D);
  var Cropping2D = class extends Layer {
    constructor(args) {
      super(args);
      if (typeof args.cropping === "number") {
        this.cropping = [[args.cropping, args.cropping], [args.cropping, args.cropping]];
      } else if (typeof args.cropping[0] === "number") {
        this.cropping = [
          [args.cropping[0], args.cropping[0]],
          [args.cropping[1], args.cropping[1]]
        ];
      } else {
        this.cropping = args.cropping;
      }
      this.dataFormat = args.dataFormat === void 0 ? "channelsLast" : args.dataFormat;
      this.inputSpec = [{ndim: 4}];
    }
    computeOutputShape(inputShape) {
      if (this.dataFormat === "channelsFirst") {
        return [
          inputShape[0],
          inputShape[1],
          inputShape[2] - this.cropping[0][0] - this.cropping[0][1],
          inputShape[3] - this.cropping[1][0] - this.cropping[1][1]
        ];
      } else {
        return [
          inputShape[0],
          inputShape[1] - this.cropping[0][0] - this.cropping[0][1],
          inputShape[2] - this.cropping[1][0] - this.cropping[1][1],
          inputShape[3]
        ];
      }
    }
    call(inputs, kwargs) {
      return tidy(() => {
        inputs = getExactlyOneTensor(inputs);
        if (this.dataFormat === "channelsLast") {
          const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);
          return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);
        } else {
          const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);
          return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);
        }
      });
    }
    getConfig() {
      const config = {cropping: this.cropping, dataFormat: this.dataFormat};
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  Cropping2D.className = "Cropping2D";
  serialization_exports.registerClass(Cropping2D);
  var UpSampling2D = class extends Layer {
    constructor(args) {
      super(args);
      this.DEFAULT_SIZE = [2, 2];
      this.inputSpec = [{ndim: 4}];
      this.size = args.size == null ? this.DEFAULT_SIZE : args.size;
      this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
      checkDataFormat(this.dataFormat);
      this.interpolation = args.interpolation == null ? "nearest" : args.interpolation;
      checkInterpolationFormat(this.interpolation);
    }
    computeOutputShape(inputShape) {
      if (this.dataFormat === "channelsFirst") {
        const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];
        const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];
        return [inputShape[0], inputShape[1], height, width];
      } else {
        const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];
        const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];
        return [inputShape[0], height, width, inputShape[3]];
      }
    }
    call(inputs, kwargs) {
      return tidy(() => {
        let input2 = getExactlyOneTensor(inputs);
        const inputShape = input2.shape;
        if (this.dataFormat === "channelsFirst") {
          input2 = transpose(input2, [0, 2, 3, 1]);
          const height = this.size[0] * inputShape[2];
          const width = this.size[1] * inputShape[3];
          const resized = this.interpolation === "nearest" ? input2.resizeNearestNeighbor([height, width]) : input2.resizeBilinear([height, width]);
          return transpose(resized, [0, 3, 1, 2]);
        } else {
          const height = this.size[0] * inputShape[1];
          const width = this.size[1] * inputShape[2];
          return this.interpolation === "nearest" ? input2.resizeNearestNeighbor([height, width]) : input2.resizeBilinear([height, width]);
        }
      });
    }
    getConfig() {
      const config = {size: this.size, dataFormat: this.dataFormat};
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  UpSampling2D.className = "UpSampling2D";
  serialization_exports.registerClass(UpSampling2D);
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function depthwiseConv2d3(x, depthwiseKernel, strides = [1, 1], padding = "valid", dataFormat, dilationRate) {
    return tidy(() => {
      if (dataFormat == null) {
        dataFormat = imageDataFormat();
      }
      checkDataFormat(dataFormat);
      let y = preprocessConv2DInput(x, dataFormat);
      if (x.rank !== 4) {
        throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);
      }
      if (depthwiseKernel.rank !== 4) {
        throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);
      }
      y = depthwiseConv2d(y, depthwiseKernel, strides, padding === "same" ? "same" : "valid", "NHWC", dilationRate);
      if (dataFormat === "channelsFirst") {
        y = transpose(y, [0, 3, 1, 2]);
      }
      return y;
    });
  }
  var DepthwiseConv2D = class extends BaseConv {
    constructor(args) {
      super(2, args);
      this.depthwiseKernel = null;
      this.depthMultiplier = args.depthMultiplier == null ? 1 : args.depthMultiplier;
      this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);
      this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);
      this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);
    }
    build(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      if (inputShape.length < 4) {
        throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);
      }
      const channelAxis = this.dataFormat === "channelsFirst" ? 1 : 3;
      if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {
        throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);
      }
      const inputDim = inputShape[channelAxis];
      const depthwiseKernelShape = [
        this.kernelSize[0],
        this.kernelSize[1],
        inputDim,
        this.depthMultiplier
      ];
      this.depthwiseKernel = this.addWeight("depthwise_kernel", depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);
      if (this.useBias) {
        this.bias = this.addWeight("bias", [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
      } else {
        this.bias = null;
      }
      this.built = true;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        inputs = getExactlyOneTensor(inputs);
        let outputs = depthwiseConv2d3(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);
        if (this.useBias) {
          outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);
        }
        if (this.activation != null) {
          outputs = this.activation.apply(outputs);
        }
        return outputs;
      });
    }
    computeOutputShape(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      const rows = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1];
      const cols = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2];
      const outFilters = this.dataFormat === "channelsFirst" ? inputShape[1] * this.depthMultiplier : inputShape[3] * this.depthMultiplier;
      const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);
      const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);
      if (this.dataFormat === "channelsFirst") {
        return [inputShape[0], outFilters, outRows, outCols];
      } else {
        return [inputShape[0], outRows, outCols, outFilters];
      }
    }
    getConfig() {
      const config = super.getConfig();
      config["depthMultiplier"] = this.depthMultiplier;
      config["depthwiseInitializer"] = serializeInitializer(this.depthwiseInitializer);
      config["depthwiseRegularizer"] = serializeRegularizer(this.depthwiseRegularizer);
      config["depthwiseConstraint"] = serializeConstraint(this.depthwiseRegularizer);
      return config;
    }
  };
  DepthwiseConv2D.className = "DepthwiseConv2D";
  serialization_exports.registerClass(DepthwiseConv2D);
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function standardizeArgs(inputs, initialState, constants, numConstants) {
    if (Array.isArray(inputs)) {
      if (initialState != null || constants != null) {
        throw new ValueError("When inputs is an array, neither initialState or constants should be provided");
      }
      if (numConstants != null) {
        constants = inputs.slice(inputs.length - numConstants, inputs.length);
        inputs = inputs.slice(0, inputs.length - numConstants);
      }
      if (inputs.length > 1) {
        initialState = inputs.slice(1, inputs.length);
      }
      inputs = inputs[0];
    }
    function toListOrNull(x) {
      if (x == null || Array.isArray(x)) {
        return x;
      } else {
        return [x];
      }
    }
    initialState = toListOrNull(initialState);
    constants = toListOrNull(constants);
    return {inputs, initialState, constants};
  }
  function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {
    return tidy(() => {
      const ndim = inputs.shape.length;
      if (ndim < 3) {
        throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);
      }
      const axes = [1, 0].concat(range2(2, ndim));
      inputs = transpose(inputs, axes);
      if (constants != null) {
        throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");
      }
      if (unroll) {
        console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.");
      }
      if (mask != null) {
        mask = mask.asType("bool").asType("float32");
        if (mask.rank === ndim - 1) {
          mask = expandDims(mask, -1);
        }
        mask = transpose(mask, axes);
      }
      if (goBackwards) {
        inputs = reverse(inputs, 0);
        if (mask != null) {
          mask = reverse(mask, 0);
        }
      }
      const perStepOutputs = [];
      let lastOutput;
      let states = initialStates;
      const timeSteps = inputs.shape[0];
      const perStepInputs = unstack(inputs);
      let perStepMasks;
      if (mask != null) {
        perStepMasks = unstack(mask);
      }
      for (let t = 0; t < timeSteps; ++t) {
        const currentInput = perStepInputs[t];
        const stepOutputs = tidy(() => stepFunction(currentInput, states));
        if (mask == null) {
          lastOutput = stepOutputs[0];
          states = stepOutputs[1];
        } else {
          const maskedOutputs = tidy(() => {
            const stepMask = perStepMasks[t];
            const negStepMask = onesLike(stepMask).sub(stepMask);
            const output = stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask));
            const newStates = states.map((state, i) => {
              return stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask));
            });
            return {output, newStates};
          });
          lastOutput = maskedOutputs.output;
          states = maskedOutputs.newStates;
        }
        if (needPerStepOutputs) {
          perStepOutputs.push(lastOutput);
        }
      }
      let outputs;
      if (needPerStepOutputs) {
        const axis = 1;
        outputs = stack(perStepOutputs, axis);
      }
      return [lastOutput, outputs, states];
    });
  }
  var RNN = class extends Layer {
    constructor(args) {
      super(args);
      let cell;
      if (args.cell == null) {
        throw new ValueError("cell property is missing for the constructor of RNN.");
      } else if (Array.isArray(args.cell)) {
        cell = new StackedRNNCells({cells: args.cell});
      } else {
        cell = args.cell;
      }
      if (cell.stateSize == null) {
        throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");
      }
      this.cell = cell;
      this.returnSequences = args.returnSequences == null ? false : args.returnSequences;
      this.returnState = args.returnState == null ? false : args.returnState;
      this.goBackwards = args.goBackwards == null ? false : args.goBackwards;
      this._stateful = args.stateful == null ? false : args.stateful;
      this.unroll = args.unroll == null ? false : args.unroll;
      this.supportsMasking = true;
      this.inputSpec = [new InputSpec({ndim: 3})];
      this.stateSpec = null;
      this.states_ = null;
      this.numConstants = null;
      this.keptStates = [];
    }
    getStates() {
      if (this.states_ == null) {
        const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
        return range2(0, numStates).map((x) => null);
      } else {
        return this.states_;
      }
    }
    setStates(states) {
      this.states_ = states;
    }
    computeOutputShape(inputShape) {
      if (isArrayOfShapes(inputShape)) {
        inputShape = inputShape[0];
      }
      inputShape = inputShape;
      let stateSize = this.cell.stateSize;
      if (!Array.isArray(stateSize)) {
        stateSize = [stateSize];
      }
      const outputDim = stateSize[0];
      let outputShape;
      if (this.returnSequences) {
        outputShape = [inputShape[0], inputShape[1], outputDim];
      } else {
        outputShape = [inputShape[0], outputDim];
      }
      if (this.returnState) {
        const stateShape = [];
        for (const dim of stateSize) {
          stateShape.push([inputShape[0], dim]);
        }
        return [outputShape].concat(stateShape);
      } else {
        return outputShape;
      }
    }
    computeMask(inputs, mask) {
      return tidy(() => {
        if (Array.isArray(mask)) {
          mask = mask[0];
        }
        const outputMask = this.returnSequences ? mask : null;
        if (this.returnState) {
          const stateMask = this.states.map((s) => null);
          return [outputMask].concat(stateMask);
        } else {
          return outputMask;
        }
      });
    }
    get states() {
      if (this.states_ == null) {
        const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
        const output = [];
        for (let i = 0; i < numStates; ++i) {
          output.push(null);
        }
        return output;
      } else {
        return this.states_;
      }
    }
    set states(s) {
      this.states_ = s;
    }
    build(inputShape) {
      const constantShape = null;
      if (this.numConstants != null) {
        throw new NotImplementedError("Constants support is not implemented in RNN yet.");
      }
      if (isArrayOfShapes(inputShape)) {
        inputShape = inputShape[0];
      }
      inputShape = inputShape;
      const batchSize = this.stateful ? inputShape[0] : null;
      const inputDim = inputShape.slice(2);
      this.inputSpec[0] = new InputSpec({shape: [batchSize, null, ...inputDim]});
      const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));
      if (constantShape != null) {
        throw new NotImplementedError("Constants support is not implemented in RNN yet.");
      } else {
        this.cell.build(stepInputShape);
      }
      let stateSize;
      if (Array.isArray(this.cell.stateSize)) {
        stateSize = this.cell.stateSize;
      } else {
        stateSize = [this.cell.stateSize];
      }
      if (this.stateSpec != null) {
        if (!util_exports.arraysEqual(this.stateSpec.map((spec) => spec.shape[spec.shape.length - 1]), stateSize)) {
          throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`);
        }
      } else {
        this.stateSpec = stateSize.map((dim) => new InputSpec({shape: [null, dim]}));
      }
      if (this.stateful) {
        this.resetStates();
      }
    }
    resetStates(states, training = false) {
      tidy(() => {
        if (!this.stateful) {
          throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");
        }
        const batchSize = this.inputSpec[0].shape[0];
        if (batchSize == null) {
          throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");
        }
        if (this.states_ == null) {
          if (Array.isArray(this.cell.stateSize)) {
            this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));
          } else {
            this.states_ = [zeros([batchSize, this.cell.stateSize])];
          }
        } else if (states == null) {
          dispose(this.states_);
          if (this.keptStates != null) {
            dispose(this.keptStates);
            this.keptStates = [];
          }
          if (Array.isArray(this.cell.stateSize)) {
            this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));
          } else {
            this.states_[0] = zeros([batchSize, this.cell.stateSize]);
          }
        } else {
          if (!Array.isArray(states)) {
            states = [states];
          }
          if (states.length !== this.states_.length) {
            throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);
          }
          if (training === true) {
            this.keptStates.push(this.states_.slice());
          } else {
            dispose(this.states_);
          }
          for (let index = 0; index < this.states_.length; ++index) {
            const value = states[index];
            const dim = Array.isArray(this.cell.stateSize) ? this.cell.stateSize[index] : this.cell.stateSize;
            const expectedShape = [batchSize, dim];
            if (!util_exports.arraysEqual(value.shape, expectedShape)) {
              throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);
            }
            this.states_[index] = value;
          }
        }
        this.states_ = this.states_.map((state) => keep(state.clone()));
      });
    }
    apply(inputs, kwargs) {
      let initialState = kwargs == null ? null : kwargs["initialState"];
      let constants = kwargs == null ? null : kwargs["constants"];
      if (kwargs == null) {
        kwargs = {};
      }
      const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);
      inputs = standardized.inputs;
      initialState = standardized.initialState;
      constants = standardized.constants;
      let additionalInputs = [];
      let additionalSpecs = [];
      if (initialState != null) {
        kwargs["initialState"] = initialState;
        additionalInputs = additionalInputs.concat(initialState);
        this.stateSpec = [];
        for (const state of initialState) {
          this.stateSpec.push(new InputSpec({shape: state.shape}));
        }
        additionalSpecs = additionalSpecs.concat(this.stateSpec);
      }
      if (constants != null) {
        kwargs["constants"] = constants;
        additionalInputs = additionalInputs.concat(constants);
        this.numConstants = constants.length;
      }
      const isTensor2 = additionalInputs[0] instanceof SymbolicTensor;
      if (isTensor2) {
        const fullInput = [inputs].concat(additionalInputs);
        const fullInputSpec = this.inputSpec.concat(additionalSpecs);
        const originalInputSpec = this.inputSpec;
        this.inputSpec = fullInputSpec;
        const output = super.apply(fullInput, kwargs);
        this.inputSpec = originalInputSpec;
        return output;
      } else {
        return super.apply(inputs, kwargs);
      }
    }
    call(inputs, kwargs) {
      return tidy(() => {
        const mask = kwargs == null ? null : kwargs["mask"];
        const training = kwargs == null ? null : kwargs["training"];
        let initialState = kwargs == null ? null : kwargs["initialState"];
        inputs = getExactlyOneTensor(inputs);
        if (initialState == null) {
          if (this.stateful) {
            initialState = this.states_;
          } else {
            initialState = this.getInitialState(inputs);
          }
        }
        const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
        if (initialState.length !== numStates) {
          throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);
        }
        if (this.unroll) {
          console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");
        }
        const cellCallKwargs = {training};
        const step5 = (inputs2, states2) => {
          const outputs2 = this.cell.call([inputs2].concat(states2), cellCallKwargs);
          return [outputs2[0], outputs2.slice(1)];
        };
        const rnnOutputs = rnn(step5, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);
        const lastOutput = rnnOutputs[0];
        const outputs = rnnOutputs[1];
        const states = rnnOutputs[2];
        if (this.stateful) {
          this.resetStates(states, training);
        }
        const output = this.returnSequences ? outputs : lastOutput;
        if (this.returnState) {
          return [output].concat(states);
        } else {
          return output;
        }
      });
    }
    getInitialState(inputs) {
      return tidy(() => {
        let initialState = zeros(inputs.shape);
        initialState = sum2(initialState, [1, 2]);
        initialState = expandDims2(initialState);
        if (Array.isArray(this.cell.stateSize)) {
          return this.cell.stateSize.map((dim) => dim > 1 ? tile2(initialState, [1, dim]) : initialState);
        } else {
          return this.cell.stateSize > 1 ? [tile2(initialState, [1, this.cell.stateSize])] : [initialState];
        }
      });
    }
    get trainableWeights() {
      if (!this.trainable) {
        return [];
      }
      return this.cell.trainableWeights;
    }
    get nonTrainableWeights() {
      if (!this.trainable) {
        return this.cell.weights;
      }
      return this.cell.nonTrainableWeights;
    }
    setFastWeightInitDuringBuild(value) {
      super.setFastWeightInitDuringBuild(value);
      if (this.cell != null) {
        this.cell.setFastWeightInitDuringBuild(value);
      }
    }
    getConfig() {
      const baseConfig = super.getConfig();
      const config = {
        returnSequences: this.returnSequences,
        returnState: this.returnState,
        goBackwards: this.goBackwards,
        stateful: this.stateful,
        unroll: this.unroll
      };
      if (this.numConstants != null) {
        config["numConstants"] = this.numConstants;
      }
      const cellConfig = this.cell.getConfig();
      if (this.getClassName() === RNN.className) {
        config["cell"] = {
          className: this.cell.getClassName(),
          config: cellConfig
        };
      }
      return Object.assign({}, cellConfig, baseConfig, config);
    }
    static fromConfig(cls, config, customObjects = {}) {
      const cellConfig = config["cell"];
      const cell = deserialize(cellConfig, customObjects);
      return new cls(Object.assign(config, {cell}));
    }
  };
  RNN.className = "RNN";
  serialization_exports.registerClass(RNN);
  var RNNCell = class extends Layer {
  };
  var SimpleRNNCell = class extends RNNCell {
    constructor(args) {
      super(args);
      this.DEFAULT_ACTIVATION = "tanh";
      this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
      this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal";
      this.DEFAULT_BIAS_INITIALIZER = "zeros";
      this.units = args.units;
      assertPositiveInteger(this.units, `units`);
      this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);
      this.useBias = args.useBias == null ? true : args.useBias;
      this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
      this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
      this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
      this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
      this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
      this.biasRegularizer = getRegularizer(args.biasRegularizer);
      this.kernelConstraint = getConstraint(args.kernelConstraint);
      this.recurrentConstraint = getConstraint(args.recurrentConstraint);
      this.biasConstraint = getConstraint(args.biasConstraint);
      this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);
      this.recurrentDropout = min2([
        1,
        max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
      ]);
      this.stateSize = this.units;
      this.dropoutMask = null;
      this.recurrentDropoutMask = null;
    }
    build(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      this.kernel = this.addWeight("kernel", [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
      this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
      if (this.useBias) {
        this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
      } else {
        this.bias = null;
      }
      this.built = true;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        inputs = inputs;
        if (inputs.length !== 2) {
          throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);
        }
        let prevOutput = inputs[1];
        inputs = inputs[0];
        const training = kwargs["training"] == null ? false : kwargs["training"];
        if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
          this.dropoutMask = generateDropoutMask({
            ones: () => onesLike(inputs),
            rate: this.dropout,
            training
          });
        }
        if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
          this.recurrentDropoutMask = generateDropoutMask({
            ones: () => onesLike(prevOutput),
            rate: this.recurrentDropout,
            training
          });
        }
        let h;
        const dpMask = this.dropoutMask;
        const recDpMask = this.recurrentDropoutMask;
        if (dpMask != null) {
          h = dot2(mul(inputs, dpMask), this.kernel.read());
        } else {
          h = dot2(inputs, this.kernel.read());
        }
        if (this.bias != null) {
          h = biasAdd(h, this.bias.read());
        }
        if (recDpMask != null) {
          prevOutput = mul(prevOutput, recDpMask);
        }
        let output = add2(h, dot2(prevOutput, this.recurrentKernel.read()));
        if (this.activation != null) {
          output = this.activation.apply(output);
        }
        return [output, output];
      });
    }
    getConfig() {
      const baseConfig = super.getConfig();
      const config = {
        units: this.units,
        activation: serializeActivation(this.activation),
        useBias: this.useBias,
        kernelInitializer: serializeInitializer(this.kernelInitializer),
        recurrentInitializer: serializeInitializer(this.recurrentInitializer),
        biasInitializer: serializeInitializer(this.biasInitializer),
        kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
        recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
        biasRegularizer: serializeRegularizer(this.biasRegularizer),
        activityRegularizer: serializeRegularizer(this.activityRegularizer),
        kernelConstraint: serializeConstraint(this.kernelConstraint),
        recurrentConstraint: serializeConstraint(this.recurrentConstraint),
        biasConstraint: serializeConstraint(this.biasConstraint),
        dropout: this.dropout,
        recurrentDropout: this.recurrentDropout
      };
      return Object.assign({}, baseConfig, config);
    }
  };
  SimpleRNNCell.className = "SimpleRNNCell";
  serialization_exports.registerClass(SimpleRNNCell);
  var SimpleRNN = class extends RNN {
    constructor(args) {
      args.cell = new SimpleRNNCell(args);
      super(args);
    }
    call(inputs, kwargs) {
      return tidy(() => {
        if (this.cell.dropoutMask != null) {
          dispose(this.cell.dropoutMask);
          this.cell.dropoutMask = null;
        }
        if (this.cell.recurrentDropoutMask != null) {
          dispose(this.cell.recurrentDropoutMask);
          this.cell.recurrentDropoutMask = null;
        }
        const mask = kwargs == null ? null : kwargs["mask"];
        const training = kwargs == null ? null : kwargs["training"];
        const initialState = kwargs == null ? null : kwargs["initialState"];
        return super.call(inputs, {mask, training, initialState});
      });
    }
    static fromConfig(cls, config) {
      return new cls(config);
    }
  };
  SimpleRNN.className = "SimpleRNN";
  serialization_exports.registerClass(SimpleRNN);
  var GRUCell = class extends RNNCell {
    constructor(args) {
      super(args);
      this.DEFAULT_ACTIVATION = "tanh";
      this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid";
      this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
      this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal";
      this.DEFAULT_BIAS_INITIALIZER = "zeros";
      if (args.resetAfter) {
        throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);
      }
      this.units = args.units;
      assertPositiveInteger(this.units, "units");
      this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);
      this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);
      this.useBias = args.useBias == null ? true : args.useBias;
      this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
      this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
      this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
      this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
      this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
      this.biasRegularizer = getRegularizer(args.biasRegularizer);
      this.kernelConstraint = getConstraint(args.kernelConstraint);
      this.recurrentConstraint = getConstraint(args.recurrentConstraint);
      this.biasConstraint = getConstraint(args.biasConstraint);
      this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);
      this.recurrentDropout = min2([
        1,
        max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
      ]);
      this.implementation = args.implementation;
      this.stateSize = this.units;
      this.dropoutMask = null;
      this.recurrentDropoutMask = null;
    }
    build(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      const inputDim = inputShape[inputShape.length - 1];
      this.kernel = this.addWeight("kernel", [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
      this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
      if (this.useBias) {
        this.bias = this.addWeight("bias", [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
      } else {
        this.bias = null;
      }
      this.built = true;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        inputs = inputs;
        if (inputs.length !== 2) {
          throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);
        }
        const training = kwargs["training"] == null ? false : kwargs["training"];
        let hTMinus1 = inputs[1];
        inputs = inputs[0];
        if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
          this.dropoutMask = generateDropoutMask({
            ones: () => onesLike(inputs),
            rate: this.dropout,
            training,
            count: 3
          });
        }
        if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
          this.recurrentDropoutMask = generateDropoutMask({
            ones: () => onesLike(hTMinus1),
            rate: this.recurrentDropout,
            training,
            count: 3
          });
        }
        const dpMask = this.dropoutMask;
        const recDpMask = this.recurrentDropoutMask;
        let z;
        let r;
        let hh;
        if (0 < this.dropout && this.dropout < 1) {
          inputs = mul(inputs, dpMask[0]);
        }
        let matrixX = dot2(inputs, this.kernel.read());
        if (this.useBias) {
          matrixX = biasAdd(matrixX, this.bias.read());
        }
        if (0 < this.recurrentDropout && this.recurrentDropout < 1) {
          hTMinus1 = mul(hTMinus1, recDpMask[0]);
        }
        const recurrentKernelValue = this.recurrentKernel.read();
        const [rk1, rk2] = split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);
        const matrixInner = dot2(hTMinus1, rk1);
        const [xZ, xR, xH] = split(matrixX, 3, matrixX.rank - 1);
        const [recurrentZ, recurrentR] = split(matrixInner, 2, matrixInner.rank - 1);
        z = this.recurrentActivation.apply(add2(xZ, recurrentZ));
        r = this.recurrentActivation.apply(add2(xR, recurrentR));
        const recurrentH = dot2(mul(r, hTMinus1), rk2);
        hh = this.activation.apply(add2(xH, recurrentH));
        const h = add2(mul(z, hTMinus1), mul(add2(1, neg(z)), hh));
        return [h, h];
      });
    }
    getConfig() {
      const baseConfig = super.getConfig();
      const config = {
        units: this.units,
        activation: serializeActivation(this.activation),
        recurrentActivation: serializeActivation(this.recurrentActivation),
        useBias: this.useBias,
        kernelInitializer: serializeInitializer(this.kernelInitializer),
        recurrentInitializer: serializeInitializer(this.recurrentInitializer),
        biasInitializer: serializeInitializer(this.biasInitializer),
        kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
        recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
        biasRegularizer: serializeRegularizer(this.biasRegularizer),
        activityRegularizer: serializeRegularizer(this.activityRegularizer),
        kernelConstraint: serializeConstraint(this.kernelConstraint),
        recurrentConstraint: serializeConstraint(this.recurrentConstraint),
        biasConstraint: serializeConstraint(this.biasConstraint),
        dropout: this.dropout,
        recurrentDropout: this.recurrentDropout,
        implementation: this.implementation,
        resetAfter: false
      };
      return Object.assign({}, baseConfig, config);
    }
  };
  GRUCell.className = "GRUCell";
  serialization_exports.registerClass(GRUCell);
  var GRU = class extends RNN {
    constructor(args) {
      if (args.implementation === 0) {
        console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.");
      }
      args.cell = new GRUCell(args);
      super(args);
    }
    call(inputs, kwargs) {
      return tidy(() => {
        if (this.cell.dropoutMask != null) {
          dispose(this.cell.dropoutMask);
          this.cell.dropoutMask = null;
        }
        if (this.cell.recurrentDropoutMask != null) {
          dispose(this.cell.recurrentDropoutMask);
          this.cell.recurrentDropoutMask = null;
        }
        const mask = kwargs == null ? null : kwargs["mask"];
        const training = kwargs == null ? null : kwargs["training"];
        const initialState = kwargs == null ? null : kwargs["initialState"];
        return super.call(inputs, {mask, training, initialState});
      });
    }
    static fromConfig(cls, config) {
      if (config["implmentation"] === 0) {
        config["implementation"] = 1;
      }
      return new cls(config);
    }
  };
  GRU.className = "GRU";
  serialization_exports.registerClass(GRU);
  var LSTMCell = class extends RNNCell {
    constructor(args) {
      super(args);
      this.DEFAULT_ACTIVATION = "tanh";
      this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid";
      this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
      this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal";
      this.DEFAULT_BIAS_INITIALIZER = "zeros";
      this.units = args.units;
      assertPositiveInteger(this.units, "units");
      this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);
      this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);
      this.useBias = args.useBias == null ? true : args.useBias;
      this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
      this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
      this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
      this.unitForgetBias = args.unitForgetBias;
      this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
      this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
      this.biasRegularizer = getRegularizer(args.biasRegularizer);
      this.kernelConstraint = getConstraint(args.kernelConstraint);
      this.recurrentConstraint = getConstraint(args.recurrentConstraint);
      this.biasConstraint = getConstraint(args.biasConstraint);
      this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);
      this.recurrentDropout = min2([
        1,
        max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
      ]);
      this.implementation = args.implementation;
      this.stateSize = [this.units, this.units];
      this.dropoutMask = null;
      this.recurrentDropoutMask = null;
    }
    build(inputShape) {
      var _a;
      inputShape = getExactlyOneShape(inputShape);
      const inputDim = inputShape[inputShape.length - 1];
      this.kernel = this.addWeight("kernel", [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
      this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
      let biasInitializer;
      if (this.useBias) {
        if (this.unitForgetBias) {
          const capturedBiasInit = this.biasInitializer;
          const capturedUnits = this.units;
          biasInitializer = new (_a = class CustomInit extends Initializer {
            apply(shape, dtype) {
              const bI = capturedBiasInit.apply([capturedUnits]);
              const bF = new Ones().apply([capturedUnits]);
              const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);
              return concatAlongFirstAxis(concatAlongFirstAxis(bI, bF), bCAndH);
            }
          }, _a.className = "CustomInit", _a)();
        } else {
          biasInitializer = this.biasInitializer;
        }
        this.bias = this.addWeight("bias", [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);
      } else {
        this.bias = null;
      }
      this.built = true;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        const training = kwargs["training"] == null ? false : kwargs["training"];
        inputs = inputs;
        if (inputs.length !== 3) {
          throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);
        }
        let hTMinus1 = inputs[1];
        const cTMinus1 = inputs[2];
        inputs = inputs[0];
        if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
          this.dropoutMask = generateDropoutMask({
            ones: () => onesLike(inputs),
            rate: this.dropout,
            training,
            count: 4
          });
        }
        if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
          this.recurrentDropoutMask = generateDropoutMask({
            ones: () => onesLike(hTMinus1),
            rate: this.recurrentDropout,
            training,
            count: 4
          });
        }
        const dpMask = this.dropoutMask;
        const recDpMask = this.recurrentDropoutMask;
        let i;
        let f;
        let c;
        let o;
        if (0 < this.dropout && this.dropout < 1) {
          inputs = mul(inputs, dpMask[0]);
        }
        let z = dot2(inputs, this.kernel.read());
        if (0 < this.recurrentDropout && this.recurrentDropout < 1) {
          hTMinus1 = mul(hTMinus1, recDpMask[0]);
        }
        z = add2(z, dot2(hTMinus1, this.recurrentKernel.read()));
        if (this.useBias) {
          z = biasAdd(z, this.bias.read());
        }
        const [z0, z1, z2, z3] = split(z, 4, z.rank - 1);
        i = this.recurrentActivation.apply(z0);
        f = this.recurrentActivation.apply(z1);
        c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(z2)));
        o = this.recurrentActivation.apply(z3);
        const h = mul(o, this.activation.apply(c));
        return [h, h, c];
      });
    }
    getConfig() {
      const baseConfig = super.getConfig();
      const config = {
        units: this.units,
        activation: serializeActivation(this.activation),
        recurrentActivation: serializeActivation(this.recurrentActivation),
        useBias: this.useBias,
        kernelInitializer: serializeInitializer(this.kernelInitializer),
        recurrentInitializer: serializeInitializer(this.recurrentInitializer),
        biasInitializer: serializeInitializer(this.biasInitializer),
        unitForgetBias: this.unitForgetBias,
        kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
        recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
        biasRegularizer: serializeRegularizer(this.biasRegularizer),
        activityRegularizer: serializeRegularizer(this.activityRegularizer),
        kernelConstraint: serializeConstraint(this.kernelConstraint),
        recurrentConstraint: serializeConstraint(this.recurrentConstraint),
        biasConstraint: serializeConstraint(this.biasConstraint),
        dropout: this.dropout,
        recurrentDropout: this.recurrentDropout,
        implementation: this.implementation
      };
      return Object.assign({}, baseConfig, config);
    }
  };
  LSTMCell.className = "LSTMCell";
  serialization_exports.registerClass(LSTMCell);
  var LSTM = class extends RNN {
    constructor(args) {
      if (args.implementation === 0) {
        console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.");
      }
      args.cell = new LSTMCell(args);
      super(args);
    }
    call(inputs, kwargs) {
      return tidy(() => {
        if (this.cell.dropoutMask != null) {
          dispose(this.cell.dropoutMask);
          this.cell.dropoutMask = null;
        }
        if (this.cell.recurrentDropoutMask != null) {
          dispose(this.cell.recurrentDropoutMask);
          this.cell.recurrentDropoutMask = null;
        }
        const mask = kwargs == null ? null : kwargs["mask"];
        const training = kwargs == null ? null : kwargs["training"];
        const initialState = kwargs == null ? null : kwargs["initialState"];
        return super.call(inputs, {mask, training, initialState});
      });
    }
    static fromConfig(cls, config) {
      if (config["implmentation"] === 0) {
        config["implementation"] = 1;
      }
      return new cls(config);
    }
  };
  LSTM.className = "LSTM";
  serialization_exports.registerClass(LSTM);
  var StackedRNNCells = class extends RNNCell {
    constructor(args) {
      super(args);
      this.cells = args.cells;
    }
    get stateSize() {
      const stateSize = [];
      for (const cell of this.cells.slice().reverse()) {
        if (Array.isArray(cell.stateSize)) {
          stateSize.push(...cell.stateSize);
        } else {
          stateSize.push(cell.stateSize);
        }
      }
      return stateSize;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        inputs = inputs;
        let states = inputs.slice(1);
        const nestedStates = [];
        for (const cell of this.cells.slice().reverse()) {
          if (Array.isArray(cell.stateSize)) {
            nestedStates.push(states.splice(0, cell.stateSize.length));
          } else {
            nestedStates.push(states.splice(0, 1));
          }
        }
        nestedStates.reverse();
        const newNestedStates = [];
        let callInputs;
        for (let i = 0; i < this.cells.length; ++i) {
          const cell = this.cells[i];
          states = nestedStates[i];
          if (i === 0) {
            callInputs = [inputs[0]].concat(states);
          } else {
            callInputs = [callInputs[0]].concat(states);
          }
          callInputs = cell.call(callInputs, kwargs);
          newNestedStates.push(callInputs.slice(1));
        }
        states = [];
        for (const cellStates of newNestedStates.slice().reverse()) {
          states.push(...cellStates);
        }
        return [callInputs[0]].concat(states);
      });
    }
    build(inputShape) {
      if (isArrayOfShapes(inputShape)) {
        inputShape = inputShape[0];
      }
      inputShape = inputShape;
      let outputDim;
      this.cells.forEach((cell, i) => {
        nameScope(`RNNCell_${i}`, () => {
          cell.build(inputShape);
          if (Array.isArray(cell.stateSize)) {
            outputDim = cell.stateSize[0];
          } else {
            outputDim = cell.stateSize;
          }
          inputShape = [inputShape[0], outputDim];
        });
      });
      this.built = true;
    }
    getConfig() {
      const baseConfig = super.getConfig();
      const getCellConfig = (cell) => {
        return {
          className: cell.getClassName(),
          config: cell.getConfig()
        };
      };
      const cellConfigs = this.cells.map(getCellConfig);
      const config = {cells: cellConfigs};
      return Object.assign({}, baseConfig, config);
    }
    static fromConfig(cls, config, customObjects = {}) {
      const cells = [];
      for (const cellConfig of config["cells"]) {
        cells.push(deserialize(cellConfig, customObjects));
      }
      return new cls({cells});
    }
    get trainableWeights() {
      if (!this.trainable) {
        return [];
      }
      const weights = [];
      for (const cell of this.cells) {
        weights.push(...cell.trainableWeights);
      }
      return weights;
    }
    get nonTrainableWeights() {
      const weights = [];
      for (const cell of this.cells) {
        weights.push(...cell.nonTrainableWeights);
      }
      if (!this.trainable) {
        const trainableWeights = [];
        for (const cell of this.cells) {
          trainableWeights.push(...cell.trainableWeights);
        }
        return trainableWeights.concat(weights);
      }
      return weights;
    }
    getWeights() {
      const weights = [];
      for (const cell of this.cells) {
        weights.push(...cell.weights);
      }
      return batchGetValue(weights);
    }
    setWeights(weights) {
      const tuples = [];
      for (const cell of this.cells) {
        const numParams = cell.weights.length;
        const inputWeights = weights.splice(numParams);
        for (let i = 0; i < cell.weights.length; ++i) {
          tuples.push([cell.weights[i], inputWeights[i]]);
        }
      }
      batchSetValue(tuples);
    }
  };
  StackedRNNCells.className = "StackedRNNCells";
  serialization_exports.registerClass(StackedRNNCells);
  function generateDropoutMask(args) {
    const {ones: ones4, rate, training = false, count: count2 = 1} = args;
    const droppedInputs = () => dropout2(ones4(), rate);
    const createMask = () => inTrainPhase(droppedInputs, ones4, training);
    if (!count2 || count2 <= 1) {
      return keep(createMask().clone());
    }
    const masks = Array(count2).fill(void 0).map(createMask);
    return masks.map((m) => keep(m.clone()));
  }
  /**
   * @license
   * Copyright 2020 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var __rest = function(s, e) {
    var t = {};
    for (var p2 in s)
      if (Object.prototype.hasOwnProperty.call(s, p2) && e.indexOf(p2) < 0)
        t[p2] = s[p2];
    if (s != null && typeof Object.getOwnPropertySymbols === "function")
      for (var i = 0, p2 = Object.getOwnPropertySymbols(s); i < p2.length; i++) {
        if (e.indexOf(p2[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p2[i]))
          t[p2[i]] = s[p2[i]];
      }
    return t;
  };
  var ConvRNN2D = class extends RNN {
    constructor(args) {
      if (args.unroll) {
        throw new NotImplementedError("Unrolling is not possible with convolutional RNNs.");
      }
      if (Array.isArray(args.cell)) {
        throw new NotImplementedError("It is not possible at the moment to stack convolutional cells.");
      }
      super(args);
      this.inputSpec = [new InputSpec({ndim: 5})];
    }
    call(inputs, kwargs) {
      return tidy(() => {
        if (this.cell.dropoutMask != null) {
          dispose(this.cell.dropoutMask);
          this.cell.dropoutMask = null;
        }
        if (this.cell.recurrentDropoutMask != null) {
          dispose(this.cell.recurrentDropoutMask);
          this.cell.recurrentDropoutMask = null;
        }
        if (kwargs && kwargs["constants"]) {
          throw new ValueError("ConvRNN2D cell does not support constants");
        }
        const mask = kwargs == null ? null : kwargs["mask"];
        const training = kwargs == null ? null : kwargs["training"];
        const initialState = kwargs == null ? null : kwargs["initialState"];
        return super.call(inputs, {mask, training, initialState});
      });
    }
    computeOutputShape(inputShape) {
      let outShape = this.computeSingleOutputShape(inputShape);
      if (!this.returnSequences) {
        outShape = [outShape[0], ...outShape.slice(2)];
      }
      if (this.returnState) {
        outShape = [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];
      }
      return outShape;
    }
    getInitialState(inputs) {
      return tidy(() => {
        const {stateSize} = this.cell;
        const inputShape = inputs.shape;
        const outputShape = this.computeSingleOutputShape(inputShape);
        const stateShape = [outputShape[0], ...outputShape.slice(2)];
        const initialState = zeros(stateShape);
        if (Array.isArray(stateSize)) {
          return Array(stateSize.length).fill(initialState);
        }
        return [initialState];
      });
    }
    resetStates(states, training = false) {
      tidy(() => {
        if (!this.stateful) {
          throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");
        }
        const inputShape = this.inputSpec[0].shape;
        const outputShape = this.computeSingleOutputShape(inputShape);
        const stateShape = [outputShape[0], ...outputShape.slice(2)];
        const batchSize = inputShape[0];
        if (batchSize == null) {
          throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");
        }
        if (this.getStates() == null) {
          if (Array.isArray(this.cell.stateSize)) {
            this.states_ = this.cell.stateSize.map(() => zeros(stateShape));
          } else {
            this.states_ = [zeros(stateShape)];
          }
        } else if (states == null) {
          dispose(this.states_);
          if (this.keptStates != null) {
            dispose(this.keptStates);
            this.keptStates = [];
          }
          if (Array.isArray(this.cell.stateSize)) {
            this.states_ = this.cell.stateSize.map(() => zeros(stateShape));
          } else {
            this.states_[0] = zeros(stateShape);
          }
        } else {
          if (!Array.isArray(states)) {
            states = [states];
          }
          if (states.length !== this.states_.length) {
            throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);
          }
          if (training) {
            this.keptStates.push(this.states_.slice());
          } else {
            dispose(this.states_);
          }
          for (let index = 0; index < this.states_.length; ++index) {
            const value = states[index];
            const expectedShape = stateShape;
            if (!util_exports.arraysEqual(value.shape, expectedShape)) {
              throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);
            }
            this.states_[index] = value;
          }
        }
        this.states_ = this.states_.map((state) => keep(state.clone()));
      });
    }
    computeSingleOutputShape(inputShape) {
      const {dataFormat, filters, kernelSize, padding, strides, dilationRate} = this.cell;
      const isChannelsFirst = dataFormat === "channelsFirst";
      const h = inputShape[isChannelsFirst ? 3 : 2];
      const w = inputShape[isChannelsFirst ? 4 : 3];
      const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]);
      const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);
      const outShape = [
        ...inputShape.slice(0, 2),
        ...isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters]
      ];
      return outShape;
    }
  };
  ConvRNN2D.className = "ConvRNN2D";
  var ConvLSTM2DCell = class extends LSTMCell {
    constructor(args) {
      const {filters, kernelSize, strides, padding, dataFormat, dilationRate} = args;
      super(Object.assign({}, args, {units: filters}));
      this.filters = filters;
      assertPositiveInteger(this.filters, "filters");
      this.kernelSize = normalizeArray(kernelSize, 2, "kernelSize");
      this.kernelSize.forEach((size) => assertPositiveInteger(size, "kernelSize"));
      this.strides = normalizeArray(strides || 1, 2, "strides");
      this.strides.forEach((stride) => assertPositiveInteger(stride, "strides"));
      this.padding = padding || "valid";
      checkPaddingMode(this.padding);
      this.dataFormat = dataFormat || "channelsLast";
      checkDataFormat(this.dataFormat);
      this.dilationRate = normalizeArray(dilationRate || 1, 2, "dilationRate");
      this.dilationRate.forEach((rate) => assertPositiveInteger(rate, "dilationRate"));
    }
    build(inputShape) {
      var _a;
      inputShape = getExactlyOneShape(inputShape);
      const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
      if (inputShape[channelAxis] == null) {
        throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);
      }
      const inputDim = inputShape[channelAxis];
      const numOfKernels = 4;
      const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);
      this.kernel = this.addWeight("kernel", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
      const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);
      this.recurrentKernel = this.addWeight("recurrent_kernel", recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
      if (this.useBias) {
        let biasInitializer;
        if (this.unitForgetBias) {
          const init2 = this.biasInitializer;
          const filters = this.filters;
          biasInitializer = new (_a = class CustomInit extends Initializer {
            apply(shape, dtype) {
              const biasI = init2.apply([filters]);
              const biasF = ones2([filters]);
              const biasCAndO = init2.apply([filters * 2]);
              return concatenate([biasI, biasF, biasCAndO]);
            }
          }, _a.className = "CustomInit", _a)();
        } else {
          biasInitializer = this.biasInitializer;
        }
        this.bias = this.addWeight("bias", [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);
      }
      this.built = true;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        if (inputs.length !== 3) {
          throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);
        }
        const training = kwargs["training"] || false;
        const x = inputs[0];
        const hTMinus1 = inputs[1];
        const cTMinus1 = inputs[2];
        const numOfKernels = 4;
        if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
          this.dropoutMask = generateDropoutMask({
            ones: () => onesLike(x),
            rate: this.dropout,
            training,
            count: numOfKernels
          });
        }
        const dropoutMask = this.dropoutMask;
        const applyDropout = (x2, mask, index) => {
          if (!mask || !mask[index]) {
            return x2;
          }
          return mul(mask[index], x2);
        };
        let xI = applyDropout(x, dropoutMask, 0);
        let xF = applyDropout(x, dropoutMask, 1);
        let xC = applyDropout(x, dropoutMask, 2);
        let xO = applyDropout(x, dropoutMask, 3);
        if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
          this.recurrentDropoutMask = generateDropoutMask({
            ones: () => onesLike(hTMinus1),
            rate: this.recurrentDropout,
            training,
            count: numOfKernels
          });
        }
        const recDropoutMask = this.recurrentDropoutMask;
        let hI = applyDropout(hTMinus1, recDropoutMask, 0);
        let hF = applyDropout(hTMinus1, recDropoutMask, 1);
        let hC = applyDropout(hTMinus1, recDropoutMask, 2);
        let hO = applyDropout(hTMinus1, recDropoutMask, 3);
        const kernelChannelAxis = 3;
        const [kernelI, kernelF, kernelC, kernelO] = split(this.kernel.read(), numOfKernels, kernelChannelAxis);
        const [biasI, biasF, biasC, biasO] = this.useBias ? split(this.bias.read(), numOfKernels) : [null, null, null, null];
        xI = this.inputConv(xI, kernelI, biasI, this.padding);
        xF = this.inputConv(xF, kernelF, biasF, this.padding);
        xC = this.inputConv(xC, kernelC, biasC, this.padding);
        xO = this.inputConv(xO, kernelO, biasO, this.padding);
        const [recKernelI, recKernelF, recKernelC, recKernelO] = split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);
        hI = this.recurrentConv(hI, recKernelI);
        hF = this.recurrentConv(hF, recKernelF);
        hC = this.recurrentConv(hC, recKernelC);
        hO = this.recurrentConv(hO, recKernelO);
        const i = this.recurrentActivation.apply(add2(xI, hI));
        const f = this.recurrentActivation.apply(add2(xF, hF));
        const c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(add2(xC, hC))));
        const h = mul(this.recurrentActivation.apply(add2(xO, hO)), this.activation.apply(c));
        return [h, h, c];
      });
    }
    getConfig() {
      const _a = super.getConfig(), {units: _} = _a, baseConfig = __rest(_a, ["units"]);
      const config = {
        filters: this.filters,
        kernelSize: this.kernelSize,
        padding: this.padding,
        dataFormat: this.dataFormat,
        dilationRate: this.dilationRate,
        strides: this.strides
      };
      return Object.assign({}, baseConfig, config);
    }
    inputConv(x, w, b, padding) {
      const out = conv2d(x, w, this.strides, padding || "valid", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC", this.dilationRate);
      if (b) {
        return biasAdd(out, b, this.dataFormat);
      }
      return out;
    }
    recurrentConv(x, w) {
      const strides = 1;
      return conv2d(x, w, strides, "same", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC");
    }
  };
  ConvLSTM2DCell.className = "ConvLSTM2DCell";
  serialization_exports.registerClass(ConvLSTM2DCell);
  var ConvLSTM2D = class extends ConvRNN2D {
    constructor(args) {
      const cell = new ConvLSTM2DCell(args);
      super(Object.assign({}, args, {cell}));
    }
    static fromConfig(cls, config) {
      return new cls(config);
    }
  };
  ConvLSTM2D.className = "ConvLSTM2D";
  serialization_exports.registerClass(ConvLSTM2D);
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var Dropout = class extends Layer {
    constructor(args) {
      super(args);
      this.rate = Math.max(Math.min(args.rate, 1), 0);
      this.noiseShape = args.noiseShape;
      this.seed = args.seed;
      this.supportsMasking = true;
    }
    getNoiseShape(input2) {
      if (this.noiseShape == null) {
        return this.noiseShape;
      }
      const inputShape = input2.shape;
      const noiseShape = [];
      for (let i = 0; i < this.noiseShape.length; ++i) {
        noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);
      }
      return noiseShape;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        this.invokeCallHook(inputs, kwargs);
        const input2 = getExactlyOneTensor(inputs);
        if (0 < this.rate && this.rate < 1) {
          const training = kwargs["training"] == null ? false : kwargs["training"];
          const noiseShape = this.getNoiseShape(input2);
          const output = inTrainPhase(() => dropout2(input2, this.rate, noiseShape, this.seed), () => input2, training);
          return output;
        }
        return inputs;
      });
    }
    getConfig() {
      const config = {
        rate: this.rate,
        noiseShape: this.noiseShape,
        seed: this.seed
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
    dispose() {
      return super.dispose();
    }
  };
  Dropout.className = "Dropout";
  serialization_exports.registerClass(Dropout);
  var SpatialDropout1D = class extends Dropout {
    constructor(args) {
      super(args);
      this.inputSpec = [{ndim: 3}];
    }
    getNoiseShape(input2) {
      const inputShape = input2.shape;
      return [inputShape[0], 1, inputShape[2]];
    }
  };
  SpatialDropout1D.className = "SpatialDropout1D";
  serialization_exports.registerClass(SpatialDropout1D);
  var Dense = class extends Layer {
    constructor(args) {
      super(args);
      this.activation = null;
      this.useBias = true;
      this.kernel = null;
      this.bias = null;
      this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
      this.DEFAULT_BIAS_INITIALIZER = "zeros";
      if (args.batchInputShape == null && args.inputShape == null && args.inputDim != null) {
        let batchSize = null;
        if (args.batchSize != null) {
          batchSize = args.batchSize;
        }
        this.batchInputShape = [batchSize, args.inputDim];
      }
      this.units = args.units;
      assertPositiveInteger(this.units, "units");
      this.activation = getActivation(args.activation);
      if (args.useBias != null) {
        this.useBias = args.useBias;
      }
      this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
      this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
      this.kernelConstraint = getConstraint(args.kernelConstraint);
      this.biasConstraint = getConstraint(args.biasConstraint);
      this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
      this.biasRegularizer = getRegularizer(args.biasRegularizer);
      this.activityRegularizer = getRegularizer(args.activityRegularizer);
      this.supportsMasking = true;
      this.inputSpec = [{minNDim: 2}];
    }
    build(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      const inputLastDim = inputShape[inputShape.length - 1];
      if (this.kernel == null) {
        this.kernel = this.addWeight("kernel", [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
        if (this.useBias) {
          this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
        }
      }
      this.inputSpec = [{minNDim: 2, axes: {[-1]: inputLastDim}}];
      this.built = true;
    }
    computeOutputShape(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      const outputShape = inputShape.slice();
      outputShape[outputShape.length - 1] = this.units;
      return outputShape;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        this.invokeCallHook(inputs, kwargs);
        const input2 = getExactlyOneTensor(inputs);
        const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());
        let output;
        if (fusedActivationName != null) {
          output = dot2(input2, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);
        } else {
          output = dot2(input2, this.kernel.read());
          if (this.bias != null) {
            output = biasAdd(output, this.bias.read());
          }
          if (this.activation != null) {
            output = this.activation.apply(output);
          }
        }
        return output;
      });
    }
    getConfig() {
      const config = {
        units: this.units,
        activation: serializeActivation(this.activation),
        useBias: this.useBias,
        kernelInitializer: serializeInitializer(this.kernelInitializer),
        biasInitializer: serializeInitializer(this.biasInitializer),
        kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
        biasRegularizer: serializeRegularizer(this.biasRegularizer),
        activityRegularizer: serializeRegularizer(this.activityRegularizer),
        kernelConstraint: serializeConstraint(this.kernelConstraint),
        biasConstraint: serializeConstraint(this.biasConstraint)
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  Dense.className = "Dense";
  serialization_exports.registerClass(Dense);
  var Flatten = class extends Layer {
    constructor(args) {
      args = args || {};
      super(args);
      this.inputSpec = [{minNDim: 3}];
      this.dataFormat = args.dataFormat;
    }
    computeOutputShape(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      for (const dim of inputShape.slice(1)) {
        if (dim == null) {
          throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);
        }
      }
      return [inputShape[0], arrayProd(inputShape, 1)];
    }
    call(inputs, kwargs) {
      return tidy(() => {
        this.invokeCallHook(inputs, kwargs);
        let input2 = getExactlyOneTensor(inputs);
        if (this.dataFormat === "channelsFirst" && input2.rank > 1) {
          const permutation = [0];
          for (let i = 2; i < input2.rank; ++i) {
            permutation.push(i);
          }
          permutation.push(1);
          input2 = input2.transpose(permutation);
        }
        return batchFlatten(input2);
      });
    }
    getConfig() {
      const config = {};
      if (this.dataFormat != null) {
        config["dataFormat"] = this.dataFormat;
      }
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  Flatten.className = "Flatten";
  serialization_exports.registerClass(Flatten);
  var Activation2 = class extends Layer {
    constructor(args) {
      super(args);
      this.supportsMasking = true;
      this.activation = getActivation(args.activation);
    }
    call(inputs, kwargs) {
      return tidy(() => {
        this.invokeCallHook(inputs, kwargs);
        const input2 = getExactlyOneTensor(inputs);
        return this.activation.apply(input2);
      });
    }
    getConfig() {
      const config = {activation: serializeActivation(this.activation)};
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  Activation2.className = "Activation";
  serialization_exports.registerClass(Activation2);
  var RepeatVector = class extends Layer {
    constructor(args) {
      super(args);
      this.n = args.n;
      this.inputSpec = [{ndim: 2}];
    }
    computeOutputShape(inputShape) {
      return [inputShape[0], this.n, inputShape[1]];
    }
    call(inputs, kwargs) {
      return tidy(() => {
        inputs = getExactlyOneTensor(inputs);
        return repeat(inputs, this.n);
      });
    }
    getConfig() {
      const config = {
        n: this.n
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  RepeatVector.className = "RepeatVector";
  serialization_exports.registerClass(RepeatVector);
  var Reshape2 = class extends Layer {
    constructor(args) {
      super(args);
      this.targetShape = args.targetShape;
      for (let i = 0; i < this.targetShape.length; ++i) {
        if (this.isUnknown(this.targetShape[i])) {
          this.targetShape[i] = null;
        }
      }
    }
    isUnknown(dim) {
      return dim < 0 || dim == null;
    }
    fixUnknownDimension(inputShape, outputShape) {
      const errorMsg = "Total size of new array must be unchanged.";
      const finalShape = outputShape.slice();
      let known = 1;
      let unknown = null;
      for (let i = 0; i < finalShape.length; ++i) {
        const dim = finalShape[i];
        if (this.isUnknown(dim)) {
          if (unknown === null) {
            unknown = i;
          } else {
            throw new ValueError("Can only specifiy one unknown dimension.");
          }
        } else {
          known *= dim;
        }
      }
      const originalSize = arrayProd(inputShape);
      if (unknown !== null) {
        if (known === 0 || originalSize % known !== 0) {
          throw new ValueError(errorMsg);
        }
        finalShape[unknown] = originalSize / known;
      } else if (originalSize !== known) {
        throw new ValueError(errorMsg);
      }
      return finalShape;
    }
    computeOutputShape(inputShape) {
      let anyUnknownDims = false;
      for (let i = 0; i < inputShape.length; ++i) {
        if (this.isUnknown(inputShape[i])) {
          anyUnknownDims = true;
          break;
        }
      }
      if (anyUnknownDims) {
        return inputShape.slice(0, 1).concat(this.targetShape);
      } else {
        return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));
      }
    }
    call(inputs, kwargs) {
      return tidy(() => {
        this.invokeCallHook(inputs, kwargs);
        const input2 = getExactlyOneTensor(inputs);
        const inputShape = input2.shape;
        const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));
        return input2.reshape(outputShape);
      });
    }
    getConfig() {
      const config = {
        targetShape: this.targetShape
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  Reshape2.className = "Reshape";
  serialization_exports.registerClass(Reshape2);
  var Permute = class extends Layer {
    constructor(args) {
      super(args);
      if (args.dims == null) {
        throw new Error("Required configuration field `dims` is missing during Permute constructor call.");
      }
      if (!Array.isArray(args.dims)) {
        throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);
      }
      const expectedSortedIndices = range2(1, args.dims.length + 1);
      if (!util_exports.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {
        throw new Error("Invalid permutation `dims`: " + JSON.stringify(args.dims) + " `dims` must contain consecutive integers starting from 1.");
      }
      this.dims = args.dims;
      this.dimsIncludingBatch = [0].concat(this.dims);
      this.inputSpec = [new InputSpec({ndim: this.dims.length + 1})];
    }
    computeOutputShape(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      const outputShape = inputShape.slice();
      this.dims.forEach((dim, i) => {
        outputShape[i + 1] = inputShape[dim];
      });
      return outputShape;
    }
    call(inputs, kwargs) {
      return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);
    }
    getConfig() {
      const config = {
        dims: this.dims
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  Permute.className = "Permute";
  serialization_exports.registerClass(Permute);
  var Masking = class extends Layer {
    constructor(args) {
      super(args == null ? {} : args);
      this.supportsMasking = true;
      if (args != null) {
        this.maskValue = args.maskValue == null ? 0 : args.maskValue;
      } else {
        this.maskValue = 0;
      }
    }
    computeOutputShape(inputShape) {
      return inputShape;
    }
    getConfig() {
      const baseConfig = super.getConfig();
      const config = {maskValue: this.maskValue};
      Object.assign(config, baseConfig);
      return config;
    }
    computeMask(inputs, mask) {
      const input2 = getExactlyOneTensor(inputs);
      const axis = -1;
      return any(notEqual(input2, this.maskValue), axis);
    }
    call(inputs, kwargs) {
      return tidy(() => {
        this.invokeCallHook(inputs, kwargs);
        const input2 = getExactlyOneTensor(inputs);
        const axis = -1;
        const keepDims = true;
        const booleanMask = any(notEqual(input2, this.maskValue), axis, keepDims);
        const output = input2.mul(booleanMask.asType(input2.dtype));
        return output;
      });
    }
  };
  Masking.className = "Masking";
  serialization_exports.registerClass(Masking);
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var Embedding = class extends Layer {
    constructor(args) {
      super(args);
      this.embeddings = null;
      this.DEFAULT_EMBEDDINGS_INITIALIZER = "randomUniform";
      if (args.batchInputShape == null && args.inputShape == null) {
        let batchSize = null;
        if (args.batchSize != null) {
          batchSize = args.batchSize;
        }
        if (args.inputLength == null) {
          this.batchInputShape = [batchSize, null];
        } else {
          this.batchInputShape = [batchSize].concat(toList(args.inputLength));
        }
      }
      this.inputDim = args.inputDim;
      assertPositiveInteger(this.inputDim, "inputDim");
      this.outputDim = args.outputDim;
      assertPositiveInteger(this.outputDim, "outputDim");
      this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);
      this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);
      this.activityRegularizer = getRegularizer(args.activityRegularizer);
      this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);
      this.maskZero = args.maskZero;
      this.supportsMasking = args.maskZero;
      this.inputLength = args.inputLength;
    }
    build(inputShape) {
      this.embeddings = this.addWeight("embeddings", [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);
      this.built = true;
    }
    warnOnIncompatibleInputShape(inputShape) {
    }
    computeMask(inputs, mask) {
      return tidy(() => {
        if (!this.maskZero) {
          return null;
        } else {
          inputs = getExactlyOneTensor(inputs);
          return notEqual(inputs, zerosLike(inputs));
        }
      });
    }
    computeOutputShape(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      if (this.inputLength == null) {
        return [...inputShape, this.outputDim];
      }
      const inLens = toList(this.inputLength);
      if (inLens.length !== inputShape.length - 1) {
        throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);
      } else {
        let i = 0;
        for (let k = 0; k < inLens.length; ++k) {
          const s1 = inLens[k];
          const s2 = inputShape[k + 1];
          if (s1 != null && s2 != null && s1 !== s2) {
            throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);
          } else if (s1 == null) {
            inLens[i] = s2;
          }
          i++;
        }
      }
      return [inputShape[0], ...inLens, this.outputDim];
    }
    call(inputs, kwargs) {
      return tidy(() => {
        this.invokeCallHook(inputs, kwargs);
        let input2 = getExactlyOneTensor(inputs);
        if (input2.dtype !== "int32") {
          input2 = cast2(input2, "int32");
        }
        const output = gather2(this.embeddings.read(), input2.as1D());
        return output.reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)));
      });
    }
    getConfig() {
      const config = {
        inputDim: this.inputDim,
        outputDim: this.outputDim,
        embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),
        embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),
        activityRegularizer: serializeRegularizer(this.activityRegularizer),
        embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),
        maskZero: this.maskZero,
        inputLength: this.inputLength
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  Embedding.className = "Embedding";
  serialization_exports.registerClass(Embedding);
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var Merge = class extends Layer {
    constructor(args) {
      super(args || {});
      this.supportsMasking = true;
    }
    mergeFunction(inputs) {
      throw new NotImplementedError();
    }
    computeElementwiseOpOutputShape(shape1, shape2) {
      if (shape1 == null || shape2 == null) {
        return null;
      } else if (shape1.length < shape2.length) {
        return this.computeElementwiseOpOutputShape(shape2, shape1);
      } else if (shape2.length === 0) {
        return shape1;
      }
      const outputShape = shape1.slice(0, shape1.length - shape2.length);
      for (let k = 0; k < shape2.length; ++k) {
        const i = shape1[shape1.length - shape2.length + k];
        const j = shape2[k];
        if (i == null || j == null || i < 0 || j < 0) {
          outputShape.push(null);
        } else if (i === 1) {
          outputShape.push(j);
        } else if (j === 1) {
          outputShape.push(i);
        } else {
          if (i !== j) {
            throw new ValueError("Operands could not be broadcast together with shapes " + JSON.stringify(shape1) + " " + JSON.stringify(shape2));
          }
          outputShape.push(i);
        }
      }
      return outputShape;
    }
    build(inputShape) {
      if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {
        inputShape = [getExactlyOneShape(inputShape)];
      }
      inputShape = inputShape;
      if (inputShape.length < 2) {
        throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);
      }
      let batchSizes = [];
      for (const shape of inputShape) {
        if (shape != null && shape[0] !== null) {
          batchSizes.push(shape[0]);
        }
      }
      batchSizes = unique2(batchSizes);
      if (batchSizes.length > 1) {
        throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);
      }
      let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);
      for (let i = 1; i < inputShape.length; ++i) {
        const shape = inputShape[i] == null ? null : inputShape[i].slice(1);
        outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);
      }
      const allRanks = inputShape.map((shape) => shape.length);
      if (inputShape.indexOf(null) === -1 && unique2(allRanks).length === 1) {
        this.reshapeRequired = false;
      } else {
        this.reshapeRequired = true;
      }
    }
    call(inputs, kwargs) {
      return tidy(() => {
        inputs = inputs;
        if (this.reshapeRequired) {
          const reshapedInputs = [];
          const inputDims = inputs.map((input2) => input2.rank);
          if (inputDims.indexOf(null) === -1) {
            const maxNDim = max2(inputDims);
            for (let x of inputs) {
              const xNDim = x.rank;
              for (let k = 0; k < maxNDim - xNDim; ++k) {
                x = expandDims2(x, 1);
              }
              reshapedInputs.push(x);
            }
            return this.mergeFunction(reshapedInputs);
          } else {
            let transposed = false;
            for (const x of inputs) {
              const xNDim = x.rank;
              if (xNDim == null) {
                const xShape = x.shape;
                const batchSize = xShape[0];
                const newShape = xShape.slice(1).concat([batchSize]);
                let xTransposed = x.reshape([batchSize].concat(arrayProd(xShape.slice(1))));
                xTransposed = transpose(xTransposed, [1, 0]);
                xTransposed = xTransposed.reshape(newShape);
                reshapedInputs.push(xTransposed);
                transposed = true;
              } else if (xNDim > 1) {
                const dims = range2(1, xNDim).concat([0]);
                reshapedInputs.push(transpose(x, dims));
                transposed = true;
              } else {
                reshapedInputs.push(x);
              }
            }
            let y = this.mergeFunction(reshapedInputs);
            const yNDim = y.rank;
            if (transposed) {
              if (yNDim == null) {
                const yShape = y.shape;
                const yNDim2 = yShape.length;
                const batchSize = yShape[yNDim2 - 1];
                const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));
                y = transpose(y.reshape([-1, batchSize]), [1, 0]).reshape(newShape);
              } else if (yNDim > 1) {
                const dims = [yNDim - 1].concat(range2(0, yNDim - 1));
                y = transpose(y, dims);
              }
            }
            return y;
          }
        } else {
          return this.mergeFunction(inputs);
        }
      });
    }
    computeOutputShape(inputShape) {
      inputShape = inputShape;
      let outputShape;
      if (inputShape[0] == null) {
        outputShape = null;
      } else {
        outputShape = inputShape[0].slice(1);
      }
      for (let i = 1; i < inputShape.length; ++i) {
        const shape = inputShape[i] == null ? null : inputShape[i].slice(1);
        outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);
      }
      let batchSizes = [];
      for (const shape of inputShape) {
        if (shape != null && shape[0] !== null) {
          batchSizes.push(shape[0]);
        }
      }
      batchSizes = unique2(batchSizes);
      if (batchSizes.length === 1) {
        outputShape = batchSizes.concat(outputShape);
      } else {
        outputShape = [null].concat(outputShape);
      }
      return outputShape;
    }
    computeMask(inputs, mask) {
      return tidy(() => {
        if (mask == null) {
          return null;
        }
        if (!Array.isArray(mask)) {
          throw new ValueError("`mask` should be an Array");
        }
        if (!Array.isArray(inputs)) {
          throw new ValueError("`inputs` should be an Array");
        }
        if (mask.length !== inputs.length) {
          throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);
        }
        if (mask.every((m) => m == null)) {
          return null;
        }
        mask = mask.map((m) => m == null ? m : expandDims(m, 0));
        let output = mask[0];
        for (let i = 1; i < mask.length - 1; ++i) {
          output = logicalAnd(output, mask[i]);
        }
        return output;
      });
    }
  };
  var Add2 = class extends Merge {
    constructor(args) {
      super(args);
    }
    mergeFunction(inputs) {
      return tidy(() => {
        let output = inputs[0].clone();
        for (let i = 1; i < inputs.length; ++i) {
          output = add2(output, inputs[i]);
        }
        return output;
      });
    }
  };
  Add2.className = "Add";
  serialization_exports.registerClass(Add2);
  var Multiply2 = class extends Merge {
    constructor(args) {
      super(args);
    }
    mergeFunction(inputs) {
      return tidy(() => {
        let output = inputs[0].clone();
        for (let i = 1; i < inputs.length; ++i) {
          output = mul(output, inputs[i]);
        }
        return output;
      });
    }
  };
  Multiply2.className = "Multiply";
  serialization_exports.registerClass(Multiply2);
  var Average = class extends Merge {
    constructor(args) {
      super(args);
    }
    mergeFunction(inputs) {
      return tidy(() => {
        let output = inputs[0].clone();
        for (let i = 1; i < inputs.length; ++i) {
          output = add2(output, inputs[i]);
        }
        return mul(1 / inputs.length, output);
      });
    }
  };
  Average.className = "Average";
  serialization_exports.registerClass(Average);
  var Maximum2 = class extends Merge {
    constructor(args) {
      super(args);
    }
    mergeFunction(inputs) {
      return tidy(() => {
        let output = inputs[0];
        for (let i = 1; i < inputs.length; ++i) {
          output = maximum(output, inputs[i]);
        }
        return output;
      });
    }
  };
  Maximum2.className = "Maximum";
  serialization_exports.registerClass(Maximum2);
  var Minimum2 = class extends Merge {
    constructor(args) {
      super(args);
    }
    mergeFunction(inputs) {
      return tidy(() => {
        let output = inputs[0];
        for (let i = 1; i < inputs.length; ++i) {
          output = minimum(output, inputs[i]);
        }
        return output;
      });
    }
  };
  Minimum2.className = "Minimum";
  serialization_exports.registerClass(Minimum2);
  var Concatenate = class extends Merge {
    constructor(args) {
      super(args);
      this.DEFAULT_AXIS = -1;
      if (args == null) {
        args = {};
      }
      this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;
      this.supportsMasking = true;
      this.reshapeRequired = false;
    }
    build(inputShape) {
      if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) || inputShape.length === 1) {
        throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs");
      }
      inputShape = inputShape;
      let allNoneShape = true;
      for (const shape of inputShape) {
        if (shape != null) {
          allNoneShape = false;
          break;
        }
      }
      if (allNoneShape) {
        return;
      }
      const shapeSet = [];
      for (let i = 0; i < inputShape.length; ++i) {
        const shapeWithoutConcatAxis = inputShape[i].slice();
        shapeWithoutConcatAxis.splice(this.axis, 1);
        let exists = false;
        for (const shape of shapeSet) {
          if (util_exports.arraysEqual(shape, shapeWithoutConcatAxis)) {
            exists = true;
            break;
          }
        }
        if (!exists) {
          shapeSet.push(shapeWithoutConcatAxis);
        }
      }
      if (shapeSet.length > 1) {
        throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: " + JSON.stringify(inputShape));
      }
    }
    mergeFunction(inputs) {
      return tidy(() => {
        return concatenate(inputs, this.axis);
      });
    }
    computeOutputShape(inputShape) {
      if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {
        throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");
      }
      const inputShapes = inputShape;
      const outputShape = inputShapes[0].slice();
      const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;
      for (const shape of inputShapes.slice(1)) {
        if (outputShape[axis] == null || shape[axis] == null) {
          outputShape[axis] = null;
          break;
        }
        outputShape[axis] += shape[axis];
      }
      return outputShape;
    }
    computeMask(inputs, mask) {
      if (mask == null) {
        return null;
      }
      if (!Array.isArray(mask)) {
        throw new ValueError("`mask` should be an array for Concatenate");
      }
      if (!Array.isArray(inputs)) {
        throw new ValueError("`inputs` should be an array for Concatenate");
      }
      if (mask.length !== inputs.length) {
        throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);
      }
      return tidy(() => {
        let allNullMasks = true;
        mask.forEach((m) => {
          if (m != null) {
            allNullMasks = false;
            return;
          }
        });
        if (allNullMasks) {
          return null;
        }
        const outputMasks = [];
        for (let i = 0; i < inputs.length; ++i) {
          if (mask[i] == null) {
            outputMasks.push(onesLike(inputs[i]).asType("bool"));
          } else if (mask[i].rank < inputs[i].rank) {
            outputMasks.push(expandDims(mask[i], -1));
          } else {
            outputMasks.push(mask[i]);
          }
        }
        const concatenatedMasks = concat(outputMasks, this.axis);
        return all(concatenatedMasks, -1, false);
      });
    }
    getConfig() {
      const config = {
        axis: this.axis
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  Concatenate.className = "Concatenate";
  serialization_exports.registerClass(Concatenate);
  function interpretAxis(axis, dim) {
    while (axis < 0) {
      axis += dim;
    }
    return axis;
  }
  function batchDot(x, y, axes) {
    if (x.shape.length > 3 || y.shape.length > 3) {
      throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");
    }
    util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`);
    util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`);
    if (typeof axes === "number") {
      axes = [axes, axes];
    }
    if (x.dtype === "complex64" || y.dtype === "complex64") {
      throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");
    }
    const xNDim = x.shape.length;
    const yNDim = y.shape.length;
    if (axes == null) {
      axes = [xNDim - 1, yNDim - 2];
    }
    const axesArray = axes;
    return tidy(() => {
      let diff;
      if (xNDim > yNDim) {
        diff = xNDim - yNDim;
        const diffShape = [];
        for (let i = 0; i < diff; ++i) {
          diffShape.push(1);
        }
        y = y.reshape(y.shape.concat(diffShape));
      } else if (yNDim > xNDim) {
        diff = yNDim - xNDim;
        const diffShape = [];
        for (let i = 0; i < diff; ++i) {
          diffShape.push(1);
        }
        x = x.reshape(x.shape.concat(diffShape));
      } else {
        diff = 0;
      }
      let out;
      if (x.shape.length === 2 && y.shape.length === 2) {
        if (axesArray[0] === axesArray[1]) {
          out = x.mul(y).sum(axesArray[0]);
        } else {
          out = x.transpose([1, 0]).mul(y).sum(axesArray[1]);
        }
      } else {
        const adjX = axesArray[0] !== x.shape.length - 1;
        const adjY = axesArray[1] === y.shape.length - 1;
        out = x.matMul(y, adjX, adjY);
      }
      if (diff > 0) {
        let idx;
        if (xNDim > yNDim) {
          idx = xNDim + yNDim - 3;
        } else {
          idx = xNDim - 1;
        }
        const squeezeAxes = [];
        for (let i = idx; i < idx + diff; ++i) {
          squeezeAxes.push(i);
        }
        out = out.squeeze(squeezeAxes);
      }
      if (out.shape.length === 1) {
        out = out.expandDims(1);
      }
      return out;
    });
  }
  var Dot = class extends Merge {
    constructor(args) {
      super(args);
      this.axes = args.axes;
      this.normalize = args.normalize == null ? false : args.normalize;
      this.supportsMasking = true;
      this.reshapeRequired = false;
    }
    build(inputShape) {
      util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs.");
      const shape1 = inputShape[0];
      const shape2 = inputShape[1];
      if (shape1.length > 3 || shape2.length > 3) {
        throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");
      }
      const axes = this.interpretAxes(shape1, shape2);
      if (shape1[axes[0]] !== shape2[axes[1]]) {
        throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`);
      }
    }
    mergeFunction(inputs) {
      if (inputs.length !== 2) {
        throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);
      }
      let x1 = inputs[0];
      let x2 = inputs[1];
      let axes;
      if (!Array.isArray(this.axes)) {
        axes = [
          interpretAxis(this.axes, x1.shape.length),
          interpretAxis(this.axes, x2.shape.length)
        ];
      } else {
        axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length));
      }
      if (this.normalize) {
        x1 = l2Normalize(x1, axes[0]);
        x2 = l2Normalize(x2, axes[1]);
      }
      return batchDot(x1, x2, axes);
    }
    interpretAxes(shape1, shape2) {
      let axes;
      if (!Array.isArray(this.axes)) {
        axes = [
          interpretAxis(this.axes, shape1.length),
          interpretAxis(this.axes, shape2.length)
        ];
      } else {
        axes = this.axes;
      }
      return axes;
    }
    computeOutputShape(inputShape) {
      util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs.");
      const shape1 = inputShape[0].slice();
      const shape2 = inputShape[1].slice();
      if (shape1.length > 3 || shape2.length > 3) {
        throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");
      }
      const axes = this.interpretAxes(shape1, shape2);
      shape1.splice(axes[0], 1);
      shape2.splice(axes[1], 1);
      shape2.splice(0, 1);
      const outputShape = shape1.concat(shape2);
      if (outputShape.length === 1) {
        outputShape.push(1);
      }
      return outputShape;
    }
    computeMask(inputs, mask) {
      return null;
    }
    getConfig() {
      const config = {
        axes: this.axes,
        normalize: this.normalize
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  Dot.className = "Dot";
  serialization_exports.registerClass(Dot);
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var GaussianNoise = class extends Layer {
    constructor(args) {
      super(args);
      this.supportsMasking = true;
      this.stddev = args.stddev;
    }
    computeOutputShape(inputShape) {
      return inputShape;
    }
    getConfig() {
      const baseConfig = super.getConfig();
      const config = {stddev: this.stddev};
      Object.assign(config, baseConfig);
      return config;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        this.invokeCallHook(inputs, kwargs);
        const input2 = getExactlyOneTensor(inputs);
        const noised = () => randomNormal2(input2.shape, 0, this.stddev).add(input2);
        const output = inTrainPhase(noised, () => input2, kwargs["training"] || false);
        return output;
      });
    }
  };
  GaussianNoise.className = "GaussianNoise";
  serialization_exports.registerClass(GaussianNoise);
  var GaussianDropout = class extends Layer {
    constructor(args) {
      super(args);
      this.supportsMasking = true;
      this.rate = args.rate;
    }
    computeOutputShape(inputShape) {
      return inputShape;
    }
    getConfig() {
      const baseConfig = super.getConfig();
      const config = {rate: this.rate};
      Object.assign(config, baseConfig);
      return config;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        this.invokeCallHook(inputs, kwargs);
        const input2 = getExactlyOneTensor(inputs);
        if (this.rate > 0 && this.rate < 1) {
          const noised = () => {
            const stddev = Math.sqrt(this.rate / (1 - this.rate));
            return input2.mul(randomNormal2(input2.shape, 1, stddev));
          };
          return inTrainPhase(noised, () => input2, kwargs["training"] || false);
        }
        return input2;
      });
    }
  };
  GaussianDropout.className = "GaussianDropout";
  serialization_exports.registerClass(GaussianDropout);
  var AlphaDropout = class extends Layer {
    constructor(args) {
      super(args);
      this.supportsMasking = true;
      this.rate = args.rate;
      this.noiseShape = args.noiseShape;
    }
    _getNoiseShape(inputs) {
      return this.noiseShape || getExactlyOneTensor(inputs).shape;
    }
    computeOutputShape(inputShape) {
      return inputShape;
    }
    getConfig() {
      const baseConfig = super.getConfig();
      const config = {rate: this.rate};
      Object.assign(config, baseConfig);
      return config;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        if (this.rate < 1 && this.rate > 0) {
          const noiseShape = this._getNoiseShape(inputs);
          const droppedInputs = () => {
            const input2 = getExactlyOneTensor(inputs);
            const alpha = 1.6732632423543772;
            const scale22 = 1.0507009873554805;
            const alphaP = -alpha * scale22;
            let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);
            keptIdx = cast2(keptIdx, "float32");
            const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;
            const b = -a * alphaP * this.rate;
            const x = input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP));
            return x.mul(a).add(b);
          };
          return inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs["training"] || false);
        }
        return inputs;
      });
    }
  };
  AlphaDropout.className = "AlphaDropout";
  serialization_exports.registerClass(AlphaDropout);
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function batchNormalization(x, mean4, variance, beta, gamma, epsilon32 = 1e-3) {
    let out;
    if (x.rank === 2) {
      out = batchNorm2d(x, mean4, variance, beta, gamma, epsilon32);
    } else if (x.rank === 3) {
      out = batchNorm3d(x, mean4, variance, beta, gamma, epsilon32);
    } else if (x.rank === 4) {
      out = batchNorm4d(x, mean4, variance, beta, gamma, epsilon32);
    } else {
      throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);
    }
    return out;
  }
  function regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon32 = 1e-3) {
    return tidy(() => {
      const meanAndVariance = moments(x, reductionAxes);
      const mean4 = meanAndVariance.mean;
      const variance = meanAndVariance.variance;
      const normed = batchNormalization(x, mean4, variance, beta, gamma, epsilon32);
      return [normed, mean4, variance];
    });
  }
  function broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon32 = 1e-3) {
    return tidy(() => {
      const meanAndVariance = moments(x, reductionAxes);
      const mean4 = meanAndVariance.mean;
      const variance = meanAndVariance.variance;
      const targetShape = [];
      for (const axis of range2(0, x.rank)) {
        if (reductionAxes.indexOf(axis) !== -1) {
          targetShape.push(1);
        } else {
          targetShape.push(x.shape[axis]);
        }
      }
      const broadcastMean = mean4.reshape(targetShape);
      const broadcastVariance = variance.reshape(targetShape);
      const broadcastGamma = gamma == null ? null : gamma.reshape(targetShape);
      const broadcastBeta = beta == null ? null : beta.reshape(targetShape);
      const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon32);
      return [normed, mean4, variance];
    });
  }
  function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon32 = 1e-3) {
    if (util_exports.arraysEqual(reductionAxes.slice().sort(), range2(0, x.rank - 1))) {
      return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon32);
    } else {
      return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon32);
    }
  }
  var BatchNormalization = class extends Layer {
    constructor(args) {
      if (args == null) {
        args = {};
      }
      super(args);
      this.supportsMasking = true;
      this.axis = args.axis == null ? -1 : args.axis;
      this.momentum = args.momentum == null ? 0.99 : args.momentum;
      this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;
      this.center = args.center == null ? true : args.center;
      this.scale = args.scale == null ? true : args.scale;
      this.betaInitializer = getInitializer(args.betaInitializer || "zeros");
      this.gammaInitializer = getInitializer(args.gammaInitializer || "ones");
      this.movingMeanInitializer = getInitializer(args.movingMeanInitializer || "zeros");
      this.movingVarianceInitializer = getInitializer(args.movingVarianceInitializer || "ones");
      this.betaConstraint = getConstraint(args.betaConstraint);
      this.gammaConstraint = getConstraint(args.gammaConstraint);
      this.betaRegularizer = getRegularizer(args.betaRegularizer);
      this.gammaRegularizer = getRegularizer(args.gammaRegularizer);
    }
    build(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      const axis = this.axis >= 0 ? this.axis : this.axis + inputShape.length;
      const dim = inputShape[axis];
      if (dim == null) {
        throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);
      }
      this.inputSpec = [new InputSpec({ndim: inputShape.length, axes: {[axis]: dim}})];
      const shape = [dim];
      if (this.scale) {
        this.gamma = this.addWeight("gamma", shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);
      }
      if (this.center) {
        this.beta = this.addWeight("beta", shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);
      }
      this.movingMean = this.addWeight("moving_mean", shape, null, this.movingMeanInitializer, null, false);
      this.movingVariance = this.addWeight("moving_variance", shape, null, this.movingVarianceInitializer, null, false);
      this.built = true;
    }
    call(inputs, kwargs) {
      return tidy(() => {
        const training = kwargs["training"] == null ? false : kwargs["training"];
        const input2 = getExactlyOneTensor(inputs);
        const inputShape = input2.shape;
        const ndim = inputShape.length;
        const reductionAxes = range2(0, ndim);
        const axis = this.axis >= 0 ? this.axis : this.axis + ndim;
        reductionAxes.splice(axis, 1);
        const broadcastShape = pyListRepeat(1, ndim);
        broadcastShape[axis] = inputShape[axis];
        const sortedReductionAxes = reductionAxes.slice();
        sortedReductionAxes.sort();
        const needsBroadcasting = !util_exports.arraysEqual(sortedReductionAxes, range2(0, ndim).slice(0, ndim - 1));
        const normalizeInference = () => {
          if (needsBroadcasting) {
            const broadcastMovingMean = this.movingMean.read().reshape(broadcastShape);
            const broadcastMovingVariance = this.movingVariance.read().reshape(broadcastShape);
            const broadcastBeta = this.center ? this.beta.read().reshape(broadcastShape) : null;
            const broadcastGamma = this.scale ? this.gamma.read().reshape(broadcastShape) : null;
            return batchNormalization(input2, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);
          } else {
            return batchNormalization(input2, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);
          }
        };
        if (!training) {
          return normalizeInference();
        }
        const [normedTraining, mean4, variance] = normalizeBatchInTraining(input2, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);
        const doMovingAverage = (variable2, value, momentum) => {
          tidy(() => {
            const decay = 1 - momentum;
            const origValue = variable2.read();
            const updateDelta = origValue.sub(value).mul(decay);
            variable2.write(origValue.sub(updateDelta));
          });
        };
        const updateMovingMeanAndVariance = () => {
          doMovingAverage(this.movingMean, mean4, this.momentum);
          doMovingAverage(this.movingVariance, variance, this.momentum);
        };
        updateMovingMeanAndVariance();
        return normedTraining;
      });
    }
    getConfig() {
      const config = {
        axis: this.axis,
        momentum: this.momentum,
        epsilon: this.epsilon,
        center: this.center,
        scale: this.scale,
        betaInitializer: serializeInitializer(this.betaInitializer),
        gammaInitializer: serializeInitializer(this.gammaInitializer),
        movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),
        movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),
        betaRegularizer: serializeRegularizer(this.betaRegularizer),
        gammaRegularizer: serializeRegularizer(this.gammaRegularizer),
        betaConstraint: serializeConstraint(this.betaConstraint),
        gammaConstraint: serializeConstraint(this.gammaConstraint)
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  BatchNormalization.className = "BatchNormalization";
  serialization_exports.registerClass(BatchNormalization);
  var LayerNormalization = class extends Layer {
    constructor(args) {
      if (args == null) {
        args = {};
      }
      super(args);
      this.axis = args.axis == null ? -1 : args.axis;
      if (typeof this.axis === "number") {
        if (!Number.isInteger(this.axis)) {
          throw new Error(`Expected axis to be an integer, but received ${this.axis}`);
        }
      } else if (Array.isArray(this.axis)) {
        for (const axis of this.axis) {
          if (!Number.isInteger(axis)) {
            throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`);
          }
        }
      } else {
        throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);
      }
      this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;
      this.center = args.center == null ? true : args.center;
      this.scale = args.scale == null ? true : args.scale;
      this.betaInitializer = getInitializer(args.betaInitializer || "zeros");
      this.gammaInitializer = getInitializer(args.gammaInitializer || "ones");
      this.betaRegularizer = getRegularizer(args.betaRegularizer);
      this.gammaRegularizer = getRegularizer(args.gammaRegularizer);
      this.supportsMasking = true;
    }
    build(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      const nDims = inputShape.length;
      if (typeof this.axis === "number") {
        this.axis = [this.axis];
      }
      for (let i = 0; i < this.axis.length; ++i) {
        if (this.axis[i] < 0) {
          this.axis[i] += nDims;
        }
      }
      for (const axis of this.axis) {
        if (axis < 0 || axis >= nDims) {
          throw new Error(`Invalid axis: ${axis}`);
        }
      }
      if (this.axis.length !== unique2(this.axis).length) {
        throw new Error(`Found duplicate axes in: ${this.axis}`);
      }
      const paramShape = this.axis.map((axis) => inputShape[axis]);
      const trainable = true;
      if (this.scale) {
        this.gamma = this.addWeight("gamma", paramShape, "float32", this.gammaInitializer, this.gammaRegularizer, trainable);
      } else {
        this.gamma = null;
      }
      if (this.center) {
        this.beta = this.addWeight("beta", paramShape, "float32", this.betaInitializer, this.betaRegularizer, trainable);
      } else {
        this.beta = null;
      }
      this.built = true;
    }
    call(inputs, kwargs) {
      const input2 = getExactlyOneTensor(inputs);
      const inputShape = input2.shape;
      const nDims = inputShape.length;
      return tidy(() => {
        const keepDims = true;
        let {mean: mean4, variance} = moments(input2, this.axis, keepDims);
        const broadcastShape = pyListRepeat(1, nDims);
        for (const dim of this.axis) {
          broadcastShape[dim] = inputShape[dim];
        }
        const broadcast = (v) => {
          if (v != null && v.shape.length !== nDims && this.axis !== [nDims - 1]) {
            return v.reshape(broadcastShape);
          } else {
            return v;
          }
        };
        let scale22 = broadcast(this.gamma.read());
        let offset = broadcast(this.beta.read());
        const momentsTiling = [];
        const scaleOffsetTiling = [];
        for (let i = 0; i < nDims; ++i) {
          if (this.axis.indexOf(i) !== -1) {
            momentsTiling.push(inputShape[i]);
            scaleOffsetTiling.push(1);
          } else {
            momentsTiling.push(1);
            scaleOffsetTiling.push(inputShape[i]);
          }
        }
        mean4 = mean4.tile(momentsTiling);
        variance = variance.tile(momentsTiling);
        scale22 = scale22.tile(scaleOffsetTiling);
        offset = offset.tile(scaleOffsetTiling);
        return batchNormalization(input2, mean4, variance, offset, scale22, this.epsilon);
      });
    }
    getConfig() {
      const config = {
        axis: this.axis,
        epsilon: this.epsilon,
        center: this.center,
        scale: this.scale,
        betaInitializer: serializeInitializer(this.betaInitializer),
        gammaInitializer: serializeInitializer(this.gammaInitializer),
        betaRegularizer: serializeRegularizer(this.betaRegularizer),
        gammaRegularizer: serializeRegularizer(this.gammaRegularizer)
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  LayerNormalization.className = "LayerNormalization";
  serialization_exports.registerClass(LayerNormalization);
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function spatial2dPadding(x, padding, dataFormat) {
    return tidy(() => {
      if (x.rank !== 4) {
        throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);
      }
      if (padding == null) {
        padding = [[1, 1], [1, 1]];
      }
      if (padding.length !== 2 || padding[0].length !== 2 || padding[1].length !== 2) {
        throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");
      }
      if (dataFormat == null) {
        dataFormat = imageDataFormat();
      }
      if (dataFormat !== "channelsLast" && dataFormat !== "channelsFirst") {
        throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);
      }
      let pattern;
      if (dataFormat === "channelsFirst") {
        pattern = [[0, 0], [0, 0], padding[0], padding[1]];
      } else {
        pattern = [[0, 0], padding[0], padding[1], [0, 0]];
      }
      return pad(x, pattern);
    });
  }
  var ZeroPadding2D = class extends Layer {
    constructor(args) {
      if (args == null) {
        args = {};
      }
      super(args);
      this.dataFormat = args.dataFormat == null ? imageDataFormat() : args.dataFormat;
      if (args.padding == null) {
        this.padding = [[1, 1], [1, 1]];
      } else if (typeof args.padding === "number") {
        this.padding = [[args.padding, args.padding], [args.padding, args.padding]];
      } else {
        args.padding = args.padding;
        if (args.padding.length !== 2) {
          throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);
        }
        let heightPadding;
        let widthPadding;
        if (typeof args.padding[0] === "number") {
          heightPadding = [args.padding[0], args.padding[0]];
          widthPadding = [args.padding[1], args.padding[1]];
        } else {
          args.padding = args.padding;
          if (args.padding[0].length !== 2) {
            throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);
          }
          heightPadding = args.padding[0];
          if (args.padding[1].length !== 2) {
            throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);
          }
          widthPadding = args.padding[1];
        }
        this.padding = [heightPadding, widthPadding];
      }
      this.inputSpec = [new InputSpec({ndim: 4})];
    }
    computeOutputShape(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      let rows;
      let cols;
      if (this.dataFormat === "channelsFirst") {
        if (inputShape[2] != null && inputShape[2] >= 0) {
          rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];
        } else {
          rows = null;
        }
        if (inputShape[3] != null && inputShape[3] >= 0) {
          cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];
        } else {
          cols = null;
        }
        return [inputShape[0], inputShape[1], rows, cols];
      } else {
        if (inputShape[1] != null && inputShape[1] >= 0) {
          rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];
        } else {
          rows = null;
        }
        if (inputShape[2] != null && inputShape[2] >= 0) {
          cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];
        } else {
          cols = null;
        }
        return [inputShape[0], rows, cols, inputShape[3]];
      }
    }
    call(inputs, kwargs) {
      return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));
    }
    getConfig() {
      const config = {
        padding: this.padding,
        dataFormat: this.dataFormat
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  ZeroPadding2D.className = "ZeroPadding2D";
  serialization_exports.registerClass(ZeroPadding2D);
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {
    return tidy(() => {
      checkDataFormat(dataFormat);
      checkPoolMode(poolMode);
      checkPaddingMode(padding);
      if (strides == null) {
        strides = [1, 1];
      }
      if (padding == null) {
        padding = "valid";
      }
      if (dataFormat == null) {
        dataFormat = imageDataFormat();
      }
      if (poolMode == null) {
        poolMode = "max";
      }
      x = preprocessConv2DInput(x, dataFormat);
      let y;
      const paddingString = padding === "same" ? "same" : "valid";
      if (poolMode === "max") {
        y = maxPool(x, poolSize, strides, paddingString);
      } else {
        y = avgPool(x, poolSize, strides, paddingString);
      }
      if (dataFormat === "channelsFirst") {
        y = transpose(y, [0, 3, 1, 2]);
      }
      return y;
    });
  }
  function pool3d(x, poolSize, strides, padding, dataFormat, poolMode) {
    return tidy(() => {
      checkDataFormat(dataFormat);
      checkPoolMode(poolMode);
      checkPaddingMode(padding);
      if (strides == null) {
        strides = [1, 1, 1];
      }
      if (padding == null) {
        padding = "valid";
      }
      if (dataFormat == null) {
        dataFormat = imageDataFormat();
      }
      if (poolMode == null) {
        poolMode = "max";
      }
      x = preprocessConv3DInput(x, dataFormat);
      let y;
      const paddingString = padding === "same" ? "same" : "valid";
      if (poolMode === "max") {
        y = maxPool3d(x, poolSize, strides, paddingString);
      } else {
        y = avgPool3d(x, poolSize, strides, paddingString);
      }
      if (dataFormat === "channelsFirst") {
        y = transpose(y, [0, 4, 1, 2, 3]);
      }
      return y;
    });
  }
  var Pooling1D = class extends Layer {
    constructor(args) {
      if (args.poolSize == null) {
        args.poolSize = 2;
      }
      super(args);
      if (typeof args.poolSize === "number") {
        this.poolSize = [args.poolSize];
      } else if (Array.isArray(args.poolSize) && args.poolSize.length === 1 && typeof args.poolSize[0] === "number") {
        this.poolSize = args.poolSize;
      } else {
        throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);
      }
      assertPositiveInteger(this.poolSize, "poolSize");
      if (args.strides == null) {
        this.strides = this.poolSize;
      } else {
        if (typeof args.strides === "number") {
          this.strides = [args.strides];
        } else if (Array.isArray(args.strides) && args.strides.length === 1 && typeof args.strides[0] === "number") {
          this.strides = args.strides;
        } else {
          throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);
        }
      }
      assertPositiveInteger(this.strides, "strides");
      this.padding = args.padding == null ? "valid" : args.padding;
      checkPaddingMode(this.padding);
      this.inputSpec = [new InputSpec({ndim: 3})];
    }
    computeOutputShape(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);
      return [inputShape[0], length, inputShape[2]];
    }
    call(inputs, kwargs) {
      return tidy(() => {
        this.invokeCallHook(inputs, kwargs);
        inputs = expandDims2(getExactlyOneTensor(inputs), 2);
        const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, "channelsLast");
        return squeeze(output, [2]);
      });
    }
    getConfig() {
      const config = {
        poolSize: this.poolSize,
        padding: this.padding,
        strides: this.strides
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  var MaxPooling1D = class extends Pooling1D {
    constructor(args) {
      super(args);
    }
    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
      checkDataFormat(dataFormat);
      checkPaddingMode(padding);
      return pool2d(inputs, poolSize, strides, padding, dataFormat, "max");
    }
  };
  MaxPooling1D.className = "MaxPooling1D";
  serialization_exports.registerClass(MaxPooling1D);
  var AveragePooling1D = class extends Pooling1D {
    constructor(args) {
      super(args);
    }
    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
      checkDataFormat(dataFormat);
      checkPaddingMode(padding);
      return pool2d(inputs, poolSize, strides, padding, dataFormat, "avg");
    }
  };
  AveragePooling1D.className = "AveragePooling1D";
  serialization_exports.registerClass(AveragePooling1D);
  var Pooling2D = class extends Layer {
    constructor(args) {
      if (args.poolSize == null) {
        args.poolSize = [2, 2];
      }
      super(args);
      this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize];
      if (args.strides == null) {
        this.strides = this.poolSize;
      } else if (Array.isArray(args.strides)) {
        if (args.strides.length !== 2) {
          throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);
        }
        this.strides = args.strides;
      } else {
        this.strides = [args.strides, args.strides];
      }
      assertPositiveInteger(this.poolSize, "poolSize");
      assertPositiveInteger(this.strides, "strides");
      this.padding = args.padding == null ? "valid" : args.padding;
      this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
      checkDataFormat(this.dataFormat);
      checkPaddingMode(this.padding);
      this.inputSpec = [new InputSpec({ndim: 4})];
    }
    computeOutputShape(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      let rows = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1];
      let cols = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2];
      rows = convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);
      cols = convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);
      if (this.dataFormat === "channelsFirst") {
        return [inputShape[0], inputShape[1], rows, cols];
      } else {
        return [inputShape[0], rows, cols, inputShape[3]];
      }
    }
    call(inputs, kwargs) {
      return tidy(() => {
        this.invokeCallHook(inputs, kwargs);
        return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);
      });
    }
    getConfig() {
      const config = {
        poolSize: this.poolSize,
        padding: this.padding,
        strides: this.strides,
        dataFormat: this.dataFormat
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  var MaxPooling2D = class extends Pooling2D {
    constructor(args) {
      super(args);
    }
    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
      checkDataFormat(dataFormat);
      checkPaddingMode(padding);
      return pool2d(inputs, poolSize, strides, padding, dataFormat, "max");
    }
  };
  MaxPooling2D.className = "MaxPooling2D";
  serialization_exports.registerClass(MaxPooling2D);
  var AveragePooling2D = class extends Pooling2D {
    constructor(args) {
      super(args);
    }
    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
      checkDataFormat(dataFormat);
      checkPaddingMode(padding);
      return pool2d(inputs, poolSize, strides, padding, dataFormat, "avg");
    }
  };
  AveragePooling2D.className = "AveragePooling2D";
  serialization_exports.registerClass(AveragePooling2D);
  var Pooling3D = class extends Layer {
    constructor(args) {
      if (args.poolSize == null) {
        args.poolSize = [2, 2, 2];
      }
      super(args);
      this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize, args.poolSize];
      if (args.strides == null) {
        this.strides = this.poolSize;
      } else if (Array.isArray(args.strides)) {
        if (args.strides.length !== 3) {
          throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);
        }
        this.strides = args.strides;
      } else {
        this.strides = [args.strides, args.strides, args.strides];
      }
      assertPositiveInteger(this.poolSize, "poolSize");
      assertPositiveInteger(this.strides, "strides");
      this.padding = args.padding == null ? "valid" : args.padding;
      this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
      checkDataFormat(this.dataFormat);
      checkPaddingMode(this.padding);
      this.inputSpec = [new InputSpec({ndim: 5})];
    }
    computeOutputShape(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      let depths = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1];
      let rows = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2];
      let cols = this.dataFormat === "channelsFirst" ? inputShape[4] : inputShape[3];
      depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);
      rows = convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);
      cols = convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);
      if (this.dataFormat === "channelsFirst") {
        return [inputShape[0], inputShape[1], depths, rows, cols];
      } else {
        return [inputShape[0], depths, rows, cols, inputShape[4]];
      }
    }
    call(inputs, kwargs) {
      return tidy(() => {
        this.invokeCallHook(inputs, kwargs);
        return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);
      });
    }
    getConfig() {
      const config = {
        poolSize: this.poolSize,
        padding: this.padding,
        strides: this.strides,
        dataFormat: this.dataFormat
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  var MaxPooling3D = class extends Pooling3D {
    constructor(args) {
      super(args);
    }
    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
      checkDataFormat(dataFormat);
      checkPaddingMode(padding);
      return pool3d(inputs, poolSize, strides, padding, dataFormat, "max");
    }
  };
  MaxPooling3D.className = "MaxPooling3D";
  serialization_exports.registerClass(MaxPooling3D);
  var AveragePooling3D = class extends Pooling3D {
    constructor(args) {
      super(args);
    }
    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
      checkDataFormat(dataFormat);
      checkPaddingMode(padding);
      return pool3d(inputs, poolSize, strides, padding, dataFormat, "avg");
    }
  };
  AveragePooling3D.className = "AveragePooling3D";
  serialization_exports.registerClass(AveragePooling3D);
  var GlobalPooling1D = class extends Layer {
    constructor(args) {
      super(args);
      this.inputSpec = [new InputSpec({ndim: 3})];
    }
    computeOutputShape(inputShape) {
      return [inputShape[0], inputShape[2]];
    }
    call(inputs, kwargs) {
      throw new NotImplementedError();
    }
  };
  var GlobalAveragePooling1D = class extends GlobalPooling1D {
    constructor(args) {
      super(args || {});
    }
    call(inputs, kwargs) {
      return tidy(() => {
        const input2 = getExactlyOneTensor(inputs);
        return mean(input2, 1);
      });
    }
  };
  GlobalAveragePooling1D.className = "GlobalAveragePooling1D";
  serialization_exports.registerClass(GlobalAveragePooling1D);
  var GlobalMaxPooling1D = class extends GlobalPooling1D {
    constructor(args) {
      super(args || {});
    }
    call(inputs, kwargs) {
      return tidy(() => {
        const input2 = getExactlyOneTensor(inputs);
        return max(input2, 1);
      });
    }
  };
  GlobalMaxPooling1D.className = "GlobalMaxPooling1D";
  serialization_exports.registerClass(GlobalMaxPooling1D);
  var GlobalPooling2D = class extends Layer {
    constructor(args) {
      super(args);
      this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
      checkDataFormat(this.dataFormat);
      this.inputSpec = [new InputSpec({ndim: 4})];
    }
    computeOutputShape(inputShape) {
      inputShape = inputShape;
      if (this.dataFormat === "channelsLast") {
        return [inputShape[0], inputShape[3]];
      } else {
        return [inputShape[0], inputShape[1]];
      }
    }
    call(inputs, kwargs) {
      throw new NotImplementedError();
    }
    getConfig() {
      const config = {dataFormat: this.dataFormat};
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
  };
  var GlobalAveragePooling2D = class extends GlobalPooling2D {
    call(inputs, kwargs) {
      return tidy(() => {
        const input2 = getExactlyOneTensor(inputs);
        if (this.dataFormat === "channelsLast") {
          return mean(input2, [1, 2]);
        } else {
          return mean(input2, [2, 3]);
        }
      });
    }
  };
  GlobalAveragePooling2D.className = "GlobalAveragePooling2D";
  serialization_exports.registerClass(GlobalAveragePooling2D);
  var GlobalMaxPooling2D = class extends GlobalPooling2D {
    call(inputs, kwargs) {
      return tidy(() => {
        const input2 = getExactlyOneTensor(inputs);
        if (this.dataFormat === "channelsLast") {
          return max(input2, [1, 2]);
        } else {
          return max(input2, [2, 3]);
        }
      });
    }
  };
  GlobalMaxPooling2D.className = "GlobalMaxPooling2D";
  serialization_exports.registerClass(GlobalMaxPooling2D);
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var Wrapper = class extends Layer {
    constructor(args) {
      super(args);
      this.layer = args.layer;
    }
    build(inputShape) {
      this.built = true;
    }
    get trainable() {
      if (this.layer != null) {
        return this.layer.trainable;
      } else {
        return false;
      }
    }
    set trainable(value) {
      if (this.layer != null) {
        this.layer.trainable = value;
      }
    }
    get trainableWeights() {
      return this.layer.trainableWeights;
    }
    get nonTrainableWeights() {
      return this.layer.nonTrainableWeights;
    }
    get updates() {
      return this.layer._updates;
    }
    get losses() {
      return this.layer.losses;
    }
    getWeights() {
      return this.layer.getWeights();
    }
    setWeights(weights) {
      this.layer.setWeights(weights);
    }
    getConfig() {
      const config = {
        layer: {
          className: this.layer.getClassName(),
          config: this.layer.getConfig()
        }
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
    setFastWeightInitDuringBuild(value) {
      super.setFastWeightInitDuringBuild(value);
      if (this.layer != null) {
        this.layer.setFastWeightInitDuringBuild(value);
      }
    }
    static fromConfig(cls, config, customObjects = {}) {
      const layerConfig = config["layer"];
      const layer = deserialize(layerConfig, customObjects);
      delete config["layer"];
      const newConfig = {layer};
      Object.assign(newConfig, config);
      return new cls(newConfig);
    }
  };
  var TimeDistributed = class extends Wrapper {
    constructor(args) {
      super(args);
      this.supportsMasking = true;
    }
    build(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      if (inputShape.length < 3) {
        throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);
      }
      this.inputSpec = [{shape: inputShape}];
      const childInputShape = [inputShape[0]].concat(inputShape.slice(2));
      if (!this.layer.built) {
        this.layer.build(childInputShape);
        this.layer.built = true;
      }
      super.build(inputShape);
    }
    computeOutputShape(inputShape) {
      inputShape = getExactlyOneShape(inputShape);
      const childInputShape = [inputShape[0]].concat(inputShape.slice(2));
      const childOutputShape = this.layer.computeOutputShape(childInputShape);
      const timesteps = inputShape[1];
      return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));
    }
    call(inputs, kwargs) {
      return tidy(() => {
        inputs = getExactlyOneTensor(inputs);
        const step5 = (inputs2, states) => {
          const output = getExactlyOneTensor(this.layer.call(inputs2, kwargs));
          return [output, []];
        };
        const rnnOutputs = rnn(step5, inputs, [], false, null, null, false, true);
        const y = rnnOutputs[1];
        return y;
      });
    }
  };
  TimeDistributed.className = "TimeDistributed";
  serialization_exports.registerClass(TimeDistributed);
  function checkBidirectionalMergeMode(value) {
    checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, "BidirectionalMergeMode", value);
  }
  var DEFAULT_BIDIRECTIONAL_MERGE_MODE = "concat";
  var Bidirectional = class extends Wrapper {
    constructor(args) {
      super(args);
      const layerConfig = args.layer.getConfig();
      const forwDict = {};
      forwDict["className"] = args.layer.getClassName();
      forwDict["config"] = layerConfig;
      this.forwardLayer = deserialize(forwDict);
      layerConfig["goBackwards"] = layerConfig["goBackwards"] === true ? false : true;
      const backDict = {};
      backDict["className"] = args.layer.getClassName();
      backDict["config"] = layerConfig;
      this.backwardLayer = deserialize(backDict);
      this.forwardLayer.name = "forward_" + this.forwardLayer.name;
      this.backwardLayer.name = "backward_" + this.backwardLayer.name;
      this.mergeMode = args.mergeMode === void 0 ? DEFAULT_BIDIRECTIONAL_MERGE_MODE : args.mergeMode;
      checkBidirectionalMergeMode(this.mergeMode);
      if (args.weights) {
        throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");
      }
      this._stateful = args.layer.stateful;
      this.returnSequences = args.layer.returnSequences;
      this.returnState = args.layer.returnState;
      this.supportsMasking = true;
      this._trainable = true;
      this.inputSpec = args.layer.inputSpec;
      this.numConstants = null;
    }
    get trainable() {
      return this._trainable;
    }
    set trainable(value) {
      this._trainable = value;
      if (this.forwardLayer != null) {
        this.forwardLayer.trainable = value;
      }
      if (this.backwardLayer != null) {
        this.backwardLayer.trainable = value;
      }
    }
    getWeights() {
      return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());
    }
    setWeights(weights) {
      const numWeights = weights.length;
      const numeightsOver2 = Math.floor(numWeights / 2);
      this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));
      this.backwardLayer.setWeights(weights.slice(numeightsOver2));
    }
    computeOutputShape(inputShape) {
      let layerShapes = this.forwardLayer.computeOutputShape(inputShape);
      if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {
        layerShapes = [layerShapes];
      }
      layerShapes = layerShapes;
      let outputShape;
      let outputShapes;
      let stateShape;
      if (this.returnState) {
        stateShape = layerShapes.slice(1);
        outputShape = layerShapes[0];
      } else {
        outputShape = layerShapes[0];
      }
      outputShape = outputShape;
      if (this.mergeMode === "concat") {
        outputShape[outputShape.length - 1] *= 2;
        outputShapes = [outputShape];
      } else if (this.mergeMode == null) {
        outputShapes = [outputShape, outputShape.slice()];
      } else {
        outputShapes = [outputShape];
      }
      if (this.returnState) {
        if (this.mergeMode == null) {
          return outputShapes.concat(stateShape).concat(stateShape.slice());
        }
        return [outputShape].concat(stateShape).concat(stateShape.slice());
      }
      return singletonOrArray(outputShapes);
    }
    apply(inputs, kwargs) {
      let initialState = kwargs == null ? null : kwargs["initialState"];
      let constants = kwargs == null ? null : kwargs["constants"];
      if (kwargs == null) {
        kwargs = {};
      }
      const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);
      inputs = standardized.inputs;
      initialState = standardized.initialState;
      constants = standardized.constants;
      if (Array.isArray(inputs)) {
        initialState = inputs.slice(1);
        inputs = inputs[0];
      }
      if ((initialState == null || initialState.length === 0) && constants == null) {
        return super.apply(inputs, kwargs);
      }
      const additionalInputs = [];
      const additionalSpecs = [];
      if (initialState != null) {
        const numStates = initialState.length;
        if (numStates % 2 > 0) {
          throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");
        }
        kwargs["initialState"] = initialState;
        additionalInputs.push(...initialState);
        const stateSpecs = initialState.map((state) => new InputSpec({shape: state.shape}));
        this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);
        this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);
        additionalSpecs.push(...stateSpecs);
      }
      if (constants != null) {
        throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");
      }
      const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;
      for (const tensor2 of additionalInputs) {
        if (tensor2 instanceof SymbolicTensor !== isSymbolicTensor) {
          throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");
        }
      }
      if (isSymbolicTensor) {
        const fullInput = [inputs].concat(additionalInputs);
        const fullInputSpec = this.inputSpec.concat(additionalSpecs);
        const originalInputSpec = this.inputSpec;
        this.inputSpec = fullInputSpec;
        const output = super.apply(fullInput, kwargs);
        this.inputSpec = originalInputSpec;
        return output;
      } else {
        return super.apply(inputs, kwargs);
      }
    }
    call(inputs, kwargs) {
      return tidy(() => {
        const initialState = kwargs["initialState"];
        let y;
        let yRev;
        if (initialState == null) {
          y = this.forwardLayer.call(inputs, kwargs);
          yRev = this.backwardLayer.call(inputs, kwargs);
        } else {
          const forwardState = initialState.slice(0, initialState.length / 2);
          const backwardState = initialState.slice(initialState.length / 2);
          y = this.forwardLayer.call(inputs, Object.assign(kwargs, {initialState: forwardState}));
          yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, {initialState: backwardState}));
        }
        let states;
        if (this.returnState) {
          if (Array.isArray(y)) {
            states = y.slice(1).concat(yRev.slice(1));
          } else {
          }
          y = y[0];
          yRev = yRev[0];
        }
        if (this.returnSequences) {
          yRev = reverse(yRev, 1);
        }
        let output;
        if (this.mergeMode === "concat") {
          output = concatenate([y, yRev]);
        } else if (this.mergeMode === "sum") {
          output = add2(y, yRev);
        } else if (this.mergeMode === "ave") {
          output = mul(0.5, add2(y, yRev));
        } else if (this.mergeMode === "mul") {
          output = mul(y, yRev);
        } else if (this.mergeMode == null) {
          output = [y, yRev];
        }
        if (this.returnState) {
          if (this.mergeMode == null) {
            return output.concat(states);
          }
          return [output].concat(states);
        }
        return output;
      });
    }
    resetStates(states) {
      this.forwardLayer.resetStates();
      this.backwardLayer.resetStates();
    }
    build(inputShape) {
      nameScope(this.forwardLayer.name, () => {
        this.forwardLayer.build(inputShape);
      });
      nameScope(this.backwardLayer.name, () => {
        this.backwardLayer.build(inputShape);
      });
      this.built = true;
    }
    computeMask(inputs, mask) {
      if (Array.isArray(mask)) {
        mask = mask[0];
      }
      let outputMask;
      if (this.returnSequences) {
        if (this.mergeMode == null) {
          outputMask = [mask, mask];
        } else {
          outputMask = mask;
        }
      } else {
        if (this.mergeMode == null) {
          outputMask = [null, null];
        } else {
          outputMask = null;
        }
      }
      if (this.returnState) {
        const states = this.forwardLayer.states;
        const stateMask = states.map((state) => null);
        if (Array.isArray(outputMask)) {
          return outputMask.concat(stateMask).concat(stateMask);
        } else {
          return [outputMask].concat(stateMask).concat(stateMask);
        }
      } else {
        return outputMask;
      }
    }
    get trainableWeights() {
      return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);
    }
    get nonTrainableWeights() {
      return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);
    }
    setFastWeightInitDuringBuild(value) {
      super.setFastWeightInitDuringBuild(value);
      if (this.forwardLayer != null) {
        this.forwardLayer.setFastWeightInitDuringBuild(value);
      }
      if (this.backwardLayer != null) {
        this.backwardLayer.setFastWeightInitDuringBuild(value);
      }
    }
    getConfig() {
      const config = {
        mergeMode: this.mergeMode
      };
      const baseConfig = super.getConfig();
      Object.assign(config, baseConfig);
      return config;
    }
    static fromConfig(cls, config) {
      const rnnLayer = deserialize(config["layer"]);
      delete config["layer"];
      if (config["numConstants"] != null) {
        throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants present is not supported yet.`);
      }
      const newConfig = config;
      newConfig["layer"] = rnnLayer;
      return new cls(newConfig);
    }
  };
  Bidirectional.className = "Bidirectional";
  serialization_exports.registerClass(Bidirectional);
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function inputLayer(args) {
    return new InputLayer(args);
  }
  function elu3(args) {
    return new ELU(args);
  }
  function reLU(args) {
    return new ReLU(args);
  }
  function leakyReLU(args) {
    return new LeakyReLU(args);
  }
  function prelu2(args) {
    return new PReLU(args);
  }
  function softmax2(args) {
    return new Softmax3(args);
  }
  function thresholdedReLU(args) {
    return new ThresholdedReLU(args);
  }
  function conv1d2(args) {
    return new Conv1D(args);
  }
  function conv2d3(args) {
    return new Conv2D2(args);
  }
  function conv2dTranspose2(args) {
    return new Conv2DTranspose(args);
  }
  function conv3d2(args) {
    return new Conv3D2(args);
  }
  function separableConv2d2(args) {
    return new SeparableConv2D(args);
  }
  function cropping2D(args) {
    return new Cropping2D(args);
  }
  function upSampling2d(args) {
    return new UpSampling2D(args);
  }
  function depthwiseConv2d4(args) {
    return new DepthwiseConv2D(args);
  }
  function activation(args) {
    return new Activation2(args);
  }
  function dense(args) {
    return new Dense(args);
  }
  function dropout3(args) {
    return new Dropout(args);
  }
  function spatialDropout1d(args) {
    return new SpatialDropout1D(args);
  }
  function flatten3(args) {
    return new Flatten(args);
  }
  function repeatVector(args) {
    return new RepeatVector(args);
  }
  function reshape2(args) {
    return new Reshape2(args);
  }
  function permute(args) {
    return new Permute(args);
  }
  function embedding(args) {
    return new Embedding(args);
  }
  function add3(args) {
    return new Add2(args);
  }
  function average(args) {
    return new Average(args);
  }
  function concatenate2(args) {
    return new Concatenate(args);
  }
  function maximum2(args) {
    return new Maximum2(args);
  }
  function minimum2(args) {
    return new Minimum2(args);
  }
  function multiply(args) {
    return new Multiply2(args);
  }
  function dot3(args) {
    return new Dot(args);
  }
  function batchNormalization2(args) {
    return new BatchNormalization(args);
  }
  function layerNormalization(args) {
    return new LayerNormalization(args);
  }
  function zeroPadding2d(args) {
    return new ZeroPadding2D(args);
  }
  function averagePooling1d(args) {
    return new AveragePooling1D(args);
  }
  function avgPool1d(args) {
    return averagePooling1d(args);
  }
  function avgPooling1d(args) {
    return averagePooling1d(args);
  }
  function averagePooling2d(args) {
    return new AveragePooling2D(args);
  }
  function avgPool2d(args) {
    return averagePooling2d(args);
  }
  function avgPooling2d(args) {
    return averagePooling2d(args);
  }
  function averagePooling3d(args) {
    return new AveragePooling3D(args);
  }
  function avgPool3d2(args) {
    return averagePooling3d(args);
  }
  function avgPooling3d(args) {
    return averagePooling3d(args);
  }
  function globalAveragePooling1d(args) {
    return new GlobalAveragePooling1D(args);
  }
  function globalAveragePooling2d(args) {
    return new GlobalAveragePooling2D(args);
  }
  function globalMaxPooling1d(args) {
    return new GlobalMaxPooling1D(args);
  }
  function globalMaxPooling2d(args) {
    return new GlobalMaxPooling2D(args);
  }
  function maxPooling1d(args) {
    return new MaxPooling1D(args);
  }
  function maxPooling2d(args) {
    return new MaxPooling2D(args);
  }
  function maxPooling3d(args) {
    return new MaxPooling3D(args);
  }
  function gru(args) {
    return new GRU(args);
  }
  function gruCell(args) {
    return new GRUCell(args);
  }
  function lstm(args) {
    return new LSTM(args);
  }
  function lstmCell(args) {
    return new LSTMCell(args);
  }
  function simpleRNN(args) {
    return new SimpleRNN(args);
  }
  function simpleRNNCell(args) {
    return new SimpleRNNCell(args);
  }
  function convLstm2d(args) {
    return new ConvLSTM2D(args);
  }
  function convLstm2dCell(args) {
    return new ConvLSTM2DCell(args);
  }
  function rnn2(args) {
    return new RNN(args);
  }
  function stackedRNNCells(args) {
    return new StackedRNNCells(args);
  }
  function bidirectional(args) {
    return new Bidirectional(args);
  }
  function timeDistributed(args) {
    return new TimeDistributed(args);
  }
  var globalMaxPool1d = globalMaxPooling1d;
  var globalMaxPool2d = globalMaxPooling2d;
  var maxPool1d = maxPooling1d;
  var maxPool2d = maxPooling2d;
  function gaussianNoise(args) {
    return new GaussianNoise(args);
  }
  function gaussianDropout(args) {
    return new GaussianDropout(args);
  }
  function alphaDropout(args) {
    return new AlphaDropout(args);
  }
  function masking(args) {
    return new Masking(args);
  }
  var exports_metrics_exports = {};
  __export2(exports_metrics_exports, {
    MAPE: () => MAPE2,
    MSE: () => MSE2,
    binaryAccuracy: () => binaryAccuracy2,
    binaryCrossentropy: () => binaryCrossentropy3,
    categoricalAccuracy: () => categoricalAccuracy2,
    categoricalCrossentropy: () => categoricalCrossentropy3,
    cosineProximity: () => cosineProximity2,
    mape: () => mape2,
    meanAbsoluteError: () => meanAbsoluteError2,
    meanAbsolutePercentageError: () => meanAbsolutePercentageError2,
    meanSquaredError: () => meanSquaredError3,
    mse: () => mse2,
    precision: () => precision2,
    recall: () => recall2,
    sparseCategoricalAccuracy: () => sparseCategoricalAccuracy2
  });
  function binaryAccuracy2(yTrue, yPred) {
    return binaryAccuracy(yTrue, yPred);
  }
  function binaryCrossentropy3(yTrue, yPred) {
    return binaryCrossentropy2(yTrue, yPred);
  }
  function sparseCategoricalAccuracy2(yTrue, yPred) {
    return sparseCategoricalAccuracy(yTrue, yPred);
  }
  function categoricalAccuracy2(yTrue, yPred) {
    return categoricalAccuracy(yTrue, yPred);
  }
  function categoricalCrossentropy3(yTrue, yPred) {
    return categoricalCrossentropy2(yTrue, yPred);
  }
  function precision2(yTrue, yPred) {
    return precision(yTrue, yPred);
  }
  function recall2(yTrue, yPred) {
    return recall(yTrue, yPred);
  }
  function cosineProximity2(yTrue, yPred) {
    return cosineProximity(yTrue, yPred);
  }
  function meanAbsoluteError2(yTrue, yPred) {
    return meanAbsoluteError(yTrue, yPred);
  }
  function meanAbsolutePercentageError2(yTrue, yPred) {
    return meanAbsolutePercentageError(yTrue, yPred);
  }
  function MAPE2(yTrue, yPred) {
    return meanAbsolutePercentageError(yTrue, yPred);
  }
  function mape2(yTrue, yPred) {
    return meanAbsolutePercentageError(yTrue, yPred);
  }
  function meanSquaredError3(yTrue, yPred) {
    return meanSquaredError2(yTrue, yPred);
  }
  function MSE2(yTrue, yPred) {
    return meanSquaredError2(yTrue, yPred);
  }
  function mse2(yTrue, yPred) {
    return meanSquaredError2(yTrue, yPred);
  }
  var exports_models_exports = {};
  __export2(exports_models_exports, {
    modelFromJSON: () => modelFromJSON
  });
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var exports_regularizers_exports = {};
  __export2(exports_regularizers_exports, {
    l1: () => l12,
    l1l2: () => l1l2,
    l2: () => l22
  });
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  function l1l2(config) {
    return new L1L2(config);
  }
  function l12(config) {
    return l1(config);
  }
  function l22(config) {
    return l2(config);
  }
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  var Callback = class extends BaseCallback {
    constructor() {
      super(...arguments);
      this.model = null;
    }
    setModel(model2) {
      if (!(model2 instanceof LayersModel)) {
        throw new Error("model must be a LayersModel, not some other Container");
      }
      this.model = model2;
    }
  };
  function less2(currVal, prevVal) {
    return currVal < prevVal;
  }
  function greater2(currVal, prevVal) {
    return currVal > prevVal;
  }
  var EarlyStopping = class extends Callback {
    constructor(args) {
      super();
      if (args == null) {
        args = {};
      }
      if (args.restoreBestWeights) {
        throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");
      }
      this.monitor = args.monitor || "val_loss";
      this.minDelta = Math.abs(args.minDelta || 0);
      this.patience = args.patience || 0;
      this.verbose = args.verbose || 0;
      this.mode = args.mode || "auto";
      this.baseline = args.baseline;
      if (["auto", "min", "max"].indexOf(this.mode) === -1) {
        console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`);
        this.mode = "auto";
      }
      if (this.mode === "min") {
        this.monitorFunc = less2;
      } else if (this.mode === "max") {
        this.monitorFunc = greater2;
      } else {
        if (this.monitor.indexOf("acc") !== -1) {
          this.monitorFunc = greater2;
        } else {
          this.monitorFunc = less2;
        }
      }
      if (this.monitorFunc === less2) {
        this.minDelta *= -1;
      }
    }
    async onTrainBegin(logs) {
      this.wait = 0;
      this.stoppedEpoch = 0;
      if (this.baseline != null) {
        this.best = this.baseline;
      } else {
        this.best = this.monitorFunc === less2 ? Infinity : -Infinity;
      }
    }
    async onEpochEnd(epoch, logs) {
      await resolveScalarsInLogs(logs);
      const current = this.getMonitorValue(logs);
      if (current == null) {
        return;
      }
      if (this.monitorFunc(current - this.minDelta, this.best)) {
        this.best = current;
        this.wait = 0;
      } else {
        this.wait++;
        if (this.wait >= this.patience) {
          this.stoppedEpoch = epoch;
          this.model.stopTraining = true;
        }
      }
    }
    async onTrainEnd(logs) {
      if (this.stoppedEpoch > 0 && this.verbose) {
        console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);
      }
    }
    getMonitorValue(logs) {
      if (logs == null) {
        logs = {};
      }
      const monitorValue = logs[this.monitor];
      if (monitorValue == null) {
        console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`);
      }
      return monitorValue;
    }
  };
  function earlyStopping(args) {
    return new EarlyStopping(args);
  }
  var callbacks = {earlyStopping};
  /**
   * @license
   * Copyright 2018 Google LLC
   *
   * Use of this source code is governed by an MIT-style
   * license that can be found in the LICENSE file or at
   * https://opensource.org/licenses/MIT.
   * =============================================================================
   */
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  var DataType;
  (function(DataType2) {
    DataType2[DataType2["DT_INVALID"] = 0] = "DT_INVALID";
    DataType2[DataType2["DT_FLOAT"] = 1] = "DT_FLOAT";
    DataType2[DataType2["DT_DOUBLE"] = 2] = "DT_DOUBLE";
    DataType2[DataType2["DT_INT32"] = 3] = "DT_INT32";
    DataType2[DataType2["DT_UINT8"] = 4] = "DT_UINT8";
    DataType2[DataType2["DT_INT16"] = 5] = "DT_INT16";
    DataType2[DataType2["DT_INT8"] = 6] = "DT_INT8";
    DataType2[DataType2["DT_STRING"] = 7] = "DT_STRING";
    DataType2[DataType2["DT_COMPLEX64"] = 8] = "DT_COMPLEX64";
    DataType2[DataType2["DT_INT64"] = 9] = "DT_INT64";
    DataType2[DataType2["DT_BOOL"] = 10] = "DT_BOOL";
    DataType2[DataType2["DT_QINT8"] = 11] = "DT_QINT8";
    DataType2[DataType2["DT_QUINT8"] = 12] = "DT_QUINT8";
    DataType2[DataType2["DT_QINT32"] = 13] = "DT_QINT32";
    DataType2[DataType2["DT_BFLOAT16"] = 14] = "DT_BFLOAT16";
    DataType2[DataType2["DT_FLOAT_REF"] = 101] = "DT_FLOAT_REF";
    DataType2[DataType2["DT_DOUBLE_REF"] = 102] = "DT_DOUBLE_REF";
    DataType2[DataType2["DT_INT32_REF"] = 103] = "DT_INT32_REF";
    DataType2[DataType2["DT_UINT8_REF"] = 104] = "DT_UINT8_REF";
    DataType2[DataType2["DT_INT16_REF"] = 105] = "DT_INT16_REF";
    DataType2[DataType2["DT_INT8_REF"] = 106] = "DT_INT8_REF";
    DataType2[DataType2["DT_STRING_REF"] = 107] = "DT_STRING_REF";
    DataType2[DataType2["DT_COMPLEX64_REF"] = 108] = "DT_COMPLEX64_REF";
    DataType2[DataType2["DT_INT64_REF"] = 109] = "DT_INT64_REF";
    DataType2[DataType2["DT_BOOL_REF"] = 110] = "DT_BOOL_REF";
    DataType2[DataType2["DT_QINT8_REF"] = 111] = "DT_QINT8_REF";
    DataType2[DataType2["DT_QUINT8_REF"] = 112] = "DT_QUINT8_REF";
    DataType2[DataType2["DT_QINT32_REF"] = 113] = "DT_QINT32_REF";
    DataType2[DataType2["DT_BFLOAT16_REF"] = 114] = "DT_BFLOAT16_REF";
  })(DataType || (DataType = {}));
  var SaverDef;
  (function(SaverDef2) {
    let CheckpointFormatVersion;
    (function(CheckpointFormatVersion2) {
      CheckpointFormatVersion2[CheckpointFormatVersion2["LEGACY"] = 0] = "LEGACY";
      CheckpointFormatVersion2[CheckpointFormatVersion2["V1"] = 1] = "V1";
      CheckpointFormatVersion2[CheckpointFormatVersion2["V2"] = 2] = "V2";
    })(CheckpointFormatVersion = SaverDef2.CheckpointFormatVersion || (SaverDef2.CheckpointFormatVersion = {}));
  })(SaverDef || (SaverDef = {}));
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var CUSTOM_OPS = {};
  function registerOp(name, opFunc) {
    const opMapper = {
      tfOpName: name,
      category: "custom",
      inputs: [],
      attrs: [],
      customExecutor: opFunc
    };
    CUSTOM_OPS[name] = opMapper;
  }
  function getRegisteredOp(name) {
    return CUSTOM_OPS[name];
  }
  function deregisterOp(name) {
    delete CUSTOM_OPS[name];
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function getParamValue(paramName, node2, tensorMap, context, resourceManager) {
    const inputParam = node2.inputParams[paramName];
    if (inputParam && inputParam.inputIndexStart !== void 0) {
      const start = inputParam.inputIndexStart;
      const end = inputParam.inputIndexEnd === 0 ? void 0 : inputParam.inputIndexEnd === void 0 ? start + 1 : inputParam.inputIndexEnd;
      if (inputParam.type === "tensor") {
        return getTensor(node2.inputNames[inputParam.inputIndexStart], tensorMap, context, resourceManager);
      }
      if (inputParam.type === "tensors") {
        const inputs = node2.inputNames.slice(start, end);
        return inputs.map((name) => getTensor(name, tensorMap, context, resourceManager));
      }
      const tensor2 = getTensor(node2.inputNames.slice(start)[0], tensorMap, context, resourceManager);
      const data = tensor2.dataSync();
      return inputParam.type === "number" ? data[0] : util_exports.toNestedArray(tensor2.shape, data);
    }
    const attrParam = node2.attrParams[paramName];
    return attrParam && attrParam.value;
  }
  function getTensor(name, tensorsMap, context, resourceManager) {
    const [nodeName, index] = parseNodeName(name);
    if (resourceManager != null) {
      const tensor2 = resourceManager.getHashTableHandleByName(nodeName);
      if (tensor2 != null) {
        return tensor2;
      }
    }
    const contextId = context.currentContextIds.find((contextId2) => {
      return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId2)];
    });
    return contextId !== void 0 ? tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] : void 0;
  }
  function getTensorsForCurrentContenxt(name, tensorsMap, context) {
    return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];
  }
  function getNodeNameAndIndex(inputName, context) {
    const [nodeName, index] = parseNodeName(inputName);
    return [
      getNodeNameWithContextId(nodeName, context && context.currentContextId),
      index
    ];
  }
  function getNodeNameWithContextId(name, contextId) {
    return !!contextId ? `${name}-${contextId}` : name;
  }
  function parseNodeName(name) {
    const parts = name.split(":");
    if (parts.length === 1) {
      return [name, 0];
    }
    const nodeName = parts[0];
    return [nodeName, Number(parts[parts.length - 1])];
  }
  function getPadding(node2, tensorMap, context) {
    let pad3 = getParamValue("pad", node2, tensorMap, context);
    if (pad3 === "explicit") {
      pad3 = getParamValue("explicitPaddings", node2, tensorMap, context);
      const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];
      for (let i = 0; i < 4; i++) {
        explicitPadding[i][0] = pad3[i * 2];
        explicitPadding[i][1] = pad3[i * 2 + 1];
      }
      return explicitPadding;
    }
    return pad3;
  }
  function cloneTensor(tensor2) {
    return tensor2.kept ? tensor2 : clone(tensor2);
  }
  var arithmetic_exports = {};
  __export2(arithmetic_exports, {
    json: () => json
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var json = [
    {
      tfOpName: "Add",
      category: "arithmetic",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "AddV2",
      category: "arithmetic",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "AddN",
      category: "arithmetic",
      inputs: [{start: 0, end: 0, name: "tensors", type: "tensors"}]
    },
    {
      tfOpName: "BiasAdd",
      category: "arithmetic",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true},
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          notSupported: true
        }
      ]
    },
    {
      tfOpName: "Sub",
      category: "arithmetic",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "RealDiv",
      category: "arithmetic",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Div",
      category: "arithmetic",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "DivNoNan",
      category: "arithmetic",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "FloorDiv",
      category: "arithmetic",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Mul",
      category: "arithmetic",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Maximum",
      category: "arithmetic",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Minimum",
      category: "arithmetic",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Pow",
      category: "arithmetic",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "SquaredDifference",
      category: "arithmetic",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Mod",
      category: "arithmetic",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "FloorMod",
      category: "arithmetic",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [{
        tfName: "T",
        name: "dtype",
        type: "dtype",
        notSupported: true
      }]
    }
  ];
  var basic_math_exports = {};
  __export2(basic_math_exports, {
    json: () => json2
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var json2 = [
    {
      tfOpName: "Abs",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Acos",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Asin",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Atan",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Atan2",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "y", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Ceil",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "ClipByValue",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "clipValueMin", type: "number"},
        {start: 2, name: "clipValueMax", type: "number"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Complex",
      category: "basic_math",
      inputs: [
        {start: 0, name: "real", type: "tensor"},
        {start: 1, name: "imag", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "ComplexAbs",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Cos",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Cosh",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Elu",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Exp",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Floor",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Log",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Imag",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true},
        {
          tfName: "Tout",
          name: "outputType",
          type: "dtype",
          notSupported: true
        }
      ]
    },
    {
      tfOpName: "Neg",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Real",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true},
        {
          tfName: "Tout",
          name: "outputType",
          type: "dtype",
          notSupported: true
        }
      ]
    },
    {
      tfOpName: "Prelu",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "alpha", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Relu",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Relu6",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Selu",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Sigmoid",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Sin",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Sinh",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Sqrt",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Rsqrt",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Square",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Tan",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Tanh",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Sign",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Round",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Expm1",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Log1p",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Reciprocal",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Softplus",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Asinh",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Acosh",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Atanh",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Erf",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Prod",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "axes", type: "number[]"}
      ],
      attrs: [
        {
          tfName: "keep_dims",
          name: "keepDims",
          type: "bool",
          notSupported: true
        },
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "LeakyRelu",
      category: "basic_math",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {
          tfName: "alpha",
          name: "alpha",
          type: "number",
          defaultValue: 0.2
        },
        {
          tfName: "T",
          name: "dtype",
          type: "dtype",
          notSupported: true
        }
      ]
    }
  ];
  var control_exports = {};
  __export2(control_exports, {
    json: () => json3
  });
  var json3 = [
    {
      tfOpName: "EmptyTensorList",
      category: "control",
      inputs: [
        {start: 0, name: "elementShape", type: "shape"},
        {start: 1, name: "maxNumElements", type: "number"}
      ],
      attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
    },
    {
      tfOpName: "LoopCond",
      category: "control",
      inputs: [{start: 0, name: "pred", type: "tensor"}]
    },
    {
      tfOpName: "Switch",
      category: "control",
      inputs: [
        {start: 0, name: "data", type: "tensor"},
        {start: 1, name: "pred", type: "tensor"}
      ]
    },
    {
      tfOpName: "Merge",
      category: "control",
      inputs: [{start: 0, end: 0, name: "tensors", type: "tensors"}]
    },
    {
      tfOpName: "Enter",
      category: "control",
      inputs: [
        {start: 0, name: "tensor", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true},
        {tfName: "frame_name", name: "frameName", type: "string"},
        {tfName: "is_constant", name: "isConstant", type: "bool"}
      ]
    },
    {
      tfOpName: "Exit",
      category: "control",
      inputs: [
        {start: 0, name: "tensor", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "NextIteration",
      category: "control",
      inputs: [
        {start: 0, name: "tensor", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "TensorArrayV3",
      category: "control",
      inputs: [
        {start: 0, name: "size", type: "number"}
      ],
      attrs: [
        {tfName: "dtype", name: "dtype", type: "dtype"},
        {tfName: "element_shape", name: "elementShape", type: "shape"},
        {tfName: "dynamic_size", name: "dynamicSize", type: "bool"},
        {tfName: "clear_after_read", name: "clearAfterRead", type: "bool"},
        {
          tfName: "identical_element_shapes",
          name: "identicalElementShapes",
          type: "bool"
        },
        {tfName: "tensor_array_name", name: "name", type: "string"}
      ]
    },
    {
      tfOpName: "TensorArrayWriteV3",
      category: "control",
      inputs: [
        {start: 0, name: "tensorArrayId", type: "tensor"},
        {start: 1, name: "index", type: "number"},
        {start: 2, name: "tensor", type: "tensor"},
        {start: 3, name: "flowIn", type: "number"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "TensorArrayReadV3",
      category: "control",
      inputs: [
        {start: 0, name: "tensorArrayId", type: "tensor"},
        {start: 1, name: "index", type: "number"},
        {start: 2, name: "flowIn", type: "number"}
      ],
      attrs: [{
        tfName: "dtype",
        name: "dtype",
        type: "dtype",
        notSupported: true
      }]
    },
    {
      tfOpName: "TensorArrayGatherV3",
      category: "control",
      inputs: [
        {start: 0, name: "tensorArrayId", type: "tensor"},
        {start: 1, name: "indices", type: "number[]"},
        {start: 2, name: "flowIn", type: "number"}
      ],
      attrs: [
        {tfName: "dtype", name: "dtype", type: "dtype"},
        {tfName: "element_shape", name: "elementShape", type: "shape"}
      ]
    },
    {
      tfOpName: "TensorArrayScatterV3",
      category: "control",
      inputs: [
        {start: 0, name: "tensorArrayId", type: "tensor"},
        {start: 1, name: "indices", type: "number[]"},
        {start: 2, name: "tensor", type: "tensor"},
        {start: 3, name: "flowIn", type: "number"}
      ],
      attrs: [{tfName: "T", name: "dtype", type: "dtype"}]
    },
    {
      tfOpName: "TensorArrayConcatV3",
      category: "control",
      inputs: [
        {start: 0, name: "tensorArrayId", type: "tensor"},
        {start: 1, name: "flowIn", type: "number"}
      ],
      attrs: [
        {tfName: "dtype", name: "dtype", type: "dtype"},
        {
          tfName: "element_shape_except0",
          name: "elementShapeExcept0",
          type: "shape",
          notSupported: true
        }
      ]
    },
    {
      tfOpName: "TensorArraySplitV3",
      category: "control",
      inputs: [
        {start: 0, name: "tensorArrayId", type: "tensor"},
        {start: 1, name: "tensor", type: "tensor"},
        {start: 2, name: "lengths", type: "number[]"},
        {start: 3, name: "flowIn", type: "number"}
      ],
      attrs: [{tfName: "T", name: "dtype", type: "dtype"}]
    },
    {
      tfOpName: "TensorArraySizeV3",
      category: "control",
      inputs: [
        {start: 0, name: "tensorArrayId", type: "tensor"},
        {start: 1, name: "flowIn", type: "number"}
      ]
    },
    {
      tfOpName: "TensorArrayCloseV3",
      category: "control",
      inputs: [{start: 0, name: "tensorArrayId", type: "tensor"}]
    },
    {
      tfOpName: "StatelessIf",
      category: "control",
      inputs: [
        {start: 0, name: "cond", type: "tensor"},
        {start: 1, end: 0, name: "args", type: "tensors"}
      ],
      attrs: [
        {tfName: "then_branch", name: "thenBranch", type: "func"},
        {tfName: "else_branch", name: "elseBranch", type: "func"}
      ]
    },
    {
      tfOpName: "If",
      category: "control",
      inputs: [
        {start: 0, name: "cond", type: "tensor"},
        {start: 1, end: 0, name: "args", type: "tensors"}
      ],
      attrs: [
        {tfName: "then_branch", name: "thenBranch", type: "func"},
        {tfName: "else_branch", name: "elseBranch", type: "func"}
      ]
    },
    {
      tfOpName: "StatelessWhile",
      category: "control",
      inputs: [
        {start: 0, end: 0, name: "args", type: "tensors"}
      ],
      attrs: [
        {tfName: "cond", name: "cond", type: "func"},
        {tfName: "body", name: "body", type: "func"}
      ]
    },
    {
      tfOpName: "While",
      category: "control",
      inputs: [
        {start: 0, end: 0, name: "args", type: "tensors"}
      ],
      attrs: [
        {tfName: "cond", name: "cond", type: "func"},
        {tfName: "body", name: "body", type: "func"}
      ]
    },
    {
      tfOpName: "TensorListScatter",
      category: "control",
      inputs: [
        {start: 0, name: "tensor", type: "tensor"},
        {start: 1, name: "indices", type: "number[]"},
        {start: 2, name: "elementShape", type: "shape"}
      ],
      attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
    },
    {
      tfOpName: "TensorListScatterV2",
      category: "control",
      inputs: [
        {start: 0, name: "tensor", type: "tensor"},
        {start: 1, name: "indices", type: "number[]"},
        {start: 2, name: "elementShape", type: "shape"},
        {start: 3, name: "numElements", type: "number"}
      ],
      attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
    },
    {
      tfOpName: "TensorListGather",
      category: "control",
      inputs: [
        {start: 0, name: "tensorListId", type: "tensor"},
        {start: 1, name: "indices", type: "number[]"},
        {start: 2, name: "elementShape", type: "shape"}
      ],
      attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
    },
    {
      tfOpName: "TensorListGetItem",
      category: "control",
      inputs: [
        {start: 0, name: "tensorListId", type: "tensor"},
        {start: 1, name: "index", type: "number"},
        {start: 2, name: "elementShape", type: "shape"}
      ],
      attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
    },
    {
      tfOpName: "TensorListSetItem",
      category: "control",
      inputs: [
        {start: 0, name: "tensorListId", type: "tensor"},
        {start: 1, name: "index", type: "number"},
        {start: 2, name: "tensor", type: "tensor"}
      ],
      attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
    },
    {
      tfOpName: "TensorListReserve",
      category: "control",
      inputs: [
        {start: 0, name: "elementShape", type: "shape"},
        {start: 1, name: "numElements", type: "number"}
      ],
      attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
    },
    {
      tfOpName: "TensorListFromTensor",
      category: "control",
      inputs: [
        {start: 0, name: "tensor", type: "tensor"},
        {start: 1, name: "elementShape", type: "shape"}
      ],
      attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
    },
    {
      tfOpName: "TensorListStack",
      category: "control",
      inputs: [
        {start: 0, name: "tensorListId", type: "tensor"},
        {start: 1, name: "elementShape", type: "shape"}
      ],
      attrs: [
        {tfName: "element_dtype", name: "elementDType", type: "dtype"},
        {tfName: "num_elements", name: "numElements", type: "dtype"}
      ]
    },
    {
      tfOpName: "TensorListSplit",
      category: "control",
      inputs: [
        {start: 0, name: "tensor", type: "tensor"},
        {start: 1, name: "elementShape", type: "shape"},
        {start: 2, name: "lengths", type: "number[]"}
      ],
      attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
    },
    {
      tfOpName: "TensorListConcat",
      category: "control",
      inputs: [
        {start: 0, name: "tensorListId", type: "tensor"}
      ],
      attrs: [
        {tfName: "element_shape", name: "elementShape", type: "shape"},
        {tfName: "element_dtype", name: "elementDType", type: "dtype"}
      ]
    },
    {
      tfOpName: "TensorListPopBack",
      category: "control",
      inputs: [
        {start: 0, name: "tensorListId", type: "tensor"},
        {start: 1, name: "elementShape", type: "shape"}
      ],
      attrs: [{tfName: "element_dtype", name: "elementDType", type: "dtype"}]
    },
    {
      tfOpName: "TensorListPushBack",
      category: "control",
      inputs: [
        {start: 0, name: "tensorListId", type: "tensor"},
        {start: 1, name: "tensor", type: "tensor"}
      ],
      attrs: [
        {tfName: "element_dtype", name: "elementDType", type: "dtype"}
      ]
    }
  ];
  var convolution_exports = {};
  __export2(convolution_exports, {
    json: () => json4
  });
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var json4 = [
    {
      tfOpName: "AvgPool",
      category: "convolution",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "strides", name: "strides", type: "number[]"},
        {tfName: "padding", name: "pad", type: "string"},
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          notSupported: true
        },
        {tfName: "ksize", name: "kernelSize", type: "number[]"},
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "MaxPool",
      category: "convolution",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "strides", name: "strides", type: "number[]"},
        {tfName: "padding", name: "pad", type: "string"},
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          notSupported: true
        },
        {tfName: "ksize", name: "kernelSize", type: "number[]"},
        {
          tfName: "explicit_paddings",
          name: "explicitPaddings",
          type: "number[]",
          defaultValue: [],
          notSupported: true
        },
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "MaxPoolWithArgmax",
      category: "convolution",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "strides", name: "strides", type: "number[]"},
        {tfName: "padding", name: "pad", type: "string"},
        {tfName: "ksize", name: "kernelSize", type: "number[]"},
        {
          tfName: "include_batch_in_index",
          name: "includeBatchInIndex",
          type: "bool"
        },
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "AvgPool3D",
      category: "convolution",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "strides", name: "strides", type: "number[]"},
        {tfName: "padding", name: "pad", type: "string"},
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          notSupported: true
        },
        {tfName: "ksize", name: "kernelSize", type: "number[]"},
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "MaxPool3D",
      category: "convolution",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "strides", name: "strides", type: "number[]"},
        {tfName: "padding", name: "pad", type: "string"},
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          notSupported: true
        },
        {tfName: "ksize", name: "kernelSize", type: "number[]"},
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Conv1D",
      category: "convolution",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "filter", type: "tensor"}
      ],
      attrs: [
        {tfName: "stride", name: "stride", type: "number"},
        {tfName: "padding", name: "pad", type: "string"},
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          defaultValue: "NWC"
        },
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true},
        {
          tfName: "dilation",
          name: "dilation",
          type: "number",
          defaultValue: 1
        }
      ]
    },
    {
      tfOpName: "Conv2D",
      category: "convolution",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "filter", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true},
        {tfName: "strides", name: "strides", type: "number[]"},
        {tfName: "padding", name: "pad", type: "string"},
        {tfName: "useCudnnOnGpu", name: "useCudnnOnGpu", type: "bool"},
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          defaultValue: "NHWC"
        },
        {
          tfName: "explicit_paddings",
          name: "explicitPaddings",
          type: "number[]",
          defaultValue: []
        },
        {tfName: "dilations", name: "dilations", type: "number[]"}
      ]
    },
    {
      tfOpName: "_FusedConv2D",
      category: "convolution",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "filter", type: "tensor"},
        {start: 2, end: 0, name: "args", type: "tensors"}
      ],
      attrs: [
        {tfName: "num_args", name: "numArgs", type: "number"},
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true},
        {tfName: "strides", name: "strides", type: "number[]"},
        {tfName: "padding", name: "pad", type: "string"},
        {
          tfName: "explicit_paddings",
          name: "explicitPaddings",
          type: "number[]",
          defaultValue: []
        },
        {
          tfName: "use_cudnn_on_gpu",
          name: "useCudnnOnGpu",
          type: "bool",
          defaultValue: true
        },
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          defaultValue: "NHWC"
        },
        {
          tfName: "dilations",
          name: "dilations",
          type: "number[]",
          defaultValue: [1, 1, 1, 1]
        },
        {
          tfName: "fused_ops",
          name: "fusedOps",
          type: "string[]",
          defaultValue: []
        },
        {
          tfName: "epsilon",
          name: "epsilon",
          type: "number",
          defaultValue: 1e-4
        },
        {
          tfName: "leakyrelu_alpha",
          name: "leakyreluAlpha",
          type: "number"
        }
      ]
    },
    {
      tfOpName: "Conv2DBackpropInput",
      category: "convolution",
      inputs: [
        {start: 2, name: "x", type: "tensor"},
        {start: 1, name: "filter", type: "tensor"},
        {start: 0, name: "outputShape", type: "number[]"}
      ],
      attrs: [
        {tfName: "strides", name: "strides", type: "number[]"},
        {tfName: "padding", name: "pad", type: "string"},
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          notSupported: true
        },
        {
          tfName: "explicit_paddings",
          name: "explicitPaddings",
          type: "number[]",
          defaultValue: []
        },
        {
          tfName: "dilations",
          name: "dilations",
          type: "number[]",
          notSupported: true
        }
      ]
    },
    {
      tfOpName: "DepthwiseConv2d",
      category: "convolution",
      inputs: [
        {start: 0, name: "input", type: "tensor"},
        {start: 1, name: "filter", type: "tensor"}
      ],
      attrs: [
        {tfName: "strides", name: "strides", type: "number[]"},
        {tfName: "padding", name: "pad", type: "string"},
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          defaultValue: "NHWC"
        },
        {
          tfName: "explicit_paddings",
          name: "explicitPaddings",
          type: "number[]",
          defaultValue: []
        },
        {tfName: "dilations", name: "dilations", type: "number[]"}
      ]
    },
    {
      tfOpName: "DepthwiseConv2dNative",
      category: "convolution",
      inputs: [
        {start: 0, name: "input", type: "tensor"},
        {start: 1, name: "filter", type: "tensor"}
      ],
      attrs: [
        {tfName: "strides", name: "strides", type: "number[]"},
        {tfName: "padding", name: "pad", type: "string"},
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          defaultValue: "NHWC"
        },
        {
          tfName: "explicit_paddings",
          name: "explicitPaddings",
          type: "number[]",
          defaultValue: []
        },
        {tfName: "dilations", name: "dilations", type: "number[]"}
      ]
    },
    {
      tfOpName: "FusedDepthwiseConv2dNative",
      category: "convolution",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "filter", type: "tensor"},
        {start: 2, end: 0, name: "args", type: "tensors"}
      ],
      attrs: [
        {tfName: "num_args", name: "numArgs", type: "number"},
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true},
        {tfName: "strides", name: "strides", type: "number[]"},
        {tfName: "padding", name: "pad", type: "string"},
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          defaultValue: "NHWC"
        },
        {
          tfName: "dilations",
          name: "dilations",
          type: "number[]",
          defaultValue: [1, 1, 1, 1]
        },
        {
          tfName: "fused_ops",
          name: "fusedOps",
          type: "string[]",
          defaultValue: []
        },
        {
          tfName: "explicit_paddings",
          name: "explicitPaddings",
          type: "number[]",
          defaultValue: []
        }
      ]
    },
    {
      tfOpName: "Conv3D",
      category: "convolution",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "filter", type: "tensor"}
      ],
      attrs: [
        {tfName: "strides", name: "strides", type: "number[]"},
        {tfName: "padding", name: "pad", type: "string"},
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          defaultValue: "NHWC"
        },
        {tfName: "dilations", name: "dilations", type: "number[]"}
      ]
    },
    {
      tfOpName: "Dilation2D",
      category: "convolution",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "filter", type: "tensor"}
      ],
      attrs: [
        {tfName: "strides", name: "strides", type: "number[]"},
        {tfName: "rates", name: "dilations", type: "number[]"},
        {tfName: "padding", name: "pad", type: "string"}
      ]
    }
  ];
  var creation_exports = {};
  __export2(creation_exports, {
    json: () => json5
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var json5 = [
    {
      tfOpName: "Fill",
      category: "creation",
      inputs: [
        {start: 0, name: "shape", type: "number[]"},
        {start: 1, name: "value", type: "number"}
      ],
      attrs: [{tfName: "T", name: "dtype", type: "dtype"}]
    },
    {
      tfOpName: "LinSpace",
      category: "creation",
      inputs: [
        {start: 0, name: "start", type: "number"},
        {start: 1, name: "stop", type: "number"},
        {start: 2, name: "num", type: "number"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "OneHot",
      category: "creation",
      inputs: [
        {start: 0, name: "indices", type: "tensor"},
        {start: 1, name: "depth", type: "number"},
        {start: 2, name: "onValue", type: "number", defaultValue: 1},
        {start: 3, name: "offValue", type: "number", defaultValue: 0}
      ],
      attrs: [
        {
          tfName: "axis",
          name: "axis",
          type: "number",
          notSupported: true
        },
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Ones",
      category: "creation",
      inputs: [
        {start: 0, name: "shape", type: "number[]"}
      ],
      attrs: [{tfName: "T", name: "dtype", type: "dtype"}]
    },
    {
      tfOpName: "OnesLike",
      category: "creation",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [{tfName: "dtype", name: "dtype", type: "dtype"}]
    },
    {
      tfOpName: "RandomUniform",
      category: "creation",
      inputs: [
        {start: 0, name: "shape", type: "number[]"}
      ],
      attrs: [
        {
          tfName: "minval",
          name: "minval",
          type: "number",
          defaultValue: 0
        },
        {
          tfName: "maxval",
          name: "maxval",
          type: "number",
          defaultValue: 1
        },
        {tfName: "dtype", name: "dtype", type: "dtype"},
        {tfName: "seed", name: "seed", type: "number", defaultValue: 0},
        {
          tfName: "seed2",
          name: "seed2",
          type: "number",
          defaultValue: 0,
          notSupported: true
        },
        {tfName: "T", name: "T", type: "number", notSupported: true}
      ]
    },
    {
      tfOpName: "Range",
      category: "creation",
      inputs: [
        {start: 0, name: "start", type: "number"},
        {start: 1, name: "stop", type: "number"},
        {start: 2, name: "step", type: "number", defaultValue: 0}
      ],
      attrs: [{tfName: "Tidx", name: "dtype", type: "dtype"}]
    },
    {
      tfOpName: "TruncatedNormal",
      category: "creation",
      inputs: [
        {start: 0, name: "shape", type: "number[]"}
      ],
      attrs: [
        {
          tfName: "means",
          name: "mean",
          type: "number",
          defaultValue: 0
        },
        {
          tfName: "stddev",
          name: "stdDev",
          type: "number",
          defaultValue: 1
        },
        {tfName: "seed", name: "seed", type: "number"},
        {
          tfName: "seed2",
          name: "seed2",
          type: "number",
          defaultValue: 0,
          notSupported: true
        },
        {tfName: "dtype", name: "dtype", type: "dtype"},
        {tfName: "T", name: "T", type: "number", notSupported: true}
      ]
    },
    {
      tfOpName: "Zeros",
      category: "creation",
      inputs: [
        {start: 0, name: "shape", type: "number[]"}
      ],
      attrs: [{tfName: "T", name: "dtype", type: "dtype"}]
    },
    {
      tfOpName: "ZerosLike",
      category: "creation",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [{tfName: "T", name: "dtype", type: "dtype"}]
    },
    {
      tfOpName: "Multinomial",
      category: "creation",
      inputs: [
        {start: 0, name: "logits", type: "tensor"},
        {start: 1, name: "numSamples", type: "number"}
      ],
      attrs: [
        {tfName: "seed", name: "seed", type: "number"},
        {tfName: "seed2", name: "seed2", type: "number"},
        {tfName: "T", name: "dtype", type: "dtype"},
        {tfName: "output_dtype", name: "output_dtype", type: "dtype"}
      ]
    }
  ];
  var dynamic_exports = {};
  __export2(dynamic_exports, {
    json: () => json6
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var json6 = [
    {
      tfOpName: "NonMaxSuppressionV2",
      category: "dynamic",
      inputs: [
        {start: 0, name: "boxes", type: "tensor"},
        {start: 1, name: "scores", type: "tensor"},
        {start: 2, name: "maxOutputSize", type: "number"},
        {start: 3, name: "iouThreshold", type: "number"}
      ]
    },
    {
      tfOpName: "NonMaxSuppressionV3",
      category: "dynamic",
      inputs: [
        {start: 0, name: "boxes", type: "tensor"},
        {start: 1, name: "scores", type: "tensor"},
        {start: 2, name: "maxOutputSize", type: "number"},
        {start: 3, name: "iouThreshold", type: "number"},
        {start: 4, name: "scoreThreshold", type: "number"}
      ]
    },
    {
      tfOpName: "NonMaxSuppressionV4",
      category: "dynamic",
      inputs: [
        {start: 0, name: "boxes", type: "tensor"},
        {start: 1, name: "scores", type: "tensor"},
        {start: 2, name: "maxOutputSize", type: "number"},
        {start: 3, name: "iouThreshold", type: "number"},
        {start: 4, name: "scoreThreshold", type: "number"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true},
        {
          tfName: "T_threshold",
          name: "threshold",
          type: "dtype",
          notSupported: true
        },
        {
          tfName: "pad_to_max_output_size",
          name: "padToMaxOutputSize",
          type: "bool"
        }
      ]
    },
    {
      tfOpName: "NonMaxSuppressionV5",
      category: "dynamic",
      inputs: [
        {start: 0, name: "boxes", type: "tensor"},
        {start: 1, name: "scores", type: "tensor"},
        {start: 2, name: "maxOutputSize", type: "number"},
        {start: 3, name: "iouThreshold", type: "number"},
        {start: 4, name: "scoreThreshold", type: "number"},
        {start: 5, name: "softNmsSigma", type: "number"}
      ]
    },
    {
      tfOpName: "Where",
      category: "dynamic",
      inputs: [
        {start: 0, name: "condition", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "ListDiff",
      category: "dynamic",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "y", type: "tensor"}
      ],
      attrs: [{
        tfName: "T",
        name: "dtype",
        type: "dtype",
        notSupported: true
      }]
    }
  ];
  var evaluation_exports = {};
  __export2(evaluation_exports, {
    json: () => json7
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var json7 = [
    {
      tfOpName: "TopKV2",
      category: "evaluation",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "k", type: "number"}
      ],
      attrs: [{tfName: "sorted", name: "sorted", type: "bool"}]
    },
    {
      tfOpName: "Unique",
      category: "evaluation",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ]
    },
    {
      tfOpName: "UniqueV2",
      category: "evaluation",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "axis", type: "number"}
      ]
    }
  ];
  var graph_exports = {};
  __export2(graph_exports, {
    json: () => json8
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var json8 = [
    {
      tfOpName: "PlaceholderWithDefault",
      category: "graph",
      inputs: [
        {start: 0, name: "default", type: "tensor"}
      ],
      attrs: [
        {tfName: "shape", name: "shape", type: "shape"},
        {tfName: "dtype", name: "dtype", type: "dtype"}
      ]
    },
    {
      tfOpName: "Placeholder",
      category: "graph",
      attrs: [
        {tfName: "shape", name: "shape", type: "shape"},
        {tfName: "dtype", name: "dtype", type: "dtype"}
      ]
    },
    {tfOpName: "Const", category: "graph"},
    {
      tfOpName: "Identity",
      category: "graph",
      inputs: [{start: 0, name: "x", type: "tensor"}]
    },
    {
      tfOpName: "IdentityN",
      category: "graph",
      inputs: [{start: 0, end: 0, name: "x", type: "tensors"}]
    },
    {
      tfOpName: "Snapshot",
      category: "graph",
      inputs: [{start: 0, name: "x", type: "tensor"}]
    },
    {
      tfOpName: "Rank",
      category: "graph",
      inputs: [{start: 0, name: "x", type: "tensor"}]
    },
    {
      tfOpName: "Size",
      category: "graph",
      inputs: [{start: 0, name: "x", type: "tensor"}]
    },
    {
      tfOpName: "Shape",
      category: "graph",
      inputs: [{start: 0, name: "x", type: "tensor"}]
    },
    {
      tfOpName: "ShapeN",
      category: "graph",
      inputs: [{start: 0, end: 0, name: "x", type: "tensors"}]
    },
    {
      tfOpName: "Print",
      category: "graph",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "data", type: "tensors"}
      ],
      attrs: [
        {tfName: "message", name: "message", type: "string"},
        {
          tfName: "first_n",
          name: "firstN",
          type: "number",
          notSupported: true
        },
        {
          tfName: "summarize",
          name: "summarize",
          type: "number",
          defaultValue: 3
        }
      ]
    },
    {tfOpName: "NoOp", category: "graph", inputs: []},
    {
      tfOpName: "StopGradient",
      category: "graph",
      inputs: [{start: 0, name: "x", type: "tensor"}]
    },
    {
      tfOpName: "FakeQuantWithMinMaxVars",
      category: "graph",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "min", name: "min", type: "number"},
        {tfName: "max", name: "max", type: "number"}
      ]
    }
  ];
  var hash_table_exports = {};
  __export2(hash_table_exports, {
    json: () => json9
  });
  var json9 = [
    {
      tfOpName: "HashTable",
      category: "hash_table",
      inputs: [],
      attrs: [
        {tfName: "shared_name", name: "sharedName", type: "string"},
        {
          tfName: "use_node_name_sharing",
          name: "useNodeNameSharing",
          type: "bool"
        },
        {tfName: "key_dtype", name: "keyDType", type: "dtype"},
        {tfName: "value_dtype", name: "valueDType", type: "dtype"}
      ]
    },
    {
      tfOpName: "HashTableV2",
      category: "hash_table",
      inputs: [],
      attrs: [
        {tfName: "shared_name", name: "sharedName", type: "string"},
        {
          tfName: "use_node_name_sharing",
          name: "useNodeNameSharing",
          type: "bool"
        },
        {tfName: "key_dtype", name: "keyDType", type: "dtype"},
        {tfName: "value_dtype", name: "valueDType", type: "dtype"}
      ]
    },
    {
      tfOpName: "LookupTableImport",
      category: "hash_table",
      inputs: [
        {start: 0, name: "tableHandle", type: "tensor"},
        {start: 1, name: "keys", type: "tensor"},
        {start: 2, name: "values", type: "tensor"}
      ],
      attrs: [
        {tfName: "Tin", name: "tIn", type: "dtype", notSupported: true},
        {
          tfName: "Tout",
          name: "tOut",
          type: "dtype",
          notSupported: true
        }
      ]
    },
    {
      tfOpName: "LookupTableImportV2",
      category: "hash_table",
      inputs: [
        {start: 0, name: "tableHandle", type: "tensor"},
        {start: 1, name: "keys", type: "tensor"},
        {start: 2, name: "values", type: "tensor"}
      ],
      attrs: [
        {tfName: "Tin", name: "tIn", type: "dtype", notSupported: true},
        {
          tfName: "Tout",
          name: "tOut",
          type: "dtype",
          notSupported: true
        }
      ]
    },
    {
      tfOpName: "LookupTableFind",
      category: "hash_table",
      inputs: [
        {start: 0, name: "tableHandle", type: "tensor"},
        {start: 1, name: "keys", type: "tensor"},
        {start: 2, name: "defaultValue", type: "tensor"}
      ],
      attrs: [
        {tfName: "Tin", name: "tIn", type: "dtype", notSupported: true},
        {
          tfName: "Tout",
          name: "tOut",
          type: "dtype",
          notSupported: true
        }
      ]
    },
    {
      tfOpName: "LookupTableFindV2",
      category: "hash_table",
      inputs: [
        {start: 0, name: "tableHandle", type: "tensor"},
        {start: 1, name: "keys", type: "tensor"},
        {start: 2, name: "defaultValue", type: "tensor"}
      ],
      attrs: [
        {tfName: "Tin", name: "tIn", type: "dtype", notSupported: true},
        {
          tfName: "Tout",
          name: "tOut",
          type: "dtype",
          notSupported: true
        }
      ]
    },
    {
      tfOpName: "LookupTableSize",
      category: "hash_table",
      inputs: [
        {start: 0, name: "tableHandle", type: "tensor"}
      ]
    },
    {
      tfOpName: "LookupTableSizeV2",
      category: "hash_table",
      inputs: [
        {start: 0, name: "tableHandle", type: "tensor"}
      ]
    }
  ];
  var image_exports = {};
  __export2(image_exports, {
    json: () => json10
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var json10 = [
    {
      tfOpName: "ResizeBilinear",
      category: "image",
      inputs: [
        {start: 0, name: "images", type: "tensor"},
        {start: 1, name: "size", type: "number[]"}
      ],
      attrs: [
        {tfName: "align_corners", name: "alignCorners", type: "bool"},
        {
          tfName: "half_pixel_centers",
          name: "halfPixelCenters",
          type: "bool"
        },
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "ResizeNearestNeighbor",
      category: "image",
      inputs: [
        {start: 0, name: "images", type: "tensor"},
        {start: 1, name: "size", type: "number[]"}
      ],
      attrs: [
        {tfName: "align_corners", name: "alignCorners", type: "bool"},
        {
          tfName: "half_pixel_centers",
          name: "halfPixelCenters",
          type: "bool"
        },
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "CropAndResize",
      category: "image",
      inputs: [
        {start: 0, name: "image", type: "tensor"},
        {start: 1, name: "boxes", type: "tensor"},
        {start: 2, name: "boxInd", type: "tensor"},
        {start: 3, name: "cropSize", type: "number[]"}
      ],
      attrs: [
        {tfName: "method", name: "method", type: "string"},
        {
          tfName: "extrapolation_value",
          name: "extrapolationValue",
          type: "number"
        }
      ]
    }
  ];
  var logical_exports = {};
  __export2(logical_exports, {
    json: () => json11
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var json11 = [
    {
      tfOpName: "Equal",
      category: "logical",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "NotEqual",
      category: "logical",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Greater",
      category: "logical",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "GreaterEqual",
      category: "logical",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Less",
      category: "logical",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "LessEqual",
      category: "logical",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "LogicalAnd",
      category: "logical",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "LogicalNot",
      category: "logical",
      inputs: [
        {start: 0, name: "a", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "LogicalOr",
      category: "logical",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Select",
      category: "logical",
      inputs: [
        {start: 0, name: "condition", type: "tensor"},
        {start: 1, name: "a", type: "tensor"},
        {start: 2, name: "b", type: "tensor"}
      ],
      attrs: [
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "SelectV2",
      category: "logical",
      inputs: [
        {start: 0, name: "condition", type: "tensor"},
        {start: 1, name: "a", type: "tensor"},
        {start: 2, name: "b", type: "tensor"}
      ],
      attrs: [{
        tfName: "T",
        name: "dtype",
        type: "dtype",
        notSupported: true
      }]
    }
  ];
  var matrices_exports = {};
  __export2(matrices_exports, {
    json: () => json12
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var json12 = [
    {
      tfOpName: "_FusedMatMul",
      category: "matrices",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"},
        {start: 2, end: 0, name: "args", type: "tensors"}
      ],
      attrs: [
        {tfName: "num_args", name: "numArgs", type: "number"},
        {
          tfName: "fused_ops",
          name: "fusedOps",
          type: "string[]",
          defaultValue: []
        },
        {
          tfName: "epsilon",
          name: "epsilon",
          type: "number",
          defaultValue: 1e-4
        },
        {
          tfName: "transpose_a",
          name: "transposeA",
          type: "bool",
          defaultValue: false
        },
        {
          tfName: "transpose_b",
          name: "transposeB",
          type: "bool",
          defaultValue: false
        },
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "MatMul",
      category: "matrices",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {
          tfName: "transpose_a",
          name: "transposeA",
          type: "bool",
          defaultValue: false
        },
        {
          tfName: "transpose_b",
          name: "transposeB",
          type: "bool",
          defaultValue: false
        },
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "BatchMatMul",
      category: "matrices",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {
          tfName: "adj_x",
          name: "transposeA",
          type: "bool",
          defaultValue: false
        },
        {
          tfName: "adj_y",
          name: "transposeB",
          type: "bool",
          defaultValue: false
        },
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "BatchMatMulV2",
      category: "matrices",
      inputs: [
        {start: 0, name: "a", type: "tensor"},
        {start: 1, name: "b", type: "tensor"}
      ],
      attrs: [
        {
          tfName: "adj_x",
          name: "transposeA",
          type: "bool",
          defaultValue: false
        },
        {
          tfName: "adj_y",
          name: "transposeB",
          type: "bool",
          defaultValue: false
        },
        {tfName: "T", name: "dtype", type: "dtype", notSupported: true}
      ]
    },
    {
      tfOpName: "Transpose",
      category: "matrices",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "perm", type: "number[]"}
      ],
      attrs: [{
        tfName: "T",
        name: "dtype",
        type: "dtype",
        notSupported: true
      }]
    }
  ];
  var normalization_exports = {};
  __export2(normalization_exports, {
    json: () => json13
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var json13 = [
    {
      tfOpName: "FusedBatchNorm",
      category: "normalization",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "scale", type: "tensor"},
        {start: 2, name: "offset", type: "tensor"},
        {start: 3, name: "mean", type: "tensor"},
        {start: 4, name: "variance", type: "tensor"}
      ],
      attrs: [
        {
          tfName: "epsilon",
          name: "epsilon",
          type: "number",
          defaultValue: 1e-3
        },
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          notSupported: true
        }
      ]
    },
    {
      tfOpName: "FusedBatchNormV2",
      category: "normalization",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "scale", type: "tensor"},
        {start: 2, name: "offset", type: "tensor"},
        {start: 3, name: "mean", type: "tensor"},
        {start: 4, name: "variance", type: "tensor"}
      ],
      attrs: [
        {
          tfName: "epsilon",
          name: "epsilon",
          type: "number",
          defaultValue: 1e-3
        },
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          notSupported: true
        }
      ]
    },
    {
      tfOpName: "FusedBatchNormV3",
      category: "normalization",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "scale", type: "tensor"},
        {start: 2, name: "offset", type: "tensor"},
        {start: 3, name: "mean", type: "tensor"},
        {start: 4, name: "variance", type: "tensor"}
      ],
      attrs: [
        {
          tfName: "epsilon",
          name: "epsilon",
          type: "number",
          defaultValue: 1e-3
        },
        {
          tfName: "data_format",
          name: "dataFormat",
          type: "string",
          notSupported: true
        }
      ]
    },
    {
      tfOpName: "LRN",
      category: "normalization",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {
          tfName: "depth_radius",
          name: "radius",
          type: "number",
          defaultValue: 5
        },
        {tfName: "bias", name: "bias", type: "number", defaultValue: 1},
        {
          tfName: "alpha",
          name: "alpha",
          type: "number",
          defaultValue: 1
        },
        {
          tfName: "beta",
          name: "beta",
          type: "number",
          defaultValue: 0.5
        }
      ]
    },
    {
      tfOpName: "Softmax",
      category: "normalization",
      inputs: [{start: 0, name: "x", type: "tensor"}]
    },
    {
      tfOpName: "LogSoftmax",
      category: "normalization",
      inputs: [{start: 0, name: "x", type: "tensor"}]
    },
    {
      tfOpName: "SparseToDense",
      category: "normalization",
      inputs: [
        {start: 0, name: "sparseIndices", type: "tensor"},
        {start: 1, name: "outputShape", type: "number[]"},
        {start: 2, name: "sparseValues", type: "tensor"},
        {start: 3, name: "defaultValue", type: "tensor"}
      ],
      attrs: [{
        tfName: "validate_indices",
        name: "validateIndices",
        type: "bool",
        defaultValue: true,
        notSupported: true
      }]
    }
  ];
  var reduction_exports = {};
  __export2(reduction_exports, {
    json: () => json14
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var json14 = [
    {
      tfOpName: "Bincount",
      category: "reduction",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "size", type: "number"},
        {start: 2, name: "weights", type: "tensor"}
      ]
    },
    {
      tfOpName: "DenseBincount",
      category: "reduction",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "size", type: "number"},
        {start: 2, name: "weights", type: "tensor"}
      ],
      attrs: [{tfName: "binary_output", name: "binaryOutput", type: "bool"}]
    },
    {
      tfOpName: "Max",
      category: "reduction",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "axis", type: "number[]"}
      ],
      attrs: [{tfName: "keep_dims", name: "keepDims", type: "bool"}]
    },
    {
      tfOpName: "Mean",
      category: "reduction",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "axis", type: "number[]"}
      ],
      attrs: [{tfName: "keep_dims", name: "keepDims", type: "bool"}]
    },
    {
      tfOpName: "Min",
      category: "reduction",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "axis", type: "number[]"}
      ],
      attrs: [{tfName: "keep_dims", name: "keepDims", type: "bool"}]
    },
    {
      tfOpName: "Sum",
      category: "reduction",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "axis", type: "number[]"}
      ],
      attrs: [{tfName: "keep_dims", name: "keepDims", type: "bool"}]
    },
    {
      tfOpName: "All",
      category: "reduction",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "axis", type: "number[]"}
      ],
      attrs: [{tfName: "keep_dims", name: "keepDims", type: "bool"}]
    },
    {
      tfOpName: "Any",
      category: "reduction",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "axis", type: "number[]"}
      ],
      attrs: [{tfName: "keep_dims", name: "keepDims", type: "bool"}]
    },
    {
      tfOpName: "ArgMax",
      category: "reduction",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "axis", type: "number"}
      ]
    },
    {
      tfOpName: "ArgMin",
      category: "reduction",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "axis", type: "number"}
      ]
    },
    {
      tfOpName: "Prod",
      category: "reduction",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "axis", type: "number[]"}
      ],
      attrs: [{tfName: "keep_dims", name: "keepDims", type: "bool"}]
    },
    {
      tfOpName: "Cumsum",
      category: "reduction",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "axis", type: "number"}
      ],
      attrs: [
        {tfName: "exclusive", name: "exclusive", type: "bool"},
        {tfName: "reverse", name: "reverse", type: "bool"}
      ]
    }
  ];
  var slice_join_exports = {};
  __export2(slice_join_exports, {
    json: () => json15
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var json15 = [
    {
      tfOpName: "ConcatV2",
      category: "slice_join",
      inputs: [
        {start: 0, end: -1, name: "tensors", type: "tensors"},
        {start: -1, name: "axis", type: "number"}
      ],
      attrs: [{tfName: "N", name: "n", type: "number", defaultValue: 2}]
    },
    {
      tfOpName: "Concat",
      category: "slice_join",
      inputs: [
        {start: 1, end: 0, name: "tensors", type: "tensors"},
        {start: 0, name: "axis", type: "number"}
      ],
      attrs: [{tfName: "N", name: "n", type: "number", defaultValue: 2}]
    },
    {
      tfOpName: "GatherV2",
      category: "slice_join",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "indices", type: "tensor"},
        {start: 2, name: "axis", type: "number", defaultValue: 0}
      ],
      attrs: [{
        tfName: "batch_dims",
        name: "batchDims",
        type: "number",
        defaultValue: 0
      }]
    },
    {
      tfOpName: "Gather",
      category: "slice_join",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "indices", type: "tensor"}
      ],
      attrs: [{
        tfName: "validate_indices",
        name: "validateIndices",
        type: "bool",
        notSupported: true
      }]
    },
    {
      tfOpName: "Reverse",
      category: "slice_join",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "dims", type: "bool[]"}
      ]
    },
    {
      tfOpName: "ReverseV2",
      category: "slice_join",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "axis", type: "number[]"}
      ]
    },
    {
      tfOpName: "Slice",
      category: "slice_join",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "begin", type: "number[]"},
        {start: 2, name: "size", type: "number[]"}
      ]
    },
    {
      tfOpName: "StridedSlice",
      category: "slice_join",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "begin", type: "number[]"},
        {start: 2, name: "end", type: "number[]"},
        {start: 3, name: "strides", type: "number[]"}
      ],
      attrs: [
        {
          tfName: "begin_mask",
          name: "beginMask",
          type: "number",
          defaultValue: 0
        },
        {
          tfName: "end_mask",
          name: "endMask",
          type: "number",
          defaultValue: 0
        },
        {
          tfName: "new_axis_mask",
          name: "newAxisMask",
          type: "number",
          defaultValue: 0
        },
        {
          tfName: "ellipsis_mask",
          name: "ellipsisMask",
          type: "number",
          defaultValue: 0
        },
        {
          tfName: "shrink_axis_mask",
          name: "shrinkAxisMask",
          type: "number",
          defaultValue: 0
        }
      ]
    },
    {
      tfOpName: "Pack",
      category: "slice_join",
      inputs: [
        {start: 0, end: 0, name: "tensors", type: "tensors"}
      ],
      attrs: [
        {tfName: "axis", name: "axis", type: "number", defaultValue: 0}
      ]
    },
    {
      tfOpName: "Unpack",
      category: "slice_join",
      inputs: [
        {start: 0, name: "tensor", type: "tensor"}
      ],
      attrs: [
        {tfName: "axis", name: "axis", type: "number", defaultValue: 0},
        {
          tfName: "num",
          name: "num",
          type: "number",
          defaultValue: 0,
          notSupported: true
        }
      ]
    },
    {
      tfOpName: "Tile",
      category: "slice_join",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "reps", type: "number[]"}
      ]
    },
    {
      tfOpName: "Split",
      category: "slice_join",
      inputs: [
        {start: 0, name: "axis", type: "number", defaultValue: 0},
        {start: 1, name: "x", type: "tensor"}
      ],
      attrs: [{
        tfName: "num_split",
        name: "numOrSizeSplits",
        type: "number",
        defaultValue: 1
      }]
    },
    {
      tfOpName: "SplitV",
      category: "slice_join",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "numOrSizeSplits", type: "number[]"},
        {start: 2, name: "axis", type: "number", defaultValue: 0}
      ]
    },
    {
      tfOpName: "ScatterNd",
      category: "slice_join",
      inputs: [
        {start: 0, name: "indices", type: "tensor"},
        {start: 1, name: "values", type: "tensor"},
        {start: 2, name: "shape", type: "number[]"}
      ]
    },
    {
      tfOpName: "GatherNd",
      category: "slice_join",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "indices", type: "tensor"}
      ]
    },
    {
      tfOpName: "SparseToDense",
      category: "slice_join",
      inputs: [
        {start: 0, name: "sparseIndices", type: "tensor"},
        {start: 1, name: "outputShape", type: "number[]"},
        {start: 2, name: "sparseValues", type: "tensor"},
        {start: 3, name: "defaultValue", type: "tensor"}
      ],
      attrs: [{
        tfName: "validate_indices",
        name: "validateIndices",
        type: "bool",
        defaultValue: false,
        notSupported: true
      }]
    }
  ];
  var spectral_exports = {};
  __export2(spectral_exports, {
    json: () => json16
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var json16 = [
    {
      tfOpName: "FFT",
      category: "spectral",
      inputs: [{start: 0, name: "x", type: "tensor"}]
    },
    {
      tfOpName: "IFFT",
      category: "spectral",
      inputs: [{start: 0, name: "x", type: "tensor"}]
    },
    {
      tfOpName: "RFFT",
      category: "spectral",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {
          start: 1,
          name: "fft_length",
          type: "number",
          notSupported: true
        }
      ]
    },
    {
      tfOpName: "IRFFT",
      category: "spectral",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {
          start: 1,
          name: "fft_length",
          type: "number",
          notSupported: true
        }
      ]
    }
  ];
  var transformation_exports = {};
  __export2(transformation_exports, {
    json: () => json17
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var json17 = [
    {
      tfOpName: "Cast",
      category: "transformation",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {
          tfName: "SrcT",
          name: "sdtype",
          type: "dtype",
          notSupported: true
        },
        {tfName: "DstT", name: "dtype", type: "dtype"}
      ]
    },
    {
      tfOpName: "ExpandDims",
      category: "transformation",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "axis", type: "number"}
      ]
    },
    {
      tfOpName: "MirrorPad",
      category: "transformation",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "padding", type: "number[]"}
      ],
      attrs: [{tfName: "mode", name: "mode", type: "string"}]
    },
    {
      tfOpName: "Pad",
      category: "transformation",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "padding", type: "number[]"}
      ],
      attrs: [{
        tfName: "constant_value",
        name: "constantValue",
        type: "number",
        defaultValue: 0
      }]
    },
    {
      tfOpName: "PadV2",
      category: "transformation",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "padding", type: "number[]"},
        {
          start: 2,
          name: "constantValue",
          type: "number",
          defaultValue: 0
        }
      ]
    },
    {
      tfOpName: "Reshape",
      category: "transformation",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "shape", type: "number[]"}
      ]
    },
    {
      tfOpName: "Squeeze",
      category: "transformation",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [{
        tfName: "axis",
        tfDeprecatedName: "squeeze_dims",
        name: "axis",
        type: "number[]"
      }]
    },
    {
      tfOpName: "SpaceToBatchND",
      category: "transformation",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "blockShape", type: "number[]"},
        {start: 2, name: "paddings", type: "number[]"}
      ]
    },
    {
      tfOpName: "BatchToSpaceND",
      category: "transformation",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "blockShape", type: "number[]"},
        {start: 2, name: "crops", type: "number[]"}
      ]
    },
    {
      tfOpName: "DepthToSpace",
      category: "transformation",
      inputs: [
        {start: 0, name: "x", type: "tensor"}
      ],
      attrs: [
        {tfName: "block_size", name: "blockSize", type: "number"},
        {tfName: "data_format", name: "dataFormat", type: "string"}
      ]
    },
    {
      tfOpName: "BroadcastTo",
      category: "transformation",
      inputs: [
        {start: 0, name: "x", type: "tensor"},
        {start: 1, name: "shape", type: "number[]"}
      ],
      attrs: []
    }
  ];
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var OperationMapper = class {
    static get Instance() {
      return this._instance || (this._instance = new this());
    }
    constructor() {
      const ops = [
        arithmetic_exports,
        basic_math_exports,
        control_exports,
        convolution_exports,
        creation_exports,
        dynamic_exports,
        evaluation_exports,
        logical_exports,
        image_exports,
        graph_exports,
        matrices_exports,
        normalization_exports,
        reduction_exports,
        slice_join_exports,
        spectral_exports,
        transformation_exports,
        hash_table_exports
      ];
      const mappersJson = [].concat(...ops.map((op2) => op2.json));
      this.opMappers = mappersJson.reduce((map, mapper) => {
        map[mapper.tfOpName] = mapper;
        return map;
      }, {});
    }
    transformGraph(graph2, signature = {}) {
      const tfNodes = graph2.node;
      const placeholders = [];
      const weights = [];
      const initNodes = [];
      const nodes = tfNodes.reduce((map, node2) => {
        map[node2.name] = this.mapNode(node2);
        if (node2.op.startsWith("Placeholder")) {
          placeholders.push(map[node2.name]);
        } else if (node2.op === "Const") {
          weights.push(map[node2.name]);
        } else if (node2.input == null || node2.input.length === 0) {
          initNodes.push(map[node2.name]);
        }
        return map;
      }, {});
      let inputs = [];
      const outputs = [];
      let inputNodeNameToKey = {};
      let outputNodeNameToKey = {};
      if (signature != null) {
        inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);
        outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);
      }
      const allNodes = Object.keys(nodes);
      allNodes.forEach((key) => {
        const node2 = nodes[key];
        node2.inputNames.forEach((name) => {
          const [nodeName] = getNodeNameAndIndex(name);
          node2.inputs.push(nodes[nodeName]);
          nodes[nodeName].children.push(node2);
        });
      });
      if (Object.keys(outputNodeNameToKey).length === 0) {
        allNodes.forEach((key) => {
          const node2 = nodes[key];
          if (node2.children.length === 0) {
            outputs.push(node2);
          }
        });
      } else {
        Object.keys(outputNodeNameToKey).forEach((name) => {
          const [nodeName] = getNodeNameAndIndex(name);
          const node2 = nodes[nodeName];
          if (node2 != null) {
            node2.signatureKey = outputNodeNameToKey[name];
            outputs.push(node2);
          }
        });
      }
      if (Object.keys(inputNodeNameToKey).length > 0) {
        Object.keys(inputNodeNameToKey).forEach((name) => {
          const [nodeName] = getNodeNameAndIndex(name);
          const node2 = nodes[nodeName];
          if (node2) {
            node2.signatureKey = inputNodeNameToKey[name];
            inputs.push(node2);
          }
        });
      } else {
        inputs = placeholders;
      }
      let functions = {};
      if (graph2.library != null && graph2.library.function != null) {
        functions = graph2.library.function.reduce((functions2, func2) => {
          functions2[func2.signature.name] = this.mapFunction(func2);
          return functions2;
        }, {});
      }
      const result = {nodes, inputs, outputs, weights, placeholders, signature, functions};
      if (initNodes.length > 0) {
        result.initNodes = initNodes;
      }
      return result;
    }
    mapSignatureEntries(entries) {
      return Object.keys(entries || {}).reduce((prev, curr) => {
        prev[entries[curr].name] = curr;
        return prev;
      }, {});
    }
    mapNode(node2) {
      const mapper = getRegisteredOp(node2.op) || this.opMappers[node2.op] || {};
      if (node2.attr == null) {
        node2.attr = {};
      }
      const newNode = {
        name: node2.name,
        op: node2.op,
        category: mapper.category,
        inputNames: (node2.input || []).map((input2) => input2.startsWith("^") ? input2.substr(1) : input2),
        inputs: [],
        children: [],
        inputParams: {},
        attrParams: {},
        rawAttrs: node2.attr
      };
      if (mapper.inputs != null) {
        newNode.inputParams = mapper.inputs.reduce((map, param) => {
          map[param.name] = {
            type: param.type,
            inputIndexStart: param.start,
            inputIndexEnd: param.end
          };
          return map;
        }, {});
      }
      if (mapper.attrs != null) {
        newNode.attrParams = mapper.attrs.reduce((map, param) => {
          const type = param.type;
          let value = void 0;
          switch (param.type) {
            case "string":
              value = getStringParam(node2.attr, param.tfName, param.defaultValue);
              if (value === void 0 && !!param.tfDeprecatedName) {
                value = getStringParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
              }
              break;
            case "string[]":
              value = getStringArrayParam(node2.attr, param.tfName, param.defaultValue);
              if (value === void 0 && !!param.tfDeprecatedName) {
                value = getStringArrayParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
              }
              break;
            case "number":
              value = getNumberParam(node2.attr, param.tfName, param.defaultValue || 0);
              if (value === void 0 && !!param.tfDeprecatedName) {
                value = getNumberParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
              }
              break;
            case "number[]":
              value = getNumericArrayParam(node2.attr, param.tfName, param.defaultValue);
              if (value === void 0 && !!param.tfDeprecatedName) {
                value = getNumericArrayParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
              }
              break;
            case "bool":
              value = getBoolParam(node2.attr, param.tfName, param.defaultValue);
              if (value === void 0 && !!param.tfDeprecatedName) {
                value = getBoolParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
              }
              break;
            case "bool[]":
              value = getBoolArrayParam(node2.attr, param.tfName, param.defaultValue);
              if (value === void 0 && !!param.tfDeprecatedName) {
                value = getBoolArrayParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
              }
              break;
            case "shape":
              value = getTensorShapeParam(node2.attr, param.tfName, param.defaultValue);
              if (value === void 0 && !!param.tfDeprecatedName) {
                value = getTensorShapeParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
              }
              break;
            case "shape[]":
              value = getTensorShapeArrayParam(node2.attr, param.tfName, param.defaultValue);
              if (value === void 0 && !!param.tfDeprecatedName) {
                value = getTensorShapeArrayParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
              }
              break;
            case "dtype":
              value = getDtypeParam(node2.attr, param.tfName, param.defaultValue);
              if (value === void 0 && !!param.tfDeprecatedName) {
                value = getDtypeParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
              }
              break;
            case "dtype[]":
              value = getDtypeArrayParam(node2.attr, param.tfName, param.defaultValue);
              if (value === void 0 && !!param.tfDeprecatedName) {
                value = getDtypeArrayParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
              }
              break;
            case "func":
              value = getFuncParam(node2.attr, param.tfName, param.defaultValue);
              if (value === void 0 && !!param.tfDeprecatedName) {
                value = getFuncParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
              }
              break;
            case "tensor":
            case "tensors":
              break;
            default:
              throw new Error(`Unsupported param type: ${param.type} for op: ${node2.op}`);
          }
          map[param.name] = {value, type};
          return map;
        }, {});
      }
      return newNode;
    }
    mapFunction(functionDef) {
      const tfNodes = functionDef.nodeDef;
      const placeholders = [];
      const weights = [];
      let nodes = {};
      if (tfNodes != null) {
        nodes = tfNodes.reduce((map, node2) => {
          map[node2.name] = this.mapNode(node2);
          if (node2.op === "Const") {
            weights.push(map[node2.name]);
          }
          return map;
        }, {});
      }
      const inputs = [];
      const outputs = [];
      functionDef.signature.inputArg.forEach((arg) => {
        const [nodeName] = getNodeNameAndIndex(arg.name);
        const node2 = {
          name: nodeName,
          op: "Placeholder",
          inputs: [],
          inputNames: [],
          category: "graph",
          inputParams: {},
          attrParams: {dtype: {value: parseDtypeParam(arg.type), type: "dtype"}},
          children: []
        };
        node2.signatureKey = arg.name;
        inputs.push(node2);
        nodes[nodeName] = node2;
      });
      const allNodes = Object.keys(nodes);
      allNodes.forEach((key) => {
        const node2 = nodes[key];
        node2.inputNames.forEach((name) => {
          const [nodeName] = getNodeNameAndIndex(name);
          node2.inputs.push(nodes[nodeName]);
          nodes[nodeName].children.push(node2);
        });
      });
      const returnNodeMap = functionDef.ret;
      functionDef.signature.outputArg.forEach((output) => {
        const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);
        const node2 = nodes[nodeName];
        if (node2 != null) {
          node2.defaultOutput = index;
          outputs.push(node2);
        }
      });
      const signature = this.mapArgsToSignature(functionDef);
      return {nodes, inputs, outputs, weights, placeholders, signature};
    }
    mapArgsToSignature(functionDef) {
      return {
        methodName: functionDef.signature.name,
        inputs: functionDef.signature.inputArg.reduce((map, arg) => {
          map[arg.name] = this.mapArgToTensorInfo(arg);
          return map;
        }, {}),
        outputs: functionDef.signature.outputArg.reduce((map, arg) => {
          map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);
          return map;
        }, {})
      };
    }
    mapArgToTensorInfo(arg, nameMap2) {
      let name = arg.name;
      if (nameMap2 != null) {
        name = nameMap2[name];
      }
      return {name, dtype: arg.type};
    }
  };
  function decodeBase64(text) {
    const global2 = env().global;
    if (typeof global2.atob !== "undefined") {
      return global2.atob(text);
    } else if (typeof Buffer !== "undefined") {
      return new Buffer(text, "base64").toString();
    } else {
      throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()");
    }
  }
  function parseStringParam(s, keepCase) {
    const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);
    return keepCase ? value : value.toLowerCase();
  }
  function getStringParam(attrs, name, def, keepCase = false) {
    const param = attrs[name];
    if (param != null) {
      return parseStringParam(param.s, keepCase);
    }
    return def;
  }
  function getBoolParam(attrs, name, def) {
    const param = attrs[name];
    return param ? param.b : def;
  }
  function getNumberParam(attrs, name, def) {
    const param = attrs[name] || {};
    const value = param["i"] != null ? param["i"] : param["f"] != null ? param["f"] : def;
    return typeof value === "number" ? value : parseInt(value, 10);
  }
  function parseDtypeParam(value) {
    if (typeof value === "string") {
      value = DataType[value];
    }
    switch (value) {
      case DataType.DT_FLOAT:
        return "float32";
      case DataType.DT_INT32:
      case DataType.DT_INT64:
      case DataType.DT_INT8:
      case DataType.DT_UINT8:
        return "int32";
      case DataType.DT_BOOL:
        return "bool";
      case DataType.DT_DOUBLE:
        return "float32";
      case DataType.DT_STRING:
        return "string";
      default:
        return null;
    }
  }
  function getFuncParam(attrs, name, def) {
    const param = attrs[name];
    if (param && param.func) {
      return param.func.name;
    }
    return def;
  }
  function getDtypeParam(attrs, name, def) {
    const param = attrs[name];
    if (param && param.type) {
      return parseDtypeParam(param.type);
    }
    return def;
  }
  function getDtypeArrayParam(attrs, name, def) {
    const param = attrs[name];
    if (param && param.list && param.list.type) {
      return param.list.type.map((v) => parseDtypeParam(v));
    }
    return def;
  }
  function parseTensorShapeParam(shape) {
    if (shape.unknownRank) {
      return void 0;
    }
    if (shape.dim != null) {
      return shape.dim.map((dim) => typeof dim.size === "number" ? dim.size : parseInt(dim.size, 10));
    }
    return [];
  }
  function getTensorShapeParam(attrs, name, def) {
    const param = attrs[name];
    if (param && param.shape) {
      return parseTensorShapeParam(param.shape);
    }
    return def;
  }
  function getNumericArrayParam(attrs, name, def) {
    const param = attrs[name];
    if (param) {
      return ((param.list.f && param.list.f.length ? param.list.f : param.list.i) || []).map((v) => typeof v === "number" ? v : parseInt(v, 10));
    }
    return def;
  }
  function getStringArrayParam(attrs, name, def, keepCase = false) {
    const param = attrs[name];
    if (param && param.list && param.list.s) {
      return param.list.s.map((v) => {
        return parseStringParam(v, keepCase);
      });
    }
    return def;
  }
  function getTensorShapeArrayParam(attrs, name, def) {
    const param = attrs[name];
    if (param && param.list && param.list.shape) {
      return param.list.shape.map((v) => {
        return parseTensorShapeParam(v);
      });
    }
    return def;
  }
  function getBoolArrayParam(attrs, name, def) {
    const param = attrs[name];
    if (param && param.list && param.list.b) {
      return param.list.b;
    }
    return def;
  }
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var NodeValueImpl = class {
    constructor(node2, tensorMap, context) {
      this.node = node2;
      this.tensorMap = tensorMap;
      this.context = context;
      this.inputs = [];
      this.attrs = {};
      this.inputs = node2.inputNames.map((name) => this.getInput(name));
      if (node2.rawAttrs != null) {
        this.attrs = Object.keys(node2.rawAttrs).reduce((attrs, key) => {
          attrs[key] = this.getAttr(key);
          return attrs;
        }, {});
      }
    }
    getInput(name) {
      return getTensor(name, this.tensorMap, this.context);
    }
    getAttr(name, defaultValue) {
      const value = this.node.rawAttrs[name];
      if (value.tensor != null) {
        return getTensor(name, this.tensorMap, this.context);
      }
      if (value.i != null || value.f != null) {
        return getNumberParam(this.node.rawAttrs, name, defaultValue);
      }
      if (value.s != null) {
        return getStringParam(this.node.rawAttrs, name, defaultValue);
      }
      if (value.b != null) {
        return getBoolParam(this.node.rawAttrs, name, defaultValue);
      }
      if (value.shape != null) {
        return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);
      }
      if (value.type != null) {
        return getDtypeParam(this.node.rawAttrs, name, defaultValue);
      }
      if (value.list != null) {
        if (value.list.i != null || value.list.f != null) {
          return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);
        }
        if (value.list.s != null) {
          return getStringArrayParam(this.node.rawAttrs, name, defaultValue);
        }
        if (value.list.shape != null) {
          return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);
        }
        if (value.list.b != null) {
          return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);
        }
        if (value.list.type != null) {
          return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);
        }
      }
      return defaultValue;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var executeOp = (node2, tensorMap, context) => {
    switch (node2.op) {
      case "BiasAdd":
      case "AddV2":
      case "Add": {
        return [add2(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "AddN": {
        return [addN(getParamValue("tensors", node2, tensorMap, context))];
      }
      case "FloorMod":
      case "Mod":
        return [mod(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      case "Mul":
        return [mul(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      case "RealDiv":
      case "Div": {
        return [div(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "DivNoNan": {
        return [divNoNan(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "FloorDiv": {
        return [floorDiv(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "Sub": {
        return [sub(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "Minimum": {
        return [minimum(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "Maximum": {
        return [maximum(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "Pow": {
        return [pow(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "SquaredDifference": {
        return [squaredDifference(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var executeOp2 = (node2, tensorMap, context) => {
    switch (node2.op) {
      case "Abs":
      case "ComplexAbs":
        return [abs(getParamValue("x", node2, tensorMap, context))];
      case "Acos":
        return [acos(getParamValue("x", node2, tensorMap, context))];
      case "Acosh":
        return [acosh(getParamValue("x", node2, tensorMap, context))];
      case "Asin":
        return [asin(getParamValue("x", node2, tensorMap, context))];
      case "Asinh":
        return [asinh(getParamValue("x", node2, tensorMap, context))];
      case "Atan":
        return [atan(getParamValue("x", node2, tensorMap, context))];
      case "Atan2":
        return [atan2(getParamValue("x", node2, tensorMap, context), getParamValue("y", node2, tensorMap, context))];
      case "Atanh":
        return [atanh(getParamValue("x", node2, tensorMap, context))];
      case "Ceil":
        return [ceil(getParamValue("x", node2, tensorMap, context))];
      case "Complex":
        return [complex(getParamValue("real", node2, tensorMap, context), getParamValue("imag", node2, tensorMap, context))];
      case "Cos":
        return [cos(getParamValue("x", node2, tensorMap, context))];
      case "Cosh":
        return [cosh(getParamValue("x", node2, tensorMap, context))];
      case "Elu":
        return [elu(getParamValue("x", node2, tensorMap, context))];
      case "Erf":
        return [erf(getParamValue("x", node2, tensorMap, context))];
      case "Exp":
        return [exp(getParamValue("x", node2, tensorMap, context))];
      case "Expm1": {
        return [expm1(getParamValue("x", node2, tensorMap, context))];
      }
      case "Floor":
        return [floor(getParamValue("x", node2, tensorMap, context))];
      case "Log":
        return [log(getParamValue("x", node2, tensorMap, context))];
      case "Log1p": {
        return [log1p(getParamValue("x", node2, tensorMap, context))];
      }
      case "Imag":
        return [imag(getParamValue("x", node2, tensorMap, context))];
      case "Neg":
        return [neg(getParamValue("x", node2, tensorMap, context))];
      case "Reciprocal": {
        return [reciprocal(getParamValue("x", node2, tensorMap, context))];
      }
      case "Real":
        return [real(getParamValue("x", node2, tensorMap, context))];
      case "Relu":
        return [relu(getParamValue("x", node2, tensorMap, context))];
      case "Round": {
        return [round2(getParamValue("x", node2, tensorMap, context))];
      }
      case "Selu":
        return [selu(getParamValue("x", node2, tensorMap, context))];
      case "Sigmoid":
        return [sigmoid(getParamValue("x", node2, tensorMap, context))];
      case "Sin":
        return [sin(getParamValue("x", node2, tensorMap, context))];
      case "Sign": {
        return [sign(getParamValue("x", node2, tensorMap, context))];
      }
      case "Sinh": {
        return [sinh(getParamValue("x", node2, tensorMap, context))];
      }
      case "Softplus": {
        return [softplus(getParamValue("x", node2, tensorMap, context))];
      }
      case "Sqrt": {
        return [sqrt(getParamValue("x", node2, tensorMap, context))];
      }
      case "Square": {
        return [square(getParamValue("x", node2, tensorMap, context))];
      }
      case "Tanh": {
        return [tanh2(getParamValue("x", node2, tensorMap, context))];
      }
      case "Tan":
        return [tan(getParamValue("x", node2, tensorMap, context))];
      case "ClipByValue":
        return [clipByValue(getParamValue("x", node2, tensorMap, context), getParamValue("clipValueMin", node2, tensorMap, context), getParamValue("clipValueMax", node2, tensorMap, context))];
      case "Relu6":
        return [relu6(getParamValue("x", node2, tensorMap, context))];
      case "Rsqrt":
        return [rsqrt(getTensor(node2.inputNames[0], tensorMap, context))];
      case "Prod":
        return [prod(getParamValue("x", node2, tensorMap, context), getParamValue("axes", node2, tensorMap, context))];
      case "LeakyRelu":
        return [leakyRelu(getParamValue("x", node2, tensorMap, context), getParamValue("alpha", node2, tensorMap, context))];
      case "Prelu":
        return [prelu(getParamValue("x", node2, tensorMap, context), getParamValue("alpha", node2, tensorMap, context))];
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = "") {
    if (typeof shapeA === "number" || typeof shapeB === "number") {
      return;
    }
    util_exports.assert(shapeA.length === shapeB.length, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
    for (let i = 0; i < shapeA.length; i++) {
      const dim0 = shapeA[i];
      const dim1 = shapeB[i];
      util_exports.assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
    }
  }
  function fullDefinedShape(elementShape) {
    if (typeof elementShape === "number" || elementShape.some((dim) => dim < 0)) {
      return false;
    }
    return true;
  }
  function inferElementShape(listElementShape, tensors, elementShape) {
    let partialShape = mergeElementShape(listElementShape, elementShape);
    const notfullDefinedShape = !fullDefinedShape(partialShape);
    if (notfullDefinedShape && tensors.length === 0) {
      throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${partialShape}`);
    }
    if (notfullDefinedShape) {
      tensors.forEach((tensor2) => {
        partialShape = mergeElementShape(tensor2.shape, partialShape);
      });
    }
    if (!fullDefinedShape(partialShape)) {
      throw new Error(`Non-fully-defined elementShape: ${partialShape}`);
    }
    return partialShape;
  }
  function mergeElementShape(elementShapeA, elementShapeB) {
    if (typeof elementShapeA === "number") {
      return elementShapeB;
    }
    if (typeof elementShapeB === "number") {
      return elementShapeA;
    }
    if (elementShapeA.length !== elementShapeB.length) {
      throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${elementShapeB}`);
    }
    const result = [];
    for (let i = 0; i < elementShapeA.length; ++i) {
      const dim0 = elementShapeA[i];
      const dim1 = elementShapeB[i];
      if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) {
        throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${elementShapeB}`);
      }
      result[i] = dim0 >= 0 ? dim0 : dim1;
    }
    return result;
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var TensorArray = class {
    constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {
      this.name = name;
      this.dtype = dtype;
      this.maxSize = maxSize;
      this.elementShape = elementShape;
      this.identicalElementShapes = identicalElementShapes;
      this.dynamicSize = dynamicSize;
      this.clearAfterRead = clearAfterRead;
      this.tensors = [];
      this.closed_ = false;
      this.idTensor = scalar(0);
      keep(this.idTensor);
    }
    get id() {
      return this.idTensor.id;
    }
    get closed() {
      return this.closed_;
    }
    clearAndClose(keepIds) {
      this.tensors.forEach((tensor2) => {
        if (keepIds == null || !keepIds.has(tensor2.tensor.id)) {
          tensor2.tensor.dispose();
        }
      });
      this.tensors = [];
      this.closed_ = true;
      this.idTensor.dispose();
    }
    size() {
      return this.tensors.length;
    }
    read(index) {
      if (this.closed_) {
        throw new Error(`TensorArray ${this.name} has already been closed.`);
      }
      if (index < 0 || index >= this.size()) {
        throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);
      }
      const tensorWithState = this.tensors[index];
      if (tensorWithState.cleared) {
        throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);
      }
      if (this.clearAfterRead) {
        tensorWithState.cleared = true;
      }
      tensorWithState.read = true;
      return tensorWithState.tensor;
    }
    readMany(indices) {
      return indices.map((index) => this.read(index));
    }
    write(index, tensor2) {
      if (this.closed_) {
        throw new Error(`TensorArray ${this.name} has already been closed.`);
      }
      if (index < 0 || !this.dynamicSize && index >= this.maxSize) {
        throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);
      }
      const t = this.tensors[index] || {};
      if (tensor2.dtype !== this.dtype) {
        throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},
          because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`);
      }
      if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0)) {
        this.elementShape = tensor2.shape;
      }
      assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);
      if (t.read) {
        throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);
      }
      if (t.written) {
        throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);
      }
      t.tensor = tensor2;
      keep(tensor2);
      t.written = true;
      this.tensors[index] = t;
    }
    writeMany(indices, tensors) {
      if (indices.length !== tensors.length) {
        throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);
      }
      indices.forEach((i, index) => this.write(i, tensors[index]));
    }
    gather(indices, dtype) {
      if (!!dtype && dtype !== this.dtype) {
        throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);
      }
      if (!indices) {
        indices = [];
        for (let i = 0; i < this.size(); i++) {
          indices.push(i);
        }
      } else {
        indices = indices.slice(0, this.size());
      }
      if (indices.length === 0) {
        return tensor([], [0].concat(this.elementShape));
      }
      const tensors = this.readMany(indices);
      assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, "TensorArray shape mismatch: ");
      return stack(tensors, 0);
    }
    concat(dtype) {
      if (!!dtype && dtype !== this.dtype) {
        throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);
      }
      if (this.size() === 0) {
        return tensor([], [0].concat(this.elementShape));
      }
      const indices = [];
      for (let i = 0; i < this.size(); i++) {
        indices.push(i);
      }
      const tensors = this.readMany(indices);
      assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);
      return concat(tensors, 0);
    }
    scatter(indices, tensor2) {
      if (tensor2.dtype !== this.dtype) {
        throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);
      }
      if (indices.length !== tensor2.shape[0]) {
        throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);
      }
      const maxIndex = Math.max(...indices);
      if (!this.dynamicSize && maxIndex >= this.maxSize) {
        throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);
      }
      this.writeMany(indices, unstack(tensor2, 0));
    }
    split(length, tensor2) {
      if (tensor2.dtype !== this.dtype) {
        throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);
      }
      let totalLength = 0;
      const cumulativeLengths = length.map((len) => {
        totalLength += len;
        return totalLength;
      });
      if (totalLength !== tensor2.shape[0]) {
        throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);
      }
      if (!this.dynamicSize && length.length !== this.maxSize) {
        throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);
      }
      const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength;
      const tensors = [];
      tidy(() => {
        tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);
        for (let i = 0; i < length.length; ++i) {
          const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1];
          const indices2 = [0, previousLength, 0];
          const sizes = [1, length[i], elementPerRow];
          tensors[i] = reshape(slice(tensor2, indices2, sizes), this.elementShape);
        }
        return tensors;
      });
      const indices = [];
      for (let i = 0; i < length.length; i++) {
        indices[i] = i;
      }
      this.writeMany(indices, tensors);
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var TensorList = class {
    constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {
      this.tensors = tensors;
      this.elementShape = elementShape;
      this.elementDtype = elementDtype;
      if (tensors != null) {
        tensors.forEach((tensor2) => {
          if (elementDtype !== tensor2.dtype) {
            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor2.dtype}`);
          }
          assertShapesMatchAllowUndefinedSize(elementShape, tensor2.shape, "TensorList shape mismatch: ");
          keep(tensor2);
        });
      }
      this.idTensor = scalar(0);
      this.maxNumElements = maxNumElements;
      keep(this.idTensor);
    }
    get id() {
      return this.idTensor.id;
    }
    copy() {
      return new TensorList([...this.tensors], this.elementShape, this.elementDtype);
    }
    clearAndClose(keepIds) {
      this.tensors.forEach((tensor2) => {
        if (keepIds == null || !keepIds.has(tensor2.id)) {
          tensor2.dispose();
        }
      });
      this.tensors.length = 0;
      this.idTensor.dispose();
    }
    size() {
      return this.tensors.length;
    }
    stack(elementShape, elementDtype, numElements = -1) {
      if (elementDtype !== this.elementDtype) {
        throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
      }
      if (numElements !== -1 && this.tensors.length !== numElements) {
        throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);
      }
      assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, "TensorList shape mismatch: ");
      const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
      return tidy(() => {
        const reshapedTensors = this.tensors.map((tensor2) => reshape(tensor2, outputElementShape));
        return stack(reshapedTensors, 0);
      });
    }
    popBack(elementShape, elementDtype) {
      if (elementDtype !== this.elementDtype) {
        throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
      }
      if (this.size() === 0) {
        throw new Error("Trying to pop from an empty list.");
      }
      const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
      const tensor2 = this.tensors.pop();
      assertShapesMatchAllowUndefinedSize(tensor2.shape, elementShape, "TensorList shape mismatch: ");
      return reshape(tensor2, outputElementShape);
    }
    pushBack(tensor2) {
      if (tensor2.dtype !== this.elementDtype) {
        throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);
      }
      assertShapesMatchAllowUndefinedSize(tensor2.shape, this.elementShape, "TensorList shape mismatch: ");
      if (this.maxNumElements === this.size()) {
        throw new Error(`Trying to push element into a full list.`);
      }
      keep(tensor2);
      this.tensors.push(tensor2);
    }
    resize(size) {
      if (size < 0) {
        throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);
      }
      if (this.maxNumElements !== -1 && size > this.maxNumElements) {
        throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);
      }
      this.tensors.length = size;
    }
    getItem(elementIndex, elementShape, elementDtype) {
      if (elementDtype !== this.elementDtype) {
        throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
      }
      if (elementIndex < 0 || elementIndex > this.tensors.length) {
        throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);
      }
      if (this.tensors[elementIndex] == null) {
        throw new Error(`element at index ${elementIndex} is null.`);
      }
      assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, "TensorList shape mismatch: ");
      const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
      return reshape(this.tensors[elementIndex], outputElementShape);
    }
    setItem(elementIndex, tensor2) {
      if (tensor2.dtype !== this.elementDtype) {
        throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);
      }
      if (elementIndex < 0 || this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {
        throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);
      }
      assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, "TensorList shape mismatch: ");
      keep(tensor2);
      this.tensors[elementIndex] = tensor2;
    }
    gather(indices, elementDtype, elementShape) {
      if (elementDtype !== this.elementDtype) {
        throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
      }
      assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, "TensorList shape mismatch: ");
      indices = indices.slice(0, this.size());
      const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
      if (indices.length === 0) {
        return tensor([], [0].concat(outputElementShape));
      }
      return tidy(() => {
        const tensors = indices.map((i) => reshape(this.tensors[i], outputElementShape));
        return stack(tensors, 0);
      });
    }
    concat(elementDtype, elementShape) {
      if (!!elementDtype && elementDtype !== this.elementDtype) {
        throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);
      }
      assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, "TensorList shape mismatch: ");
      const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
      if (this.size() === 0) {
        return tensor([], [0].concat(outputElementShape));
      }
      return tidy(() => {
        const tensors = this.tensors.map((t) => reshape(t, outputElementShape));
        return concat(tensors, 0);
      });
    }
  };
  function fromTensor(tensor2, elementShape, elementDtype) {
    const dtype = tensor2.dtype;
    if (tensor2.shape.length < 1) {
      throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor2.shape}`);
    }
    if (tensor2.dtype !== elementDtype) {
      throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${elementDtype}`);
    }
    const tensorElementShape = tensor2.shape.slice(1);
    assertShapesMatchAllowUndefinedSize(tensorElementShape, elementShape, "TensorList shape mismatch: ");
    const tensorList = unstack(tensor2);
    return new TensorList(tensorList, elementShape, dtype);
  }
  function reserve(elementShape, elementDtype, numElements) {
    return new TensorList([], elementShape, elementDtype, numElements);
  }
  function scatter(tensor2, indices, elementShape, numElements) {
    if (indices.length !== tensor2.shape[0]) {
      throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);
    }
    const maxIndex = Math.max(...indices);
    if (numElements != null && numElements !== -1 && maxIndex >= numElements) {
      throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);
    }
    const list = new TensorList([], elementShape, tensor2.dtype, numElements);
    const tensors = unstack(tensor2, 0);
    indices.forEach((value, index) => {
      list.setItem(value, tensors[index]);
    });
    return list;
  }
  function split2(tensor2, length, elementShape) {
    let totalLength = 0;
    const cumulativeLengths = length.map((len) => {
      totalLength += len;
      return totalLength;
    });
    if (totalLength !== tensor2.shape[0]) {
      throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);
    }
    const shapeWithoutFirstDim = tensor2.shape.slice(1);
    const outputElementShape = mergeElementShape(shapeWithoutFirstDim, elementShape);
    const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength;
    const tensors = tidy(() => {
      const tensors2 = [];
      tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);
      for (let i = 0; i < length.length; ++i) {
        const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1];
        const indices = [0, previousLength, 0];
        const sizes = [1, length[i], elementPerRow];
        tensors2[i] = reshape(slice(tensor2, indices, sizes), outputElementShape);
      }
      tensor2.dispose();
      return tensors2;
    });
    const list = new TensorList([], elementShape, tensor2.dtype, length.length);
    for (let i = 0; i < tensors.length; i++) {
      list.setItem(i, tensors[i]);
    }
    return list;
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var executeOp3 = async (node2, tensorMap, context) => {
    switch (node2.op) {
      case "If":
      case "StatelessIf": {
        const thenFunc = getParamValue("thenBranch", node2, tensorMap, context);
        const elseFunc = getParamValue("elseBranch", node2, tensorMap, context);
        const cond = getParamValue("cond", node2, tensorMap, context);
        const args = getParamValue("args", node2, tensorMap, context);
        const condValue = await cond.data();
        if (condValue[0]) {
          return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);
        } else {
          return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);
        }
      }
      case "While":
      case "StatelessWhile": {
        const bodyFunc = getParamValue("body", node2, tensorMap, context);
        const condFunc = getParamValue("cond", node2, tensorMap, context);
        const args = getParamValue("args", node2, tensorMap, context);
        const condResult = await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);
        const argIds = args.map((tensor2) => tensor2.id);
        let condValue = await condResult[0].data();
        condResult.forEach((tensor2) => {
          if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1) {
            tensor2.dispose();
          }
        });
        let result = args;
        while (condValue[0]) {
          const origResult = result;
          result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);
          const resultIds = result.map((tensor2) => tensor2.id);
          origResult.forEach((tensor2) => {
            if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) {
              tensor2.dispose();
            }
          });
          const condResult2 = await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);
          condValue = await condResult2[0].data();
          condResult2.forEach((tensor2) => {
            if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) {
              tensor2.dispose();
            }
          });
        }
        return result;
      }
      case "LoopCond": {
        const pred = getParamValue("pred", node2, tensorMap, context);
        return [cloneTensor(pred)];
      }
      case "Switch": {
        const pred = getParamValue("pred", node2, tensorMap, context);
        let data = getParamValue("data", node2, tensorMap, context);
        if (!data.kept) {
          data = cloneTensor(data);
        }
        return (await pred.data())[0] ? [void 0, data] : [data, void 0];
      }
      case "Merge": {
        const inputName = node2.inputNames.find((name) => getTensor(name, tensorMap, context) !== void 0);
        if (inputName) {
          const data = getTensor(inputName, tensorMap, context);
          return [cloneTensor(data)];
        }
        return void 0;
      }
      case "Enter": {
        const frameId = getParamValue("frameName", node2, tensorMap, context);
        const data = getParamValue("tensor", node2, tensorMap, context);
        context.enterFrame(frameId);
        return [cloneTensor(data)];
      }
      case "Exit": {
        const data = getParamValue("tensor", node2, tensorMap, context);
        context.exitFrame();
        return [cloneTensor(data)];
      }
      case "NextIteration": {
        const data = getParamValue("tensor", node2, tensorMap, context);
        context.nextIteration();
        return [cloneTensor(data)];
      }
      case "TensorArrayV3": {
        const size = getParamValue("size", node2, tensorMap, context);
        const dtype = getParamValue("dtype", node2, tensorMap, context);
        const elementShape = getParamValue("elementShape", node2, tensorMap, context);
        const dynamicSize = getParamValue("dynamicSize", node2, tensorMap, context);
        const clearAfterRead = getParamValue("clearAfterRead", node2, tensorMap, context);
        const identicalElementShapes = getParamValue("identicalElementShapes", node2, tensorMap, context);
        const name = getParamValue("name", node2, tensorMap, context);
        const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);
        context.addTensorArray(tensorArray);
        return [tensorArray.idTensor, scalar(1)];
      }
      case "TensorArrayWriteV3": {
        const id = getParamValue("tensorArrayId", node2, tensorMap, context);
        const index = getParamValue("index", node2, tensorMap, context);
        const writeTensor = getParamValue("tensor", node2, tensorMap, context);
        const writeTensorArray = context.getTensorArray(id.id);
        writeTensorArray.write(index, writeTensor);
        return [writeTensorArray.idTensor];
      }
      case "TensorArrayReadV3": {
        const readId = getParamValue("tensorArrayId", node2, tensorMap, context);
        const readIndex = getParamValue("index", node2, tensorMap, context);
        const readTensorArray = context.getTensorArray(readId.id);
        return [readTensorArray.read(readIndex)];
      }
      case "TensorArrayGatherV3": {
        const gatherId = getParamValue("tensorArrayId", node2, tensorMap, context);
        const gatherIndices = getParamValue("indices", node2, tensorMap, context);
        const gatherDtype = getParamValue("dtype", node2, tensorMap, context);
        const gatherTensorArray = context.getTensorArray(gatherId.id);
        return [gatherTensorArray.gather(gatherIndices, gatherDtype)];
      }
      case "TensorArrayScatterV3": {
        const scatterId = getParamValue("tensorArrayId", node2, tensorMap, context);
        const scatterIndices = getParamValue("indices", node2, tensorMap, context);
        const scatterTensor = getParamValue("tensor", node2, tensorMap, context);
        const scatterTensorArray = context.getTensorArray(scatterId.id);
        scatterTensorArray.scatter(scatterIndices, scatterTensor);
        return [scatterTensorArray.idTensor];
      }
      case "TensorArrayConcatV3": {
        const concatId = getParamValue("tensorArrayId", node2, tensorMap, context);
        const concatTensorArray = context.getTensorArray(concatId.id);
        const concatDtype = getParamValue("dtype", node2, tensorMap, context);
        return [concatTensorArray.concat(concatDtype)];
      }
      case "TensorArraySplitV3": {
        const splitId = getParamValue("tensorArrayId", node2, tensorMap, context);
        const splitTensor = getParamValue("tensor", node2, tensorMap, context);
        const lengths = getParamValue("lengths", node2, tensorMap, context);
        const splitTensorArray = context.getTensorArray(splitId.id);
        splitTensorArray.split(lengths, splitTensor);
        return [splitTensorArray.idTensor];
      }
      case "TensorArraySizeV3": {
        const sizeId = getParamValue("tensorArrayId", node2, tensorMap, context);
        const sizeTensorArray = context.getTensorArray(sizeId.id);
        return [scalar(sizeTensorArray.size(), "int32")];
      }
      case "TensorArrayCloseV3": {
        const closeId = getParamValue("tensorArrayId", node2, tensorMap, context);
        const closeTensorArray = context.getTensorArray(closeId.id);
        closeTensorArray.clearAndClose();
        return [closeTensorArray.idTensor];
      }
      case "TensorListSetItem": {
        const idTensor = getParamValue("tensorListId", node2, tensorMap, context);
        const index = getParamValue("index", node2, tensorMap, context);
        const writeTensor = getParamValue("tensor", node2, tensorMap, context);
        const tensorList = context.getTensorList(idTensor.id);
        tensorList.setItem(index, writeTensor);
        return [tensorList.idTensor];
      }
      case "TensorListGetItem": {
        const idTensor = getParamValue("tensorListId", node2, tensorMap, context);
        const readIndex = getParamValue("index", node2, tensorMap, context);
        const elementShape = getParamValue("elementShape", node2, tensorMap, context);
        const elementDType = getParamValue("elementDType", node2, tensorMap, context);
        const tensorList = context.getTensorList(idTensor.id);
        return [tensorList.getItem(readIndex, elementShape, elementDType)];
      }
      case "TensorListScatterV2":
      case "TensorListScatter": {
        const scatterIndices = getParamValue("indices", node2, tensorMap, context);
        const scatterTensor = getParamValue("tensor", node2, tensorMap, context);
        const elementShape = getParamValue("elementShape", node2, tensorMap, context);
        const numElements = getParamValue("numElements", node2, tensorMap, context);
        const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);
        context.addTensorList(tensorList);
        return [tensorList.idTensor];
      }
      case "TensorListReserve":
      case "EmptyTensorList": {
        const elementShape = getParamValue("elementShape", node2, tensorMap, context);
        const elementDtype = getParamValue("elementDType", node2, tensorMap, context);
        let numElementsParam;
        if (node2.op === "TensorListReserve") {
          numElementsParam = "numElements";
        } else {
          numElementsParam = "maxNumElements";
        }
        const numElements = getParamValue(numElementsParam, node2, tensorMap, context);
        const tensorList = reserve(elementShape, elementDtype, numElements);
        context.addTensorList(tensorList);
        return [tensorList.idTensor];
      }
      case "TensorListGather": {
        const gatherId = getParamValue("tensorListId", node2, tensorMap, context);
        const gatherIndices = getParamValue("indices", node2, tensorMap, context);
        const elementShape = getParamValue("elementShape", node2, tensorMap, context);
        const elementDtype = getParamValue("elementDType", node2, tensorMap, context);
        const tensorList = context.getTensorList(gatherId.id);
        return [tensorList.gather(gatherIndices, elementDtype, elementShape)];
      }
      case "TensorListStack": {
        const idTensor = getParamValue("tensorListId", node2, tensorMap, context);
        const elementShape = getParamValue("elementShape", node2, tensorMap, context);
        const elementDtype = getParamValue("elementDType", node2, tensorMap, context);
        const numElements = getParamValue("numElements", node2, tensorMap, context);
        const tensorList = context.getTensorList(idTensor.id);
        return [tensorList.stack(elementShape, elementDtype, numElements)];
      }
      case "TensorListFromTensor": {
        const tensor2 = getParamValue("tensor", node2, tensorMap, context);
        const elementShape = getParamValue("elementShape", node2, tensorMap, context);
        const elementDtype = getParamValue("elementDType", node2, tensorMap, context);
        const tensorList = fromTensor(tensor2, elementShape, elementDtype);
        context.addTensorList(tensorList);
        return [tensorList.idTensor];
      }
      case "TensorListConcat": {
        const concatId = getParamValue("tensorListId", node2, tensorMap, context);
        const tensorList = context.getTensorList(concatId.id);
        const concatDtype = getParamValue("dtype", node2, tensorMap, context);
        const elementShape = getParamValue("elementShape", node2, tensorMap, context);
        return [tensorList.concat(concatDtype, elementShape)];
      }
      case "TensorListPushBack": {
        const idTensor = getParamValue("tensorListId", node2, tensorMap, context);
        const writeTensor = getParamValue("tensor", node2, tensorMap, context);
        const tensorList = context.getTensorList(idTensor.id);
        tensorList.pushBack(writeTensor);
        return [tensorList.idTensor];
      }
      case "TensorListPopBack": {
        const idTensor = getParamValue("tensorListId", node2, tensorMap, context);
        const elementShape = getParamValue("elementShape", node2, tensorMap, context);
        const elementDType = getParamValue("elementDType", node2, tensorMap, context);
        const tensorList = context.getTensorList(idTensor.id);
        return [tensorList.popBack(elementShape, elementDType)];
      }
      case "TensorListSplit": {
        const splitTensor = getParamValue("tensor", node2, tensorMap, context);
        const elementShape = getParamValue("elementShape", node2, tensorMap, context);
        const lengths = getParamValue("lengths", node2, tensorMap, context);
        const tensorList = split2(splitTensor, lengths, elementShape);
        context.addTensorList(tensorList);
        return [tensorList.idTensor];
      }
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fusedConvAndDepthWiseParams(node2, tensorMap, context) {
    const [extraOp, activationFunc] = getParamValue("fusedOps", node2, tensorMap, context);
    const isBiasAdd = extraOp === "biasadd";
    const isPrelu = activationFunc === "prelu";
    const isBatchNorm = extraOp === "fusedbatchnorm";
    const numArgs = getParamValue("numArgs", node2, tensorMap, context);
    if (isBiasAdd) {
      if (isPrelu && numArgs !== 2) {
        throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
      }
      if (!isPrelu && numArgs !== 1) {
        throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.");
      }
    }
    if (isBatchNorm) {
      throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");
    }
    const stride = getParamValue("strides", node2, tensorMap, context);
    const pad3 = getPadding(node2, tensorMap, context);
    const dataFormat = getParamValue("dataFormat", node2, tensorMap, context).toUpperCase();
    const dilations = getParamValue("dilations", node2, tensorMap, context);
    const [biasArg, preluArg] = getParamValue("args", node2, tensorMap, context);
    const leakyreluAlpha = getParamValue("leakyreluAlpha", node2, tensorMap, context);
    return {
      stride,
      pad: pad3,
      dataFormat,
      dilations,
      biasArg,
      preluArg,
      activationFunc,
      leakyreluAlpha
    };
  }
  var executeOp4 = (node2, tensorMap, context) => {
    switch (node2.op) {
      case "Conv1D": {
        const stride = getParamValue("stride", node2, tensorMap, context);
        const pad3 = getParamValue("pad", node2, tensorMap, context);
        const dataFormat = getParamValue("dataFormat", node2, tensorMap, context).toUpperCase();
        const dilation = getParamValue("dilation", node2, tensorMap, context);
        return [conv1d(getParamValue("x", node2, tensorMap, context), getParamValue("filter", node2, tensorMap, context), stride, pad3, dataFormat, dilation)];
      }
      case "Conv2D": {
        const stride = getParamValue("strides", node2, tensorMap, context);
        const pad3 = getPadding(node2, tensorMap, context);
        const dataFormat = getParamValue("dataFormat", node2, tensorMap, context).toUpperCase();
        const dilations = getParamValue("dilations", node2, tensorMap, context);
        return [conv2d(getParamValue("x", node2, tensorMap, context), getParamValue("filter", node2, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])];
      }
      case "_FusedConv2D": {
        const {stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha} = fusedConvAndDepthWiseParams(node2, tensorMap, context);
        return [fused_ops_exports.conv2d({
          x: getParamValue("x", node2, tensorMap, context),
          filter: getParamValue("filter", node2, tensorMap, context),
          strides: [stride[1], stride[2]],
          pad: pad3,
          dataFormat,
          dilations: [dilations[1], dilations[2]],
          bias: biasArg,
          activation: activationFunc,
          preluActivationWeights: preluArg,
          leakyreluAlpha
        })];
      }
      case "FusedDepthwiseConv2dNative": {
        const {stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha} = fusedConvAndDepthWiseParams(node2, tensorMap, context);
        return [fused_ops_exports.depthwiseConv2d({
          x: getParamValue("x", node2, tensorMap, context),
          filter: getParamValue("filter", node2, tensorMap, context),
          strides: [stride[1], stride[2]],
          pad: pad3,
          dataFormat,
          dilations: [dilations[1], dilations[2]],
          bias: biasArg,
          activation: activationFunc,
          preluActivationWeights: preluArg,
          leakyreluAlpha
        })];
      }
      case "Conv2DBackpropInput":
      case "Conv2dTranspose": {
        const shape = getParamValue("outputShape", node2, tensorMap, context);
        const stride = getParamValue("strides", node2, tensorMap, context);
        const pad3 = getPadding(node2, tensorMap, context);
        return [conv2dTranspose(getParamValue("x", node2, tensorMap, context), getParamValue("filter", node2, tensorMap, context), shape, [stride[1], stride[2]], pad3)];
      }
      case "DepthwiseConv2dNative":
      case "DepthwiseConv2d": {
        const stride = getParamValue("strides", node2, tensorMap, context);
        const pad3 = getPadding(node2, tensorMap, context);
        const dilations = getParamValue("dilations", node2, tensorMap, context);
        const dataFormat = getParamValue("dataFormat", node2, tensorMap, context).toUpperCase();
        return [depthwiseConv2d(getParamValue("input", node2, tensorMap, context), getParamValue("filter", node2, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])];
      }
      case "Conv3D": {
        const stride = getParamValue("strides", node2, tensorMap, context);
        const pad3 = getParamValue("pad", node2, tensorMap, context);
        const dataFormat = getParamValue("dataFormat", node2, tensorMap, context).toUpperCase();
        const dilations = getParamValue("dilations", node2, tensorMap, context);
        return [conv3d(getParamValue("x", node2, tensorMap, context), getParamValue("filter", node2, tensorMap, context), [stride[1], stride[2], stride[3]], pad3, dataFormat, [dilations[1], dilations[2], dilations[3]])];
      }
      case "AvgPool": {
        const stride = getParamValue("strides", node2, tensorMap, context);
        const pad3 = getParamValue("pad", node2, tensorMap, context);
        const kernelSize = getParamValue("kernelSize", node2, tensorMap, context);
        return [avgPool(getParamValue("x", node2, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)];
      }
      case "MaxPool": {
        const stride = getParamValue("strides", node2, tensorMap, context);
        const pad3 = getParamValue("pad", node2, tensorMap, context);
        const kernelSize = getParamValue("kernelSize", node2, tensorMap, context);
        return [maxPool(getParamValue("x", node2, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)];
      }
      case "MaxPoolWithArgmax": {
        const stride = getParamValue("strides", node2, tensorMap, context);
        const pad3 = getParamValue("pad", node2, tensorMap, context);
        const kernelSize = getParamValue("kernelSize", node2, tensorMap, context);
        const includeBatchInIndex = getParamValue("includeBatchInIndex", node2, tensorMap, context);
        const {result, indexes} = maxPoolWithArgmax(getParamValue("x", node2, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3, includeBatchInIndex);
        return [result, indexes];
      }
      case "AvgPool3D": {
        const stride = getParamValue("strides", node2, tensorMap, context);
        const pad3 = getParamValue("pad", node2, tensorMap, context);
        const kernelSize = getParamValue("kernelSize", node2, tensorMap, context);
        return [avgPool3d(getParamValue("x", node2, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)];
      }
      case "MaxPool3D": {
        const stride = getParamValue("strides", node2, tensorMap, context);
        const pad3 = getParamValue("pad", node2, tensorMap, context);
        const kernelSize = getParamValue("kernelSize", node2, tensorMap, context);
        return [maxPool3d(getParamValue("x", node2, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)];
      }
      case "Dilation2D": {
        const strides = getParamValue("strides", node2, tensorMap, context);
        const pad3 = getParamValue("pad", node2, tensorMap, context);
        const dilations = getParamValue("dilations", node2, tensorMap, context);
        const strideHeight = strides[1];
        const strideWidth = strides[2];
        const dilationHeight = dilations[1];
        const dilationWidth = dilations[2];
        return [dilation2d(getParamValue("x", node2, tensorMap, context), getParamValue("filter", node2, tensorMap, context), [strideHeight, strideWidth], pad3, [dilationHeight, dilationWidth], "NHWC")];
      }
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var executeOp5 = (node2, tensorMap, context) => {
    switch (node2.op) {
      case "Fill": {
        const shape = getParamValue("shape", node2, tensorMap, context);
        const dtype = getParamValue("dtype", node2, tensorMap, context);
        const value = getParamValue("value", node2, tensorMap, context);
        return [fill(shape, value, dtype)];
      }
      case "LinSpace": {
        const start = getParamValue("start", node2, tensorMap, context);
        const stop = getParamValue("stop", node2, tensorMap, context);
        const num = getParamValue("num", node2, tensorMap, context);
        return [linspace(start, stop, num)];
      }
      case "Multinomial": {
        const logits = getParamValue("logits", node2, tensorMap, context);
        const numSamples = getParamValue("numSamples", node2, tensorMap, context);
        const seed = getParamValue("seed", node2, tensorMap, context);
        return [multinomial(logits, numSamples, seed)];
      }
      case "OneHot": {
        const indices = getParamValue("indices", node2, tensorMap, context);
        const depth = getParamValue("depth", node2, tensorMap, context);
        const onValue = getParamValue("onValue", node2, tensorMap, context);
        const offValue = getParamValue("offValue", node2, tensorMap, context);
        return [oneHot(indices, depth, onValue, offValue)];
      }
      case "Ones": {
        return [ones2(getParamValue("shape", node2, tensorMap, context), getParamValue("dtype", node2, tensorMap, context))];
      }
      case "OnesLike": {
        return [onesLike(getParamValue("x", node2, tensorMap, context))];
      }
      case "RandomUniform": {
        return [randomUniform(getParamValue("shape", node2, tensorMap, context), getParamValue("minval", node2, tensorMap, context), getParamValue("maxval", node2, tensorMap, context), getParamValue("dtype", node2, tensorMap, context))];
      }
      case "Range": {
        const start = getParamValue("start", node2, tensorMap, context);
        const stop = getParamValue("stop", node2, tensorMap, context);
        const step5 = getParamValue("step", node2, tensorMap, context);
        return [range(start, stop, step5, getParamValue("dtype", node2, tensorMap, context))];
      }
      case "TruncatedNormal": {
        const shape = getParamValue("shape", node2, tensorMap, context);
        const mean4 = getParamValue("mean", node2, tensorMap, context);
        const stdDev = getParamValue("stdDev", node2, tensorMap, context);
        const seed = getParamValue("seed", node2, tensorMap, context);
        return [truncatedNormal(shape, mean4, stdDev, getParamValue("dtype", node2, tensorMap, context), seed)];
      }
      case "Zeros": {
        return [zeros(getParamValue("shape", node2, tensorMap, context), getParamValue("dtype", node2, tensorMap, context))];
      }
      case "ZerosLike": {
        return [zerosLike(getParamValue("x", node2, tensorMap, context))];
      }
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function nmsParams(node2, tensorMap, context) {
    const boxes = getParamValue("boxes", node2, tensorMap, context);
    const scores = getParamValue("scores", node2, tensorMap, context);
    const maxOutputSize = getParamValue("maxOutputSize", node2, tensorMap, context);
    const iouThreshold = getParamValue("iouThreshold", node2, tensorMap, context);
    const scoreThreshold = getParamValue("scoreThreshold", node2, tensorMap, context);
    const softNmsSigma = getParamValue("softNmsSigma", node2, tensorMap, context);
    return {
      boxes,
      scores,
      maxOutputSize,
      iouThreshold,
      scoreThreshold,
      softNmsSigma
    };
  }
  var executeOp6 = async (node2, tensorMap, context) => {
    switch (node2.op) {
      case "NonMaxSuppressionV5": {
        const {boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma} = nmsParams(node2, tensorMap, context);
        const result = await image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
        return [result.selectedIndices, result.selectedScores];
      }
      case "NonMaxSuppressionV4": {
        const {boxes, scores, maxOutputSize, iouThreshold, scoreThreshold} = nmsParams(node2, tensorMap, context);
        const padToMaxOutputSize = getParamValue("padToMaxOutputSize", node2, tensorMap, context);
        const result = await image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
        return [result.selectedIndices, result.validOutputs];
      }
      case "NonMaxSuppressionV3":
      case "NonMaxSuppressionV2": {
        const {boxes, scores, maxOutputSize, iouThreshold, scoreThreshold} = nmsParams(node2, tensorMap, context);
        return [await image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];
      }
      case "Where": {
        const condition = cast(getParamValue("condition", node2, tensorMap, context), "bool");
        const result = [await whereAsync(condition)];
        condition.dispose();
        return result;
      }
      case "ListDiff": {
        return setdiff1dAsync(getParamValue("x", node2, tensorMap, context), getParamValue("y", node2, tensorMap, context));
      }
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var executeOp7 = (node2, tensorMap, context) => {
    switch (node2.op) {
      case "TopKV2": {
        const x = getParamValue("x", node2, tensorMap, context);
        const k = getParamValue("k", node2, tensorMap, context);
        const sorted = getParamValue("sorted", node2, tensorMap, context);
        const result = topk(x, k, sorted);
        return [result.values, result.indices];
      }
      case "Unique": {
        const x = getParamValue("x", node2, tensorMap, context);
        const result = unique(x);
        return [result.values, result.indices];
      }
      case "UniqueV2": {
        const x = getParamValue("x", node2, tensorMap, context);
        const axis = getParamValue("axis", node2, tensorMap, context);
        const result = unique(x, axis);
        return [result.values, result.indices];
      }
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var executeOp8 = (node2, tensorMap, context) => {
    switch (node2.op) {
      case "Const": {
        return tensorMap[node2.name];
      }
      case "PlaceholderWithDefault":
        const def = getParamValue("default", node2, tensorMap, context);
        return [getTensor(node2.name, tensorMap, context) || def];
      case "Placeholder":
        return [getTensor(node2.name, tensorMap, context)];
      case "Identity":
      case "StopGradient":
      case "FakeQuantWithMinMaxVars": {
        const data2 = getParamValue("x", node2, tensorMap, context);
        return [cloneTensor(data2)];
      }
      case "IdentityN":
        return getParamValue("x", node2, tensorMap, context).map((t) => cloneTensor(t));
      case "Snapshot":
        const snapshot = getParamValue("x", node2, tensorMap, context);
        return [cloneTensor(snapshot)];
      case "Shape":
        return [tensor1d(getParamValue("x", node2, tensorMap, context).shape, "int32")];
      case "ShapeN":
        return getParamValue("x", node2, tensorMap, context).map((t) => tensor1d(t.shape));
      case "Size":
        return [scalar(getParamValue("x", node2, tensorMap, context).size, "int32")];
      case "Rank":
        return [scalar(getParamValue("x", node2, tensorMap, context).rank, "int32")];
      case "NoOp":
        return [scalar(1)];
      case "Print":
        const input2 = getParamValue("x", node2, tensorMap, context);
        const data = getParamValue("data", node2, tensorMap, context);
        const message = getParamValue("message", node2, tensorMap, context);
        const summarize = getParamValue("summarize", node2, tensorMap, context);
        console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance.");
        console.log(message);
        for (let i = 0; i < data.length; i++) {
          console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0, summarize));
        }
        return [input2];
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var HashTable = class {
    constructor(keyDType, valueDType) {
      this.keyDType = keyDType;
      this.valueDType = valueDType;
      this.handle = scalar(0);
      this.tensorMap = new Map();
      keep(this.handle);
    }
    get id() {
      return this.handle.id;
    }
    clearAndClose() {
      this.tensorMap.forEach((value) => value.dispose());
      this.tensorMap.clear();
      this.handle.dispose();
    }
    size() {
      return this.tensorMap.size;
    }
    tensorSize() {
      return scalar(this.size(), "int32");
    }
    async import(keys, values) {
      this.checkKeyAndValueTensor(keys, values);
      const $keys = await keys.data();
      this.tensorMap.forEach((value) => value.dispose());
      this.tensorMap.clear();
      return tidy(() => {
        const $values = unstack(values);
        const keysLength = $keys.length;
        const valuesLength = $values.length;
        util_exports.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);
        for (let i = 0; i < keysLength; i++) {
          const key = $keys[i];
          const value = $values[i];
          keep(value);
          this.tensorMap.set(key, value);
        }
        return this.handle;
      });
    }
    async find(keys, defaultValue) {
      this.checkKeyAndValueTensor(keys, defaultValue);
      const $keys = await keys.data();
      return tidy(() => {
        const result = [];
        for (let i = 0; i < $keys.length; i++) {
          const key = $keys[i];
          const value = this.findWithDefault(key, defaultValue);
          result.push(value);
        }
        return stack(result);
      });
    }
    findWithDefault(key, defaultValue) {
      const result = this.tensorMap.get(key);
      return result != null ? result : defaultValue;
    }
    checkKeyAndValueTensor(key, value) {
      if (key.dtype !== this.keyDType) {
        throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);
      }
      if (value.dtype !== this.valueDType) {
        throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`);
      }
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var executeOp9 = async (node2, tensorMap, context, resourceManager) => {
    switch (node2.op) {
      case "HashTable":
      case "HashTableV2": {
        const keyDType = getParamValue("keyDType", node2, tensorMap, context);
        const valueDType = getParamValue("valueDType", node2, tensorMap, context);
        const hashTable2 = new HashTable(keyDType, valueDType);
        resourceManager.addHashTable(node2.name, hashTable2);
        return [hashTable2.handle];
      }
      case "LookupTableImport":
      case "LookupTableImportV2": {
        const handle = getParamValue("tableHandle", node2, tensorMap, context, resourceManager);
        const keys = getParamValue("keys", node2, tensorMap, context);
        const values = getParamValue("values", node2, tensorMap, context);
        const hashTable2 = resourceManager.getHashTableById(handle.id);
        return [await hashTable2.import(keys, values)];
      }
      case "LookupTableFind":
      case "LookupTableFindV2": {
        const handle = getParamValue("tableHandle", node2, tensorMap, context, resourceManager);
        const keys = getParamValue("keys", node2, tensorMap, context);
        const defaultValue = getParamValue("defaultValue", node2, tensorMap, context);
        const hashTable2 = resourceManager.getHashTableById(handle.id);
        return [await hashTable2.find(keys, defaultValue)];
      }
      case "LookupTableSize":
      case "LookupTableSizeV2": {
        const handle = getParamValue("tableHandle", node2, tensorMap, context, resourceManager);
        const hashTable2 = resourceManager.getHashTableById(handle.id);
        return [hashTable2.tensorSize()];
      }
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var executeOp10 = (node2, tensorMap, context) => {
    switch (node2.op) {
      case "ResizeBilinear": {
        const images = getParamValue("images", node2, tensorMap, context);
        const size = getParamValue("size", node2, tensorMap, context);
        const alignCorners = getParamValue("alignCorners", node2, tensorMap, context);
        const halfPixelCenters = getParamValue("halfPixelCenters", node2, tensorMap, context);
        return [image.resizeBilinear(images, [size[0], size[1]], alignCorners, halfPixelCenters)];
      }
      case "ResizeNearestNeighbor": {
        const images = getParamValue("images", node2, tensorMap, context);
        const size = getParamValue("size", node2, tensorMap, context);
        const alignCorners = getParamValue("alignCorners", node2, tensorMap, context);
        const halfPixelCenters = getParamValue("halfPixelCenters", node2, tensorMap, context);
        return [image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners, halfPixelCenters)];
      }
      case "CropAndResize": {
        const image3 = getParamValue("image", node2, tensorMap, context);
        const boxes = getParamValue("boxes", node2, tensorMap, context);
        const boxInd = getParamValue("boxInd", node2, tensorMap, context);
        const cropSize = getParamValue("cropSize", node2, tensorMap, context);
        const method = getParamValue("method", node2, tensorMap, context);
        const extrapolationValue = getParamValue("extrapolationValue", node2, tensorMap, context);
        return [image.cropAndResize(image3, boxes, boxInd, cropSize, method, extrapolationValue)];
      }
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var executeOp11 = (node2, tensorMap, context) => {
    switch (node2.op) {
      case "Equal": {
        return [equal(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "NotEqual": {
        return [notEqual(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "Greater": {
        return [greater(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "GreaterEqual": {
        return [greaterEqual(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "Less": {
        return [less(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "LessEqual": {
        return [lessEqual(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "LogicalAnd": {
        return [logicalAnd(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "LogicalNot": {
        return [logicalNot(getParamValue("a", node2, tensorMap, context))];
      }
      case "LogicalOr": {
        return [logicalOr(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      case "Select":
      case "SelectV2": {
        return [where(getParamValue("condition", node2, tensorMap, context), getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
      }
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var executeOp12 = (node2, tensorMap, context) => {
    switch (node2.op) {
      case "BatchMatMul":
      case "BatchMatMulV2":
      case "MatMul":
        return [matMul(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context), getParamValue("transposeA", node2, tensorMap, context), getParamValue("transposeB", node2, tensorMap, context))];
      case "Transpose":
        return [transpose(getParamValue("x", node2, tensorMap, context), getParamValue("perm", node2, tensorMap, context))];
      case "_FusedMatMul":
        const [extraOp, activationFunc] = getParamValue("fusedOps", node2, tensorMap, context);
        const isBiasAdd = extraOp === "biasadd";
        const isPrelu = activationFunc === "prelu";
        const numArgs = getParamValue("numArgs", node2, tensorMap, context);
        const leakyreluAlpha = getParamValue("leakyreluAlpha", node2, tensorMap, context);
        if (isBiasAdd) {
          if (isPrelu && numArgs !== 2) {
            throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
          }
          if (!isPrelu && numArgs !== 1) {
            throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.");
          }
        }
        const [biasArg, preluArg] = getParamValue("args", node2, tensorMap, context);
        return [fused_ops_exports.matMul({
          a: getParamValue("a", node2, tensorMap, context),
          b: getParamValue("b", node2, tensorMap, context),
          transposeA: getParamValue("transposeA", node2, tensorMap, context),
          transposeB: getParamValue("transposeB", node2, tensorMap, context),
          bias: biasArg,
          activation: activationFunc,
          preluActivationWeights: preluArg,
          leakyreluAlpha
        })];
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var executeOp13 = (node2, tensorMap, context) => {
    switch (node2.op) {
      case "FusedBatchNorm":
      case "FusedBatchNormV2": {
        return [batchNorm(getParamValue("x", node2, tensorMap, context), getParamValue("mean", node2, tensorMap, context), getParamValue("variance", node2, tensorMap, context), getParamValue("offset", node2, tensorMap, context), getParamValue("scale", node2, tensorMap, context), getParamValue("epsilon", node2, tensorMap, context))];
      }
      case "FusedBatchNormV3": {
        return [batchNorm(getParamValue("x", node2, tensorMap, context), getParamValue("mean", node2, tensorMap, context), getParamValue("variance", node2, tensorMap, context), getParamValue("offset", node2, tensorMap, context), getParamValue("scale", node2, tensorMap, context), getParamValue("epsilon", node2, tensorMap, context))];
      }
      case "LRN": {
        return [localResponseNormalization(getParamValue("x", node2, tensorMap, context), getParamValue("radius", node2, tensorMap, context), getParamValue("bias", node2, tensorMap, context), getParamValue("alpha", node2, tensorMap, context), getParamValue("beta", node2, tensorMap, context))];
      }
      case "Softmax": {
        return [softmax(getParamValue("x", node2, tensorMap, context))];
      }
      case "LogSoftmax": {
        return [logSoftmax(getParamValue("x", node2, tensorMap, context))];
      }
      case "SparseToDense": {
        return [sparseToDense(getParamValue("sparseIndices", node2, tensorMap, context), getParamValue("outputShape", node2, tensorMap, context), getParamValue("sparseValues", node2, tensorMap, context), getParamValue("defaultValue", node2, tensorMap, context))];
      }
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var executeOp14 = (node2, tensorMap, context) => {
    switch (node2.op) {
      case "Max": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        const keepDims = getParamValue("keepDims", node2, tensorMap, context);
        return [max(getParamValue("x", node2, tensorMap, context), axis, keepDims)];
      }
      case "Mean": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        const keepDims = getParamValue("keepDims", node2, tensorMap, context);
        return [mean(getParamValue("x", node2, tensorMap, context), axis, keepDims)];
      }
      case "Min": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        const keepDims = getParamValue("keepDims", node2, tensorMap, context);
        return [min(getParamValue("x", node2, tensorMap, context), axis, keepDims)];
      }
      case "Sum": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        const keepDims = getParamValue("keepDims", node2, tensorMap, context);
        return [sum2(getParamValue("x", node2, tensorMap, context), axis, keepDims)];
      }
      case "All": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        const keepDims = getParamValue("keepDims", node2, tensorMap, context);
        return [all(getParamValue("x", node2, tensorMap, context), axis, keepDims)];
      }
      case "Any": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        const keepDims = getParamValue("keepDims", node2, tensorMap, context);
        return [any(getParamValue("x", node2, tensorMap, context), axis, keepDims)];
      }
      case "ArgMax": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        return [argMax(getParamValue("x", node2, tensorMap, context), axis)];
      }
      case "ArgMin": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        return [argMin(getParamValue("x", node2, tensorMap, context), axis)];
      }
      case "Prod": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        const keepDims = getParamValue("keepDims", node2, tensorMap, context);
        return [prod(getParamValue("x", node2, tensorMap, context), axis, keepDims)];
      }
      case "Cumsum": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        const exclusive = getParamValue("exclusive", node2, tensorMap, context);
        const reverse5 = getParamValue("reverse", node2, tensorMap, context);
        return [cumsum(getParamValue("x", node2, tensorMap, context), axis, exclusive, reverse5)];
      }
      case "Bincount":
        const x = getParamValue("x", node2, tensorMap, context);
        const weights = getParamValue("weights", node2, tensorMap, context);
        const size = getParamValue("size", node2, tensorMap, context);
        return [bincount(x, weights, size)];
      case "DenseBincount": {
        const x2 = getParamValue("x", node2, tensorMap, context);
        const weights2 = getParamValue("weights", node2, tensorMap, context);
        const size2 = getParamValue("size", node2, tensorMap, context);
        const binaryOutput = getParamValue("binaryOutput", node2, tensorMap, context);
        return [denseBincount(x2, weights2, size2, binaryOutput)];
      }
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var executeOp15 = (node2, tensorMap, context) => {
    switch (node2.op) {
      case "ConcatV2":
      case "Concat": {
        const n = getParamValue("n", node2, tensorMap, context);
        const axis = getParamValue("axis", node2, tensorMap, context);
        let inputs = getParamValue("tensors", node2, tensorMap, context);
        inputs = inputs.slice(0, n);
        return [concat(inputs, axis)];
      }
      case "Gather": {
        const input2 = getParamValue("x", node2, tensorMap, context);
        const indices = getParamValue("indices", node2, tensorMap, context);
        return [gather(input2, cast(indices, "int32"), 0)];
      }
      case "GatherV2": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        const batchDims = getParamValue("batchDims", node2, tensorMap, context);
        const input2 = getParamValue("x", node2, tensorMap, context);
        const indices = getParamValue("indices", node2, tensorMap, context);
        return [gather(input2, cast(indices, "int32"), axis, batchDims)];
      }
      case "Reverse": {
        const dims = getParamValue("dims", node2, tensorMap, context);
        const axis = [];
        for (let i = 0; i < dims.length; i++) {
          if (dims[i]) {
            axis.push(i);
          }
        }
        const input2 = getParamValue("x", node2, tensorMap, context);
        return [reverse(input2, axis)];
      }
      case "ReverseV2": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        const input2 = getParamValue("x", node2, tensorMap, context);
        return [reverse(input2, axis)];
      }
      case "Slice": {
        const begin = getParamValue("begin", node2, tensorMap, context);
        const size = getParamValue("size", node2, tensorMap, context);
        return [slice(getParamValue("x", node2, tensorMap, context), begin, size)];
      }
      case "StridedSlice": {
        const begin = getParamValue("begin", node2, tensorMap, context);
        const end = getParamValue("end", node2, tensorMap, context);
        const strides = getParamValue("strides", node2, tensorMap, context);
        const beginMask = getParamValue("beginMask", node2, tensorMap, context);
        const endMask = getParamValue("endMask", node2, tensorMap, context);
        const ellipsisMask = getParamValue("ellipsisMask", node2, tensorMap, context);
        const newAxisMask = getParamValue("newAxisMask", node2, tensorMap, context);
        const shrinkAxisMask = getParamValue("shrinkAxisMask", node2, tensorMap, context);
        const tensor2 = getParamValue("x", node2, tensorMap, context);
        return [stridedSlice(tensor2, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];
      }
      case "Pack": {
        return tidy(() => {
          const axis = getParamValue("axis", node2, tensorMap, context);
          const tensors = getParamValue("tensors", node2, tensorMap, context);
          const shape = tensors[0].shape;
          const squeezedShape = squeeze(tensors[0]).shape;
          const mapped = tensors.map((tensor2) => {
            const sameShape = util_exports.arraysEqual(tensor2.shape, shape);
            if (!sameShape && !util_exports.arraysEqual(squeeze(tensor2).shape, squeezedShape)) {
              throw new Error("the input tensors shape does not match");
            }
            return sameShape ? tensor2 : reshape(tensor2, shape);
          });
          return [stack(mapped, axis)];
        });
      }
      case "Unpack": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        const tensor2 = getParamValue("tensor", node2, tensorMap, context);
        return unstack(tensor2, axis);
      }
      case "Tile": {
        const reps = getParamValue("reps", node2, tensorMap, context);
        return [tile(getParamValue("x", node2, tensorMap, context), reps)];
      }
      case "Split":
      case "SplitV": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        const numOrSizeSplits = getParamValue("numOrSizeSplits", node2, tensorMap, context);
        const tensor2 = getParamValue("x", node2, tensorMap, context);
        return split(tensor2, numOrSizeSplits, axis);
      }
      case "ScatterNd": {
        const indices = getParamValue("indices", node2, tensorMap, context);
        const values = getParamValue("values", node2, tensorMap, context);
        const shape = getParamValue("shape", node2, tensorMap, context);
        return [scatterND(indices, values, shape)];
      }
      case "GatherNd": {
        const x = getParamValue("x", node2, tensorMap, context);
        const indices = getParamValue("indices", node2, tensorMap, context);
        return [gatherND(x, indices)];
      }
      case "SparseToDense": {
        const indices = getParamValue("sparseIndices", node2, tensorMap, context);
        const shape = getParamValue("outputShape", node2, tensorMap, context);
        const sparseValues = getParamValue("sparseValues", node2, tensorMap, context);
        const defaultValue = getParamValue("defaultValue", node2, tensorMap, context);
        return [sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ? defaultValue : cast(defaultValue, sparseValues.dtype))];
      }
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var executeOp16 = (node2, tensorMap, context) => {
    switch (node2.op) {
      case "FFT": {
        return [fft(getParamValue("x", node2, tensorMap, context))];
      }
      case "IFFT": {
        return [ifft(getParamValue("x", node2, tensorMap, context))];
      }
      case "RFFT": {
        return [rfft(getParamValue("x", node2, tensorMap, context))];
      }
      case "IRFFT": {
        return [irfft(getParamValue("x", node2, tensorMap, context))];
      }
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var executeOp17 = (node2, tensorMap, context) => {
    switch (node2.op) {
      case "Cast": {
        return [cast(getParamValue("x", node2, tensorMap, context), getParamValue("dtype", node2, tensorMap, context))];
      }
      case "ExpandDims": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        return [expandDims(getParamValue("x", node2, tensorMap, context), axis)];
      }
      case "Squeeze": {
        const axis = getParamValue("axis", node2, tensorMap, context);
        return [squeeze(getParamValue("x", node2, tensorMap, context), axis)];
      }
      case "Reshape": {
        return [reshape(getParamValue("x", node2, tensorMap, context), getParamValue("shape", node2, tensorMap, context))];
      }
      case "MirrorPad": {
        return [mirrorPad(getParamValue("x", node2, tensorMap, context), getParamValue("padding", node2, tensorMap, context), getParamValue("mode", node2, tensorMap, context))];
      }
      case "PadV2":
      case "Pad": {
        return [pad(getParamValue("x", node2, tensorMap, context), getParamValue("padding", node2, tensorMap, context), getParamValue("constantValue", node2, tensorMap, context))];
      }
      case "SpaceToBatchND": {
        const blockShape = getParamValue("blockShape", node2, tensorMap, context);
        const paddings = getParamValue("paddings", node2, tensorMap, context);
        return [spaceToBatchND(getParamValue("x", node2, tensorMap, context), blockShape, paddings)];
      }
      case "BatchToSpaceND": {
        const blockShape = getParamValue("blockShape", node2, tensorMap, context);
        const crops = getParamValue("crops", node2, tensorMap, context);
        return [batchToSpaceND(getParamValue("x", node2, tensorMap, context), blockShape, crops)];
      }
      case "DepthToSpace": {
        const blockSize = getParamValue("blockSize", node2, tensorMap, context);
        const dataFormat = getParamValue("dataFormat", node2, tensorMap, context).toUpperCase();
        return [depthToSpace(getParamValue("x", node2, tensorMap, context), blockSize, dataFormat)];
      }
      case "BroadcastTo": {
        return [broadcastTo(getParamValue("x", node2, tensorMap, context), getParamValue("shape", node2, tensorMap, context))];
      }
      default:
        throw TypeError(`Node type ${node2.op} is not implemented`);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function executeOp18(node2, tensorMap, context, resourceManager) {
    const value = ((node22, tensorMap2, context2) => {
      switch (node22.category) {
        case "arithmetic":
          return tidy(() => executeOp(node22, tensorMap2, context2));
        case "basic_math":
          return tidy(() => executeOp2(node22, tensorMap2, context2));
        case "control":
          return executeOp3(node22, tensorMap2, context2);
        case "convolution":
          return tidy(() => executeOp4(node22, tensorMap2, context2));
        case "creation":
          return tidy(() => executeOp5(node22, tensorMap2, context2));
        case "dynamic":
          return executeOp6(node22, tensorMap2, context2);
        case "evaluation":
          return tidy(() => executeOp7(node22, tensorMap2, context2));
        case "image":
          return tidy(() => executeOp10(node22, tensorMap2, context2));
        case "graph":
          return tidy(() => executeOp8(node22, tensorMap2, context2));
        case "logical":
          return tidy(() => executeOp11(node22, tensorMap2, context2));
        case "matrices":
          return tidy(() => executeOp12(node22, tensorMap2, context2));
        case "normalization":
          return tidy(() => executeOp13(node22, tensorMap2, context2));
        case "reduction":
          return tidy(() => executeOp14(node22, tensorMap2, context2));
        case "slice_join":
          return tidy(() => executeOp15(node22, tensorMap2, context2));
        case "spectral":
          return tidy(() => executeOp16(node22, tensorMap2, context2));
        case "transformation":
          return tidy(() => executeOp17(node22, tensorMap2, context2));
        case "hash_table":
          return executeOp9(node22, tensorMap2, context2, resourceManager);
        case "custom":
          const opMapper = getRegisteredOp(node22.op);
          if (opMapper && opMapper.customExecutor) {
            return opMapper.customExecutor(new NodeValueImpl(node22, tensorMap2, context2));
          } else {
            throw TypeError(`Custom op ${node22.op} is not registered.`);
          }
        default:
          throw TypeError(`Unknown op '${node22.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`);
      }
    })(node2, tensorMap, context);
    if (util_exports.isPromise(value)) {
      return value.then((data) => [].concat(data));
    }
    return [].concat(value);
  }
  var ExecutionContext = class {
    constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {
      this.weightMap = weightMap;
      this.tensorArrayMap = tensorArrayMap;
      this.tensorListMap = tensorListMap;
      this.functionMap = functionMap;
      this.rootContext = {id: 0, frameName: "", iterationId: 0};
      this.contexts = [this.rootContext];
      this.lastId = 0;
      this.generateCurrentContextIds();
    }
    newFrame(id, frameName) {
      return {id, frameName, iterationId: 0};
    }
    set currentContext(contexts2) {
      if (this.contexts !== contexts2) {
        this.contexts = contexts2;
        this.generateCurrentContextIds();
      }
    }
    get currentContext() {
      return this.contexts;
    }
    get currentContextId() {
      return this._currentContextIds[0];
    }
    get currentContextIds() {
      return this._currentContextIds;
    }
    generateCurrentContextIds() {
      const names = [];
      for (let i = 0; i < this.contexts.length - 1; i++) {
        const contexts2 = this.contexts.slice(0, this.contexts.length - i);
        names.push(this.contextIdforContexts(contexts2));
      }
      names.push("");
      this._currentContextIds = names;
    }
    contextIdforContexts(contexts2) {
      return contexts2 ? contexts2.map((context) => context.id === 0 && context.iterationId === 0 ? "" : `${context.frameName}-${context.iterationId}`).join("/") : "";
    }
    enterFrame(frameId) {
      if (this.contexts) {
        this.lastId++;
        this.contexts = this.contexts.slice();
        this.contexts.push(this.newFrame(this.lastId, frameId));
        this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));
      }
    }
    exitFrame() {
      if (this.contexts && this.contexts.length > 1) {
        this.contexts = this.contexts.slice();
        this.contexts.splice(-1);
        this.currentContextIds.shift();
      } else {
        throw new Error("Cannot exit frame, the context is empty");
      }
    }
    nextIteration() {
      if (this.contexts && this.contexts.length > 0) {
        this.contexts = this.contexts.slice();
        this.lastId++;
        const context = Object.assign({}, this.contexts[this.contexts.length - 1]);
        context.iterationId += 1;
        context.id = this.lastId;
        this.contexts.splice(-1, 1, context);
        this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));
      } else {
        throw new Error("Cannot increase frame iteration, the context is empty");
      }
    }
    getWeight(name) {
      return this.weightMap[name];
    }
    addTensorArray(tensorArray) {
      this.tensorArrayMap[tensorArray.id] = tensorArray;
    }
    getTensorArray(id) {
      return this.tensorArrayMap[id];
    }
    addTensorList(tensorList) {
      this.tensorListMap[tensorList.id] = tensorList;
    }
    getTensorList(id) {
      return this.tensorListMap[id];
    }
    dispose(keepIds) {
      for (const key in this.tensorArrayMap) {
        this.tensorArrayMap[key].clearAndClose(keepIds);
      }
      for (const key in this.tensorListMap) {
        this.tensorListMap[key].clearAndClose(keepIds);
      }
    }
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {
    const usedNodes = new Set();
    const missingInputs = [];
    let dynamicNode = null;
    let syncInputs = null;
    const seen = new Set();
    const inputNodeNames = Object.keys(inputs).map((name) => parseNodeName(name)[0]);
    let initNodeNames = [];
    if (initNodes != null) {
      initNodeNames = initNodes.map((node2) => parseNodeName(node2.name)[0]);
    }
    const frontier = [...outputs];
    while (frontier.length > 0) {
      const node2 = frontier.pop();
      if (isControlFlow(node2) || isDynamicShape(node2) || isHashTable(node2)) {
        if (dynamicNode == null) {
          dynamicNode = node2;
          syncInputs = dynamicNode.children.map((child) => child.name).filter((name) => usedNodes.has(name));
        }
      }
      usedNodes.add(node2.name);
      if (weightMap[node2.name] != null) {
        continue;
      }
      if (inputNodeNames.indexOf(node2.name) !== -1) {
        continue;
      }
      if (initNodeNames.indexOf(node2.name) !== -1) {
        continue;
      }
      if (node2.inputs.length === 0) {
        missingInputs.push(node2.name);
        continue;
      }
      node2.inputs.forEach((input2) => {
        if (seen.has(input2.name)) {
          return;
        }
        seen.add(input2.name);
        frontier.push(input2);
      });
    }
    return {inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs};
  }
  function getNodesInTopologicalOrder(graph2, weightMap, executionInfo) {
    const {usedNodes, inputs} = executionInfo;
    const frontier = [];
    const inputNodes = Object.keys(inputs).map((name) => parseNodeName(name)[0]).map((name) => graph2.nodes[name]);
    const initNodes = graph2.initNodes;
    inputNodes.forEach((input2) => {
      if (usedNodes.has(input2.name)) {
        frontier.push(input2);
      }
    });
    graph2.weights.forEach((weight) => {
      if (usedNodes.has(weight.name)) {
        frontier.push(weight);
      }
    });
    if (initNodes != null) {
      initNodes.forEach((node2) => {
        if (usedNodes.has(node2.name)) {
          frontier.push(node2);
        }
      });
    }
    const seen = new Set();
    const orderedNodes = [];
    while (frontier.length > 0) {
      const node2 = frontier.pop();
      seen.add(node2.name);
      if (!weightMap[node2.name]) {
        orderedNodes.push(node2);
      }
      node2.children.forEach((child) => {
        if (!seen.has(child.name) && usedNodes.has(child.name) && child.inputs.every((input2) => seen.has(input2.name))) {
          frontier.push(child);
        }
      });
    }
    return orderedNodes;
  }
  var CONTROL_FLOW_OPS = [
    "Switch",
    "Merge",
    "Enter",
    "Exit",
    "NextIteration",
    "StatelessIf",
    "StatelessWhile",
    "if",
    "While"
  ];
  var DYNAMIC_SHAPE_OPS = [
    "NonMaxSuppressionV2",
    "NonMaxSuppressionV3",
    "NonMaxSuppressionV5",
    "Where"
  ];
  var HASH_TABLE_OPS = [
    "HashTable",
    "HashTableV2",
    "LookupTableImport",
    "LookupTableImportV2",
    "LookupTableFind",
    "LookupTableFindV2",
    "LookupTableSize",
    "LookupTableSizeV2"
  ];
  function isControlFlow(node2) {
    return CONTROL_FLOW_OPS.indexOf(node2.op) >= 0;
  }
  function isDynamicShape(node2) {
    return DYNAMIC_SHAPE_OPS.indexOf(node2.op) >= 0;
  }
  function isHashTable(node2) {
    return HASH_TABLE_OPS.indexOf(node2.op) >= 0;
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var GraphExecutor = class {
    constructor(graph2, parent) {
      this.graph = graph2;
      this.parent = parent;
      this.compiledMap = new Map();
      this._weightMap = {};
      this.SEPERATOR = ",";
      this._functions = {};
      this._functionExecutorMap = {};
      this._outputs = graph2.outputs;
      this._inputs = graph2.inputs;
      this._initNodes = graph2.initNodes;
      this._signature = graph2.signature;
      this._functions = graph2.functions;
      if (graph2.functions != null) {
        Object.keys(graph2.functions).forEach((name) => {
          this._functionExecutorMap[name] = new GraphExecutor(graph2.functions[name], this);
        });
      }
    }
    get weightIds() {
      return this.parent ? this.parent.weightIds : this._weightIds;
    }
    get functionExecutorMap() {
      return this.parent ? this.parent.functionExecutorMap : this._functionExecutorMap;
    }
    get weightMap() {
      return this.parent ? this.parent.weightMap : this._weightMap;
    }
    set weightMap(weightMap) {
      const weightIds = Object.keys(weightMap).map((key) => weightMap[key].map((tensor2) => tensor2.id));
      this._weightIds = [].concat(...weightIds);
      this._weightMap = weightMap;
    }
    set resourceManager(resourceManager) {
      this._resourceManager = resourceManager;
    }
    get inputs() {
      return this._inputs.map((node2) => {
        return {
          name: node2.name,
          shape: node2.attrParams["shape"] ? node2.attrParams["shape"].value : void 0,
          dtype: node2.attrParams["dtype"] ? node2.attrParams["dtype"].value : void 0
        };
      });
    }
    get outputs() {
      return this._outputs.map((node2) => {
        return {
          name: node2.name,
          shape: node2.attrParams["shape"] ? node2.attrParams["shape"].value : void 0,
          dtype: node2.attrParams["dtype"] ? node2.attrParams["dtype"].value : void 0
        };
      });
    }
    get inputNodes() {
      return this._inputs.map((node2) => node2.signatureKey || node2.name);
    }
    get outputNodes() {
      return this._outputs.map((node2) => {
        const name = node2.signatureKey || node2.name;
        return node2.defaultOutput ? `${name}:${node2.defaultOutput}` : name;
      });
    }
    get functions() {
      return Object.keys(this._functions).reduce((map, key) => {
        map[key] = this._functions[key].signature;
        return map;
      }, {});
    }
    getCompilationKey(inputs, outputs) {
      const sortedInputs = inputs.map((node2) => node2.name).sort();
      const sortedOutputs = outputs.map((node2) => node2.name).sort();
      return sortedInputs.join(this.SEPERATOR) + "--" + sortedOutputs.join(this.SEPERATOR);
    }
    compile(inputs, outputs) {
      const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);
      const {missingInputs, dynamicNode, syncInputs} = executionInfo;
      if (dynamicNode != null) {
        throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);
      }
      if (missingInputs.length > 0) {
        const outNames = outputs.map((n) => n.name);
        const inNames = Object.keys(inputs);
        throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`);
      }
      return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);
    }
    execute(inputs, outputs) {
      inputs = this.mapInputs(inputs);
      const names = Object.keys(inputs).sort();
      this.checkInputs(inputs);
      this.checkInputShapeAndType(inputs);
      outputs = this.mapOutputs(outputs);
      this.checkOutputs(outputs);
      const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);
      const outputNodeNames = outputs.map((name) => parseNodeName(name)[0]);
      let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]);
      if (outputNodes.length === 0) {
        outputNodes = this._outputs;
      }
      const compilationKey = this.getCompilationKey(inputNodes, outputNodes);
      let orderedNodes = this.compiledMap.get(compilationKey);
      if (orderedNodes == null) {
        orderedNodes = this.compile(inputs, outputNodes);
        this.compiledMap.set(compilationKey, orderedNodes);
      }
      const tensorArrayMap = {};
      const tensorListMap = {};
      return tidy(() => {
        const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);
        const tensorsMap = Object.assign({}, this.weightMap);
        Object.keys(inputs).forEach((name) => {
          const [nodeName, index] = parseNodeName(name);
          const tensors = [];
          tensors[index] = inputs[name];
          tensorsMap[nodeName] = tensors;
        });
        const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
        const intermediateTensorConsumerCount = {};
        for (let i = 0; i < orderedNodes.length; i++) {
          const node2 = orderedNodes[i];
          if (!tensorsMap[node2.name]) {
            const tensors = executeOp18(node2, tensorsMap, context, this._resourceManager);
            if (util_exports.isPromise(tensors)) {
              throw new Error(`The execution of the op '${node2.op}' returned a promise. Please use model.executeAsync() instead.`);
            }
            tensorsMap[node2.name] = tensors;
            this.checkTensorForDisposal(node2.name, node2, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);
          }
        }
        if (this.parent == null) {
          context.dispose(tensorsToKeep);
        }
        return outputs.map((name) => getTensor(name, tensorsMap, context));
      });
    }
    getFrozenTensorIds(tensorMap) {
      const ids = [].concat.apply([], Object.keys(tensorMap).map((key) => tensorMap[key]).map((tensors) => tensors.map((tensor2) => tensor2.id)));
      return new Set(ids);
    }
    checkTensorForDisposal(nodeName, node2, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {
      if (node2.category === "control" || outputNames.indexOf(nodeName) !== -1) {
        return;
      }
      tensorMap[nodeName].forEach((tensor2) => {
        if (tensor2 != null) {
          intermediateTensorConsumerCount[tensor2.id] = (intermediateTensorConsumerCount[tensor2.id] || 0) + node2.children.length;
        }
      });
      node2.inputs.forEach((input2) => {
        if (input2.category !== "control") {
          const tensors = getTensorsForCurrentContenxt(input2.name, tensorMap, context);
          if (tensors != null) {
            tensors.forEach((tensor2) => {
              if (tensor2 && !tensorsToKeep.has(tensor2.id)) {
                const count2 = intermediateTensorConsumerCount[tensor2.id];
                if (count2 === 1) {
                  tensor2.dispose();
                  delete intermediateTensorConsumerCount[tensor2.id];
                } else if (count2 != null) {
                  intermediateTensorConsumerCount[tensor2.id]--;
                }
              }
            });
          }
        }
      });
    }
    async executeAsync(inputs, outputs) {
      return this._executeAsync(inputs, outputs);
    }
    async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {
      if (!isFunctionExecution) {
        inputs = this.mapInputs(inputs);
        this.checkInputs(inputs);
        this.checkInputShapeAndType(inputs);
        outputs = this.mapOutputs(outputs);
        this.checkOutputs(outputs);
      }
      const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);
      const tensorMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);
      const results = outputs.map((name) => getTensor(name, tensorMap, context));
      const outputIds = results.map((t) => t.id);
      const inputIds = Object.keys(inputs).map((name) => inputs[name].id);
      const keepIds = new Set([...outputIds, ...inputIds, ...this.weightIds]);
      Object.keys(tensorMap).forEach((key) => {
        const tensorArray = tensorMap[key];
        tensorArray.forEach((tensor2) => {
          if (tensor2 && !tensor2.isDisposed && !keepIds.has(tensor2.id)) {
            tensor2.dispose();
          }
        });
      });
      if (this.parent == null) {
        context.dispose(keepIds);
      }
      return results;
    }
    async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {
      const mappedInputs = inputs.reduce((map, tensor2, index) => {
        map[this.inputs[index].name] = tensor2;
        return map;
      }, {});
      return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);
    }
    async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {
      const names = Object.keys(inputs);
      const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);
      const outputNodeNames = outputNames.map((name) => parseNodeName(name)[0]);
      let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]);
      if (outputNodes.length === 0) {
        outputNodes = this._outputs;
      }
      const {usedNodes, missingInputs, dynamicNode, syncInputs} = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes);
      const stack2 = [
        ...inputNodes,
        ...this.graph.weights,
        ...this._initNodes || []
      ].map((node2) => {
        return {node: node2, contexts: context.currentContext};
      });
      const tensorsMap = Object.assign({}, this.weightMap);
      Object.keys(inputs).forEach((name) => {
        const [nodeName, index] = parseNodeName(name);
        const tensors = [];
        tensors[index] = inputs[name];
        tensorsMap[nodeName] = tensors;
      });
      const intermediateTensorConsumerCount = {};
      const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
      const added = {};
      while (stack2.length > 0) {
        const promises = this.processStack(inputNodes, stack2, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);
        await Promise.all(promises);
      }
      if (dynamicNode == null && !isFunctionExecution) {
        console.warn(`This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.`);
      }
      const missingOutputs = outputNodes.filter((node2) => !isControlFlow(node2) && !getTensor(node2.name, tensorsMap, context)).map((node2) => node2.name);
      if (missingOutputs.length > 0) {
        let alternativeMsg = "";
        if (dynamicNode != null) {
          alternativeMsg = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`;
        }
        throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`);
      }
      return tensorsMap;
    }
    processStack(inputNodes, stack2, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {
      const promises = [];
      while (stack2.length > 0) {
        const item = stack2.pop();
        context.currentContext = item.contexts;
        let nodeName = "";
        if (item.node.op === "Enter" && getParamValue("isConstant", item.node, tensorMap, context)) {
          [nodeName] = getNodeNameAndIndex(item.node.name, context);
        }
        if (tensorMap[item.node.name] == null) {
          const tensors = executeOp18(item.node, tensorMap, context, this._resourceManager);
          if (!nodeName) {
            [nodeName] = getNodeNameAndIndex(item.node.name, context);
          }
          const currentContext = context.currentContext;
          if (util_exports.isPromise(tensors)) {
            promises.push(tensors.then((t) => {
              tensorMap[nodeName] = t;
              context.currentContext = currentContext;
              this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);
              this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);
              return t;
            }));
          } else {
            tensorMap[nodeName] = tensors;
            this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);
            this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);
          }
        } else {
          this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);
        }
      }
      return promises;
    }
    processChildNodes(node2, stack2, context, tensorMap, added, usedNodes) {
      node2.children.forEach((childNode) => {
        const [nodeName] = getNodeNameAndIndex(childNode.name, context);
        if (added[nodeName] || !usedNodes.has(childNode.name)) {
          return;
        }
        if (childNode.op === "Merge") {
          if (childNode.inputNames.some((name) => {
            return !!getTensor(name, tensorMap, context);
          })) {
            added[nodeName] = true;
            stack2.push({contexts: context.currentContext, node: childNode});
          }
        } else if (childNode.inputNames.every((name) => {
          return !!getTensor(name, tensorMap, context);
        })) {
          added[nodeName] = true;
          stack2.push({contexts: context.currentContext, node: childNode});
        }
      });
    }
    dispose() {
      Object.keys(this.weightMap).forEach((key) => this.weightMap[key].forEach((tensor2) => tensor2.dispose()));
    }
    checkInputShapeAndType(inputs) {
      Object.keys(inputs).forEach((name) => {
        const input2 = inputs[name];
        const [nodeName] = parseNodeName(name);
        const node2 = this.graph.nodes[nodeName];
        if (node2.attrParams["shape"] && node2.attrParams["shape"].value) {
          const shape = node2.attrParams["shape"].value;
          const match = shape.length === input2.shape.length && input2.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);
          util_exports.assert(match, () => `The shape of dict['${node2.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`);
        }
        if (node2.attrParams["dtype"] && node2.attrParams["dtype"].value) {
          util_exports.assert(input2.dtype === node2.attrParams["dtype"].value, () => `The dtype of dict['${node2.name}'] provided in model.execute(dict) must be ${node2.attrParams["dtype"].value}, but was ${input2.dtype}`);
        }
      });
    }
    mapInputs(inputs) {
      const result = {};
      for (const inputName in inputs) {
        if (this._signature != null && this._signature.inputs != null && this._signature.inputs[inputName] != null) {
          const tensor2 = this._signature.inputs[inputName];
          result[tensor2.name] = inputs[inputName];
        } else {
          result[inputName] = inputs[inputName];
        }
      }
      return result;
    }
    checkInputs(inputs) {
      const notInGraph = Object.keys(inputs).filter((name) => {
        const [nodeName] = parseNodeName(name);
        return this.graph.nodes[nodeName] == null;
      });
      if (notInGraph.length > 0) {
        throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`);
      }
    }
    mapOutputs(outputs) {
      return outputs.map((name) => {
        if (this._signature != null && this._signature.outputs != null && this._signature.outputs[name] != null) {
          const tensor2 = this._signature.outputs[name];
          return tensor2.name;
        }
        return name;
      }, {});
    }
    checkOutputs(outputs) {
      outputs.forEach((name) => {
        const [normalizedName] = parseNodeName(name);
        if (!this.graph.nodes[normalizedName]) {
          throw new Error(`The output '${name}' is not found in the graph`);
        }
      });
    }
  };
  var ResourceManager = class {
    constructor(hashTableNameToHandle = {}, hashTableMap = {}) {
      this.hashTableNameToHandle = hashTableNameToHandle;
      this.hashTableMap = hashTableMap;
    }
    addHashTable(name, hashTable2) {
      this.hashTableNameToHandle[name] = hashTable2.handle;
      this.hashTableMap[hashTable2.id] = hashTable2;
    }
    getHashTableHandleByName(name) {
      return this.hashTableNameToHandle[name];
    }
    getHashTableById(id) {
      return this.hashTableMap[id];
    }
    dispose() {
      for (const key in this.hashTableMap) {
        this.hashTableMap[key].clearAndClose();
        delete this.hashTableMap[key];
      }
      for (const name in this.hashTableNameToHandle) {
        this.hashTableNameToHandle[name].dispose();
        delete this.hashTableNameToHandle[name];
      }
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var TFHUB_SEARCH_PARAM = "?tfjs-format=file";
  var DEFAULT_MODEL_NAME = "model.json";
  var GraphModel = class {
    constructor(modelUrl, loadOptions = {}) {
      this.modelUrl = modelUrl;
      this.loadOptions = loadOptions;
      this.version = "n/a";
      if (loadOptions == null) {
        this.loadOptions = {};
      }
      this.resourceManager = new ResourceManager();
    }
    get modelVersion() {
      return this.version;
    }
    get inputNodes() {
      return this.executor.inputNodes;
    }
    get outputNodes() {
      return this.executor.outputNodes;
    }
    get inputs() {
      return this.executor.inputs;
    }
    get outputs() {
      return this.executor.outputs;
    }
    get weights() {
      return this.executor.weightMap;
    }
    get metadata() {
      return this.artifacts.userDefinedMetadata;
    }
    get modelSignature() {
      return this.signature;
    }
    findIOHandler() {
      const path = this.modelUrl;
      if (path.load != null) {
        this.handler = path;
      } else if (this.loadOptions.requestInit != null) {
        this.handler = io_exports.browserHTTPRequest(path, this.loadOptions);
      } else {
        const handlers = io_exports.getLoadHandlers(path, this.loadOptions);
        if (handlers.length === 0) {
          handlers.push(io_exports.browserHTTPRequest(path, this.loadOptions));
        } else if (handlers.length > 1) {
          throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);
        }
        this.handler = handlers[0];
      }
    }
    async load() {
      this.findIOHandler();
      if (this.handler.load == null) {
        throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
      }
      const artifacts = await this.handler.load();
      return this.loadSync(artifacts);
    }
    loadSync(artifacts) {
      this.artifacts = artifacts;
      const graph2 = this.artifacts.modelTopology;
      let signature;
      if (this.artifacts.userDefinedMetadata != null && this.artifacts.userDefinedMetadata.signature != null) {
        signature = this.artifacts.userDefinedMetadata.signature;
      } else {
        signature = this.artifacts.signature;
      }
      this.signature = signature;
      this.version = `${graph2.versions.producer}.${graph2.versions.minConsumer}`;
      const weightMap = io_exports.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);
      this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph2, this.signature));
      this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);
      this.executor.resourceManager = this.resourceManager;
      if (artifacts.modelInitializer != null && artifacts.modelInitializer.node != null) {
        const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);
        this.initializer = new GraphExecutor(initializer);
        this.initializer.weightMap = this.executor.weightMap;
        this.initializer.resourceManager = this.resourceManager;
        this.initializer.executeAsync({}, []);
      }
      return true;
    }
    async save(handlerOrURL, config) {
      if (typeof handlerOrURL === "string") {
        const handlers = io_exports.getSaveHandlers(handlerOrURL);
        if (handlers.length === 0) {
          throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);
        } else if (handlers.length > 1) {
          throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);
        }
        handlerOrURL = handlers[0];
      }
      if (handlerOrURL.save == null) {
        throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
      }
      return handlerOrURL.save(this.artifacts);
    }
    predict(inputs, config) {
      return this.execute(inputs, this.outputNodes);
    }
    normalizeInputs(inputs) {
      if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {
        return inputs;
      }
      inputs = Array.isArray(inputs) ? inputs : [inputs];
      if (inputs.length !== this.inputNodes.length) {
        throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);
      }
      return this.inputNodes.reduce((map, inputName, i) => {
        map[inputName] = inputs[i];
        return map;
      }, {});
    }
    normalizeOutputs(outputs) {
      outputs = outputs || this.outputNodes;
      return !Array.isArray(outputs) ? [outputs] : outputs;
    }
    execute(inputs, outputs) {
      inputs = this.normalizeInputs(inputs);
      outputs = this.normalizeOutputs(outputs);
      const result = this.executor.execute(inputs, outputs);
      return result.length > 1 ? result : result[0];
    }
    async executeAsync(inputs, outputs) {
      inputs = this.normalizeInputs(inputs);
      outputs = this.normalizeOutputs(outputs);
      const result = await this.executor.executeAsync(inputs, outputs);
      return result.length > 1 ? result : result[0];
    }
    convertTensorMapToTensorsMap(map) {
      return Object.keys(map).reduce((newMap, key) => {
        newMap[key] = [map[key]];
        return newMap;
      }, {});
    }
    dispose() {
      this.executor.dispose();
      if (this.initializer) {
        this.initializer.dispose();
      }
      this.resourceManager.dispose();
    }
  };
  async function loadGraphModel(modelUrl, options = {}) {
    if (modelUrl == null) {
      throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");
    }
    if (options == null) {
      options = {};
    }
    if (options.fromTFHub) {
      if (modelUrl.load == null) {
        if (!modelUrl.endsWith("/")) {
          modelUrl = modelUrl + "/";
        }
        modelUrl = `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;
      }
    }
    const model2 = new GraphModel(modelUrl, options);
    await model2.load();
    return model2;
  }
  /** @license See the LICENSE file. */
  var version3 = "3.3.0";
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var dist_exports = {};
  __export2(dist_exports, {
    CSVDataset: () => CSVDataset,
    Dataset: () => Dataset,
    FileDataSource: () => FileDataSource,
    TextLineDataset: () => TextLineDataset,
    URLDataSource: () => URLDataSource,
    array: () => array,
    csv: () => csv,
    func: () => func,
    generator: () => generator,
    microphone: () => microphone,
    version_data: () => version4,
    webcam: () => webcam,
    zip: () => zip
  });
  var seedrandom3 = __toModule(require_seedrandom4());
  var seedrandom2 = __toModule(require_seedrandom4());
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  function deepMap(input2, mapFn) {
    return deepMapInternal(input2, mapFn);
  }
  function deepMapInternal(input2, mapFn, seen = new Map(), containedIn = new Set()) {
    if (input2 == null) {
      return null;
    }
    if (containedIn.has(input2)) {
      throw new Error("Circular references are not supported.");
    }
    if (seen.has(input2)) {
      return seen.get(input2);
    }
    const result = mapFn(input2);
    if (result.recurse && result.value !== null) {
      throw new Error("A deep map function may not return both a value and recurse=true.");
    }
    if (!result.recurse) {
      seen.set(input2, result.value);
      return result.value;
    } else if (isIterable2(input2)) {
      const mappedIterable = Array.isArray(input2) ? [] : {};
      containedIn.add(input2);
      for (const k in input2) {
        const child = input2[k];
        const childResult = deepMapInternal(child, mapFn, seen, containedIn);
        mappedIterable[k] = childResult;
      }
      containedIn.delete(input2);
      return mappedIterable;
    } else {
      throw new Error(`Can't recurse into non-iterable type: ${input2}`);
    }
  }
  function deepZip(inputs, zipFn = zipToList) {
    return deepZipInternal(inputs, zipFn);
  }
  function deepZipInternal(inputs, zipFn, containedIn = new Set()) {
    const input2 = inputs[0];
    if (containedIn.has(input2)) {
      throw new Error("Circular references are not supported.");
    }
    const result = zipFn(inputs);
    if (result.recurse && result.value !== null) {
      throw new Error("A deep zip function may not return both a value and recurse=true.");
    }
    if (!result.recurse) {
      return result.value;
    } else if (isIterable2(input2)) {
      const mappedIterable = Array.isArray(input2) ? [] : {};
      containedIn.add(input2);
      for (const k in input2) {
        const children = inputs.map((x) => x[k]);
        const childResult = deepZipInternal(children, zipFn, containedIn);
        mappedIterable[k] = childResult;
      }
      containedIn.delete(input2);
      return mappedIterable;
    } else {
      throw new Error(`Can't recurse into non-iterable type: ${input2}`);
    }
  }
  function zipToList(x) {
    if (x === null) {
      return null;
    }
    if (isIterable2(x[0])) {
      return {value: null, recurse: true};
    } else {
      return {value: x, recurse: false};
    }
  }
  async function deepMapAndAwaitAll(input2, mapFn) {
    const seen = new Map();
    deepMapInternal(input2, mapFn, seen);
    for (const key of Array.from(seen.keys())) {
      const value = seen.get(key);
      if (util_exports.isPromise(value)) {
        const mappedValue = await value;
        seen.set(key, mappedValue);
      }
    }
    const result = deepMapInternal(input2, mapFn, seen);
    return result;
  }
  function isIterable2(obj) {
    return obj != null && !ArrayBuffer.isView(obj) && (Array.isArray(obj) || typeof obj === "object" && !(obj instanceof Tensor));
  }
  function canTensorify(obj) {
    return obj == null || isPrimitive(obj) || Array.isArray(obj) || typeof obj === "object" && obj instanceof Tensor || util_exports.isTypedArray(obj);
  }
  function isPrimitive(value) {
    return value === null || typeof value !== "object" && typeof value !== "function";
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  function deepClone(container) {
    return deepMap(container, cloneIfTensor);
  }
  function cloneIfTensor(item) {
    if (item instanceof Tensor) {
      return {value: item.clone(), recurse: false};
    } else if (isIterable2(item)) {
      return {value: null, recurse: true};
    } else {
      return {value: item, recurse: false};
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  var RingBuffer = class {
    constructor(capacity) {
      this.capacity = capacity;
      this.begin = 0;
      this.end = 0;
      if (capacity == null) {
        throw new RangeError("Can't create a ring buffer of unknown capacity.");
      }
      if (capacity < 1) {
        throw new RangeError("Can't create ring buffer of capacity < 1.");
      }
      this.data = new Array(capacity);
      this.doubledCapacity = 2 * capacity;
    }
    wrap(index) {
      while (index < 0) {
        index += this.doubledCapacity;
      }
      return index % this.doubledCapacity;
    }
    get(index) {
      if (index < 0) {
        throw new RangeError("Can't get item at a negative index.");
      }
      return this.data[index % this.capacity];
    }
    set(index, value) {
      if (index < 0) {
        throw new RangeError("Can't set item at a negative index.");
      }
      this.data[index % this.capacity] = value;
    }
    length() {
      let length = this.end - this.begin;
      if (length < 0) {
        length = this.doubledCapacity + length;
      }
      return length;
    }
    isFull() {
      return this.length() === this.capacity;
    }
    isEmpty() {
      return this.length() === 0;
    }
    push(value) {
      if (this.isFull()) {
        throw new RangeError("Ring buffer is full.");
      }
      this.set(this.end, value);
      this.end = this.wrap(this.end + 1);
    }
    pushAll(values) {
      for (const value of values) {
        this.push(value);
      }
    }
    pop() {
      if (this.isEmpty()) {
        throw new RangeError("Ring buffer is empty.");
      }
      this.end = this.wrap(this.end - 1);
      const result = this.get(this.end);
      this.set(this.end, void 0);
      return result;
    }
    unshift(value) {
      if (this.isFull()) {
        throw new RangeError("Ring buffer is full.");
      }
      this.begin = this.wrap(this.begin - 1);
      this.set(this.begin, value);
    }
    shift() {
      if (this.isEmpty()) {
        throw new RangeError("Ring buffer is empty.");
      }
      const result = this.get(this.begin);
      this.set(this.begin, void 0);
      this.begin = this.wrap(this.begin + 1);
      return result;
    }
    shuffleExcise(relativeIndex) {
      if (this.isEmpty()) {
        throw new RangeError("Ring buffer is empty.");
      }
      const index = this.wrap(this.begin + relativeIndex);
      const result = this.get(index);
      this.set(index, this.pop());
      return result;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  var GrowingRingBuffer = class extends RingBuffer {
    constructor() {
      super(GrowingRingBuffer.INITIAL_CAPACITY);
    }
    isFull() {
      return false;
    }
    push(value) {
      if (super.isFull()) {
        this.expand();
      }
      super.push(value);
    }
    unshift(value) {
      if (super.isFull()) {
        this.expand();
      }
      super.unshift(value);
    }
    expand() {
      const newCapacity = this.capacity * 2;
      const newData = new Array(newCapacity);
      const len = this.length();
      for (let i = 0; i < len; i++) {
        newData[i] = this.get(this.wrap(this.begin + i));
      }
      this.data = newData;
      this.capacity = newCapacity;
      this.doubledCapacity = 2 * this.capacity;
      this.begin = 0;
      this.end = len;
    }
  };
  GrowingRingBuffer.INITIAL_CAPACITY = 32;
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  function iteratorFromItems(items) {
    return new ArrayIterator(items);
  }
  function iteratorFromFunction(func2) {
    return new FunctionCallIterator(func2);
  }
  function iteratorFromConcatenated(baseIterators, baseErrorHandler) {
    return new ChainedIterator(baseIterators, baseErrorHandler);
  }
  function iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {
    return new ZipIterator(iterators, mismatchMode);
  }
  var LazyIterator = class {
    async toArray() {
      const result = [];
      let x = await this.next();
      while (!x.done) {
        result.push(x.value);
        x = await this.next();
      }
      return result;
    }
    async toArrayForTest() {
      const stream = this.prefetch(100);
      const result = [];
      let x = await stream.next();
      while (!x.done) {
        result.push(x.value);
        x = await stream.next();
      }
      return result;
    }
    async resolveFully() {
      let x = await this.next();
      while (!x.done) {
        x = await this.next();
      }
    }
    async resolveWhile(predicate) {
      let x = await this.next();
      let shouldContinue = predicate(x.value);
      while (!x.done && shouldContinue) {
        x = await this.next();
        shouldContinue = predicate(x.value);
      }
    }
    handleErrors(handler) {
      return new ErrorHandlingLazyIterator(this, handler);
    }
    filter(predicate) {
      return new FilterIterator(this, predicate);
    }
    map(transform4) {
      return new MapIterator(this, transform4);
    }
    mapAsync(transform4) {
      return new AsyncMapIterator(this, transform4);
    }
    serialMapAsync(transform4) {
      return new AsyncMapIterator(this, transform4).serial();
    }
    flatmap(transform4) {
      return new FlatmapIterator(this, transform4);
    }
    async forEachAsync(f) {
      return this.map(f).resolveFully();
    }
    async serialForEach(f) {
      return this.serialMapAsync(f).resolveWhile((x) => x === true);
    }
    rowMajorBatch(batchSize, smallLastBatch = true) {
      return new RowMajorBatchIterator(this, batchSize, smallLastBatch);
    }
    columnMajorBatch(batchSize, smallLastBatch = true, zipFn = zipToList) {
      const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);
      return rowBatches.map((x) => deepZip(x, zipFn));
    }
    concatenate(iterator, baseErrorHandler) {
      return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);
    }
    take(count2) {
      if (count2 < 0 || count2 == null) {
        return this;
      }
      return new TakeIterator(this, count2);
    }
    skip(count2) {
      if (count2 < 0 || count2 == null) {
        return this;
      }
      return new SkipIterator(this, count2);
    }
    prefetch(bufferSize) {
      return new PrefetchIterator(this, bufferSize);
    }
    shuffle(windowSize, seed) {
      return new ShuffleIterator(this, windowSize, seed);
    }
    serial() {
      return new SerialIterator(this);
    }
  };
  var ArrayIterator = class extends LazyIterator {
    constructor(items) {
      super();
      this.items = items;
      this.trav = 0;
    }
    summary() {
      return `Array of ${this.items.length} items`;
    }
    async next() {
      if (this.trav >= this.items.length) {
        return {value: null, done: true};
      }
      const item = this.items[this.trav];
      this.trav++;
      return {value: deepClone(item), done: false};
    }
  };
  var FunctionCallIterator = class extends LazyIterator {
    constructor(nextFn) {
      super();
      this.nextFn = nextFn;
    }
    summary() {
      return `Function call`;
    }
    async next() {
      try {
        return this.nextFn();
      } catch (e) {
        e.message = `Error thrown while iterating through a dataset: ${e.message}`;
        throw e;
      }
    }
  };
  var SerialIterator = class extends LazyIterator {
    constructor(upstream) {
      super();
      this.upstream = upstream;
      this.lastRead = Promise.resolve({value: null, done: false});
    }
    summary() {
      return `${this.upstream.summary()} -> Serial`;
    }
    async next() {
      this.lastRead = this.lastRead.then(() => this.serialNext());
      return this.lastRead;
    }
    async serialNext() {
      return this.upstream.next();
    }
  };
  var SkipIterator = class extends LazyIterator {
    constructor(upstream, maxCount) {
      super();
      this.upstream = upstream;
      this.maxCount = maxCount;
      this.count = 0;
      this.lastRead = Promise.resolve({value: null, done: false});
    }
    summary() {
      return `${this.upstream.summary()} -> Skip`;
    }
    async next() {
      this.lastRead = this.lastRead.then(() => this.serialNext());
      return this.lastRead;
    }
    async serialNext() {
      while (this.count++ < this.maxCount) {
        const skipped = await this.upstream.next();
        if (skipped.done) {
          return skipped;
        }
        dispose(skipped.value);
      }
      return this.upstream.next();
    }
  };
  var TakeIterator = class extends LazyIterator {
    constructor(upstream, maxCount) {
      super();
      this.upstream = upstream;
      this.maxCount = maxCount;
      this.count = 0;
    }
    summary() {
      return `${this.upstream.summary()} -> Take`;
    }
    async next() {
      if (this.count++ >= this.maxCount) {
        return {value: null, done: true};
      }
      return this.upstream.next();
    }
  };
  var RowMajorBatchIterator = class extends LazyIterator {
    constructor(upstream, batchSize, enableSmallLastBatch = true) {
      super();
      this.upstream = upstream;
      this.batchSize = batchSize;
      this.enableSmallLastBatch = enableSmallLastBatch;
      this.lastRead = Promise.resolve({value: null, done: false});
    }
    summary() {
      return `${this.upstream.summary()} -> RowMajorBatch`;
    }
    async next() {
      this.lastRead = this.lastRead.then(() => this.serialNext());
      return this.lastRead;
    }
    async serialNext() {
      const batch = [];
      while (batch.length < this.batchSize) {
        const item = await this.upstream.next();
        if (item.done) {
          if (this.enableSmallLastBatch && batch.length > 0) {
            return {value: batch, done: false};
          }
          return {value: null, done: true};
        }
        batch.push(item.value);
      }
      return {value: batch, done: false};
    }
  };
  var FilterIterator = class extends LazyIterator {
    constructor(upstream, predicate) {
      super();
      this.upstream = upstream;
      this.predicate = predicate;
      this.lastRead = Promise.resolve({value: null, done: false});
    }
    summary() {
      return `${this.upstream.summary()} -> Filter`;
    }
    async next() {
      this.lastRead = this.lastRead.then(() => this.serialNext());
      return this.lastRead;
    }
    async serialNext() {
      while (true) {
        const item = await this.upstream.next();
        if (item.done || this.predicate(item.value)) {
          return item;
        }
        dispose(item.value);
      }
    }
  };
  var MapIterator = class extends LazyIterator {
    constructor(upstream, transform4) {
      super();
      this.upstream = upstream;
      this.transform = transform4;
    }
    summary() {
      return `${this.upstream.summary()} -> Map`;
    }
    async next() {
      const item = await this.upstream.next();
      if (item.done) {
        return {value: null, done: true};
      }
      const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
      const mapped = this.transform(item.value);
      const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);
      for (const t of inputTensors) {
        if (!tensor_util_exports.isTensorInList(t, outputTensors)) {
          t.dispose();
        }
      }
      return {value: mapped, done: false};
    }
  };
  var ErrorHandlingLazyIterator = class extends LazyIterator {
    constructor(upstream, handler) {
      super();
      this.upstream = upstream;
      this.handler = handler;
      this.count = 0;
      this.lastRead = Promise.resolve({value: null, done: false});
    }
    summary() {
      return `${this.upstream.summary()} -> handleErrors`;
    }
    async next() {
      this.lastRead = this.lastRead.then(() => this.serialNext());
      return this.lastRead;
    }
    async serialNext() {
      while (true) {
        try {
          return await this.upstream.next();
        } catch (e) {
          if (!this.handler(e)) {
            return {value: null, done: true};
          }
        }
      }
    }
  };
  var AsyncMapIterator = class extends LazyIterator {
    constructor(upstream, transform4) {
      super();
      this.upstream = upstream;
      this.transform = transform4;
    }
    summary() {
      return `${this.upstream.summary()} -> AsyncMap`;
    }
    async next() {
      const item = await this.upstream.next();
      if (item.done) {
        return {value: null, done: true};
      }
      const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
      const mapped = await this.transform(item.value);
      const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);
      for (const t of inputTensors) {
        if (!tensor_util_exports.isTensorInList(t, outputTensors)) {
          t.dispose();
        }
      }
      return {value: mapped, done: false};
    }
  };
  var OneToManyIterator = class extends LazyIterator {
    constructor() {
      super();
      this.outputQueue = new GrowingRingBuffer();
      this.lastRead = Promise.resolve({value: null, done: false});
    }
    async next() {
      this.lastRead = this.lastRead.then(() => this.serialNext());
      return this.lastRead;
    }
    async serialNext() {
      while (this.outputQueue.length() === 0) {
        if (!await this.pump()) {
          return {value: null, done: true};
        }
      }
      return {value: this.outputQueue.shift(), done: false};
    }
  };
  var FlatmapIterator = class extends OneToManyIterator {
    constructor(upstream, transform4) {
      super();
      this.upstream = upstream;
      this.transform = transform4;
    }
    summary() {
      return `${this.upstream.summary()} -> Flatmap`;
    }
    async pump() {
      const item = await this.upstream.next();
      if (item.done) {
        return false;
      }
      const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
      const mappedArray = this.transform(item.value);
      const outputTensors = tensor_util_exports.getTensorsInContainer(mappedArray);
      this.outputQueue.pushAll(mappedArray);
      for (const t of inputTensors) {
        if (!tensor_util_exports.isTensorInList(t, outputTensors)) {
          t.dispose();
        }
      }
      return true;
    }
  };
  var ChainedIterator = class extends LazyIterator {
    constructor(iterators, baseErrorHandler) {
      super();
      this.baseErrorHandler = baseErrorHandler;
      this.lastRead = null;
      this.iterator = null;
      this.moreIterators = iterators;
    }
    summary() {
      const upstreamSummaries = "TODO: fill in upstream of chained summaries";
      return `${upstreamSummaries} -> Chained`;
    }
    async next() {
      this.lastRead = this.readFromChain(this.lastRead);
      return this.lastRead;
    }
    async readFromChain(lastRead) {
      await lastRead;
      if (this.iterator == null) {
        const iteratorResult = await this.moreIterators.next();
        if (iteratorResult.done) {
          return {value: null, done: true};
        }
        this.iterator = iteratorResult.value;
        if (this.baseErrorHandler != null) {
          this.iterator = this.iterator.handleErrors(this.baseErrorHandler);
        }
      }
      const itemResult = await this.iterator.next();
      if (itemResult.done) {
        this.iterator = null;
        return this.readFromChain(lastRead);
      }
      return itemResult;
    }
  };
  var ZipMismatchMode;
  (function(ZipMismatchMode2) {
    ZipMismatchMode2[ZipMismatchMode2["FAIL"] = 0] = "FAIL";
    ZipMismatchMode2[ZipMismatchMode2["SHORTEST"] = 1] = "SHORTEST";
    ZipMismatchMode2[ZipMismatchMode2["LONGEST"] = 2] = "LONGEST";
  })(ZipMismatchMode || (ZipMismatchMode = {}));
  var ZipIterator = class extends LazyIterator {
    constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {
      super();
      this.iterators = iterators;
      this.mismatchMode = mismatchMode;
      this.count = 0;
      this.currentPromise = null;
    }
    summary() {
      const upstreamSummaries = "TODO: fill in upstream of zip summaries";
      return `{${upstreamSummaries}} -> Zip`;
    }
    async nextState(afterState) {
      await afterState;
      let numIterators = 0;
      let iteratorsDone = 0;
      function getNext(container) {
        if (container instanceof LazyIterator) {
          const result = container.next();
          return {
            value: result.then((x) => {
              numIterators++;
              if (x.done) {
                iteratorsDone++;
              }
              return x.value;
            }),
            recurse: false
          };
        } else {
          return {value: null, recurse: true};
        }
      }
      const mapped = await deepMapAndAwaitAll(this.iterators, getNext);
      if (numIterators === iteratorsDone) {
        return {value: null, done: true};
      }
      if (iteratorsDone > 0) {
        switch (this.mismatchMode) {
          case ZipMismatchMode.FAIL:
            throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);
          case ZipMismatchMode.SHORTEST:
            return {value: null, done: true};
          case ZipMismatchMode.LONGEST:
          default:
        }
      }
      this.count++;
      return {value: mapped, done: false};
    }
    async next() {
      this.currentPromise = this.nextState(this.currentPromise);
      return this.currentPromise;
    }
  };
  var PrefetchIterator = class extends LazyIterator {
    constructor(upstream, bufferSize) {
      super();
      this.upstream = upstream;
      this.bufferSize = bufferSize;
      this.buffer = new RingBuffer(bufferSize);
    }
    summary() {
      return `${this.upstream.summary()} -> Prefetch`;
    }
    refill() {
      while (!this.buffer.isFull()) {
        const v = this.upstream.next();
        this.buffer.push(v);
      }
    }
    next() {
      this.refill();
      return this.buffer.shift();
    }
  };
  var ShuffleIterator = class extends PrefetchIterator {
    constructor(upstream, windowSize, seed) {
      super(upstream, windowSize);
      this.upstream = upstream;
      this.windowSize = windowSize;
      this.upstreamExhausted = false;
      this.random = seedrandom2.alea(seed || util_exports.now().toString());
      this.lastRead = Promise.resolve({value: null, done: false});
    }
    async next() {
      this.lastRead = this.lastRead.then(() => this.serialNext());
      return this.lastRead;
    }
    randomInt(max6) {
      return Math.floor(this.random() * max6);
    }
    chooseIndex() {
      return this.randomInt(this.buffer.length());
    }
    async serialNext() {
      if (!this.upstreamExhausted) {
        this.refill();
      }
      while (!this.buffer.isEmpty()) {
        const chosenIndex = this.chooseIndex();
        const result = await this.buffer.shuffleExcise(chosenIndex);
        if (result.done) {
          this.upstreamExhausted = true;
        } else {
          this.refill();
          return result;
        }
      }
      return {value: null, done: true};
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  var Dataset = class {
    constructor() {
      this.size = null;
    }
    batch(batchSize, smallLastBatch = true) {
      const base2 = this;
      util_exports.assert(batchSize > 0, () => `batchSize needs to be positive, but it is
      ${batchSize}`);
      let size;
      if (this.size === Infinity || this.size == null) {
        size = this.size;
      } else if (smallLastBatch) {
        size = Math.ceil(this.size / batchSize);
      } else {
        size = Math.floor(this.size / batchSize);
      }
      return datasetFromIteratorFn(async () => {
        return (await base2.iterator()).columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);
      }, size);
    }
    concatenate(dataset) {
      const base2 = this;
      let size;
      if (this.size === Infinity || dataset.size === Infinity) {
        size = Infinity;
      } else if (this.size != null && dataset.size != null) {
        size = this.size + dataset.size;
      } else {
        size = null;
      }
      return datasetFromIteratorFn(async () => (await base2.iterator()).concatenate(await dataset.iterator()), size);
    }
    filter(predicate) {
      const base2 = this;
      let size;
      if (this.size === Infinity) {
        size = Infinity;
      } else {
        size = null;
      }
      return datasetFromIteratorFn(async () => {
        return (await base2.iterator()).filter((x) => tidy(() => predicate(x)));
      }, size);
    }
    async forEachAsync(f) {
      return (await this.iterator()).forEachAsync(f);
    }
    map(transform4) {
      const base2 = this;
      return datasetFromIteratorFn(async () => {
        return (await base2.iterator()).map((x) => tidy(() => transform4(x)));
      }, this.size);
    }
    mapAsync(transform4) {
      const base2 = this;
      return datasetFromIteratorFn(async () => {
        return (await base2.iterator()).mapAsync(transform4);
      }, this.size);
    }
    prefetch(bufferSize) {
      if (bufferSize == null) {
        throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");
      }
      const base2 = this;
      return datasetFromIteratorFn(async () => (await base2.iterator()).prefetch(bufferSize), this.size);
    }
    repeat(count2) {
      const base2 = this;
      let size;
      if (this.size != null && count2 > 0) {
        size = this.size * count2;
      } else if (count2 === 0) {
        size = 0;
      } else if (this.size != null && (count2 === void 0 || count2 < 0)) {
        size = Infinity;
      } else {
        size = null;
      }
      return datasetFromIteratorFn(async () => {
        const iteratorIterator = iteratorFromFunction(async () => ({value: await base2.iterator(), done: false}));
        return iteratorFromConcatenated(iteratorIterator.take(count2));
      }, size);
    }
    skip(count2) {
      const base2 = this;
      let size;
      if (this.size != null && count2 >= 0 && this.size >= count2) {
        size = this.size - count2;
      } else if (this.size != null && (this.size < count2 || count2 === void 0 || count2 < 0)) {
        size = 0;
      } else {
        size = null;
      }
      return datasetFromIteratorFn(async () => (await base2.iterator()).skip(count2), size);
    }
    shuffle(bufferSize, seed, reshuffleEachIteration = true) {
      if (bufferSize == null || bufferSize < 0) {
        if (this.size == null) {
          throw new RangeError("`Dataset.shuffle()` requires bufferSize to be specified.");
        } else {
          throw new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);
        }
      }
      const base2 = this;
      const random = seedrandom3.alea(seed || util_exports.now().toString());
      return datasetFromIteratorFn(async () => {
        let seed2 = random.int32();
        if (reshuffleEachIteration) {
          seed2 += random.int32();
        }
        return (await base2.iterator()).shuffle(bufferSize, seed2.toString());
      }, this.size);
    }
    take(count2) {
      const base2 = this;
      let size;
      if (this.size != null && this.size > count2) {
        size = count2;
      } else if (this.size != null && this.size <= count2) {
        size = this.size;
      } else {
        size = null;
      }
      return datasetFromIteratorFn(async () => (await base2.iterator()).take(count2), size);
    }
    async toArray() {
      if (this.size === Infinity) {
        throw new Error("Can not convert infinite data stream to array.");
      }
      return (await this.iterator()).toArray();
    }
    async toArrayForTest() {
      if (this.size === Infinity) {
        throw new Error("Can not convert infinite data stream to array.");
      }
      return (await this.iterator()).toArrayForTest();
    }
  };
  Dataset.MAX_BUFFER_SIZE = 1e4;
  function datasetFromIteratorFn(iteratorFn, size = null) {
    return new class extends Dataset {
      constructor() {
        super(...arguments);
        this.size = size;
      }
      async iterator() {
        return iteratorFn();
      }
    }();
  }
  function array(items) {
    return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);
  }
  function zip(datasets) {
    if (!isIterable2(datasets)) {
      throw new Error("The argument to zip() must be an object or array.");
    }
    let size;
    if (Array.isArray(datasets)) {
      for (let i = 0; i < datasets.length; i++) {
        size = size == null ? datasets[i].size : Math.min(size, datasets[i].size);
      }
    } else if (datasets instanceof Object) {
      for (const ds in datasets) {
        size = size == null ? datasets[ds].size : Math.min(size, datasets[ds].size);
      }
    }
    return datasetFromIteratorFn(async () => {
      const streams = await deepMapAndAwaitAll(datasets, (d) => {
        if (d instanceof Dataset) {
          return {value: d.iterator(), recurse: false};
        } else if (isIterable2(d)) {
          return {value: null, recurse: true};
        } else {
          throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.");
        }
      });
      return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);
    }, size);
  }
  function deepBatchConcat(rows) {
    if (rows === null) {
      return null;
    }
    const exampleRow = rows[0];
    if (canTensorify(exampleRow)) {
      const value = batchConcat(rows);
      return {value, recurse: false};
    }
    return {value: null, recurse: true};
  }
  function batchConcat(arrays) {
    if (arrays.length === 0) {
      throw new Error("Can't make a batch of zero elements.");
    }
    if (arrays[0] instanceof Tensor) {
      return stack(arrays);
    } else {
      return tensor(arrays);
    }
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  var TextLineDataset = class extends Dataset {
    constructor(input2) {
      super();
      this.input = input2;
    }
    async iterator() {
      const inputIterator = await this.input.iterator();
      const utf8Iterator = inputIterator.decodeUTF8();
      const lineIterator = utf8Iterator.split("\n").map((line) => {
        if (line.endsWith("\r")) {
          line = line.slice(0, -1);
        }
        return line;
      });
      return lineIterator;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  var CODE_QUOTE = '"';
  var STATE_OUT = Symbol("out");
  var STATE_FIELD = Symbol("field");
  var STATE_QUOTE = Symbol("quote");
  var STATE_QUOTE_AFTER_QUOTE = Symbol("quoteafterquote");
  var STATE_WITHIN_QUOTE_IN_QUOTE = Symbol("quoteinquote");
  var CSVDataset = class extends Dataset {
    constructor(input2, csvConfig) {
      super();
      this.input = input2;
      this.hasHeader = true;
      this.fullColumnNames = null;
      this.columnNamesValidated = false;
      this.columnConfigs = null;
      this.configuredColumnsOnly = false;
      this.delimiter = ",";
      this.delimWhitespace = false;
      this.base = new TextLineDataset(input2);
      if (!csvConfig) {
        csvConfig = {};
      }
      this.hasHeader = csvConfig.hasHeader === false ? false : true;
      this.fullColumnNames = csvConfig.columnNames;
      this.columnConfigs = csvConfig.columnConfigs;
      this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;
      if (csvConfig.delimWhitespace) {
        util_exports.assert(csvConfig.delimiter == null, () => "Delimiter should not be provided when delimWhitespace is true.");
        this.delimWhitespace = true;
        this.delimiter = " ";
      } else {
        this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ",";
      }
    }
    async columnNames() {
      if (!this.columnNamesValidated) {
        await this.setColumnNames();
      }
      return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) : this.fullColumnNames;
    }
    async setColumnNames() {
      const columnNamesFromFile = await this.maybeReadHeaderLine();
      if (!this.fullColumnNames && !columnNamesFromFile) {
        throw new Error("Column names must be provided if there is no header line.");
      } else if (this.fullColumnNames && columnNamesFromFile) {
        util_exports.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => "The length of provided columnNames (" + this.fullColumnNames.length.toString() + ") does not match the length of the header line read from file (" + columnNamesFromFile.length.toString() + ").");
      }
      if (!this.fullColumnNames) {
        this.fullColumnNames = columnNamesFromFile;
      }
      const counts = this.fullColumnNames.reduce((countAcc, name) => {
        countAcc[name] = countAcc[name] + 1 || 1;
        return countAcc;
      }, {});
      const duplicateNames = Object.keys(counts).filter((name) => counts[name] > 1);
      util_exports.assert(duplicateNames.length === 0, () => "Duplicate column names found: " + duplicateNames.toString());
      if (this.columnConfigs) {
        for (const key of Object.keys(this.columnConfigs)) {
          const index = this.fullColumnNames.indexOf(key);
          if (index === -1) {
            throw new Error('The key "' + key + '" provided in columnConfigs does not match any of the column names (' + this.fullColumnNames.toString() + ").");
          }
        }
      }
      this.columnNamesValidated = true;
    }
    async maybeReadHeaderLine() {
      if (this.hasHeader) {
        const iter = await this.base.iterator();
        const firstElement = await iter.next();
        if (firstElement.done) {
          throw new Error("No data was found for CSV parsing.");
        }
        const firstLine = firstElement.value;
        const headers = this.parseRow(firstLine, false);
        return headers;
      } else {
        return null;
      }
    }
    async iterator() {
      if (!this.columnNamesValidated) {
        await this.setColumnNames();
      }
      let lines = await this.base.iterator();
      if (this.hasHeader) {
        lines = lines.skip(1);
      }
      return lines.map((x) => this.makeDataElement(x));
    }
    makeDataElement(line) {
      const values = this.parseRow(line);
      const features = {};
      const labels = {};
      for (let i = 0; i < this.fullColumnNames.length; i++) {
        const key = this.fullColumnNames[i];
        const config = this.columnConfigs ? this.columnConfigs[key] : null;
        if (this.configuredColumnsOnly && !config) {
          continue;
        } else {
          const value = values[i];
          let parsedValue = null;
          if (value === "") {
            if (config && config.default !== void 0) {
              parsedValue = config.default;
            } else if (config && (config.required || config.isLabel)) {
              throw new Error(`Required column ${key} is empty in this line: ${line}`);
            } else {
              parsedValue = void 0;
            }
          } else {
            const valueAsNum = Number(value);
            if (isNaN(valueAsNum)) {
              if (config && config.dtype === "bool") {
                parsedValue = this.getBoolean(value);
              } else {
                parsedValue = value;
              }
            } else if (!config || !config.dtype) {
              parsedValue = valueAsNum;
            } else {
              switch (config.dtype) {
                case "float32":
                  parsedValue = valueAsNum;
                  break;
                case "int32":
                  parsedValue = Math.floor(valueAsNum);
                  break;
                case "bool":
                  parsedValue = this.getBoolean(value);
                  break;
                default:
                  parsedValue = valueAsNum;
              }
            }
          }
          config && config.isLabel ? labels[key] = parsedValue : features[key] = parsedValue;
        }
      }
      if (Object.keys(labels).length === 0) {
        return features;
      } else {
        return {xs: features, ys: labels};
      }
    }
    getBoolean(value) {
      if (value === "1" || value.toLowerCase() === "true") {
        return 1;
      } else {
        return 0;
      }
    }
    parseRow(line, validateElementCount = true) {
      const result = [];
      let readOffset = 0;
      const readLength = line.length;
      let currentState = STATE_OUT;
      for (let i = 0; i < readLength; i++) {
        switch (currentState) {
          case STATE_OUT:
            switch (line.charAt(i)) {
              case CODE_QUOTE:
                readOffset = i + 1;
                currentState = STATE_QUOTE;
                break;
              case this.delimiter:
                readOffset = i + 1;
                if (this.delimiter === " " && this.delimWhitespace) {
                  break;
                }
                result.push("");
                currentState = STATE_OUT;
                break;
              default:
                currentState = STATE_FIELD;
                readOffset = i;
                break;
            }
            break;
          case STATE_FIELD:
            switch (line.charAt(i)) {
              case this.delimiter:
                result.push(line.substring(readOffset, i));
                currentState = STATE_OUT;
                readOffset = i + 1;
                break;
              default:
            }
            break;
          case STATE_QUOTE:
            switch (line.charAt(i)) {
              case CODE_QUOTE:
                currentState = STATE_QUOTE_AFTER_QUOTE;
                break;
              default:
            }
            break;
          case STATE_QUOTE_AFTER_QUOTE:
            switch (line.charAt(i)) {
              case this.delimiter:
                result.push(line.substring(readOffset, i - 1));
                currentState = STATE_OUT;
                readOffset = i + 1;
                break;
              case CODE_QUOTE:
                currentState = STATE_QUOTE;
                break;
              default:
                currentState = STATE_WITHIN_QUOTE_IN_QUOTE;
                break;
            }
            break;
          case STATE_WITHIN_QUOTE_IN_QUOTE:
            switch (line.charAt(i)) {
              case CODE_QUOTE:
                currentState = STATE_QUOTE;
                break;
              default:
            }
            break;
          default:
        }
      }
      if (currentState === STATE_QUOTE_AFTER_QUOTE) {
        result.push(line.substring(readOffset, readLength - 1));
      } else {
        result.push(line.substring(readOffset));
      }
      if (validateElementCount && result.length !== this.fullColumnNames.length) {
        throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);
      }
      return result;
    }
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  var MicrophoneIterator = class extends LazyIterator {
    constructor(microphoneConfig) {
      super();
      this.microphoneConfig = microphoneConfig;
      this.isClosed = false;
      this.fftSize = microphoneConfig.fftSize || 1024;
      const fftSizeLog2 = Math.log2(this.fftSize);
      if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 || !Number.isInteger(fftSizeLog2)) {
        throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);
      }
      this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;
      this.sampleRateHz = microphoneConfig.sampleRateHz;
      this.columnTruncateLength = microphoneConfig.columnTruncateLength || this.fftSize;
      this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;
      this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;
      this.includeSpectrogram = microphoneConfig.includeSpectrogram === false ? false : true;
      this.includeWaveform = microphoneConfig.includeWaveform === true ? true : false;
      if (!this.includeSpectrogram && !this.includeWaveform) {
        throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.");
      }
    }
    summary() {
      return `microphone`;
    }
    static async create(microphoneConfig = {}) {
      if (env().get("IS_NODE")) {
        throw new Error("microphone API is only supported in browser environment.");
      }
      const microphoneIterator = new MicrophoneIterator(microphoneConfig);
      await microphoneIterator.start();
      return microphoneIterator;
    }
    async start() {
      try {
        this.stream = await navigator.mediaDevices.getUserMedia({
          audio: this.audioTrackConstraints == null ? true : this.audioTrackConstraints,
          video: false
        });
      } catch (e) {
        throw new Error(`Error thrown while initializing video stream: ${e.message}`);
      }
      if (!this.stream) {
        throw new Error("Could not obtain audio from microphone.");
      }
      const ctxConstructor = window.AudioContext || window.webkitAudioContext;
      this.audioContext = new ctxConstructor();
      if (!this.sampleRateHz) {
        this.sampleRateHz = this.audioContext.sampleRate;
      } else if (this.audioContext.sampleRate !== this.sampleRateHz) {
        throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);
      }
      const streamSource = this.audioContext.createMediaStreamSource(this.stream);
      this.analyser = this.audioContext.createAnalyser();
      this.analyser.fftSize = this.fftSize * 2;
      this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;
      streamSource.connect(this.analyser);
      this.freqData = new Float32Array(this.fftSize);
      this.timeData = new Float32Array(this.fftSize);
      return;
    }
    async next() {
      if (this.isClosed) {
        return {value: null, done: true};
      }
      let spectrogramTensor;
      let waveformTensor;
      const audioDataQueue = await this.getAudioData();
      if (this.includeSpectrogram) {
        const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);
        spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);
      }
      if (this.includeWaveform) {
        const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);
        waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);
      }
      return {
        value: {spectrogram: spectrogramTensor, waveform: waveformTensor},
        done: false
      };
    }
    async capture() {
      return (await this.next()).value;
    }
    async getAudioData() {
      const freqDataQueue = [];
      const timeDataQueue = [];
      let currentFrames = 0;
      return new Promise((resolve) => {
        const intervalID = setInterval(() => {
          if (this.includeSpectrogram) {
            this.analyser.getFloatFrequencyData(this.freqData);
            if (this.freqData[0] === -Infinity) {
              resolve({freqDataQueue, timeDataQueue});
            }
            freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));
          }
          if (this.includeWaveform) {
            this.analyser.getFloatTimeDomainData(this.timeData);
            timeDataQueue.push(this.timeData.slice());
          }
          if (++currentFrames === this.numFrames) {
            clearInterval(intervalID);
            resolve({freqDataQueue, timeDataQueue});
          }
        }, this.fftSize / this.sampleRateHz * 1e3);
      });
    }
    stop() {
      if (!this.isClosed) {
        this.isClosed = true;
        this.analyser.disconnect();
        this.audioContext.close();
        if (this.stream != null && this.stream.getTracks().length > 0) {
          this.stream.getTracks()[0].stop();
        }
      }
    }
    toArray() {
      throw new Error("Can not convert infinite audio stream to array.");
    }
    getSampleRate() {
      return this.sampleRateHz;
    }
    flattenQueue(queue) {
      const frameSize = queue[0].length;
      const freqData = new Float32Array(queue.length * frameSize);
      queue.forEach((data, i) => freqData.set(data, i * frameSize));
      return freqData;
    }
    getTensorFromAudioDataArray(freqData, shape) {
      const vals = new Float32Array(util_exports.sizeFromShape(shape));
      vals.set(freqData, vals.length - freqData.length);
      return tensor(vals, shape);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  var WebcamIterator = class extends LazyIterator {
    constructor(webcamVideoElement, webcamConfig) {
      super();
      this.webcamVideoElement = webcamVideoElement;
      this.webcamConfig = webcamConfig;
      this.isClosed = true;
      this.resize = false;
      if (this.needToResize()) {
        this.resize = true;
        this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];
        this.cropBoxInd = tensor1d([0], "int32");
        if (this.webcamConfig.centerCrop) {
          const widthCroppingRatio = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width;
          const heightCroppingRatio = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height;
          const widthCropStart = (1 - widthCroppingRatio) / 2;
          const heightCropStart = (1 - heightCroppingRatio) / 2;
          const widthCropEnd = widthCropStart + widthCroppingRatio;
          const heightCropEnd = heightCroppingRatio + heightCropStart;
          this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);
        } else {
          this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);
        }
      }
    }
    summary() {
      return `webcam`;
    }
    static async create(webcamVideoElement, webcamConfig = {}) {
      if (env().get("IS_NODE")) {
        throw new Error("tf.data.webcam is only supported in browser environment.");
      }
      if (!webcamVideoElement) {
        webcamVideoElement = document.createElement("video");
        if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {
          throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");
        }
        webcamVideoElement.width = webcamConfig.resizeWidth;
        webcamVideoElement.height = webcamConfig.resizeHeight;
      }
      const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);
      await webcamIterator.start();
      return webcamIterator;
    }
    async start() {
      if (this.webcamConfig.facingMode) {
        util_exports.assert(this.webcamConfig.facingMode === "user" || this.webcamConfig.facingMode === "environment", () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);
      }
      try {
        this.stream = await navigator.mediaDevices.getUserMedia({
          video: {
            deviceId: this.webcamConfig.deviceId,
            facingMode: this.webcamConfig.facingMode ? this.webcamConfig.facingMode : "user",
            width: this.webcamVideoElement.width,
            height: this.webcamVideoElement.height
          }
        });
      } catch (e) {
        e.message = `Error thrown while initializing video stream: ${e.message}`;
        throw e;
      }
      if (!this.stream) {
        throw new Error("Could not obtain video from webcam.");
      }
      try {
        this.webcamVideoElement.srcObject = this.stream;
      } catch (error) {
        console.log(error);
        this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);
      }
      this.webcamVideoElement.play();
      this.isClosed = false;
      return new Promise((resolve) => {
        this.webcamVideoElement.onloadedmetadata = () => {
          resolve();
        };
      });
    }
    async next() {
      if (this.isClosed) {
        return {value: null, done: true};
      }
      let img;
      try {
        img = browser_exports.fromPixels(this.webcamVideoElement);
      } catch (e) {
        throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`);
      }
      if (this.resize) {
        try {
          return {value: this.cropAndResizeFrame(img), done: false};
        } catch (e) {
          throw new Error(`Error thrown cropping the video: ${e.message}`);
        } finally {
          img.dispose();
        }
      } else {
        return {value: img, done: false};
      }
    }
    needToResize() {
      if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight && (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth || this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {
        return true;
      }
      return false;
    }
    cropAndResizeFrame(img) {
      return tidy(() => {
        const expandedImage = expandDims(cast(img, "float32"), 0);
        let resizedImage;
        resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, "bilinear");
        const shape = resizedImage.shape;
        return reshape(resizedImage, shape.slice(1));
      });
    }
    async capture() {
      return (await this.next()).value;
    }
    stop() {
      const tracks = this.stream.getTracks();
      tracks.forEach((track) => track.stop());
      try {
        this.webcamVideoElement.srcObject = null;
      } catch (error) {
        console.log(error);
        this.webcamVideoElement.src = null;
      }
      this.isClosed = true;
    }
    toArray() {
      throw new Error("Can not convert infinite video stream to array.");
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  var DataSource = class {
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  var StringIterator = class extends LazyIterator {
    split(separator) {
      return new SplitIterator(this, separator);
    }
  };
  var SplitIterator = class extends StringIterator {
    constructor(upstream, separator) {
      super();
      this.upstream = upstream;
      this.impl = new SplitIteratorImpl(upstream, separator);
    }
    summary() {
      return this.impl.summary();
    }
    async next() {
      return this.impl.next();
    }
  };
  var SplitIteratorImpl = class extends OneToManyIterator {
    constructor(upstream, separator) {
      super();
      this.upstream = upstream;
      this.separator = separator;
      this.carryover = "";
    }
    summary() {
      return `${this.upstream.summary()} -> Split('${this.separator}')`;
    }
    async pump() {
      const chunkResult = await this.upstream.next();
      if (chunkResult.done) {
        if (this.carryover === "") {
          return false;
        }
        this.outputQueue.push(this.carryover);
        this.carryover = "";
        return true;
      }
      const lines = chunkResult.value.split(this.separator);
      lines[0] = this.carryover + lines[0];
      for (const line of lines.slice(0, -1)) {
        this.outputQueue.push(line);
      }
      this.carryover = lines[lines.length - 1];
      return true;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  var ByteChunkIterator = class extends LazyIterator {
    decodeUTF8() {
      return new Utf8Iterator(this);
    }
  };
  var Utf8Iterator = class extends StringIterator {
    constructor(upstream) {
      super();
      this.upstream = upstream;
      this.impl = new Utf8IteratorImpl(upstream);
    }
    summary() {
      return this.impl.summary();
    }
    async next() {
      return this.impl.next();
    }
  };
  var Utf8IteratorImpl = class extends OneToManyIterator {
    constructor(upstream) {
      super();
      this.upstream = upstream;
      if (env().get("IS_BROWSER")) {
        this.decoder = new TextDecoder("utf-8");
      } else {
        const {StringDecoder} = require_string_decoder();
        this.decoder = new StringDecoder("utf8");
      }
    }
    summary() {
      return `${this.upstream.summary()} -> Utf8`;
    }
    async pump() {
      const chunkResult = await this.upstream.next();
      let chunk;
      if (chunkResult.done) {
        return false;
      } else {
        chunk = chunkResult.value;
      }
      let text;
      if (env().get("IS_BROWSER")) {
        text = this.decoder.decode(chunk, {stream: true});
      } else {
        text = this.decoder.write(Buffer.from(chunk.buffer));
      }
      this.outputQueue.push(text);
      return true;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  var FileChunkIterator = class extends ByteChunkIterator {
    constructor(file, options = {}) {
      super();
      this.file = file;
      this.options = options;
      util_exports.assert(file instanceof Uint8Array || (env().get("IS_BROWSER") ? file instanceof File || file instanceof Blob : false), () => "FileChunkIterator only supports File, Blob and Uint8Array right now.");
      this.offset = options.offset || 0;
      this.chunkSize = options.chunkSize || 1024 * 1024;
    }
    summary() {
      return `FileChunks ${this.file}`;
    }
    async next() {
      if (this.offset >= (this.file instanceof Uint8Array ? this.file.byteLength : this.file.size)) {
        return {value: null, done: true};
      }
      const chunk = new Promise((resolve, reject) => {
        const end = this.offset + this.chunkSize;
        if (this.file instanceof Uint8Array) {
          resolve(new Uint8Array(this.file.slice(this.offset, end)));
        } else {
          const fileReader = new FileReader();
          fileReader.onload = (event) => {
            let data = fileReader.result;
            if (data instanceof ArrayBuffer) {
              data = new Uint8Array(data);
            }
            if (!(data instanceof Uint8Array)) {
              return reject(new TypeError("FileReader returned unknown type."));
            }
            resolve(data);
          };
          fileReader.onabort = (event) => {
            return reject(new Error("Aborted"));
          };
          fileReader.onerror = (event) => {
            return reject(new Error(event.type));
          };
          const slice5 = this.file.slice(this.offset, end);
          fileReader.readAsArrayBuffer(slice5);
        }
        this.offset = end;
      });
      return {value: await chunk, done: false};
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  async function urlChunkIterator(url, options = {}) {
    let urlString;
    let requestInit;
    if (typeof url === "string") {
      urlString = url;
    } else {
      urlString = url.url;
      requestInit = getRequestInitFromRequest(url);
    }
    const response = await util_exports.fetch(urlString, requestInit);
    if (response.ok) {
      const uint8Array = new Uint8Array(await response.arrayBuffer());
      return new FileChunkIterator(uint8Array, options);
    } else {
      throw new Error(response.statusText);
    }
  }
  var getRequestInitFromRequest = (request) => {
    const init2 = {
      method: request.method,
      headers: request.headers,
      body: request.body,
      mode: request.mode,
      credentials: request.credentials,
      cache: request.cache,
      redirect: request.redirect,
      referrer: request.referrer,
      integrity: request.integrity
    };
    return init2;
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  function isLocalPath(source) {
    return typeof source === "string" && source.substr(0, 7) === "file://";
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  var FileDataSource = class extends DataSource {
    constructor(input2, options = {}) {
      super();
      this.input = input2;
      this.options = options;
    }
    async iterator() {
      if (isLocalPath(this.input) && env().get("IS_NODE")) {
        const fs = require("fs");
        this.input = fs.readFileSync(this.input.substr(7));
      }
      return new FileChunkIterator(this.input, this.options);
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  var URLDataSource = class extends DataSource {
    constructor(url, fileOptions = {}) {
      super();
      this.url = url;
      this.fileOptions = fileOptions;
    }
    async iterator() {
      if (isLocalPath(this.url)) {
        return new FileDataSource(this.url, this.fileOptions).iterator();
      } else {
        return urlChunkIterator(this.url, this.fileOptions);
      }
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * =============================================================================
   */
  function csv(source, csvConfig = {}) {
    return new CSVDataset(new URLDataSource(source), csvConfig);
  }
  function func(f) {
    const iter = iteratorFromFunction(f);
    return datasetFromIteratorFn(async () => iter);
  }
  function generator(generator2) {
    return datasetFromIteratorFn(async () => {
      const gen = await generator2();
      return iteratorFromFunction(() => gen.next());
    });
  }
  async function webcam(webcamVideoElement, webcamConfig) {
    return WebcamIterator.create(webcamVideoElement, webcamConfig);
  }
  async function microphone(microphoneConfig) {
    return MicrophoneIterator.create(microphoneConfig);
  }
  /** @license See the LICENSE file. */
  var version4 = "3.3.0";
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function assertNotComplex(tensor2, opName) {
    if (!Array.isArray(tensor2)) {
      tensor2 = [tensor2];
    }
    tensor2.forEach((t) => {
      if (t != null) {
        util_exports.assert(t.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the CPU backend.`);
      }
    });
  }
  /**
   * @license
   * Copyright 2021 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var whereImpl2 = kernel_impls_exports.whereImpl;
  var MathBackendCPU = class extends KernelBackend {
    constructor() {
      super();
      this.blockSize = 48;
      this.firstUse = true;
      this.data = new DataStorage(this, engine());
    }
    nextDataId() {
      return MathBackendCPU.nextDataId++;
    }
    write(values, shape, dtype) {
      if (this.firstUse) {
        this.firstUse = false;
        if (env().get("IS_NODE")) {
          backend_util_exports.warn("\n============================\nHi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.\n============================");
        }
      }
      const dataId = {id: this.nextDataId()};
      this.data.set(dataId, {values, dtype, refCount: 1});
      return dataId;
    }
    makeTensorInfo(shape, dtype, values) {
      let outId;
      if (dtype === "string" && values != null && values.length > 0 && util_exports.isString(values[0])) {
        const encodedValues = values.map((d) => util_exports.encodeString(d));
        outId = this.write(encodedValues, shape, dtype);
      } else {
        outId = this.write(values, shape, dtype);
      }
      return {dataId: outId, shape, dtype};
    }
    refCount(dataId) {
      if (this.data.has(dataId)) {
        const tensorData = this.data.get(dataId);
        return tensorData.refCount;
      }
      return 0;
    }
    incRef(dataId) {
      const tensorData = this.data.get(dataId);
      tensorData.refCount++;
    }
    decRef(dataId) {
      if (this.data.has(dataId)) {
        const tensorData = this.data.get(dataId);
        tensorData.refCount--;
      }
    }
    move(dataId, values, shape, dtype, refCount) {
      this.data.set(dataId, {values, dtype, refCount});
    }
    numDataIds() {
      return this.data.numDataIds();
    }
    async read(dataId) {
      return this.readSync(dataId);
    }
    readSync(dataId) {
      const {dtype, complexTensorInfos} = this.data.get(dataId);
      if (dtype === "complex64") {
        const realValues = this.readSync(complexTensorInfos.real.dataId);
        const imagValues = this.readSync(complexTensorInfos.imag.dataId);
        return backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);
      }
      return this.data.get(dataId).values;
    }
    bufferSync(t) {
      const data = this.readSync(t.dataId);
      let decodedData = data;
      if (t.dtype === "string") {
        try {
          decodedData = data.map((d) => util_exports.decodeString(d));
        } catch (_a) {
          throw new Error("Failed to decode encoded string bytes into utf-8");
        }
      }
      return buffer(t.shape, t.dtype, decodedData);
    }
    makeOutput(values, shape, dtype) {
      const dataId = this.write(values, shape, dtype);
      return engine().makeTensorFromDataId(dataId, shape, dtype, this);
    }
    disposeData(dataId, force = false) {
      if (this.data.has(dataId)) {
        this.data.get(dataId).refCount--;
        if (!force && this.data.get(dataId).refCount > 0) {
          return false;
        }
        const {complexTensorInfos} = this.data.get(dataId);
        if (complexTensorInfos != null) {
          this.disposeData(complexTensorInfos.real.dataId, true);
          this.disposeData(complexTensorInfos.imag.dataId, true);
        }
        this.data.delete(dataId);
      }
      return true;
    }
    disposeIntermediateTensorInfo(tensorInfo) {
      this.disposeData(tensorInfo.dataId);
    }
    async time(f) {
      const start = util_exports.now();
      f();
      const kernelMs = util_exports.now() - start;
      return {kernelMs};
    }
    memory() {
      return {
        unreliable: true,
        reasons: ["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]
      };
    }
    where(condition) {
      assertNotComplex([condition], "where");
      const condVals = this.readSync(condition.dataId);
      return whereImpl2(condition.shape, condVals);
    }
    dispose() {
    }
    floatPrecision() {
      return 32;
    }
    epsilon() {
      return super.epsilon();
    }
  };
  MathBackendCPU.nextDataId = 0;
  var shared_exports = {};
  __export2(shared_exports, {
    addImpl: () => addImpl,
    bincountImpl: () => bincountImpl,
    bincountReduceImpl: () => bincountReduceImpl,
    ceilImpl: () => ceilImpl,
    concatImpl: () => concatImpl,
    expImpl: () => expImpl,
    expm1Impl: () => expm1Impl,
    floorImpl: () => floorImpl,
    gatherV2Impl: () => gatherV2Impl,
    greaterImpl: () => greaterImpl,
    lessImpl: () => lessImpl,
    linSpaceImpl: () => linSpaceImpl,
    logImpl: () => logImpl,
    maxImpl: () => maxImpl,
    maximumImpl: () => maximumImpl,
    minimumImpl: () => minimumImpl,
    multiplyImpl: () => multiplyImpl,
    negImpl: () => negImpl,
    notEqualImpl: () => notEqualImpl,
    prodImpl: () => prodImpl,
    rangeImpl: () => rangeImpl,
    rsqrtImpl: () => rsqrtImpl,
    simpleAbsImpl: () => simpleAbsImpl,
    sliceImpl: () => sliceImpl,
    squaredDifferenceImpl: () => squaredDifferenceImpl,
    stridedSliceImpl: () => stridedSliceImpl,
    subImpl: () => subImpl,
    tileImpl: () => tileImpl,
    topKImpl: () => topKImpl,
    transposeImpl: () => transposeImpl,
    uniqueImpl: () => uniqueImpl
  });
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function simpleAbsImpl(vals) {
    const resultValues = new Float32Array(vals.length);
    for (let i = 0; i < vals.length; ++i) {
      resultValues[i] = Math.abs(vals[i]);
    }
    return resultValues;
  }
  var abs2 = (args) => {
    const {x} = args.inputs;
    const cpuBackend = args.backend;
    assertNotComplex(x, "abs");
    let resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));
    const values = cpuBackend.data.get(x.dataId).values;
    resultValues = simpleAbsImpl(values);
    return cpuBackend.makeOutput(resultValues, x.shape, "float32");
  };
  var absConfig = {
    kernelName: Abs,
    backendName: "cpu",
    kernelFunc: abs2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function createSimpleBinaryKernelImpl(op2) {
    return (aShape, bShape, aVals, bVals, dtype) => {
      const newShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
      const resultRank = newShape.length;
      const resultStrides = util_exports.computeStrides(newShape);
      const resultSize = util_exports.sizeFromShape(newShape);
      const result = util_exports.getTypedArrayFromDType(dtype, resultSize);
      const aRank = aShape.length;
      const bRank = bShape.length;
      const aStrides = util_exports.computeStrides(aShape);
      const bStrides = util_exports.computeStrides(bShape);
      const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, newShape);
      const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, newShape);
      if (aBroadcastDims.length + bBroadcastDims.length === 0) {
        for (let i = 0; i < result.length; ++i) {
          result[i] = op2(aVals[i % aVals.length], bVals[i % bVals.length]);
        }
      } else {
        for (let i = 0; i < result.length; ++i) {
          const loc = util_exports.indexToLoc(i, resultRank, resultStrides);
          const aLoc = loc.slice(-aRank);
          aBroadcastDims.forEach((d) => aLoc[d] = 0);
          const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);
          const bLoc = loc.slice(-bRank);
          bBroadcastDims.forEach((d) => bLoc[d] = 0);
          const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);
          result[i] = op2(aVals[aIndex], bVals[bIndex]);
        }
      }
      return [result, newShape];
    };
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function complex2(args) {
    const {inputs, backend: backend2} = args;
    const {real: real4, imag: imag4} = inputs;
    const realVals = backend2.data.get(real4.dataId).values;
    const imagVals = backend2.data.get(imag4.dataId).values;
    const complexInfo = backend2.makeTensorInfo(real4.shape, "complex64");
    const complex4 = backend2.data.get(complexInfo.dataId);
    complex4.complexTensorInfos = {
      real: backend2.makeTensorInfo(real4.shape, "float32", realVals),
      imag: backend2.makeTensorInfo(imag4.shape, "float32", imagVals)
    };
    return complexInfo;
  }
  var complexConfig = {
    kernelName: Complex,
    backendName: "cpu",
    kernelFunc: complex2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function zeros3(backend2, shape, dtype = "float32") {
    if (dtype === "complex64") {
      const real4 = zeros3(backend2, shape, "float32");
      const imag4 = zeros3(backend2, shape, "float32");
      return complex2({inputs: {real: real4, imag: imag4}, backend: backend2});
    }
    const values = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape), dtype);
    return backend2.makeTensorInfo(shape, dtype, values);
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function identity2(args) {
    const {inputs, backend: backend2} = args;
    const {x} = inputs;
    backend2.incRef(x.dataId);
    return {dataId: x.dataId, shape: x.shape, dtype: x.dtype};
  }
  var identityConfig = {
    kernelName: Identity,
    backendName: "cpu",
    kernelFunc: identity2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function real2(args) {
    const {inputs, backend: backend2} = args;
    const {input: input2} = inputs;
    const real4 = backend2.data.get(input2.dataId).complexTensorInfos.real;
    const realVal = backend2.data.get(real4.dataId).values;
    return backend2.makeTensorInfo(real4.shape, real4.dtype, realVal);
  }
  var realConfig = {
    kernelName: Real,
    backendName: "cpu",
    kernelFunc: real2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function cast3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {dtype} = attrs;
    if (dtype === "complex64") {
      if (x.dtype === "complex64") {
        return identity2({inputs: {x}, backend: backend2});
      }
      const zerosTensorInfo = zeros3(backend2, x.shape, x.dtype);
      const floatX = cast3({inputs: {x}, backend: backend2, attrs: {dtype: "float32"}});
      const result = complex2({inputs: {real: floatX, imag: zerosTensorInfo}, backend: backend2});
      backend2.disposeIntermediateTensorInfo(zerosTensorInfo);
      backend2.disposeIntermediateTensorInfo(floatX);
      return result;
    }
    if (x.dtype === "complex64") {
      const realPart = real2({inputs: {input: x}, backend: backend2});
      const result = cast3({inputs: {x: realPart}, backend: backend2, attrs: {dtype}});
      backend2.disposeIntermediateTensorInfo(realPart);
      return result;
    }
    if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {
      const result = identity2({inputs: {x}, backend: backend2});
      return {dataId: result.dataId, shape: result.shape, dtype};
    }
    if (dtype === "int32") {
      const values = backend2.data.get(x.dataId).values;
      const resultValues = Int32Array.from(values);
      return backend2.makeTensorInfo(x.shape, "int32", resultValues);
    }
    if (dtype === "bool") {
      const xVals = backend2.data.get(x.dataId).values;
      const zero = util_exports.toTypedArray([0], x.dtype);
      const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => a !== b ? 1 : 0)(x.shape, [], xVals, zero, "bool");
      return backend2.makeTensorInfo(resultShape, "bool", resultData);
    }
    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
  }
  var castConfig = {
    kernelName: Cast,
    backendName: "cpu",
    kernelFunc: cast3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {
    if (complexImpl == null) {
      return ({inputs, backend: backend2}) => {
        const {a, b} = inputs;
        const cpuBackend = backend2;
        assertNotComplex([a, b], name);
        const aVals = cpuBackend.data.get(a.dataId).values;
        const bVals = cpuBackend.data.get(b.dataId).values;
        const $dtype = dtype || a.dtype;
        const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);
        return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);
      };
    }
    return ({inputs, backend: backend2}) => {
      const {a, b} = inputs;
      const cpuBackend = backend2;
      if (a.dtype === "complex64" || b.dtype === "complex64") {
        const $aComplex = cast3({inputs: {x: a}, backend: cpuBackend, attrs: {dtype: "complex64"}});
        const $aComplexVals = cpuBackend.data.get($aComplex.dataId);
        const aReal = $aComplexVals.complexTensorInfos.real;
        const aImag = $aComplexVals.complexTensorInfos.imag;
        const aRealVals = cpuBackend.data.get(aReal.dataId).values;
        const aImagVals = cpuBackend.data.get(aImag.dataId).values;
        const $bComplex = cast3({inputs: {x: b}, backend: cpuBackend, attrs: {dtype: "complex64"}});
        const $bComplexVals = cpuBackend.data.get($bComplex.dataId);
        const bReal = $bComplexVals.complexTensorInfos.real;
        const bImag = $bComplexVals.complexTensorInfos.imag;
        const bRealVals = cpuBackend.data.get(bReal.dataId).values;
        const bImagVals = cpuBackend.data.get(bImag.dataId).values;
        const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);
        const resultReal = cpuBackend.makeTensorInfo(resultShape, "float32", resultRealData);
        const resultImag = cpuBackend.makeTensorInfo(resultShape, "float32", resultImagData);
        const result = complex2({inputs: {real: resultReal, imag: resultImag}, backend: cpuBackend});
        cpuBackend.disposeIntermediateTensorInfo($aComplex);
        cpuBackend.disposeIntermediateTensorInfo($bComplex);
        cpuBackend.disposeIntermediateTensorInfo(resultReal);
        cpuBackend.disposeIntermediateTensorInfo(resultImag);
        return result;
      } else {
        const aVals = cpuBackend.data.get(a.dataId).values;
        const bVals = cpuBackend.data.get(b.dataId).values;
        const $dtype = dtype || a.dtype;
        const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);
        return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);
      }
    };
  }
  function createComplexBinaryKernelImpl(op2) {
    return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {
      const resultShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
      const resultSize = util_exports.sizeFromShape(resultShape);
      const resultRank = resultShape.length;
      const resultStrides = util_exports.computeStrides(resultShape);
      const resultRealVals = util_exports.getTypedArrayFromDType("float32", resultSize);
      const resultImagVals = util_exports.getTypedArrayFromDType("float32", resultSize);
      const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, resultShape);
      const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, resultShape);
      const aVals = backend_util_exports.mergeRealAndImagArrays(aRealVals, aImagVals);
      const bVals = backend_util_exports.mergeRealAndImagArrays(bRealVals, bImagVals);
      const aRank = aShape.length;
      const aStrides = util_exports.computeStrides(aShape);
      const bRank = bShape.length;
      const bStrides = util_exports.computeStrides(bShape);
      if (aBroadcastDims.length + bBroadcastDims.length === 0) {
        for (let i = 0; i < resultRealVals.length; i++) {
          const aIdx = i % aVals.length;
          const bIdx = i % bVals.length;
          const result = op2(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);
          resultRealVals[i] = result.real;
          resultImagVals[i] = result.imag;
        }
      } else {
        for (let i = 0; i < resultRealVals.length; i++) {
          const loc = util_exports.indexToLoc(i, resultRank, resultStrides);
          const aLoc = loc.slice(-aRank);
          aBroadcastDims.forEach((d) => aLoc[d] = 0);
          const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);
          const bLoc = loc.slice(-bRank);
          bBroadcastDims.forEach((d) => bLoc[d] = 0);
          const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);
          const opResult = op2(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);
          resultRealVals[i] = opResult.real;
          resultImagVals[i] = opResult.imag;
        }
      }
      return [resultRealVals, resultImagVals, resultShape];
    };
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var addImpl = createSimpleBinaryKernelImpl((a, b) => a + b);
  var addComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {
    return {real: aReal + bReal, imag: aImag + bImag};
  });
  var add4 = binaryKernelFunc(Add, addImpl, addComplexImpl);
  var addConfig = {
    kernelName: Add,
    backendName: "cpu",
    kernelFunc: add4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) {
    const weightsSize = util_exports.sizeFromShape(weightsShape);
    const outVals = util_exports.makeZerosTypedArray(size, weightsDtype);
    for (let i = 0; i < xVals.length; i++) {
      const value = xVals[i];
      if (value < 0) {
        throw new Error("Input x must be non-negative!");
      }
      if (value >= size) {
        continue;
      }
      if (weightsSize > 0) {
        outVals[value] += weightsVals[i];
      } else {
        outVals[value] += 1;
      }
    }
    return outVals;
  }
  function bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) {
    const numRows = xBuf.shape[0];
    const numCols = xBuf.shape[1];
    const outBuf = buffer([numRows, size], weightsBuf.dtype);
    for (let i = 0; i < numRows; i++) {
      for (let j = 0; j < numCols; j++) {
        const value = xBuf.get(i, j);
        if (value < 0) {
          throw new Error("Input x must be non-negative!");
        }
        if (value >= size) {
          continue;
        }
        if (binaryOutput) {
          outBuf.set(1, i, value);
        } else {
          if (weightsBuf.size > 0) {
            outBuf.set(outBuf.get(i, value) + weightsBuf.get(i, j), i, value);
          } else {
            outBuf.set(outBuf.get(i, value) + 1, i, value);
          }
        }
      }
    }
    return outBuf;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function createSimpleUnaryImpl(op2) {
    return (values, dtype, attrs) => {
      const newValues = util_exports.getTypedArrayFromDType(dtype, values.length);
      for (let i = 0; i < values.length; ++i) {
        newValues[i] = op2(values[i], attrs);
      }
      return newValues;
    };
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function unaryKernelFunc(name, op2, dtype) {
    return ({inputs, attrs, backend: backend2}) => {
      const {x} = inputs;
      assertNotComplex(x, name);
      if (x.dtype === "string" || dtype === "string") {
        throw new Error("unaryKernelFunc does not support string input/output");
      }
      const cpuBackend = backend2;
      const values = cpuBackend.data.get(x.dataId).values;
      const xSize = util_exports.sizeFromShape(x.shape);
      const $dtype = dtype || x.dtype;
      const newValues = util_exports.getArrayFromDType($dtype, xSize);
      for (let i = 0; i < xSize; ++i) {
        newValues[i] = op2(values[i], attrs);
      }
      return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);
    };
  }
  function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {
    return ({inputs, attrs, backend: backend2}) => {
      const {x} = inputs;
      assertNotComplex(x, name);
      if (x.dtype === "string" || dtype === "string") {
        throw new Error("unaryKernelFunc does not support string input/output");
      }
      const cpuBackend = backend2;
      const values = cpuBackend.data.get(x.dataId).values;
      const $dtype = dtype || x.dtype;
      const newValues = unaryImpl(values, $dtype, attrs);
      return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);
    };
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));
  var ceil2 = unaryKernelFuncFromImpl(Ceil, ceilImpl);
  var ceilConfig = {
    kernelName: Ceil,
    backendName: "cpu",
    kernelFunc: ceil2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function concatImpl(inputs, outShape, dtype, simplyConcat) {
    const outVals = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(outShape));
    if (simplyConcat && dtype !== "string") {
      let offset = 0;
      inputs.forEach((input2) => {
        const size = util_exports.sizeFromShape(input2.shape);
        outVals.set(input2.vals, offset);
        offset += size;
      });
    } else {
      let colOffset = 0;
      inputs.forEach((input2) => {
        const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(input2.vals) : input2.vals;
        let tIdx = 0;
        for (let row = 0; row < input2.shape[0]; ++row) {
          const resIdx = row * outShape[1] + colOffset;
          for (let col = 0; col < input2.shape[1]; ++col) {
            outVals[resIdx + col] = decodedData[tIdx++];
          }
        }
        colOffset += input2.shape[1];
      });
    }
    return outVals;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));
  var exp2 = unaryKernelFuncFromImpl(Exp, expImpl);
  var expConfig = {
    kernelName: Exp,
    backendName: "cpu",
    kernelFunc: exp2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));
  var expm12 = unaryKernelFuncFromImpl(Expm1, expm1Impl);
  var expm1Config = {
    kernelName: Expm1,
    backendName: "cpu",
    kernelFunc: expm12
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));
  var floor2 = unaryKernelFuncFromImpl(Floor, floorImpl);
  var floorConfig = {
    kernelName: Floor,
    backendName: "cpu",
    kernelFunc: floor2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {
    const outBuf = buffer(flattenOutputShape, xBuf.dtype);
    for (let i = 0; i < outBuf.size; ++i) {
      const newLoc = outBuf.indexToLoc(i);
      const originalLoc = newLoc.slice();
      const batchIdx = originalLoc[0];
      const indicesIdx = originalLoc[2];
      const indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]);
      originalLoc[2] = indicesBuf.values[indicesIndex];
      const originalIndex = xBuf.locToIndex(originalLoc);
      outBuf.values[i] = xBuf.values[originalIndex];
    }
    return outBuf;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var greaterImpl = createSimpleBinaryKernelImpl((a, b) => a > b ? 1 : 0);
  var greater3 = binaryKernelFunc(Greater, greaterImpl, null, "bool");
  var greaterConfig = {
    kernelName: Greater,
    backendName: "cpu",
    kernelFunc: greater3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var lessImpl = createSimpleBinaryKernelImpl((a, b) => a < b ? 1 : 0);
  var less3 = binaryKernelFunc(Less, lessImpl, null, "bool");
  var lessConfig = {
    kernelName: Less,
    backendName: "cpu",
    kernelFunc: less3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function linSpaceImpl(start, stop, num) {
    const step5 = (stop - start) / (num - 1);
    const values = util_exports.makeZerosTypedArray(num, "float32");
    values[0] = start;
    for (let i = 1; i < values.length; i++) {
      values[i] = values[i - 1] + step5;
    }
    return values;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));
  var log3 = unaryKernelFuncFromImpl(Log, logImpl);
  var logConfig = {
    kernelName: Log,
    backendName: "cpu",
    kernelFunc: log3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxImpl(aVals, reduceSize, outShape, dtype) {
    const vals = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(outShape));
    for (let i = 0; i < vals.length; ++i) {
      const offset = i * reduceSize;
      let max6 = aVals[offset];
      for (let j = 0; j < reduceSize; ++j) {
        const value = aVals[offset + j];
        if (value > max6) {
          max6 = value;
        }
      }
      vals[i] = max6;
    }
    return vals;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var maximumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.max(aValue, bValue));
  var maximum3 = binaryKernelFunc(Maximum, maximumImpl);
  var maximumConfig = {
    kernelName: Maximum,
    backendName: "cpu",
    kernelFunc: maximum3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var minimumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.min(aValue, bValue));
  var minimum3 = binaryKernelFunc(Minimum, minimumImpl);
  var minimumConfig = {
    kernelName: Minimum,
    backendName: "cpu",
    kernelFunc: minimum3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var multiplyImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue * bValue);
  var multiplyComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {
    return {
      real: aReal * bReal - aImag * bImag,
      imag: aReal * bImag + aImag * bReal
    };
  });
  var multiply2 = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);
  var multiplyConfig = {
    kernelName: Multiply,
    backendName: "cpu",
    kernelFunc: multiply2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function negImpl(xVals, xShape, xDtype) {
    const minusOne = util_exports.createScalarValue(-1, xDtype);
    return multiplyImpl([], xShape, minusOne, xVals, xDtype);
  }
  function neg2(args) {
    const {inputs, backend: backend2} = args;
    const {x} = inputs;
    assertNotComplex(x, "neg");
    const xVals = backend2.data.get(x.dataId).values;
    const [res, newShape] = negImpl(xVals, x.shape, x.dtype);
    return backend2.makeTensorInfo(newShape, x.dtype, res);
  }
  var negConfig = {
    kernelName: Neg,
    backendName: "cpu",
    kernelFunc: neg2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var notEqualImpl = createSimpleBinaryKernelImpl((a, b) => a !== b ? 1 : 0);
  var notEqual2 = binaryKernelFunc(NotEqual, notEqualImpl, null, "bool");
  var notEqualConfig = {
    kernelName: NotEqual,
    backendName: "cpu",
    kernelFunc: notEqual2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function transposeImpl(xVals, xShape, dtype, perm, newShape) {
    const xRank = xShape.length;
    const xSize = util_exports.sizeFromShape(xShape);
    const xStrides = util_exports.computeStrides(xShape);
    const newStrides = util_exports.computeStrides(newShape);
    const result = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(newShape));
    for (let i = 0; i < xSize; ++i) {
      const loc = util_exports.indexToLoc(i, xRank, xStrides);
      const newLoc = new Array(loc.length);
      for (let i2 = 0; i2 < newLoc.length; i2++) {
        newLoc[i2] = loc[perm[i2]];
      }
      const newIndex = util_exports.locToIndex(newLoc, xRank, newStrides);
      result[newIndex] = xVals[i];
    }
    return result;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function transpose2(args) {
    const {inputs, attrs, backend: backend2} = args;
    const {x} = inputs;
    const {perm} = attrs;
    assertNotComplex(x, "transpose");
    const xRank = x.shape.length;
    const newShape = new Array(xRank);
    for (let i = 0; i < newShape.length; i++) {
      newShape[i] = x.shape[perm[i]];
    }
    const values = backend2.data.get(x.dataId).values;
    const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);
    const dataId = backend2.write(result, newShape, x.dtype);
    return {dataId, shape: newShape, dtype: x.dtype};
  }
  var transposeConfig = {
    kernelName: Transpose,
    backendName: "cpu",
    kernelFunc: transpose2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function prodImpl(xShape, xDtype, xVals, reductionAxes) {
    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, reductionAxes);
    const outDtype = upcastType(xDtype, "int32");
    const outVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), outDtype);
    const reduceSize = util_exports.sizeFromShape(reduceShape);
    for (let i = 0; i < outVals.length; ++i) {
      const offset = i * reduceSize;
      let prod5 = 1;
      for (let j = 0; j < reduceSize; ++j) {
        prod5 *= xVals[offset + j];
      }
      outVals[i] = prod5;
    }
    return {outVals, outShape, outDtype};
  }
  function prod2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis, keepDims} = attrs;
    assertNotComplex(x, "prod");
    const xRank = x.shape.length;
    const axes = util_exports.parseAxisParam(axis, x.shape);
    const permutation = backend_util_exports.getAxesPermutation(axes, xRank);
    let reductionAxes = axes;
    let permutedX = x;
    const intermediateTensorInfos = [];
    if (permutation != null) {
      permutedX = transpose2({inputs: {x}, backend: backend2, attrs: {perm: permutation}});
      intermediateTensorInfos.push(permutedX);
      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);
    }
    const xVals = backend2.data.get(permutedX.dataId).values;
    const {outVals, outShape, outDtype} = prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes);
    let resultShape = outShape;
    if (keepDims) {
      resultShape = backend_util_exports.expandShapeToKeepDim(outShape, axes);
    }
    intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return backend2.makeTensorInfo(resultShape, outDtype, outVals);
  }
  var prodConfig = {
    kernelName: Prod,
    backendName: "cpu",
    kernelFunc: prod2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function rangeImpl(start, stop, step5, dtype) {
    const sameStartStop = start === stop;
    const increasingRangeNegativeStep = start < stop && step5 < 0;
    const decreasingRangePositiveStep = stop < start && step5 > 1;
    if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) {
      return util_exports.makeZerosTypedArray(0, dtype);
    }
    const numElements = Math.abs(Math.ceil((stop - start) / step5));
    const values = util_exports.makeZerosTypedArray(numElements, dtype);
    if (stop < start && step5 === 1) {
      step5 = -1;
    }
    values[0] = start;
    for (let i = 1; i < values.length; i++) {
      values[i] = values[i - 1] + step5;
    }
    return values;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));
  var rsqrt2 = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);
  var rsqrtConfig = {
    kernelName: Rsqrt,
    backendName: "cpu",
    kernelFunc: rsqrt2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sliceImpl(vals, begin, size, shape, dtype) {
    const isContinous = slice_util_exports.isSliceContinous(shape, begin, size);
    const length = util_exports.sizeFromShape(size);
    const xStrides = util_exports.computeStrides(shape);
    if (isContinous) {
      const flatOffset = slice_util_exports.computeFlatOffset(begin, xStrides);
      if (dtype === "string") {
        return vals.slice(flatOffset, flatOffset + length);
      }
      return vals.subarray(flatOffset, flatOffset + length);
    }
    const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(vals) : vals;
    const inBuf = buffer(shape, dtype, decodedData);
    const outBuf = buffer(size, dtype);
    for (let i = 0; i < outBuf.size; ++i) {
      const outLoc = outBuf.indexToLoc(i);
      const inLoc = outLoc.map((idx, j) => idx + begin[j]);
      outBuf.set(inBuf.get(...inLoc), ...outLoc);
    }
    if (dtype === "string") {
      return backend_util_exports.fromStringArrayToUint8(outBuf.values);
    }
    return outBuf.values;
  }
  function slice2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {begin, size} = attrs;
    assertNotComplex(x, "slice");
    const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);
    slice_util_exports.assertParamsValid(x, $begin, $size);
    const vals = backend2.data.get(x.dataId).values;
    const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);
    return backend2.makeTensorInfo($size, x.dtype, outVals);
  }
  var sliceConfig = {
    kernelName: Slice,
    backendName: "cpu",
    kernelFunc: slice2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var squaredDifferenceImpl = createSimpleBinaryKernelImpl((a, b) => {
    const diff = a - b;
    return diff * diff;
  });
  var squaredDifference2 = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);
  var squaredDifferenceConfig = {
    kernelName: SquaredDifference,
    backendName: "cpu",
    kernelFunc: squaredDifference2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function stridedSliceImpl(outShape, xBuf, strides, begin) {
    const outBuf = buffer(outShape, xBuf.dtype);
    for (let i = 0; i < outBuf.size; i++) {
      const loc = outBuf.indexToLoc(i);
      const newLoc = new Array(loc.length);
      for (let j = 0; j < newLoc.length; j++) {
        newLoc[j] = loc[j] * strides[j] + begin[j];
      }
      outBuf.set(xBuf.get(...newLoc), ...loc);
    }
    return outBuf;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var subImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue - bValue);
  var subComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {
    return {real: aReal - bReal, imag: aImag - bImag};
  });
  var sub2 = binaryKernelFunc(Sub, subImpl, subComplexImpl);
  var subConfig = {
    kernelName: Sub,
    backendName: "cpu",
    kernelFunc: sub2
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function tileImpl(xBuf, reps) {
    const newShape = new Array(xBuf.rank);
    for (let i = 0; i < newShape.length; i++) {
      newShape[i] = xBuf.shape[i] * reps[i];
    }
    const result = buffer(newShape, xBuf.dtype);
    for (let i = 0; i < result.values.length; ++i) {
      const newLoc = result.indexToLoc(i);
      const originalLoc = new Array(xBuf.rank);
      for (let j = 0; j < originalLoc.length; j++) {
        originalLoc[j] = newLoc[j] % xBuf.shape[j];
      }
      const originalIndex = xBuf.locToIndex(originalLoc);
      result.values[i] = xBuf.values[originalIndex];
    }
    return result;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function topKImpl(x, xShape, xDtype, k, sorted) {
    const lastDim = xShape[xShape.length - 1];
    const [batch, size] = [x.length / lastDim, lastDim];
    const allTopKVals = util_exports.getTypedArrayFromDType(xDtype, batch * k);
    const allTopKIndices = util_exports.getTypedArrayFromDType("int32", batch * k);
    for (let b = 0; b < batch; b++) {
      const offset = b * size;
      const vals = x.subarray(offset, offset + size);
      const valAndInd = [];
      for (let i = 0; i < vals.length; i++) {
        valAndInd.push({value: vals[i], index: i});
      }
      valAndInd.sort((a, b2) => b2.value - a.value);
      const outOffset = b * k;
      const topKVals = allTopKVals.subarray(outOffset, outOffset + k);
      const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
      for (let i = 0; i < k; i++) {
        topKVals[i] = valAndInd[i].value;
        topKIndices[i] = valAndInd[i].index;
      }
    }
    const outputShape = xShape.slice();
    outputShape[outputShape.length - 1] = k;
    return [
      buffer(outputShape, xDtype, allTopKVals),
      buffer(outputShape, "int32", allTopKIndices)
    ];
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function uniqueImpl(values, axis, shape, dtype) {
    const $axis = util_exports.parseAxisParam(axis, shape)[0];
    const newShape = [1, shape[0], 1];
    for (let i = 0; i < $axis; i++) {
      newShape[0] *= shape[i];
    }
    newShape[1] = shape[$axis];
    for (let i = $axis + 1; i < shape.length; i++) {
      newShape[2] *= shape[i];
    }
    const uniqueElements = {};
    const indices = new Int32Array(shape[$axis]);
    const inputBuffer = new TensorBuffer(newShape, dtype, values);
    const uniqueIndices = [];
    const is1DTensor = newShape[0] === 1 && newShape[2] === 1;
    for (let i = 0; i < shape[$axis]; i++) {
      let element;
      if (is1DTensor) {
        element = values[i].toString();
      } else {
        const axisValues = [];
        for (let m = 0; m < newShape[0]; m++) {
          for (let n = 0; n < newShape[2]; n++) {
            axisValues.push(inputBuffer.get(m, i, n));
          }
        }
        element = axisValues.join(",");
      }
      if (uniqueElements[element] !== void 0) {
        indices[i] = uniqueElements[element];
      } else {
        const uniqueIndex = Object.keys(uniqueElements).length;
        uniqueElements[element] = uniqueIndex;
        indices[i] = uniqueIndex;
        uniqueIndices.push(i);
      }
    }
    const outputTmpShape = newShape.slice();
    outputTmpShape[1] = Object.keys(uniqueElements).length;
    const outputBuffer = new TensorBuffer(outputTmpShape, dtype);
    uniqueIndices.forEach((uniqueElementIndex, i) => {
      for (let m = 0; m < newShape[0]; m++) {
        for (let n = 0; n < newShape[2]; n++) {
          outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);
        }
      }
    });
    const outputShape = shape.slice();
    outputShape[$axis] = outputTmpShape[1];
    return {
      outputValues: outputBuffer.values,
      outputShape,
      indices
    };
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  /** @license See the LICENSE file. */
  var version5 = "3.3.0";
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  registerBackend("cpu", () => new MathBackendCPU(), 1);
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var elu4 = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : Math.exp(xi) - 1);
  var eluConfig = {
    kernelName: Elu,
    backendName: "cpu",
    kernelFunc: elu4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function leakyRelu2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {alpha} = attrs;
    assertNotComplex([x], "leakyRelu");
    const xSize = util_exports.sizeFromShape(x.shape);
    const xVals = backend2.data.get(x.dataId).values;
    const outVals = util_exports.getTypedArrayFromDType("float32", xSize);
    for (let i = 0; i < xVals.length; i++) {
      outVals[i] = xVals[i] < 0 ? alpha * xVals[i] : xVals[i];
    }
    return backend2.makeTensorInfo(x.shape, "float32", outVals);
  }
  var leakyReluConfig = {
    kernelName: LeakyRelu,
    backendName: "cpu",
    kernelFunc: leakyRelu2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var preluImpl = createSimpleBinaryKernelImpl((xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);
  function prelu3(args) {
    const {inputs, backend: backend2} = args;
    const {x, alpha} = inputs;
    assertNotComplex([x, alpha], "prelu");
    const aVals = backend2.data.get(x.dataId).values;
    const bVals = backend2.data.get(alpha.dataId).values;
    const [resultData, resultShape] = preluImpl(x.shape, alpha.shape, aVals, bVals, x.dtype);
    return backend2.makeTensorInfo(resultShape, x.dtype, resultData);
  }
  var preluConfig = {
    kernelName: Prelu,
    backendName: "cpu",
    kernelFunc: prelu3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var relu2 = unaryKernelFunc(Relu, (xi) => Math.max(0, xi));
  var reluConfig = {
    kernelName: Relu,
    backendName: "cpu",
    kernelFunc: relu2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var relu62 = unaryKernelFunc(Relu6, (xi) => Math.min(Math.max(0, xi), 6));
  var relu6Config = {
    kernelName: Relu6,
    backendName: "cpu",
    kernelFunc: relu62
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function applyActivation2(backend2, x, activation2, preluActivationWeights, leakyreluAlpha) {
    if (activation2 === "linear") {
      return identity2({inputs: {x}, backend: backend2});
    } else if (activation2 === "relu") {
      return relu2({inputs: {x}, backend: backend2});
    } else if (activation2 === "elu") {
      return elu4({inputs: {x}, backend: backend2});
    } else if (activation2 === "relu6") {
      return relu62({inputs: {x}, backend: backend2});
    } else if (activation2 === "prelu") {
      return prelu3({inputs: {x, alpha: preluActivationWeights}, backend: backend2});
    } else if (activation2 === "leakyrelu") {
      return leakyRelu2({inputs: {x}, backend: backend2, attrs: {alpha: leakyreluAlpha}});
    }
    throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`);
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function reshape3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {shape} = attrs;
    const xSize = util_exports.sizeFromShape(x.shape);
    const $shape = util_exports.inferFromImplicitShape(shape, xSize);
    const $xSize = util_exports.sizeFromShape($shape);
    util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);
    backend2.incRef(x.dataId);
    const xData = backend2.data.get(x.dataId);
    if (xData.complexTensorInfos != null) {
      const real4 = xData.complexTensorInfos.real;
      const imag4 = xData.complexTensorInfos.imag;
      real4.shape = $shape;
      imag4.shape = $shape;
    }
    return {dataId: x.dataId, shape: $shape, dtype: x.dtype};
  }
  var reshapeConfig = {
    kernelName: Reshape,
    backendName: "cpu",
    kernelFunc: reshape3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function batchMatMul(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {a, b} = inputs;
    const {transposeA, transposeB} = attrs;
    assertNotComplex([a, b], "matMul");
    const aRank = a.shape.length;
    const bRank = b.shape.length;
    const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];
    const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
    const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];
    const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
    const outerDimsA = a.shape.slice(0, -2);
    const outerDimsB = b.shape.slice(0, -2);
    const batchDimA = util_exports.sizeFromShape(outerDimsA);
    const batchDimB = util_exports.sizeFromShape(outerDimsB);
    const batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;
    util_exports.assert(aRank >= 2 && bRank >= 2 && batchDimsCompatible, () => `Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);
    const outShapeOuterDims = batchDimA > batchDimB ? a.shape.slice(0, -2) : b.shape.slice(0, -2);
    const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
    util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
    const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];
    const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];
    const a3d = reshape3({inputs: {x: a}, backend: backend2, attrs: {shape: a3dShape}});
    const b3d = reshape3({inputs: {x: b}, backend: backend2, attrs: {shape: b3dShape}});
    const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];
    const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];
    const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];
    const batchDim = Math.max(batchDimA, batchDimB);
    const a3dValues = backend2.data.get(a3d.dataId).values;
    const b3dValues = backend2.data.get(b3d.dataId).values;
    const a3dStrides = util_exports.computeStrides(a3d.shape);
    const b3dStrides = util_exports.computeStrides(b3d.shape);
    const [aBatch, aOuterStep, aInnerStep] = transposeA ? [a3dStrides[0], 1, a3dStrides[1]] : [a3dStrides[0], a3dStrides[1], 1];
    const [bInnerStep, bOuterStep, bBatch] = transposeB ? [1, b3dStrides[1], b3dStrides[0]] : [b3dStrides[1], 1, b3dStrides[0]];
    const size = leftDim * rightDim;
    const result = buffer([batchDim, leftDim, rightDim], a3d.dtype);
    const resVals = result.values;
    const blockSize = backend2.blockSize;
    for (let bi = 0; bi < batchDim; bi++) {
      for (let i0 = 0; i0 < leftDim; i0 += blockSize) {
        for (let j0 = 0; j0 < rightDim; j0 += blockSize) {
          for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {
            const iBlock = Math.min(i0 + blockSize, leftDim);
            const jBlock = Math.min(j0 + blockSize, rightDim);
            const kBlock = Math.min(k0 + blockSize, sharedDim);
            for (let i = i0; i < iBlock; i++) {
              for (let j = j0; j < jBlock; j++) {
                let sum6 = 0;
                for (let k = k0; k < kBlock; k++) {
                  const batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch;
                  const batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch;
                  const aVal = a3dValues[batchOffsetA + i * aOuterStep + k * aInnerStep];
                  const bVal = b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB];
                  sum6 += aVal * bVal;
                }
                resVals[bi * size + (i * rightDim + j)] += sum6;
              }
            }
          }
        }
      }
    }
    backend2.disposeIntermediateTensorInfo(a3d);
    backend2.disposeIntermediateTensorInfo(b3d);
    return backend2.makeTensorInfo(outShape, result.dtype, result.values);
  }
  var batchMatMulConfig = {
    kernelName: BatchMatMul,
    backendName: "cpu",
    kernelFunc: batchMatMul
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function _fusedMatMul(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {a, b, bias, preluActivationWeights} = inputs;
    const {transposeA, transposeB, activation: activation2, leakyreluAlpha} = attrs;
    let current;
    let addRes;
    let activationRes;
    const intermediates = [];
    const matMulRes = batchMatMul({inputs: {a, b}, attrs: {transposeA, transposeB}, backend: backend2});
    current = matMulRes;
    if (bias) {
      addRes = add4({inputs: {a: current, b: bias}, backend: backend2});
      intermediates.push(current);
      current = addRes;
    }
    if (activation2) {
      activationRes = applyActivation2(backend2, current, activation2, preluActivationWeights, leakyreluAlpha);
      intermediates.push(current);
      current = activationRes;
    }
    for (const i of intermediates) {
      backend2.disposeIntermediateTensorInfo(i);
    }
    return current;
  }
  var _fusedMatMulConfig = {
    kernelName: _FusedMatMul,
    backendName: "cpu",
    kernelFunc: _fusedMatMul
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var acos2 = unaryKernelFunc(Acos, (xi) => Math.acos(xi));
  var acosConfig = {
    kernelName: Acos,
    backendName: "cpu",
    kernelFunc: acos2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var acosh2 = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));
  var acoshConfig = {
    kernelName: Acosh,
    backendName: "cpu",
    kernelFunc: acosh2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function addN2(args) {
    const {inputs, backend: backend2} = args;
    const tensors = inputs;
    assertNotComplex(inputs, "addN");
    const vals = tensors.map((t) => backend2.data.get(t.dataId).values);
    const outBuf = buffer(tensors[0].shape, tensors[0].dtype);
    const outVals = outBuf.values;
    for (let i = 0; i < tensors.length; i++) {
      const currVals = vals[i];
      for (let j = 0; j < outVals.length; j++) {
        outVals[j] += currVals[j];
      }
    }
    return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
  }
  var addNConfig = {
    kernelName: AddN,
    backendName: "cpu",
    kernelFunc: addN2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function all2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis, keepDims} = attrs;
    assertNotComplex(x, "all");
    const origAxes = util_exports.parseAxisParam(axis, x.shape);
    let axes = origAxes;
    const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
    let $x = x;
    if (permutedAxes != null) {
      $x = transpose2({inputs: {x}, backend: backend2, attrs: {perm: permutedAxes}});
      axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
    }
    backend_util_exports.assertAxesAreInnerMostDims("all", axes, $x.shape.length);
    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
    const reduceSize = util_exports.sizeFromShape(reduceShape);
    const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
    const aVals = backend2.data.get($x.dataId).values;
    for (let i = 0; i < vals.length; ++i) {
      const offset = i * reduceSize;
      let all4 = aVals[offset];
      for (let j = 0; j < reduceSize; ++j) {
        const value = aVals[offset + j];
        all4 = all4 && value;
      }
      vals[i] = all4;
    }
    if (permutedAxes != null) {
      backend2.disposeIntermediateTensorInfo($x);
    }
    const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);
    if (keepDims) {
      const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
      const reshapedResult = reshape3({inputs: {x: result}, backend: backend2, attrs: {shape: expandedShape}});
      backend2.disposeIntermediateTensorInfo(result);
      return reshapedResult;
    }
    return result;
  }
  var allConfig = {
    kernelName: All,
    backendName: "cpu",
    kernelFunc: all2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function any2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis, keepDims} = attrs;
    assertNotComplex(x, "any");
    const origAxes = util_exports.parseAxisParam(axis, x.shape);
    let axes = origAxes;
    const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
    let $x = x;
    if (permutedAxes != null) {
      $x = transpose2({inputs: {x}, backend: backend2, attrs: {perm: permutedAxes}});
      axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
    }
    backend_util_exports.assertAxesAreInnerMostDims("any", axes, $x.shape.length);
    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
    const reduceSize = util_exports.sizeFromShape(reduceShape);
    const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
    const aVals = backend2.data.get($x.dataId).values;
    for (let i = 0; i < vals.length; ++i) {
      const offset = i * reduceSize;
      let anyVal = aVals[offset];
      for (let j = 0; j < reduceSize; ++j) {
        const value = aVals[offset + j];
        anyVal = anyVal || value;
      }
      vals[i] = anyVal;
    }
    if (permutedAxes != null) {
      backend2.disposeIntermediateTensorInfo($x);
    }
    const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);
    if (keepDims) {
      const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
      const reshapedResult = reshape3({inputs: {x: result}, backend: backend2, attrs: {shape: expandedShape}});
      backend2.disposeIntermediateTensorInfo(result);
      return reshapedResult;
    }
    return result;
  }
  var anyConfig = {
    kernelName: Any,
    backendName: "cpu",
    kernelFunc: any2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function argMax2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis} = attrs;
    assertNotComplex(x, "argMax");
    let axes = util_exports.parseAxisParam(axis, x.shape);
    const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
    let $x = x;
    const intermediateTensorInfos = [];
    if (permutedAxes != null) {
      $x = transpose2({inputs: {x}, backend: backend2, attrs: {perm: permutedAxes}});
      intermediateTensorInfos.push($x);
      axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
    }
    axes = [axes[0]];
    backend_util_exports.assertAxesAreInnerMostDims("argMax", axes, $x.shape.length);
    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
    const outSize = util_exports.sizeFromShape(outShape);
    const vals = util_exports.makeZerosTypedArray(outSize, "int32");
    const reduceSize = util_exports.sizeFromShape(reduceShape);
    const aVals = backend2.data.get($x.dataId).values;
    for (let i = 0; i < vals.length; ++i) {
      const offset = i * reduceSize;
      let max6 = aVals[offset];
      let maxIndex = 0;
      for (let j = 0; j < reduceSize; ++j) {
        const value = aVals[offset + j];
        if (value > max6) {
          max6 = value;
          maxIndex = j;
        }
      }
      vals[i] = maxIndex;
    }
    intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return backend2.makeTensorInfo(outShape, "int32", vals);
  }
  var argMaxConfig = {
    kernelName: ArgMax,
    backendName: "cpu",
    kernelFunc: argMax2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function argMin2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis} = attrs;
    assertNotComplex(x, "argMin");
    let axes = util_exports.parseAxisParam(axis, x.shape);
    const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
    let $x = x;
    const intermediateTensorInfos = [];
    if (permutedAxes != null) {
      $x = transpose2({inputs: {x}, backend: backend2, attrs: {perm: permutedAxes}});
      intermediateTensorInfos.push($x);
      axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
    }
    axes = [axes[0]];
    backend_util_exports.assertAxesAreInnerMostDims("argMin", axes, $x.shape.length);
    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
    const outSize = util_exports.sizeFromShape(outShape);
    const vals = util_exports.makeZerosTypedArray(outSize, "int32");
    const reduceSize = util_exports.sizeFromShape(reduceShape);
    const aVals = backend2.data.get($x.dataId).values;
    for (let i = 0; i < vals.length; ++i) {
      const offset = i * reduceSize;
      let min6 = aVals[offset];
      let minIndex = 0;
      for (let j = 0; j < reduceSize; ++j) {
        const value = aVals[offset + j];
        if (value < min6) {
          min6 = value;
          minIndex = j;
        }
      }
      vals[i] = minIndex;
    }
    intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return backend2.makeTensorInfo(outShape, "int32", vals);
  }
  var argMinConfig = {
    kernelName: ArgMin,
    backendName: "cpu",
    kernelFunc: argMin2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var asin2 = unaryKernelFunc(Asin, (xi) => Math.asin(xi));
  var asinConfig = {
    kernelName: Asin,
    backendName: "cpu",
    kernelFunc: asin2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var asinh2 = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));
  var asinhConfig = {
    kernelName: Asinh,
    backendName: "cpu",
    kernelFunc: asinh2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var atan3 = unaryKernelFunc(Atan, (xi) => Math.atan(xi));
  var atanConfig = {
    kernelName: Atan,
    backendName: "cpu",
    kernelFunc: atan3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var atan2Impl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.atan2(aValue, bValue));
  var atan22 = binaryKernelFunc(Atan2, atan2Impl);
  var atan2Config = {
    kernelName: Atan2,
    backendName: "cpu",
    kernelFunc: atan22
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var atanh2 = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));
  var atanhConfig = {
    kernelName: Atanh,
    backendName: "cpu",
    kernelFunc: atanh2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function pool2(xValues, xShape, dtype, strides, convInfo, poolType) {
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padTop = convInfo.padInfo.top;
    const padLeft = convInfo.padInfo.left;
    const initialValue = poolType === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;
    const output = buffer(convInfo.outShape, dtype);
    const outputVals = output.values;
    const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];
    const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];
    const outputColStrides = convInfo.outShape[3];
    for (let b = 0; b < convInfo.batchSize; ++b) {
      const outputBatchOffset = b * outputBatchStrides;
      const inputBatchOffset = b * strides[0];
      for (let d = 0; d < convInfo.inChannels; ++d) {
        for (let yR = 0; yR < convInfo.outHeight; ++yR) {
          const xRCorner = yR * strideHeight - padTop;
          const xRMin = Math.max(0, xRCorner);
          const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
          const outputRowOffset = outputBatchOffset + yR * outputRowStrides;
          for (let yC = 0; yC < convInfo.outWidth; ++yC) {
            const xCCorner = yC * strideWidth - padLeft;
            const xCMin = Math.max(0, xCCorner);
            const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
            let minMaxValue = initialValue;
            let avgValue = 0;
            let count2 = 0;
            for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {
              const xROffset = inputBatchOffset + xR * strides[1];
              for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {
                const xCOffset = xROffset + xC * strides[2];
                const pixel = xValues[xCOffset + d];
                if (poolType === "max" && pixel > minMaxValue) {
                  minMaxValue = pixel;
                } else if (poolType === "avg") {
                  avgValue += pixel;
                  count2++;
                }
              }
              if (isNaN(minMaxValue)) {
                break;
              }
            }
            const outputOffset = outputRowOffset + yC * outputColStrides + d;
            outputVals[outputOffset] = poolType === "avg" ? avgValue / count2 : minMaxValue;
          }
        }
      }
    }
    return output;
  }
  function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {
    const maxPositions = buffer(convInfo.outShape, "int32");
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padTop = convInfo.padInfo.top;
    const padLeft = convInfo.padInfo.left;
    const xBuf = buffer(xShape, dtype, xValues);
    for (let b = 0; b < convInfo.batchSize; ++b) {
      for (let d = 0; d < convInfo.inChannels; ++d) {
        for (let yR = 0; yR < convInfo.outHeight; ++yR) {
          const xRCorner = yR * strideHeight - padTop;
          let xRMin = xRCorner;
          while (xRMin < 0) {
            xRMin += dilationHeight;
          }
          const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
          for (let yC = 0; yC < convInfo.outWidth; ++yC) {
            const xCCorner = yC * strideWidth - padLeft;
            let xCMin = xCCorner;
            while (xCMin < 0) {
              xCMin += dilationWidth;
            }
            const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
            let maxValue = Number.NEGATIVE_INFINITY;
            let maxPosition = -1;
            for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {
              const wR = xR - xRCorner;
              for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {
                const wC = xC - xCCorner;
                const pixel = xBuf.get(b, xR, xC, d);
                if (pixel > maxValue) {
                  maxValue = pixel;
                  if (flattenPositions) {
                    maxPosition = includeBatchInIndex ? ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) * convInfo.inChannels + d : (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;
                  } else {
                    maxPosition = wR * effectiveFilterWidth + wC;
                  }
                }
              }
            }
            maxPositions.set(maxPosition, b, yR, yC, d);
          }
        }
      }
    }
    return maxPositions;
  }
  function pool3d2(xValues, xShape, dtype, strides, convInfo, poolType) {
    const strideDepth = convInfo.strideDepth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const dilationDepth = convInfo.dilationDepth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padFront = convInfo.padInfo.front;
    const padTop = convInfo.padInfo.top;
    const padLeft = convInfo.padInfo.left;
    const initialValue = poolType === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;
    const output = buffer(convInfo.outShape, dtype);
    const outputVals = output.values;
    const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];
    const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];
    const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];
    const outputColStrides = convInfo.outShape[4];
    for (let batch = 0; batch < convInfo.batchSize; ++batch) {
      const outputBatchOffset = batch * outputBatchStrides;
      const inputBatchOffset = batch * strides[0];
      for (let channel = 0; channel < convInfo.inChannels; ++channel) {
        for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {
          const xDepthCorner = yDepth * strideDepth - padFront;
          let xDepthMin = xDepthCorner;
          while (xDepthMin < 0) {
            xDepthMin += dilationDepth;
          }
          const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);
          const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;
          for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {
            const xRowCorner = yRow * strideHeight - padTop;
            let xRowMin = xRowCorner;
            while (xRowMin < 0) {
              xRowMin += dilationHeight;
            }
            const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);
            const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;
            for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {
              const xColCorner = yCol * strideWidth - padLeft;
              let xColMin = xColCorner;
              while (xColMin < 0) {
                xColMin += dilationWidth;
              }
              const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);
              const outputColOffset = outputRowOffset + yCol * outputColStrides;
              let minMaxValue = initialValue;
              let avgValue = 0;
              let count2 = 0;
              for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {
                const xDepthOffset = inputBatchOffset + xDepth * strides[1];
                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {
                  const xRowOffset = xDepthOffset + xRow * strides[2];
                  for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {
                    const xColOffset = xRowOffset + xCol * strides[3];
                    const pixel = xValues[xColOffset + channel];
                    if (poolType === "max" && pixel > minMaxValue) {
                      minMaxValue = pixel;
                    } else if (poolType === "avg") {
                      avgValue += pixel;
                      count2++;
                    }
                    if (isNaN(minMaxValue)) {
                      break;
                    }
                  }
                  if (isNaN(minMaxValue)) {
                    break;
                  }
                }
                if (isNaN(minMaxValue)) {
                  break;
                }
              }
              const outputOffset = outputColOffset + channel;
              outputVals[outputOffset] = poolType === "avg" ? avgValue / count2 : minMaxValue;
            }
          }
        }
      }
    }
    return output;
  }
  function maxPool3dPositions(xBuf, convInfo) {
    const maxPositions = buffer(convInfo.outShape, "int32");
    const strideDepth = convInfo.strideDepth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const dilationDepth = convInfo.dilationDepth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padFront = convInfo.padInfo.front;
    const padTop = convInfo.padInfo.top;
    const padLeft = convInfo.padInfo.left;
    for (let batch = 0; batch < convInfo.batchSize; ++batch) {
      for (let channel = 0; channel < convInfo.inChannels; ++channel) {
        for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {
          const xDepthCorner = yDepth * strideDepth - padFront;
          let xDepthMin = xDepthCorner;
          while (xDepthMin < 0) {
            xDepthMin += dilationDepth;
          }
          const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);
          for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {
            const xRowCorner = yRow * strideHeight - padTop;
            let xRowMin = xRowCorner;
            while (xRowMin < 0) {
              xRowMin += dilationHeight;
            }
            const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);
            for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {
              const xColCorner = yCol * strideWidth - padLeft;
              let xColMin = xColCorner;
              while (xColMin < 0) {
                xColMin += dilationWidth;
              }
              const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);
              let maxValue = Number.NEGATIVE_INFINITY;
              let maxPosition = -1;
              for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {
                const wDepth = xDepth - xDepthCorner;
                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {
                  const wRow = xRow - xRowCorner;
                  for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {
                    const wCol = xCol - xColCorner;
                    const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);
                    if (pixel >= maxValue) {
                      maxValue = pixel;
                      maxPosition = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterHeight + wCol;
                    }
                  }
                }
              }
              maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);
            }
          }
        }
      }
    }
    return maxPositions;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function avgPool2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    assertNotComplex(x, "avgPool");
    const {filterSize, strides, pad: pad3, dimRoundingMode} = attrs;
    const dilations = 1;
    util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);
    let res;
    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
      res = identity2({inputs: {x}, backend: backend2});
    } else {
      const xValues = backend2.data.get(x.dataId).values;
      const strides2 = util_exports.computeStrides(x.shape);
      const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, "avg");
      res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);
    }
    return res;
  }
  var avgPoolConfig = {
    kernelName: AvgPool,
    backendName: "cpu",
    kernelFunc: avgPool2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function avgPool3D(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {filterSize, strides, pad: pad3, dimRoundingMode, dataFormat} = attrs;
    assertNotComplex(x, "avgPool3d");
    const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat);
    const xValues = backend2.data.get(x.dataId).values;
    const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, "avg");
    return backend2.makeTensorInfo(outBuf.shape, "float32", outBuf.values);
  }
  var avgPool3DConfig = {
    kernelName: AvgPool3D,
    backendName: "cpu",
    kernelFunc: avgPool3D
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function avgPool3DGrad(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {dy, input: input2} = inputs;
    const {filterSize, strides, pad: pad3, dimRoundingMode} = attrs;
    assertNotComplex([dy, input2], "avgPool3DGrad");
    const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode);
    const strideDepth = convInfo.strideDepth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const filterDepth = convInfo.filterDepth;
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const dilationDepth = convInfo.dilationDepth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
    const dx = buffer(input2.shape, "float32");
    const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
    const dyBuf = backend2.bufferSync(dy);
    for (let batch = 0; batch < convInfo.batchSize; ++batch) {
      for (let channel = 0; channel < convInfo.inChannels; ++channel) {
        for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {
          for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {
            for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {
              const dyDepthCorner = dxDepth - padFront;
              const dyRowCorner = dxRow - padTop;
              const dyColCorner = dxCol - padLeft;
              let dotProd = 0;
              for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {
                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;
                if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {
                  continue;
                }
                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {
                  const dyRow = (dyRowCorner + wRow) / strideHeight;
                  if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {
                    continue;
                  }
                  for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {
                    const dyCol = (dyColCorner + wCol) / strideWidth;
                    if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {
                      continue;
                    }
                    const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);
                    dotProd += pixel;
                  }
                }
              }
              dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);
            }
          }
        }
      }
    }
    return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
  }
  var avgPool3DGradConfig2 = {
    kernelName: AvgPool3DGrad,
    backendName: "cpu",
    kernelFunc: avgPool3DGrad
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function avgPoolGrad2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {dy, input: input2} = inputs;
    const x = input2;
    assertNotComplex([dy, input2], "avgPoolGrad");
    const {filterSize, strides, pad: pad3} = attrs;
    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3);
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
    const dx = buffer(x.shape, "float32");
    const avgMultiplier = 1 / (filterHeight * filterWidth);
    const dyData = backend2.data.get(dy.dataId).values;
    const dyBuf = buffer(dy.shape, "float32", dyData);
    for (let b = 0; b < convInfo.batchSize; ++b) {
      for (let d = 0; d < convInfo.inChannels; ++d) {
        for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {
          for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {
            const dyRCorner = dxR - padTop;
            const dyCCorner = dxC - padLeft;
            let dotProd = 0;
            for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
              const dyR = (dyRCorner + wR) / strideHeight;
              if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {
                continue;
              }
              for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
                const dyC = (dyCCorner + wC) / strideWidth;
                if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {
                  continue;
                }
                const pixel = dyBuf.get(b, dyR, dyC, d);
                dotProd += pixel;
              }
            }
            dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);
          }
        }
      }
    }
    return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
  }
  var avgPoolGradConfig2 = {
    kernelName: AvgPoolGrad,
    backendName: "cpu",
    kernelFunc: avgPoolGrad2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function batchNorm2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, scale: scale22, offset, mean: mean4, variance} = inputs;
    util_exports.assert(mean4.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
    util_exports.assert(offset == null || mean4.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
    util_exports.assert(scale22 == null || mean4.shape.length === scale22.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
    assertNotComplex([x, mean4, variance, scale22, offset], "batchNorm");
    let {varianceEpsilon} = attrs;
    if (varianceEpsilon == null) {
      varianceEpsilon = 1e-3;
    }
    const xVals = backend2.data.get(x.dataId).values;
    const mVals = backend2.data.get(mean4.dataId).values;
    const varVals = backend2.data.get(variance.dataId).values;
    const sVals = scale22 ? backend2.data.get(scale22.dataId).values : new Float32Array([1]);
    const offVals = offset ? backend2.data.get(offset.dataId).values : new Float32Array([0]);
    const outVals = new Float32Array(xVals.length);
    const offValsLength = offVals.length;
    const sValsLength = sVals.length;
    const varValsLength = varVals.length;
    const mValsLength = mVals.length;
    let offi = 0;
    let mi = 0;
    let si = 0;
    let vi = 0;
    for (let i = 0; i < xVals.length; ++i) {
      outVals[i] = offVals[offi++] + (xVals[i] - mVals[mi++]) * sVals[si++] / Math.sqrt(varVals[vi++] + varianceEpsilon);
      if (offi >= offValsLength) {
        offi = 0;
      }
      if (mi >= mValsLength) {
        mi = 0;
      }
      if (si >= sValsLength) {
        si = 0;
      }
      if (vi >= varValsLength) {
        vi = 0;
      }
    }
    return backend2.makeTensorInfo(x.shape, x.dtype, outVals);
  }
  var batchNormConfig = {
    kernelName: FusedBatchNorm,
    backendName: "cpu",
    kernelFunc: batchNorm2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function batchToSpaceND2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {blockShape, crops} = attrs;
    assertNotComplex([x], "batchToSpaceND");
    const prod5 = blockShape.reduce((a, b) => a * b);
    const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod5);
    const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);
    const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod5);
    const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);
    const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);
    const xReshaped = reshape3({inputs: {x}, backend: backend2, attrs: {shape: reshaped}});
    const xTransposed = transpose2({inputs: {x: xReshaped}, backend: backend2, attrs: {perm: permuted}});
    const xTransposedReshaped = reshape3({inputs: {x: xTransposed}, backend: backend2, attrs: {shape: reshapedPermuted}});
    const result = slice2({
      inputs: {x: xTransposedReshaped},
      backend: backend2,
      attrs: {begin: sliceBeginCoords, size: sliceSize}
    });
    backend2.disposeIntermediateTensorInfo(xReshaped);
    backend2.disposeIntermediateTensorInfo(xTransposed);
    backend2.disposeIntermediateTensorInfo(xTransposedReshaped);
    return result;
  }
  var batchToSpaceNDConfig = {
    kernelName: BatchToSpaceND,
    backendName: "cpu",
    kernelFunc: batchToSpaceND2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function bincount2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, weights} = inputs;
    const {size} = attrs;
    const xVals = backend2.data.get(x.dataId).values;
    const weightsVals = backend2.data.get(weights.dataId).values;
    const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);
    return backend2.makeTensorInfo([size], weights.dtype, outVals);
  }
  var bincountConfig = {
    kernelName: Bincount,
    backendName: "cpu",
    kernelFunc: bincount2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var clip = unaryKernelFunc(ClipByValue, (xi, attrs) => {
    const clipAttrs = attrs;
    if (xi > clipAttrs.clipValueMax) {
      return clipAttrs.clipValueMax;
    }
    return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;
  });
  var clipConfig = {
    kernelName: ClipByValue,
    backendName: "cpu",
    kernelFunc: clip
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var complexAbs = (args) => {
    const {x} = args.inputs;
    const cpuBackend = args.backend;
    const resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));
    const complexVals = cpuBackend.data.get(x.dataId);
    const real4 = complexVals.complexTensorInfos.real;
    const imag4 = complexVals.complexTensorInfos.imag;
    const realVals = cpuBackend.data.get(real4.dataId).values;
    const imagVals = cpuBackend.data.get(imag4.dataId).values;
    for (let i = 0; i < realVals.length; i++) {
      const real5 = realVals[i];
      const imag5 = imagVals[i];
      resultValues[i] = Math.hypot(real5, imag5);
    }
    return cpuBackend.makeOutput(resultValues, x.shape, "float32");
  };
  var complexAbsConfig = {
    kernelName: ComplexAbs,
    backendName: "cpu",
    kernelFunc: complexAbs
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function imag2(args) {
    const {inputs, backend: backend2} = args;
    const {input: input2} = inputs;
    const imag4 = backend2.data.get(input2.dataId).complexTensorInfos.imag;
    const imagVal = backend2.data.get(imag4.dataId).values;
    return backend2.makeTensorInfo(imag4.shape, imag4.dtype, imagVal);
  }
  var imagConfig = {
    kernelName: Imag,
    backendName: "cpu",
    kernelFunc: imag2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function concat2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {axis} = attrs;
    const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];
    let outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis);
    if (util_exports.sizeFromShape(outShape) === 0) {
      return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);
    }
    const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);
    if ($inputs.length === 1) {
      return identity2({inputs: {x: $inputs[0]}, backend: backend2});
    }
    const shapes = $inputs.map((t) => t.shape);
    backend_util_exports.assertParamsConsistent(shapes, $axis);
    if ($inputs[0].dtype === "complex64") {
      const reals = $inputs.map((t) => real2({inputs: {input: t}, backend: backend2}));
      const imags = $inputs.map((t) => imag2({inputs: {input: t}, backend: backend2}));
      const realConcated = concat2({inputs: reals, backend: backend2, attrs: {axis: $axis}});
      const imagConcated = concat2({inputs: imags, backend: backend2, attrs: {axis: $axis}});
      const result = complex2({inputs: {real: realConcated, imag: imagConcated}, backend: backend2});
      reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r));
      imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i));
      backend2.disposeIntermediateTensorInfo(realConcated);
      backend2.disposeIntermediateTensorInfo(imagConcated);
      return result;
    }
    const inputs2D = $inputs.map((t) => {
      const innerSize = util_exports.sizeFromShape(t.shape.slice($axis));
      const shape = [-1, innerSize];
      return reshape3({inputs: {x: t}, backend: backend2, attrs: {shape}});
    });
    const inputsValShapes = inputs2D.map((t) => {
      return {vals: backend2.data.get(t.dataId).values, shape: t.shape};
    });
    outShape = backend_util_exports.computeOutShape(inputs2D.map((t) => t.shape), 1);
    const simplyConcat = inputs2D[0].shape[0] === 1;
    const outVals = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);
    const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t) => t.shape), $axis);
    const outInfo = backend2.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);
    inputs2D.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return outInfo;
  }
  var concatConfig = {
    kernelName: Concat,
    backendName: "cpu",
    kernelFunc: concat2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv2D(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, filter} = inputs;
    const {strides, pad: pad3, dataFormat, dilations, dimRoundingMode} = attrs;
    assertNotComplex([x, filter], "conv2d");
    const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
    const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const padLeft = convInfo.padInfo.left;
    const padTop = convInfo.padInfo.top;
    const isChannelsLast = convInfo.dataFormat === "channelsLast";
    const y = new TensorBuffer(convInfo.outShape, x.dtype);
    const xStrides = util_exports.computeStrides(x.shape);
    const filterStrides = util_exports.computeStrides(filter.shape);
    const xBatchStride = xStrides[0];
    const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2];
    const xColStride = isChannelsLast ? xStrides[2] : 1;
    const xChannelStride = isChannelsLast ? 1 : xStrides[1];
    const yBatchStride = y.strides[0];
    const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];
    const yColStride = isChannelsLast ? y.strides[2] : 1;
    const yChannelStride = isChannelsLast ? 1 : y.strides[1];
    const xVals = backend2.data.get(x.dataId).values;
    const wVals = backend2.data.get(filter.dataId).values;
    const yVals = y.values;
    for (let b = 0; b < convInfo.batchSize; ++b) {
      const xOffset1 = b * xBatchStride;
      const yOffset1 = b * yBatchStride;
      for (let yR = 0; yR < convInfo.outHeight; ++yR) {
        const yOffset2 = yOffset1 + yR * yRowStride;
        const xRCorner = yR * convInfo.strideHeight - padTop;
        for (let wR = 0; wR < filterHeight; ++wR) {
          const xR = xRCorner + wR * dilationHeight;
          if (xR < 0 || xR >= convInfo.inHeight) {
            continue;
          }
          const wOffset1 = wR * filterStrides[0];
          const xOffset2 = xOffset1 + xR * xRowStride;
          for (let yC = 0; yC < convInfo.outWidth; ++yC) {
            const yOffset3 = yOffset2 + yC * yColStride;
            const xCCorner = yC * convInfo.strideWidth - padLeft;
            for (let wC = 0; wC < filterWidth; ++wC) {
              const xC = xCCorner + wC * dilationWidth;
              if (xC < 0 || xC >= convInfo.inWidth) {
                continue;
              }
              const wOffset2 = wOffset1 + wC * filterStrides[1];
              const xOffset3 = xOffset2 + xC * xColStride;
              let wOffset3 = wOffset2;
              for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
                const xVal = xVals[xOffset3 + d1 * xChannelStride];
                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
                  yVals[yOffset3 + d2 * yChannelStride] += xVal * wVals[wOffset3 + d2];
                }
                wOffset3 += convInfo.outChannels;
              }
            }
          }
        }
      }
    }
    return backend2.makeTensorInfo(y.shape, y.dtype, yVals);
  }
  var conv2DConfig = {
    kernelName: Conv2D,
    backendName: "cpu",
    kernelFunc: conv2D
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv2DBackpropFilter2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, dy} = inputs;
    const {strides, pad: pad3, dataFormat, dimRoundingMode, filterShape} = attrs;
    assertNotComplex([x, dy], "conv2dBackpropFilter");
    const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
    const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);
    const {strideHeight, strideWidth, filterHeight, filterWidth} = convInfo;
    const isChannelsLast = convInfo.dataFormat === "channelsLast";
    const dW = new TensorBuffer(convInfo.filterShape, "float32");
    const leftPad = convInfo.padInfo.left;
    const topPad = convInfo.padInfo.top;
    const xVals = backend2.data.get(x.dataId).values;
    const dyVals = backend2.data.get(dy.dataId).values;
    const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);
    const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);
    for (let wR = 0; wR < filterHeight; ++wR) {
      const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
      const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
      for (let wC = 0; wC < filterWidth; ++wC) {
        const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
        const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
          for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
            let dotProd = 0;
            for (let b = 0; b < convInfo.batchSize; ++b) {
              for (let yR = yRMin; yR < yRMax; ++yR) {
                const xR = wR + yR * strideHeight - topPad;
                for (let yC = yCMin; yC < yCMax; ++yC) {
                  const xC = wC + yC * strideWidth - leftPad;
                  if (isChannelsLast) {
                    dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);
                  } else {
                    dotProd += xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC);
                  }
                }
              }
            }
            dW.set(dotProd, wR, wC, d1, d2);
          }
        }
      }
    }
    return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);
  }
  var conv2DBackpropFilterConfig = {
    kernelName: Conv2DBackpropFilter,
    backendName: "cpu",
    kernelFunc: conv2DBackpropFilter2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv2DBackpropInput2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {dy, filter} = inputs;
    const {inputShape, strides, pad: pad3, dataFormat, dimRoundingMode} = attrs;
    assertNotComplex([dy, filter], "conv2dBackpropInput");
    const filterStrides = util_exports.computeStrides(filter.shape);
    const dyStrides = util_exports.computeStrides(dy.shape);
    let $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
    const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);
    const dx = new TensorBuffer(convInfo.inShape, "float32");
    const dxValues = dx.values;
    const dyValues = backend2.data.get(dy.dataId).values;
    const fltValues = backend2.data.get(filter.dataId).values;
    const [fltS0, fltS1, fltS2] = filterStrides;
    const {batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth} = convInfo;
    $dataFormat = convInfo.dataFormat;
    const topPad = filterHeight - 1 - convInfo.padInfo.top;
    const leftPad = filterWidth - 1 - convInfo.padInfo.left;
    const isChannelsLast = $dataFormat === "channelsLast";
    const xBatchStride = dx.strides[0];
    const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];
    const xColStride = isChannelsLast ? dx.strides[2] : 1;
    const xChannelStride = isChannelsLast ? 1 : dx.strides[1];
    const yBatchStride = dyStrides[0];
    const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];
    const yColStride = isChannelsLast ? dyStrides[2] : 1;
    const yChannelStride = isChannelsLast ? 1 : dyStrides[1];
    for (let b = 0; b < batchSize; ++b) {
      for (let d1 = 0; d1 < inChannels; ++d1) {
        for (let xR = 0; xR < inHeight; ++xR) {
          const xRCorner = xR - topPad;
          const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
          const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
          for (let xC = 0; xC < inWidth; ++xC) {
            const xCCorner = xC - leftPad;
            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
            const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
            let dotProd = 0;
            for (let yR = xRMin; yR < yRMax; ++yR) {
              const wR = yR * strideHeight - xRCorner;
              for (let yC = xCMin; yC < yCMax; ++yC) {
                const wC = yC * strideWidth - xCCorner;
                const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;
                const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
                for (let d2 = 0; d2 < outChannels; ++d2) {
                  const pixel = dyValues[dyOffset + yChannelStride * d2];
                  const weight = fltValues[fltOffset + d2];
                  dotProd += pixel * weight;
                }
              }
            }
            const dxOffset = xBatchStride * b + xRowStride * xR + xColStride * xC + xChannelStride * d1;
            dxValues[dxOffset] = dotProd;
          }
        }
      }
    }
    return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
  }
  var conv2DBackpropInputConfig = {
    kernelName: Conv2DBackpropInput,
    backendName: "cpu",
    kernelFunc: conv2DBackpropInput2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv3D(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, filter} = inputs;
    const {strides, pad: pad3, dilations} = attrs;
    assertNotComplex([x, filter], "conv3d");
    const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3);
    const {filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo} = convInfo;
    const padFront = padInfo.front;
    const padLeft = padInfo.left;
    const padTop = padInfo.top;
    const y = new TensorBuffer(convInfo.outShape, x.dtype);
    const xVals = backend2.data.get(x.dataId).values;
    const wVals = backend2.data.get(filter.dataId).values;
    const yVals = y.values;
    const xStrides = util_exports.computeStrides(x.shape);
    const filterStrides = util_exports.computeStrides(filter.shape);
    for (let b = 0; b < convInfo.batchSize; ++b) {
      const xOffset1 = b * xStrides[0];
      const yOffset1 = b * y.strides[0];
      for (let yF = 0; yF < convInfo.outDepth; ++yF) {
        const yOffset2 = yOffset1 + yF * y.strides[1];
        const xFCorner = yF * convInfo.strideDepth - padFront;
        for (let wF = 0; wF < filterDepth; ++wF) {
          const xF = xFCorner + wF * dilationDepth;
          if (xF < 0 || xF >= convInfo.inDepth) {
            continue;
          }
          const wOffset1 = wF * filterStrides[0];
          const xOffset2 = xOffset1 + xF * xStrides[1];
          for (let yR = 0; yR < convInfo.outHeight; ++yR) {
            const yOffset3 = yOffset2 + yR * y.strides[2];
            const xRCorner = yR * convInfo.strideHeight - padTop;
            for (let wR = 0; wR < filterHeight; ++wR) {
              const xR = xRCorner + wR * dilationHeight;
              if (xR < 0 || xR >= convInfo.inHeight) {
                continue;
              }
              const wOffset2 = wOffset1 + wR * filterStrides[1];
              const xOffset3 = xOffset2 + xR * xStrides[2];
              for (let yC = 0; yC < convInfo.outWidth; ++yC) {
                const yOffset4 = yOffset3 + yC * convInfo.outChannels;
                const xCCorner = yC * convInfo.strideWidth - padLeft;
                for (let wC = 0; wC < filterWidth; ++wC) {
                  const xC = xCCorner + wC * dilationWidth;
                  if (xC < 0 || xC >= convInfo.inWidth) {
                    continue;
                  }
                  const wOffset3 = wOffset2 + wC * filterStrides[2];
                  const xOffset4 = xOffset3 + xC * convInfo.inChannels;
                  let wOffset4 = wOffset3;
                  for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
                    const xVal = xVals[xOffset4 + d1];
                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
                      yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];
                    }
                    wOffset4 += convInfo.outChannels;
                  }
                }
              }
            }
          }
        }
      }
    }
    return backend2.makeTensorInfo(y.shape, y.dtype, y.values);
  }
  var conv3DConfig = {
    kernelName: Conv3D,
    backendName: "cpu",
    kernelFunc: conv3D
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv3DBackpropFilterV2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, dy} = inputs;
    const {strides, pad: pad3, filterShape} = attrs;
    assertNotComplex([x, dy], "conv3dBackpropFilterV2");
    const xStrides = util_exports.computeStrides(x.shape);
    const dyStrides = util_exports.computeStrides(dy.shape);
    const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3);
    const strideDepth = convInfo.strideDepth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const filterDepth = convInfo.filterDepth;
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const dw = new TensorBuffer(convInfo.filterShape, "float32");
    const dwValues = dw.values;
    const [dwS0, dwS1, dwS2, dwS3] = dw.strides;
    const dyValues = backend2.data.get(dy.dataId).values;
    const [dyS0, dyS1, dyS2, dyS3] = dyStrides;
    const xValues = backend2.data.get(x.dataId).values;
    const [xS0, xS1, xS2, xS3] = xStrides;
    const frontPad = convInfo.padInfo.front;
    const leftPad = convInfo.padInfo.left;
    const topPad = convInfo.padInfo.top;
    for (let wF = 0; wF < filterDepth; ++wF) {
      const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));
      const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);
      const wOffset1 = wF * dwS0;
      for (let wR = 0; wR < filterHeight; ++wR) {
        const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
        const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
        const wOffset2 = wR * dwS1 + wOffset1;
        for (let wC = 0; wC < filterWidth; ++wC) {
          const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
          const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
          const wOffset3 = wC * dwS2 + wOffset2;
          for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
            const wOffset4 = d1 * dwS3 + wOffset3;
            for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
              let dotProd = 0;
              for (let b = 0; b < convInfo.batchSize; ++b) {
                const xOffset1 = b * xS0;
                const yOffset1 = b * dyS0;
                for (let yF = yFMin; yF < yFMax; ++yF) {
                  const xF = wF + yF * strideDepth - frontPad;
                  const xOffset2 = xF * xS1 + xOffset1;
                  const yOffset2 = yF * dyS1 + yOffset1;
                  for (let yR = yRMin; yR < yRMax; ++yR) {
                    const xR = wR + yR * strideHeight - topPad;
                    const xOffset3 = xR * xS2 + xOffset2;
                    const yOffset3 = yR * dyS2 + yOffset2;
                    for (let yC = yCMin; yC < yCMax; ++yC) {
                      const xC = wC + yC * strideWidth - leftPad;
                      const xOffset4 = xC * xS3 + xOffset3;
                      const yOffset4 = yC * dyS3 + yOffset3;
                      dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];
                    }
                  }
                }
              }
              dwValues[wOffset4 + d2] = dotProd;
            }
          }
        }
      }
    }
    return backend2.makeTensorInfo(dw.shape, dw.dtype, dw.values);
  }
  var conv3DBackpropFilterV2Config = {
    kernelName: Conv3DBackpropFilterV2,
    backendName: "cpu",
    kernelFunc: conv3DBackpropFilterV2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv3DBackpropInputV2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {dy, filter} = inputs;
    const {pad: pad3, strides, inputShape} = attrs;
    assertNotComplex([dy], "conv3dBackpropInputV2");
    const dyStrides = util_exports.computeStrides(dy.shape);
    const filterStrides = util_exports.computeStrides(filter.shape);
    const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3);
    const dx = new TensorBuffer(convInfo.inShape, "float32");
    const dxValues = dx.values;
    const [dxS0, dxS1, dxS2, dxS3] = dx.strides;
    const dyValues = backend2.data.get(dy.dataId).values;
    const [dyS0, dyS1, dyS2, dyS3] = dyStrides;
    const fltValues = backend2.data.get(filter.dataId).values;
    const [fltS0, fltS1, fltS2, fltS3] = filterStrides;
    const {batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth} = convInfo;
    const frontPad = filterDepth - 1 - convInfo.padInfo.front;
    const topPad = filterHeight - 1 - convInfo.padInfo.top;
    const leftPad = filterWidth - 1 - convInfo.padInfo.left;
    for (let b = 0; b < batchSize; ++b) {
      for (let d1 = 0; d1 < inChannels; ++d1) {
        for (let xF = 0; xF < inDepth; ++xF) {
          const xFCorner = xF - frontPad;
          const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));
          const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);
          for (let xR = 0; xR < inHeight; ++xR) {
            const xRCorner = xR - topPad;
            const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
            const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
            for (let xC = 0; xC < inWidth; ++xC) {
              const xCCorner = xC - leftPad;
              const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
              const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
              let dotProd = 0;
              for (let yF = xFMin; yF < yFMax; ++yF) {
                const wF = yF * strideDepth - xFCorner;
                for (let yR = xRMin; yR < yRMax; ++yR) {
                  const wR = yR * strideHeight - xRCorner;
                  for (let yC = xCMin; yC < yCMax; ++yC) {
                    const wC = yC * strideWidth - xCCorner;
                    const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;
                    const fltOffset = fltS0 * (filterDepth - 1 - wF) + fltS1 * (filterHeight - 1 - wR) + fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;
                    for (let d2 = 0; d2 < outChannels; ++d2) {
                      const pixel = dyValues[dyOffset + d2];
                      const weight = fltValues[fltOffset + d2];
                      dotProd += pixel * weight;
                    }
                  }
                }
              }
              dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] = dotProd;
            }
          }
        }
      }
    }
    return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
  }
  var conv3DBackpropInputV2Config = {
    kernelName: Conv3DBackpropInputV2,
    backendName: "cpu",
    kernelFunc: conv3DBackpropInputV2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var cos2 = unaryKernelFunc(Cos, (xi) => Math.cos(xi));
  var cosConfig = {
    kernelName: Cos,
    backendName: "cpu",
    kernelFunc: cos2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var cosh2 = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));
  var coshConfig = {
    kernelName: Cosh,
    backendName: "cpu",
    kernelFunc: cosh2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function cropAndResize2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {image: image3, boxes, boxInd} = inputs;
    const {cropSize, method, extrapolationValue} = attrs;
    const [batch, imageHeight, imageWidth, numChannels] = image3.shape;
    const numBoxes = boxes.shape[0];
    const [cropHeight, cropWidth] = cropSize;
    const output = buffer([numBoxes, cropHeight, cropWidth, numChannels], "float32");
    const boxVals = backend2.data.get(boxes.dataId).values;
    const boxIndVals = backend2.data.get(boxInd.dataId).values;
    const imageVals = backend2.data.get(image3.dataId).values;
    const inStride = util_exports.computeStrides(image3.shape);
    const outStride = util_exports.computeStrides(output.shape);
    for (let b = 0; b < numBoxes; b++) {
      const startInd = b * 4;
      const y1 = boxVals[startInd];
      const x1 = boxVals[startInd + 1];
      const y2 = boxVals[startInd + 2];
      const x2 = boxVals[startInd + 3];
      const bInd = boxIndVals[b];
      if (bInd >= batch) {
        continue;
      }
      const heightScale = cropHeight > 1 ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0;
      const widthScale = cropWidth > 1 ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;
      for (let y = 0; y < cropHeight; y++) {
        const yInd = cropHeight > 1 ? y1 * (imageHeight - 1) + y * heightScale : 0.5 * (y1 + y2) * (imageHeight - 1);
        if (yInd < 0 || yInd > imageHeight - 1) {
          for (let x = 0; x < cropWidth; x++) {
            for (let c = 0; c < numChannels; c++) {
              const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
              output.values[ind] = extrapolationValue;
            }
          }
          continue;
        }
        if (method === "bilinear") {
          const topInd = Math.floor(yInd);
          const bottomInd = Math.ceil(yInd);
          const yLerp = yInd - topInd;
          for (let x = 0; x < cropWidth; x++) {
            const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);
            if (xInd < 0 || xInd > imageWidth - 1) {
              for (let c = 0; c < numChannels; c++) {
                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
                output.values[ind] = extrapolationValue;
              }
              continue;
            }
            const leftInd = Math.floor(xInd);
            const rightInd = Math.ceil(xInd);
            const xLerp = xInd - leftInd;
            for (let c = 0; c < numChannels; c++) {
              let ind = c + leftInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];
              const topLeft = imageVals[ind];
              ind = c + rightInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];
              const topRight = imageVals[ind];
              ind = c + leftInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];
              const bottomLeft = imageVals[ind];
              ind = c + rightInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];
              const bottomRight = imageVals[ind];
              const top = topLeft + (topRight - topLeft) * xLerp;
              const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;
              ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
              output.values[ind] = top + (bottom - top) * yLerp;
            }
          }
        } else {
          for (let x = 0; x < cropWidth; ++x) {
            const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);
            if (xInd < 0 || xInd > imageWidth - 1) {
              for (let c = 0; c < numChannels; c++) {
                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
                output.values[ind] = extrapolationValue;
              }
              continue;
            }
            const closestX = Math.round(xInd);
            const closestY = Math.round(yInd);
            for (let c = 0; c < numChannels; c++) {
              const inInd = c + closestX * inStride[2] + closestY * inStride[1] + bInd * inStride[0];
              const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
              output.values[outInd] = imageVals[inInd];
            }
          }
        }
      }
    }
    return backend2.makeTensorInfo(output.shape, output.dtype, output.values);
  }
  var cropAndResizeConfig = {
    kernelName: CropAndResize,
    backendName: "cpu",
    kernelFunc: cropAndResize2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function cumsum2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis, exclusive, reverse: reverse5} = attrs;
    assertNotComplex(x, "cumsum");
    const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length);
    let $x = x;
    if (permutation != null) {
      $x = transpose2({inputs: {x}, backend: backend2, attrs: {perm: permutation}});
    }
    const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0];
    if (permutedAxis !== $x.shape.length - 1) {
      throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);
    }
    const resultDtype = upcastType($x.dtype, "int32");
    const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);
    const aVals = backend2.data.get($x.dataId).values;
    const finalDim = $x.shape[$x.shape.length - 1];
    const indexAdjuster = reverse5 ? (i, j) => i + finalDim - j - 1 : (i, j) => i + j;
    for (let i = 0; i < aVals.length; i += finalDim) {
      for (let j = 0; j < finalDim; j++) {
        const idx = indexAdjuster(i, j);
        if (j === 0) {
          vals[idx] = exclusive ? 0 : aVals[idx];
        } else {
          const prevIdx = indexAdjuster(i, j - 1);
          vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] : aVals[idx] + vals[prevIdx];
        }
      }
    }
    const result = backend2.makeTensorInfo($x.shape, resultDtype, vals);
    if (permutation != null) {
      const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);
      const reverseTransposedResult = transpose2({inputs: {x: result}, backend: backend2, attrs: {perm: reversePermutation}});
      backend2.disposeIntermediateTensorInfo(result);
      backend2.disposeIntermediateTensorInfo($x);
      return reverseTransposedResult;
    }
    return result;
  }
  var cumsumConfig = {
    kernelName: Cumsum,
    backendName: "cpu",
    kernelFunc: cumsum2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function denseBincount2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, weights} = inputs;
    const {size, binaryOutput} = attrs;
    if (x.shape.length === 1) {
      const xVals = backend2.data.get(x.dataId).values;
      const weightsVals = backend2.data.get(weights.dataId).values;
      const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);
      return backend2.makeTensorInfo([size], weights.dtype, outVals);
    } else if (x.shape.length === 2) {
      const xBuf = backend2.bufferSync(x);
      const weightsBuf = backend2.bufferSync(weights);
      const outBuf = bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput);
      return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);
    }
    throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);
  }
  var denseBincountConfig = {
    kernelName: DenseBincount,
    backendName: "cpu",
    kernelFunc: denseBincount2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function depthToSpace2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {blockSize, dataFormat} = attrs;
    util_exports.assert(dataFormat === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);
    util_exports.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);
    const batchSize = x.shape[0];
    const inputHeight = x.shape[1];
    const inputWidth = x.shape[2];
    const inputDepth = x.shape[3];
    const outputHeight = inputHeight * blockSize;
    const outputWidth = inputWidth * blockSize;
    const outputDepth = inputDepth / (blockSize * blockSize);
    const xValues = backend2.data.get(x.dataId).values;
    const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);
    let outputIdx = 0;
    for (let b = 0; b < batchSize; ++b) {
      for (let h = 0; h < outputHeight; ++h) {
        const inH = Math.floor(h / blockSize);
        const offsetH = h % blockSize;
        for (let w = 0; w < outputWidth; ++w) {
          const inW = Math.floor(w / blockSize);
          const offsetW = w % blockSize;
          const offsetD = (offsetH * blockSize + offsetW) * outputDepth;
          for (let d = 0; d < outputDepth; ++d) {
            const inD = d + offsetD;
            const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));
            result[outputIdx++] = xValues[inputIdx];
          }
        }
      }
    }
    return backend2.makeTensorInfo([batchSize, outputHeight, outputWidth, outputDepth], x.dtype, result);
  }
  var depthToSpaceConfig = {
    kernelName: DepthToSpace,
    backendName: "cpu",
    kernelFunc: depthToSpace2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function depthwiseConv2dNative(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, filter} = inputs;
    const {strides, pad: pad3, dilations, dimRoundingMode} = attrs;
    assertNotComplex([x, filter], "depthwiseConv2DNative");
    const xStrides = util_exports.computeStrides(x.shape);
    const filterStrides = util_exports.computeStrides(filter.shape);
    let $dilations = dilations;
    if ($dilations == null) {
      $dilations = [1, 1];
    }
    util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);
    const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);
    const {filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo} = convInfo;
    const padLeft = padInfo.left;
    const padTop = padInfo.top;
    const chMul = convInfo.outChannels / convInfo.inChannels;
    const y = new TensorBuffer(convInfo.outShape, x.dtype);
    const xVals = backend2.data.get(x.dataId).values;
    const wVals = backend2.data.get(filter.dataId).values;
    const yVals = y.values;
    for (let b = 0; b < convInfo.batchSize; ++b) {
      const xOffset1 = b * xStrides[0];
      const yOffset1 = b * y.strides[0];
      for (let yR = 0; yR < convInfo.outHeight; ++yR) {
        const yOffset2 = yOffset1 + yR * y.strides[1];
        const xRCorner = yR * convInfo.strideHeight - padLeft;
        for (let wR = 0; wR < filterHeight; ++wR) {
          const xR = xRCorner + wR * dilationHeight;
          if (xR < 0 || xR >= convInfo.inHeight) {
            continue;
          }
          const wOffset1 = wR * filterStrides[0];
          const xOffset2 = xOffset1 + xR * xStrides[1];
          for (let yC = 0; yC < convInfo.outWidth; ++yC) {
            const yOffset3 = yOffset2 + yC * y.strides[2];
            const xCCorner = yC * convInfo.strideWidth - padTop;
            for (let wC = 0; wC < filterWidth; ++wC) {
              const xC = xCCorner + wC * dilationWidth;
              if (xC < 0 || xC >= convInfo.inWidth) {
                continue;
              }
              const wOffset2 = wOffset1 + wC * filterStrides[1];
              const xOffset3 = xOffset2 + xC * convInfo.inChannels;
              let yOffset4 = yOffset3;
              let wOffset3 = wOffset2;
              for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
                const xVal = xVals[xOffset3 + d1];
                for (let q = 0; q < chMul; ++q) {
                  yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];
                }
                yOffset4 += chMul;
                wOffset3 += chMul;
              }
            }
          }
        }
      }
    }
    return backend2.makeTensorInfo(y.shape, y.dtype, y.values);
  }
  var depthwiseConv2dNativeConfig = {
    kernelName: DepthwiseConv2dNative,
    backendName: "cpu",
    kernelFunc: depthwiseConv2dNative
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function depthwiseConv2dNativeBackpropFilter2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, dy} = inputs;
    const {strides, dilations, pad: pad3, dimRoundingMode, filterShape} = attrs;
    assertNotComplex([x, dy], "depthwiseConv2dNativeBackpropFilter");
    const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad3, dimRoundingMode, true);
    const {strideHeight, strideWidth, filterHeight, filterWidth} = convInfo;
    const dW = new TensorBuffer(convInfo.filterShape, "float32");
    const leftPad = convInfo.padInfo.left;
    const topPad = convInfo.padInfo.top;
    const chMul = convInfo.outChannels / convInfo.inChannels;
    const xVals = backend2.data.get(x.dataId).values;
    const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);
    const dyVals = backend2.data.get(dy.dataId).values;
    const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);
    for (let wR = 0; wR < filterHeight; ++wR) {
      const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
      const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
      for (let wC = 0; wC < filterWidth; ++wC) {
        const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
        const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
          const d1 = Math.trunc(d2 / chMul);
          const dm = d2 % chMul;
          let dotProd = 0;
          for (let b = 0; b < convInfo.batchSize; ++b) {
            for (let yR = yRMin; yR < yRMax; ++yR) {
              const xR = wR + yR * strideHeight - topPad;
              for (let yC = yCMin; yC < yCMax; ++yC) {
                const xC = wC + yC * strideWidth - leftPad;
                dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);
              }
            }
          }
          dW.set(dotProd, wR, wC, d1, dm);
        }
      }
    }
    return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);
  }
  var depthwiseConv2dNativeBackpropFilterConfig = {
    kernelName: DepthwiseConv2dNativeBackpropFilter,
    backendName: "cpu",
    kernelFunc: depthwiseConv2dNativeBackpropFilter2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function depthwiseConv2dNativeBackpropInput2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {dy, filter} = inputs;
    const {strides, dilations, pad: pad3, dimRoundingMode, inputShape} = attrs;
    assertNotComplex([dy, filter], "depthwiseConv2DNativeBackpropInput");
    const dyStrides = util_exports.computeStrides(dy.shape);
    const filterStrides = util_exports.computeStrides(filter.shape);
    const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, true);
    const dx = new TensorBuffer(convInfo.inShape, "float32");
    const dxValues = dx.values;
    const [dxS0, dxS1, dxS2] = dx.strides;
    const dyValues = backend2.data.get(dy.dataId).values;
    const [dyS0, dyS1, dyS2] = dyStrides;
    const fltValues = backend2.data.get(filter.dataId).values;
    const [fltS0, fltS1, fltS2] = filterStrides;
    const {batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth} = convInfo;
    const topPad = filterHeight - 1 - convInfo.padInfo.top;
    const leftPad = filterWidth - 1 - convInfo.padInfo.left;
    const chMul = outChannels / inChannels;
    for (let b = 0; b < batchSize; ++b) {
      for (let d1 = 0; d1 < inChannels; ++d1) {
        for (let xR = 0; xR < inHeight; ++xR) {
          const xRCorner = xR - topPad;
          const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
          const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
          for (let xC = 0; xC < inWidth; ++xC) {
            const xCCorner = xC - leftPad;
            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
            const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
            let dotProd = 0;
            for (let yR = xRMin; yR < yRMax; ++yR) {
              const wR = yR * strideHeight - xRCorner;
              for (let yC = xCMin; yC < yCMax; ++yC) {
                const wC = yC * strideWidth - xCCorner;
                const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;
                const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
                for (let dm = 0; dm < chMul; ++dm) {
                  const d2 = d1 * chMul + dm;
                  const pixel = dyValues[dyOffset + d2];
                  const weight = fltValues[fltOffset + dm];
                  dotProd += pixel * weight;
                }
              }
            }
            dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;
          }
        }
      }
    }
    return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
  }
  var depthwiseConv2dNativeBackpropInputConfig = {
    kernelName: DepthwiseConv2dNativeBackpropInput,
    backendName: "cpu",
    kernelFunc: depthwiseConv2dNativeBackpropInput2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function diag2(args) {
    const {inputs, backend: backend2} = args;
    const {x} = inputs;
    const xSize = util_exports.sizeFromShape(x.shape);
    const xVals = backend2.data.get(x.dataId).values;
    const outBuf = buffer([xSize, xSize], x.dtype);
    const vals = outBuf.values;
    for (let i = 0; i < xVals.length; i++) {
      vals[i * xSize + i] = xVals[i];
    }
    const outShape = [...x.shape, ...x.shape];
    return backend2.makeTensorInfo(outShape, outBuf.dtype, outBuf.values);
  }
  var diagConfig = {
    kernelName: Diag,
    backendName: "cpu",
    kernelFunc: diag2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var dilation2dConfig = {
    kernelName: Dilation2D,
    backendName: "cpu",
    kernelFunc: ({inputs, backend: backend2, attrs}) => {
      const {x, filter} = inputs;
      const {strides, pad: pad3, dilations} = attrs;
      const cpuBackend = backend2;
      const xVals = cpuBackend.data.get(x.dataId).values;
      const xRank = x.shape.length;
      const filterVals = cpuBackend.data.get(filter.dataId).values;
      const filterRank = filter.shape.length;
      const {batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape} = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations);
      const outSize = util_exports.sizeFromShape(outShape);
      const outRank = outShape.length;
      const outputVals = util_exports.getArrayFromDType(x.dtype, outSize);
      for (let b = 0; b < batchSize; ++b) {
        for (let hOut = 0; hOut < outHeight; ++hOut) {
          const hBeg = hOut * strideHeight - padInfo.top;
          for (let wOut = 0; wOut < outWidth; ++wOut) {
            const wBeg = wOut * strideWidth - padInfo.left;
            for (let d = 0; d < inChannels; ++d) {
              let curVal = Number.MIN_SAFE_INTEGER;
              for (let h = 0; h < filterHeight; ++h) {
                const hIn = hBeg + h * dilationHeight;
                if (hIn >= 0 && hIn < inHeight) {
                  for (let w = 0; w < filterWidth; ++w) {
                    const wIn = wBeg + w * dilationWidth;
                    if (wIn >= 0 && wIn < inWidth) {
                      const xIndex = util_exports.locToIndex([b, hIn, wIn, d], xRank, util_exports.computeStrides(x.shape));
                      const filterIndex = util_exports.locToIndex([h, w, d], filterRank, util_exports.computeStrides(filter.shape));
                      const val = xVals[xIndex] + filterVals[filterIndex];
                      if (val > curVal) {
                        curVal = val;
                      }
                    }
                  }
                }
              }
              const outputIndex = util_exports.locToIndex([b, hOut, wOut, d], outRank, util_exports.computeStrides(outShape));
              outputVals[outputIndex] = curVal;
            }
          }
        }
      }
      const dataId = cpuBackend.write(util_exports.toTypedArray(outputVals, x.dtype), outShape, x.dtype);
      return {dataId, shape: outShape, dtype: x.dtype};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var dilation2dBackpropFilterConfig = {
    kernelName: Dilation2DBackpropFilter,
    backendName: "cpu",
    kernelFunc: ({inputs, backend: backend2, attrs}) => {
      const {x, filter, dy} = inputs;
      const {strides, pad: pad3, dilations} = attrs;
      const cpuBackend = backend2;
      const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);
      const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);
      const {batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape} = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations);
      util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);
      const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);
      const gradients = util_exports.makeZerosNestedTypedArray(filter.shape, filter.dtype);
      for (let b = 0; b < batchSize; ++b) {
        for (let hOut = 0; hOut < outHeight; ++hOut) {
          const hBeg = hOut * strideHeight - padInfo.top;
          for (let wOut = 0; wOut < outWidth; ++wOut) {
            const wBeg = wOut * strideWidth - padInfo.left;
            for (let d = 0; d < inChannels; ++d) {
              let curVal = Number.MIN_SAFE_INTEGER;
              let hMax = 0;
              let wMax = 0;
              for (let h = 0; h < filterHeight; ++h) {
                const hIn = hBeg + h * dilationHeight;
                if (hIn >= 0 && hIn < inHeight) {
                  for (let w = 0; w < filterWidth; ++w) {
                    const wIn = wBeg + w * dilationWidth;
                    if (wIn >= 0 && wIn < inWidth) {
                      const val = $x[b][hIn][wIn][d] + $filter[h][w][d];
                      if (val > curVal) {
                        curVal = val;
                        hMax = h;
                        wMax = w;
                      }
                    }
                  }
                }
              }
              gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];
            }
          }
        }
      }
      const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);
      return {dataId, shape: filter.shape, dtype: filter.dtype};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var dilation2dBackpropInputConfig = {
    kernelName: Dilation2DBackpropInput,
    backendName: "cpu",
    kernelFunc: ({inputs, backend: backend2, attrs}) => {
      const {x, filter, dy} = inputs;
      const {strides, pad: pad3, dilations} = attrs;
      const cpuBackend = backend2;
      const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);
      const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);
      const {batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape} = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations);
      util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);
      const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);
      const gradients = util_exports.makeZerosNestedTypedArray(x.shape, x.dtype);
      for (let b = 0; b < batchSize; ++b) {
        for (let hOut = 0; hOut < outHeight; ++hOut) {
          const hBeg = hOut * strideHeight - padInfo.top;
          for (let wOut = 0; wOut < outWidth; ++wOut) {
            const wBeg = wOut * strideWidth - padInfo.left;
            for (let d = 0; d < inChannels; ++d) {
              let curVal = Number.MIN_SAFE_INTEGER;
              let hInMax = hBeg < 0 ? 0 : hBeg;
              let wInMax = wBeg < 0 ? 0 : wBeg;
              for (let h = 0; h < filterHeight; ++h) {
                const hIn = hBeg + h * dilationHeight;
                if (hIn >= 0 && hIn < inHeight) {
                  for (let w = 0; w < filterWidth; ++w) {
                    const wIn = wBeg + w * dilationWidth;
                    if (wIn >= 0 && wIn < inWidth) {
                      const val = $x[b][hIn][wIn][d] + $filter[h][w][d];
                      if (val > curVal) {
                        curVal = val;
                        hInMax = hIn;
                        wInMax = wIn;
                      }
                    }
                  }
                }
              }
              gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];
            }
          }
        }
      }
      const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), x.shape, x.dtype);
      return {dataId, shape: x.shape, dtype: x.dtype};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function eluGrad(args) {
    const {inputs, backend: backend2} = args;
    const {dy, y} = inputs;
    assertNotComplex([dy, y], "eluGrad");
    const resultValues = new Float32Array(util_exports.sizeFromShape(y.shape));
    const values = backend2.data.get(y.dataId).values;
    const dyValues = backend2.data.get(dy.dataId).values;
    for (let i = 0; i < values.length; ++i) {
      const v = values[i];
      if (v >= 1) {
        resultValues[i] = dyValues[i];
      } else {
        resultValues[i] = dyValues[i] * (v + 1);
      }
    }
    return backend2.makeTensorInfo(y.shape, "float32", resultValues);
  }
  var eluGradConfig2 = {
    kernelName: EluGrad,
    backendName: "cpu",
    kernelFunc: eluGrad
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var equalImpl = createSimpleBinaryKernelImpl((a, b) => a === b ? 1 : 0);
  var equal2 = binaryKernelFunc(Equal, equalImpl, null, "bool");
  var equalConfig = {
    kernelName: Equal,
    backendName: "cpu",
    kernelFunc: equal2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var p = backend_util_exports.ERF_P;
  var a1 = backend_util_exports.ERF_A1;
  var a2 = backend_util_exports.ERF_A2;
  var a3 = backend_util_exports.ERF_A3;
  var a4 = backend_util_exports.ERF_A4;
  var a5 = backend_util_exports.ERF_A5;
  var erf2 = unaryKernelFunc(Erf, (xi) => {
    const sign4 = Math.sign(xi);
    const v = Math.abs(xi);
    const t = 1 / (1 + p * v);
    return sign4 * (1 - ((((a5 * t + a4) * t + a3) * t + a2) * t + a1) * t * Math.exp(-v * v));
  });
  var erfConfig = {
    kernelName: Erf,
    backendName: "cpu",
    kernelFunc: erf2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function expandDims3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {input: input2} = inputs;
    const {dim} = attrs;
    const inputRank = input2.shape.length;
    const newShape = input2.shape.slice();
    let $dim = dim;
    if (dim < 0) {
      util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);
      $dim = inputRank + dim + 1;
    }
    newShape.splice($dim, 0, 1);
    return reshape3({inputs: {x: input2}, backend: backend2, attrs: {shape: newShape}});
  }
  var expandDimsConfig = {
    kernelName: ExpandDims,
    backendName: "cpu",
    kernelFunc: expandDims3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var realDivImpl = createSimpleBinaryKernelImpl((a, b) => a / b);
  var div2 = binaryKernelFunc(RealDiv, realDivImpl);
  var realDivConfig = {
    kernelName: RealDiv,
    backendName: "cpu",
    kernelFunc: div2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fftBatch(input2, inverse, cpuBackend) {
    const inputShape = input2.shape;
    const batch = inputShape[0];
    const innerDim = inputShape[1];
    const inputVals = cpuBackend.data.get(input2.dataId);
    const real2D = inputVals.complexTensorInfos.real;
    const imag2D = inputVals.complexTensorInfos.imag;
    const resultShape = [batch, innerDim];
    const resultSize = util_exports.sizeFromShape(resultShape);
    const resultReal = util_exports.getTypedArrayFromDType("float32", resultSize);
    const resultImag = util_exports.getTypedArrayFromDType("float32", resultSize);
    for (let b = 0; b < batch; b++) {
      const r = slice2({
        inputs: {x: real2D},
        backend: cpuBackend,
        attrs: {begin: [b, 0], size: [1, innerDim]}
      });
      const i = slice2({
        inputs: {x: imag2D},
        backend: cpuBackend,
        attrs: {begin: [b, 0], size: [1, innerDim]}
      });
      const input3 = complex2({inputs: {real: r, imag: i}, backend: cpuBackend});
      const {real: real4, imag: imag4} = fftImpl(input3, inverse, cpuBackend);
      const res = backend_util_exports.mergeRealAndImagArrays(real4, imag4);
      for (let d = 0; d < innerDim; d++) {
        const c = backend_util_exports.getComplexWithIndex(res, d);
        resultReal[b * innerDim + d] = c.real;
        resultImag[b * innerDim + d] = c.imag;
      }
      cpuBackend.disposeIntermediateTensorInfo(r);
      cpuBackend.disposeIntermediateTensorInfo(i);
      cpuBackend.disposeIntermediateTensorInfo(input3);
    }
    const $realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultReal);
    const $imagInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultImag);
    const result = complex2({inputs: {real: $realInfo, imag: $imagInfo}, backend: cpuBackend});
    cpuBackend.disposeIntermediateTensorInfo($realInfo);
    cpuBackend.disposeIntermediateTensorInfo($imagInfo);
    return result;
  }
  function fftImpl(input2, inverse, cpuBackend) {
    const inputSize = util_exports.sizeFromShape(input2.shape);
    const inputVals = cpuBackend.data.get(input2.dataId);
    const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;
    const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;
    if (isExponentOf2(inputSize)) {
      const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);
      const resultShape = [input2.shape[0], input2.shape[1]];
      if (inverse) {
        const realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", result.real);
        const imagInfo = cpuBackend.makeTensorInfo(resultShape, "float32", result.imag);
        const sizeInfo = cpuBackend.makeTensorInfo([], "float32", util_exports.createScalarValue(inputSize, "float32"));
        const sizeInfoCopy = identity2({inputs: {x: sizeInfo}, backend: cpuBackend});
        const divRealInfo = realDivConfig.kernelFunc({inputs: {a: realInfo, b: sizeInfo}, backend: cpuBackend});
        const divImagInfo = realDivConfig.kernelFunc({inputs: {a: imagInfo, b: sizeInfoCopy}, backend: cpuBackend});
        const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;
        const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;
        cpuBackend.disposeIntermediateTensorInfo(realInfo);
        cpuBackend.disposeIntermediateTensorInfo(imagInfo);
        cpuBackend.disposeIntermediateTensorInfo(sizeInfo);
        cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);
        cpuBackend.disposeIntermediateTensorInfo(divRealInfo);
        cpuBackend.disposeIntermediateTensorInfo(divImagInfo);
        return {real: divRealVals, imag: divImagVals};
      }
      return result;
    } else {
      const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);
      const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);
      return backend_util_exports.splitRealAndImagArrays(rawOutput);
    }
  }
  function isExponentOf2(size) {
    return (size & size - 1) === 0;
  }
  function fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {
    if (size === 1) {
      return {real: realVals, imag: imagVals};
    }
    const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);
    const half = size / 2;
    const evenComplex = backend_util_exports.complexWithEvenIndex(data);
    const evenRealVals = evenComplex.real;
    const evenImagVals = evenComplex.imag;
    const evenShape = [evenRealVals.length];
    const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, "float32", evenRealVals);
    const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, "float32", evenImagVals);
    const evenTensorInfo = complex2({inputs: {real: evenRealInfo, imag: evenImagInfo}, backend: cpuBackend});
    const oddComplex = backend_util_exports.complexWithOddIndex(data);
    const oddRealVals = oddComplex.real;
    const oddImagVals = oddComplex.imag;
    const oddShape = [oddRealVals.length];
    const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, "float32", oddRealVals);
    const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, "float32", oddImagVals);
    const oddTensorInfo = complex2({inputs: {real: oddRealInfo, imag: oddImagInfo}, backend: cpuBackend});
    const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);
    const $evenRealVals = $evenComplex.real;
    const $evenImagVals = $evenComplex.imag;
    const $evenShape = [$evenRealVals.length];
    const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, "float32", $evenRealVals);
    const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, "float32", $evenImagVals);
    const $evenTensorInfo = complex2({
      inputs: {real: $evenRealInfo, imag: $evenImagInfo},
      backend: cpuBackend
    });
    const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);
    const $oddRealVals = $oddComplex.real;
    const $oddImagVals = $oddComplex.imag;
    const $oddShape = [$oddRealVals.length];
    const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddRealVals);
    const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddImagVals);
    const $oddTensorInfo = complex2({inputs: {real: $oddRealInfo, imag: $oddImagInfo}, backend: cpuBackend});
    const e = backend_util_exports.exponents(size, inverse);
    const eShape = [e.real.length];
    const eRealInfo = cpuBackend.makeTensorInfo(eShape, "float32", e.real);
    const eImagInfo = cpuBackend.makeTensorInfo(eShape, "float32", e.imag);
    const complexInfo = complex2({inputs: {real: eRealInfo, imag: eImagInfo}, backend: cpuBackend});
    const exponentInfo = multiply2({inputs: {a: complexInfo, b: $oddTensorInfo}, backend: cpuBackend});
    const addPart = add4({
      inputs: {a: $evenTensorInfo, b: exponentInfo},
      backend: cpuBackend
    });
    const subPart = sub2({
      inputs: {a: $evenTensorInfo, b: exponentInfo},
      backend: cpuBackend
    });
    const addPartReal = real2({inputs: {input: addPart}, backend: cpuBackend});
    const subPartReal = real2({inputs: {input: subPart}, backend: cpuBackend});
    const addPartImag = imag2({inputs: {input: addPart}, backend: cpuBackend});
    const subPartImag = imag2({inputs: {input: subPart}, backend: cpuBackend});
    const $real = concat2({
      inputs: [addPartReal, subPartReal],
      backend: cpuBackend,
      attrs: {axis: 0}
    });
    const $imag = concat2({
      inputs: [addPartImag, subPartImag],
      backend: cpuBackend,
      attrs: {axis: 0}
    });
    const $realVals = cpuBackend.data.get($real.dataId).values;
    const $imagVals = cpuBackend.data.get($imag.dataId).values;
    cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);
    cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);
    cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);
    cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);
    cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);
    cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);
    cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);
    cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);
    cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);
    cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);
    cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);
    cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);
    cpuBackend.disposeIntermediateTensorInfo(eRealInfo);
    cpuBackend.disposeIntermediateTensorInfo(eImagInfo);
    cpuBackend.disposeIntermediateTensorInfo(complexInfo);
    cpuBackend.disposeIntermediateTensorInfo(exponentInfo);
    cpuBackend.disposeIntermediateTensorInfo(addPart);
    cpuBackend.disposeIntermediateTensorInfo(subPart);
    cpuBackend.disposeIntermediateTensorInfo(addPartReal);
    cpuBackend.disposeIntermediateTensorInfo(addPartImag);
    cpuBackend.disposeIntermediateTensorInfo(subPartReal);
    cpuBackend.disposeIntermediateTensorInfo(subPartImag);
    cpuBackend.disposeIntermediateTensorInfo($real);
    cpuBackend.disposeIntermediateTensorInfo($imag);
    return {real: $realVals, imag: $imagVals};
  }
  function fourierTransformByMatmul(data, size, inverse) {
    const ret = new Float32Array(size * 2);
    for (let r = 0; r < size; r++) {
      let real4 = 0;
      let imag4 = 0;
      for (let c = 0; c < size; c++) {
        const e = backend_util_exports.exponent(r * c, size, inverse);
        const term = backend_util_exports.getComplexWithIndex(data, c);
        real4 += term.real * e.real - term.imag * e.imag;
        imag4 += term.real * e.imag + term.imag * e.real;
      }
      if (inverse) {
        real4 /= size;
        imag4 /= size;
      }
      backend_util_exports.assignToTypedArray(ret, real4, imag4, r);
    }
    return ret;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fft2(args) {
    const {inputs, backend: backend2} = args;
    const {input: input2} = inputs;
    const inputSize = util_exports.sizeFromShape(input2.shape);
    const innerDimensionSize = input2.shape[input2.shape.length - 1];
    const batch = inputSize / innerDimensionSize;
    const input2D = reshape3({
      inputs: {x: input2},
      backend: backend2,
      attrs: {shape: [batch, innerDimensionSize]}
    });
    const result = fftBatch(input2D, false, backend2);
    const resultReshaped = reshape3({inputs: {x: result}, backend: backend2, attrs: {shape: input2.shape}});
    backend2.disposeIntermediateTensorInfo(input2D);
    backend2.disposeIntermediateTensorInfo(result);
    return resultReshaped;
  }
  var fftConfig = {
    kernelName: FFT,
    backendName: "cpu",
    kernelFunc: fft2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fill2(args) {
    const {backend: backend2, attrs} = args;
    const {shape, value, dtype} = attrs;
    const $dtype = dtype || util_exports.inferDtype(value);
    const values = util_exports.getArrayFromDType($dtype, util_exports.sizeFromShape(shape));
    fillValues(values, value, $dtype);
    return backend2.makeTensorInfo(shape, $dtype, values);
  }
  var fillConfig = {
    kernelName: Fill,
    backendName: "cpu",
    kernelFunc: fill2
  };
  function fillValues(values, value, dtype) {
    if (dtype === "string") {
      values.fill(value);
    } else {
      values.fill(value);
    }
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var flipLeftRightConfig = {
    kernelName: FlipLeftRight,
    backendName: "cpu",
    kernelFunc: ({inputs, attrs, backend: backend2}) => {
      const {image: image3} = inputs;
      const cpuBackend = backend2;
      const output = util_exports.getTypedArrayFromDType(image3.dtype, util_exports.sizeFromShape(image3.shape));
      const [batch, imageHeight, imageWidth, numChannels] = image3.shape;
      const imageVals = cpuBackend.data.get(image3.dataId).values;
      for (let batchIdx = 0; batchIdx < batch; batchIdx++) {
        const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;
        for (let row = 0; row < imageHeight; row++) {
          const rowOffset = row * (imageWidth * numChannels);
          for (let col = 0; col < imageWidth; col++) {
            const colOffset = col * numChannels;
            for (let channel = 0; channel < numChannels; channel++) {
              const coords2 = [batch, row, col, channel];
              const x = coords2[2];
              const coordX = Math.round(imageWidth - x);
              const outIdx = batchOffset + rowOffset + colOffset + channel;
              let outputValue = imageVals[outIdx];
              if (coordX >= 0 && coordX < imageWidth) {
                const rotatedColOffset = coordX * numChannels;
                const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;
                outputValue = imageVals[imageIdx];
              }
              output[outIdx] = outputValue;
            }
          }
        }
      }
      const dataId = cpuBackend.write(output, image3.shape, image3.dtype);
      return {dataId, shape: image3.shape, dtype: image3.dtype};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var floorDivImpl = createSimpleBinaryKernelImpl((a, b) => Math.floor(a / b));
  var floorDiv2 = binaryKernelFunc(FloorDiv, floorDivImpl, null, "int32");
  var floorDivConfig = {
    kernelName: FloorDiv,
    backendName: "cpu",
    kernelFunc: floorDiv2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fusedConv2D(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, filter, bias, preluActivationWeights} = inputs;
    const {strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha} = attrs;
    let result = conv2D({
      inputs: {x, filter},
      backend: backend2,
      attrs: {strides, pad: pad3, dataFormat, dilations, dimRoundingMode}
    });
    if (bias) {
      const resultOld = result;
      result = add4({inputs: {a: result, b: bias}, backend: backend2});
      backend2.disposeIntermediateTensorInfo(resultOld);
    }
    if (activation2) {
      const resultOld = result;
      result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha);
      backend2.disposeIntermediateTensorInfo(resultOld);
    }
    return result;
  }
  var fusedConv2DConfig = {
    kernelName: FusedConv2D,
    backendName: "cpu",
    kernelFunc: fusedConv2D
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fusedDepthwiseConv2D(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, filter, bias, preluActivationWeights} = inputs;
    const {strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha} = attrs;
    let result = depthwiseConv2dNative({
      inputs: {x, filter},
      backend: backend2,
      attrs: {strides, pad: pad3, dataFormat, dilations, dimRoundingMode}
    });
    if (bias) {
      const oldResult = result;
      result = add4({inputs: {a: result, b: bias}, backend: backend2});
      backend2.disposeIntermediateTensorInfo(oldResult);
    }
    if (activation2) {
      const oldResult = result;
      result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha);
      backend2.disposeIntermediateTensorInfo(oldResult);
    }
    return result;
  }
  var fusedDepthwiseConv2DConfig = {
    kernelName: FusedDepthwiseConv2D,
    backendName: "cpu",
    kernelFunc: fusedDepthwiseConv2D
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function gatherNd(args) {
    const {inputs, backend: backend2} = args;
    const {params, indices} = inputs;
    const paramsSize = util_exports.sizeFromShape(params.shape);
    const indicesShape = indices.shape;
    const sliceRank = indicesShape[indicesShape.length - 1];
    const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);
    if (numSlices === 0) {
      return backend2.makeTensorInfo(resultShape, params.dtype, []);
    }
    const outBuf = buffer([numSlices, sliceSize], params.dtype);
    const indicesData = backend2.data.get(indices.dataId).values;
    const paramsData = backend2.data.get(params.dataId).values;
    for (let i = 0; i < numSlices; i++) {
      const index = [];
      let flattenIndex = 0;
      for (let j = 0; j < sliceRank; j++) {
        const dim = indicesData[i * sliceRank + j];
        flattenIndex += dim * strides[j];
        index.push(dim);
      }
      if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) {
        throw new Error(`Invalid indices: ${index} does not index into ${params.shape}`);
      }
      for (let k = 0; k < sliceSize; k++) {
        outBuf.values[i * sliceSize + k] = paramsData[flattenIndex * sliceSize + k];
      }
    }
    return backend2.makeTensorInfo(resultShape, outBuf.dtype, outBuf.values);
  }
  var gatherNdConfig = {
    kernelName: GatherNd,
    backendName: "cpu",
    kernelFunc: gatherNd
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function gatherV2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, indices} = inputs;
    const {axis, batchDims} = attrs;
    assertNotComplex([x, indices], "gatherV2");
    let $batchDims = batchDims;
    if (batchDims == null) {
      $batchDims = 0;
    }
    const indicesSize = util_exports.sizeFromShape(indices.shape);
    const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
    const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, $batchDims);
    const flattenX = reshape3({
      inputs: {x},
      backend: backend2,
      attrs: {
        shape: [
          shapeInfo.batchSize,
          shapeInfo.outerSize,
          shapeInfo.dimSize,
          shapeInfo.sliceSize
        ]
      }
    });
    const flattenIndex = reshape3({
      inputs: {x: indices},
      backend: backend2,
      attrs: {shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize]}
    });
    const flattenOutputShape = [
      shapeInfo.batchSize,
      shapeInfo.outerSize,
      indicesSize / shapeInfo.batchSize,
      shapeInfo.sliceSize
    ];
    const indicesBuf = backend2.bufferSync(flattenIndex);
    const xBuf = backend2.bufferSync(flattenX);
    const outBuf = gatherV2Impl(xBuf, indicesBuf, flattenOutputShape);
    backend2.disposeIntermediateTensorInfo(flattenX);
    backend2.disposeIntermediateTensorInfo(flattenIndex);
    return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
  }
  var gatherV2Config = {
    kernelName: GatherV2,
    backendName: "cpu",
    kernelFunc: gatherV2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var greaterEqualImpl = createSimpleBinaryKernelImpl((a, b) => a >= b ? 1 : 0);
  var greaterEqual2 = binaryKernelFunc(GreaterEqual, greaterEqualImpl, null, "bool");
  var greaterEqualConfig = {
    kernelName: GreaterEqual,
    backendName: "cpu",
    kernelFunc: greaterEqual2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function ifft2(args) {
    const {inputs, backend: backend2} = args;
    const {input: input2} = inputs;
    const inputSize = util_exports.sizeFromShape(input2.shape);
    const innerDimensionSize = input2.shape[input2.shape.length - 1];
    const batch = inputSize / innerDimensionSize;
    const input2D = reshape3({
      inputs: {x: input2},
      backend: backend2,
      attrs: {shape: [batch, innerDimensionSize]}
    });
    const result = fftBatch(input2D, true, backend2);
    const resultReshaped = reshape3({inputs: {x: result}, backend: backend2, attrs: {shape: input2.shape}});
    backend2.disposeIntermediateTensorInfo(input2D);
    backend2.disposeIntermediateTensorInfo(result);
    return resultReshaped;
  }
  var ifftConfig = {
    kernelName: IFFT,
    backendName: "cpu",
    kernelFunc: ifft2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var isFinite3 = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, "bool");
  var isFiniteConfig = {
    kernelName: IsFinite,
    backendName: "cpu",
    kernelFunc: isFinite3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var isInf2 = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, "bool");
  var isInfConfig = {
    kernelName: IsInf,
    backendName: "cpu",
    kernelFunc: isInf2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var isNaN3 = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, "bool");
  var isNaNConfig = {
    kernelName: IsNan,
    backendName: "cpu",
    kernelFunc: isNaN3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var lessEqualImpl = createSimpleBinaryKernelImpl((a, b) => a <= b ? 1 : 0);
  var lessEqual2 = binaryKernelFunc(LessEqual, lessEqualImpl, null, "bool");
  var lessEqualConfig = {
    kernelName: LessEqual,
    backendName: "cpu",
    kernelFunc: lessEqual2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function linSpace(args) {
    const {backend: backend2, attrs} = args;
    const {start, stop, num} = attrs;
    const outVals = linSpaceImpl(start, stop, num);
    return backend2.makeTensorInfo([outVals.length], "float32", outVals);
  }
  var linSpaceConfig = {
    kernelName: LinSpace,
    backendName: "cpu",
    kernelFunc: linSpace
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var log1p2 = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));
  var log1pConfig = {
    kernelName: Log1p,
    backendName: "cpu",
    kernelFunc: log1p2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var logicalAndImpl = createSimpleBinaryKernelImpl((a, b) => a && b);
  var logicalAnd2 = binaryKernelFunc(LogicalAnd, logicalAndImpl, null, "bool");
  var logicalAndConfig = {
    kernelName: LogicalAnd,
    backendName: "cpu",
    kernelFunc: logicalAnd2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var logicalNot2 = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, "bool");
  var logicalNotConfig = {
    kernelName: LogicalNot,
    backendName: "cpu",
    kernelFunc: logicalNot2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var logicalOrImpl = createSimpleBinaryKernelImpl((a, b) => a || b);
  var logicalOr2 = binaryKernelFunc(LogicalOr, logicalOrImpl, null, "bool");
  var logicalOrConfig = {
    kernelName: LogicalOr,
    backendName: "cpu",
    kernelFunc: logicalOr2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function lRN(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {depthRadius, bias, alpha, beta} = attrs;
    assertNotComplex(x, "LRN");
    const channels = x.shape[3];
    const maxD = channels - 1;
    const xValues = backend2.data.get(x.dataId).values;
    const size = util_exports.sizeFromShape(x.shape);
    const result = new Float32Array(size);
    function sumAcrossChannels(offset) {
      const currentChannel = offset % channels;
      let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);
      const endSumOffset = offset - currentChannel + Math.min(currentChannel + depthRadius, maxD);
      let sum6 = 0;
      for (; beginSumOffset <= endSumOffset; beginSumOffset++) {
        const z = xValues[beginSumOffset];
        sum6 += z * z;
      }
      return sum6;
    }
    for (let offset = 0; offset < size; offset++) {
      const sum6 = sumAcrossChannels(offset);
      const val = xValues[offset] * Math.pow(bias + alpha * sum6, -beta);
      result[offset] = val;
    }
    return backend2.makeTensorInfo(x.shape, x.dtype, result);
  }
  var lRNConfig = {
    kernelName: LRN,
    backendName: "cpu",
    kernelFunc: lRN
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function lRNGrad(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, y, dy} = inputs;
    const {depthRadius, bias, alpha, beta} = attrs;
    assertNotComplex(dy, "LRNGrad");
    const dySize = util_exports.sizeFromShape(dy.shape);
    const channels = dy.shape[3];
    const dyValues = backend2.data.get(dy.dataId).values;
    const xValues = backend2.data.get(x.dataId).values;
    const yValues = backend2.data.get(y.dataId).values;
    const result = new Float32Array(dySize);
    const size = dySize;
    for (let offset = 0; offset < size; offset++) {
      const currentChannel = offset % channels;
      const depthBegin = offset - currentChannel + Math.max(0, currentChannel - depthRadius);
      const depthEnd = offset - currentChannel + Math.min(channels, currentChannel + depthRadius + 1);
      let norm2 = 0;
      for (let k = depthBegin; k < depthEnd; k++) {
        norm2 += Math.pow(xValues[k], 2);
      }
      norm2 = alpha * norm2 + bias;
      for (let k = depthBegin; k < depthEnd; k++) {
        let dyi = -2 * alpha * beta * xValues[k] * yValues[offset] / norm2;
        if (offset === k) {
          dyi += Math.pow(norm2, -beta);
        }
        dyi *= dyValues[offset];
        result[k] += dyi;
      }
    }
    return backend2.makeTensorInfo(dy.shape, x.dtype, result);
  }
  var lRNGradConfig = {
    kernelName: LRNGrad,
    backendName: "cpu",
    kernelFunc: lRNGrad
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function max3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {reductionIndices, keepDims} = attrs;
    const cpuBackend = backend2;
    let xShape = x.shape;
    const xRank = xShape.length;
    const origAxes = util_exports.parseAxisParam(reductionIndices, xShape);
    let axes = origAxes;
    const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
    let xVals = cpuBackend.data.get(x.dataId).values;
    if (permutedAxes != null) {
      const newShape = new Array(xRank);
      for (let i = 0; i < newShape.length; i++) {
        newShape[i] = xShape[permutedAxes[i]];
      }
      xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);
      axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
      xShape = newShape;
    }
    assertNotComplex(x, "max");
    backend_util_exports.assertAxesAreInnerMostDims("max", axes, xRank);
    const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, axes);
    const reduceSize = util_exports.sizeFromShape(reduceShape);
    const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);
    const dataId = cpuBackend.write(result, maxOutShape, x.dtype);
    let outShape = maxOutShape;
    if (keepDims) {
      const newShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);
      outShape = newShape;
    }
    return {dataId, shape: outShape, dtype: x.dtype};
  }
  var maxConfig = {
    kernelName: Max,
    backendName: "cpu",
    kernelFunc: max3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxPool2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    assertNotComplex(x, "maxPool");
    const {filterSize, strides, pad: pad3, dimRoundingMode} = attrs;
    const dilations = 1;
    util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);
    let res;
    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
      res = identity2({inputs: {x}, backend: backend2});
    } else {
      const xValues = backend2.data.get(x.dataId).values;
      const strides2 = util_exports.computeStrides(x.shape);
      const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, "max");
      res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);
    }
    return res;
  }
  var maxPoolConfig = {
    kernelName: MaxPool,
    backendName: "cpu",
    kernelFunc: maxPool2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxPool3D(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {filterSize, strides, pad: pad3, dimRoundingMode, dataFormat} = attrs;
    assertNotComplex(x, "maxPool3d");
    const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat);
    const xValues = backend2.data.get(x.dataId).values;
    const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, "max");
    return backend2.makeTensorInfo(outBuf.shape, "float32", outBuf.values);
  }
  var maxPool3DConfig = {
    kernelName: MaxPool3D,
    backendName: "cpu",
    kernelFunc: maxPool3D
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxPool3DGrad(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {dy, input: input2} = inputs;
    const {filterSize, strides, pad: pad3, dimRoundingMode} = attrs;
    assertNotComplex([dy, input2], "maxPool3DGrad");
    const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode);
    const inputBuf = backend2.bufferSync(input2);
    const maxPosBuf = maxPool3dPositions(inputBuf, convInfo);
    const strideDepth = convInfo.strideDepth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const dilationDepth = convInfo.dilationDepth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
    const dx = buffer(input2.shape, "float32");
    const dyBuf = backend2.bufferSync(dy);
    for (let batch = 0; batch < convInfo.batchSize; ++batch) {
      for (let channel = 0; channel < convInfo.inChannels; ++channel) {
        for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {
          for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {
            for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {
              const dyDepthCorner = dxDepth - padFront;
              const dyRowCorner = dxRow - padTop;
              const dyColCorner = dxCol - padLeft;
              let dotProd = 0;
              for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {
                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;
                if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {
                  continue;
                }
                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {
                  const dyRow = (dyRowCorner + wRow) / strideHeight;
                  if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {
                    continue;
                  }
                  for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {
                    const dyCol = (dyColCorner + wCol) / strideWidth;
                    if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {
                      continue;
                    }
                    const maxPos = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);
                    const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterWidth + wCol;
                    const mask = maxPos === curPos ? 1 : 0;
                    if (mask === 0) {
                      continue;
                    }
                    const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);
                    dotProd += pixel * mask;
                  }
                }
              }
              dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);
            }
          }
        }
      }
    }
    return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
  }
  var maxPool3DGradConfig2 = {
    kernelName: MaxPool3DGrad,
    backendName: "cpu",
    kernelFunc: maxPool3DGrad
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxPoolGrad2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {dy, input: input2, output} = inputs;
    const x = input2;
    assertNotComplex([input2, output], "maxPoolGrad");
    const {filterSize, strides, pad: pad3, dimRoundingMode} = attrs;
    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);
    const xValues = backend2.data.get(x.dataId).values;
    const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
    const dx = buffer(x.shape, "float32");
    const dyData = backend2.data.get(dy.dataId).values;
    const dyBuf = buffer(dy.shape, "float32", dyData);
    for (let b = 0; b < convInfo.batchSize; ++b) {
      for (let d = 0; d < convInfo.inChannels; ++d) {
        for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {
          for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {
            const dyRCorner = dxR - padTop;
            const dyCCorner = dxC - padLeft;
            let dotProd = 0;
            for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
              const dyR = (dyRCorner + wR) / strideHeight;
              if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {
                continue;
              }
              for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
                const dyC = (dyCCorner + wC) / strideWidth;
                if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {
                  continue;
                }
                const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(b, dyR, dyC, d);
                const curPos = wR * effectiveFilterWidth + wC;
                const mask = maxPos === curPos ? 1 : 0;
                if (mask === 0) {
                  continue;
                }
                const pixel = dyBuf.get(b, dyR, dyC, d);
                dotProd += pixel * mask;
              }
            }
            dx.set(dotProd, b, dxR, dxC, d);
          }
        }
      }
    }
    return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
  }
  var maxPoolGradConfig2 = {
    kernelName: MaxPoolGrad,
    backendName: "cpu",
    kernelFunc: maxPoolGrad2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {
    const strides = util_exports.computeStrides(xShape);
    const maxPools = pool2(xValues, xShape, dtype, strides, convInfo, "max");
    const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);
    return [maxPools.values, maxPositions.values];
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var maxPoolWithArgmaxConfig = {
    kernelName: MaxPoolWithArgmax,
    backendName: "cpu",
    kernelFunc: ({inputs, attrs, backend: backend2}) => {
      const {x} = inputs;
      const {filterSize, strides, pad: pad3, includeBatchInIndex} = attrs;
      const cpuBackend = backend2;
      assertNotComplex(x, "MaxPoolWithArgmax");
      const values = cpuBackend.data.get(x.dataId).values;
      const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad3);
      const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);
      const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);
      const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);
      return [
        {dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype},
        {dataId: indexesDataId, shape: convInfo.outShape, dtype: "int32"}
      ];
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sum3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis, keepDims} = attrs;
    assertNotComplex(x, "sum");
    let $x;
    if (x.dtype === "bool") {
      $x = cast3({inputs: {x}, backend: backend2, attrs: {dtype: "int32"}});
    } else {
      $x = identity2({inputs: {x}, backend: backend2});
    }
    const xRank = $x.shape.length;
    const axes = util_exports.parseAxisParam(axis, $x.shape);
    const permutation = backend_util_exports.getAxesPermutation(axes, xRank);
    let reductionAxes = axes;
    let permutedX = $x;
    if (permutation != null) {
      permutedX = transpose2({inputs: {x: $x}, backend: backend2, attrs: {perm: permutation}});
      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);
    }
    backend_util_exports.assertAxesAreInnerMostDims("sum", reductionAxes, permutedX.shape.length);
    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, reductionAxes);
    const resultDtype = backend_util_exports.upcastType(permutedX.dtype, "int32");
    let result = zeros3(backend2, outShape, resultDtype);
    const reduceSize = util_exports.sizeFromShape(reduceShape);
    const vals = backend2.data.get(result.dataId).values;
    const aVals = backend2.data.get(permutedX.dataId).values;
    for (let i = 0; i < vals.length; ++i) {
      const offset = i * reduceSize;
      let sum6 = 0;
      for (let j = 0; j < reduceSize; ++j) {
        sum6 += aVals[offset + j];
      }
      vals[i] = sum6;
    }
    if (keepDims) {
      const newShape = backend_util_exports.expandShapeToKeepDim(result.shape, axes);
      const oldResult = result;
      result = reshape3({inputs: {x: result}, backend: backend2, attrs: {shape: newShape}});
      backend2.disposeIntermediateTensorInfo(oldResult);
    }
    backend2.disposeIntermediateTensorInfo($x);
    if (permutation != null) {
      backend2.disposeIntermediateTensorInfo(permutedX);
    }
    return result;
  }
  var sumConfig = {
    kernelName: Sum,
    backendName: "cpu",
    kernelFunc: sum3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function mean2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis, keepDims} = attrs;
    const axes = util_exports.parseAxisParam(axis, x.shape);
    const shapes = backend_util_exports.computeOutAndReduceShapes(x.shape, axes);
    const reduceShape = shapes[1];
    const reduceSize = util_exports.sizeFromShape(reduceShape);
    const toDispose = [];
    const reduceSizeScalar = backend2.makeTensorInfo([], "float32", new Float32Array([reduceSize]));
    toDispose.push(reduceSizeScalar);
    const $x = cast3({inputs: {x}, backend: backend2, attrs: {dtype: "float32"}});
    toDispose.push($x);
    const res = div2({inputs: {a: $x, b: reduceSizeScalar}, backend: backend2});
    toDispose.push(res);
    const result = sum3({inputs: {x: res}, backend: backend2, attrs: {axis, keepDims}});
    toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return result;
  }
  var meanConfig = {
    kernelName: Mean,
    backendName: "cpu",
    kernelFunc: mean2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function min3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis, keepDims} = attrs;
    assertNotComplex(x, "min");
    const origAxes = util_exports.parseAxisParam(axis, x.shape);
    let axes = origAxes;
    const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
    let $x = x;
    if (permutedAxes != null) {
      $x = transpose2({inputs: {x}, backend: backend2, attrs: {perm: permutedAxes}});
      axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
    }
    backend_util_exports.assertAxesAreInnerMostDims("min", axes, $x.shape.length);
    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
    const reduceSize = util_exports.sizeFromShape(reduceShape);
    const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
    const aVals = backend2.data.get($x.dataId).values;
    for (let i = 0; i < vals.length; ++i) {
      const offset = i * reduceSize;
      let min6 = aVals[offset];
      for (let j = 0; j < reduceSize; ++j) {
        const value = aVals[offset + j];
        if (value < min6) {
          min6 = value;
        }
      }
      vals[i] = min6;
    }
    if (permutedAxes != null) {
      backend2.disposeIntermediateTensorInfo($x);
    }
    const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);
    if (keepDims) {
      const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
      const reshapedResult = reshape3({inputs: {x: result}, backend: backend2, attrs: {shape: expandedShape}});
      backend2.disposeIntermediateTensorInfo(result);
      return reshapedResult;
    }
    return result;
  }
  var minConfig = {
    kernelName: Min,
    backendName: "cpu",
    kernelFunc: min3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function mirrorPad2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {paddings, mode} = attrs;
    assertNotComplex(x, "mirrorPad");
    const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
    const start = paddings.map((p2) => p2[0]);
    const end = paddings.map((p2, i) => p2[0] + x.shape[i]);
    const offset = mode === "reflect" ? 0 : 1;
    const xVals = backend2.data.get(x.dataId).values;
    const xRank = x.shape.length;
    const xStrides = util_exports.computeStrides(x.shape);
    const resultSize = util_exports.sizeFromShape(outShape);
    const resultRank = outShape.length;
    const resultStrides = util_exports.computeStrides(outShape);
    const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);
    for (let i = 0; i < resultSize; i++) {
      let coords2 = util_exports.indexToLoc(i, resultRank, resultStrides);
      for (let i2 = 0; i2 < resultRank; i2++) {
        if (coords2[i2] < start[i2]) {
          coords2[i2] = start[i2] * 2 - coords2[i2] - offset;
        } else if (coords2[i2] >= end[i2]) {
          coords2[i2] = (end[i2] - 1) * 2 - coords2[i2] + offset;
        }
      }
      coords2 = coords2.map((c, i2) => c - start[i2]);
      const inIndex = util_exports.locToIndex(coords2, xRank, xStrides);
      resVals[i] = xVals[inIndex];
    }
    const outId = backend2.write(resVals, outShape, x.dtype);
    return {dataId: outId, shape: outShape, dtype: x.dtype};
  }
  var mirrorPadConfig = {
    kernelName: MirrorPad,
    backendName: "cpu",
    kernelFunc: mirrorPad2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var modImpl = createSimpleBinaryKernelImpl((aValue, bValue) => {
    const rem = aValue % bValue;
    if (aValue < 0 && bValue < 0 || aValue >= 0 && bValue >= 0) {
      return rem;
    } else {
      return (rem + bValue) % bValue;
    }
  });
  var mod2 = binaryKernelFunc(Mod, modImpl);
  var modConfig = {
    kernelName: Mod,
    backendName: "cpu",
    kernelFunc: mod2
  };
  var seedrandom4 = __toModule(require_seedrandom2());
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function softmax3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {logits} = inputs;
    const {dim} = attrs;
    const logitsRank = logits.shape.length;
    let $dim = dim;
    if ($dim === -1) {
      $dim = logitsRank - 1;
    }
    if ($dim !== logitsRank - 1) {
      throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`);
    }
    const axes = util_exports.parseAxisParam([$dim], logits.shape);
    const maxLogit = max3({
      inputs: {x: logits},
      backend: backend2,
      attrs: {reductionIndices: axes, keepDims: false}
    });
    const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);
    const maxLogitReshaped = reshape3({inputs: {x: maxLogit}, backend: backend2, attrs: {shape: expandedShape}});
    const a = sub2({inputs: {a: logits, b: maxLogitReshaped}, backend: backend2});
    const b = exp2({inputs: {x: a}, backend: backend2});
    const sumExp = sum3({inputs: {x: b}, backend: backend2, attrs: {axis: axes, keepDims: false}});
    const sumReshaped = reshape3({inputs: {x: sumExp}, backend: backend2, attrs: {shape: expandedShape}});
    const result = div2({inputs: {a: b, b: sumReshaped}, backend: backend2});
    backend2.disposeIntermediateTensorInfo(maxLogit);
    backend2.disposeIntermediateTensorInfo(maxLogitReshaped);
    backend2.disposeIntermediateTensorInfo(a);
    backend2.disposeIntermediateTensorInfo(b);
    backend2.disposeIntermediateTensorInfo(sumExp);
    backend2.disposeIntermediateTensorInfo(sumReshaped);
    return result;
  }
  var softmaxConfig = {
    kernelName: Softmax,
    backendName: "cpu",
    kernelFunc: softmax3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function multinomial2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {logits} = inputs;
    const {numSamples, seed, normalized} = attrs;
    assertNotComplex(logits, "multinomial");
    const probabilities = normalized ? logits : softmax3({inputs: {logits}, backend: backend2, attrs: {dim: -1}});
    const batchSize = probabilities.shape[0];
    const numEvents = probabilities.shape[1];
    const probVals = backend2.data.get(probabilities.dataId).values;
    const resShape = [batchSize, numSamples];
    const resVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape), "int32");
    for (let b = 0; b < batchSize; ++b) {
      const offset = b * numEvents;
      const cdf = new Float32Array(numEvents - 1);
      cdf[0] = probVals[offset];
      for (let event = 1; event < cdf.length; ++event) {
        cdf[event] = cdf[event - 1] + probVals[offset + event];
      }
      const random = seedrandom4.alea(seed.toString());
      const outOffset = b * numSamples;
      for (let sampleId = 0; sampleId < numSamples; ++sampleId) {
        const r = random();
        resVals[outOffset + sampleId] = cdf.length;
        for (let event = 0; event < cdf.length; event++) {
          if (r < cdf[event]) {
            resVals[outOffset + sampleId] = event;
            break;
          }
        }
      }
    }
    if (!normalized) {
      backend2.disposeIntermediateTensorInfo(probabilities);
    }
    return backend2.makeTensorInfo(resShape, "int32", resVals);
  }
  var multinomialConfig = {
    kernelName: Multinomial,
    backendName: "cpu",
    kernelFunc: multinomial2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var nonMaxSuppressionV3Impl2 = kernel_impls_exports.nonMaxSuppressionV3Impl;
  function nonMaxSuppressionV3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {boxes, scores} = inputs;
    const {maxOutputSize, iouThreshold, scoreThreshold} = attrs;
    assertNotComplex(boxes, "NonMaxSuppression");
    const boxesVals = backend2.data.get(boxes.dataId).values;
    const scoresVals = backend2.data.get(scores.dataId).values;
    const {selectedIndices} = nonMaxSuppressionV3Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
    return backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices));
  }
  var nonMaxSuppressionV3Config = {
    kernelName: NonMaxSuppressionV3,
    backendName: "cpu",
    kernelFunc: nonMaxSuppressionV3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var nonMaxSuppressionV4Impl2 = kernel_impls_exports.nonMaxSuppressionV4Impl;
  function nonMaxSuppressionV4(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {boxes, scores} = inputs;
    const {maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize} = attrs;
    assertNotComplex(boxes, "NonMaxSuppressionPadded");
    const boxesVals = backend2.data.get(boxes.dataId).values;
    const scoresVals = backend2.data.get(scores.dataId).values;
    const {selectedIndices, validOutputs} = nonMaxSuppressionV4Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
    return [
      backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
      backend2.makeTensorInfo([], "int32", new Int32Array([validOutputs]))
    ];
  }
  var nonMaxSuppressionV4Config = {
    kernelName: NonMaxSuppressionV4,
    backendName: "cpu",
    kernelFunc: nonMaxSuppressionV4
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var nonMaxSuppressionV5Impl2 = kernel_impls_exports.nonMaxSuppressionV5Impl;
  function nonMaxSuppressionV5(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {boxes, scores} = inputs;
    const {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma} = attrs;
    assertNotComplex(boxes, "NonMaxSuppressionWithScore");
    const boxesVals = backend2.data.get(boxes.dataId).values;
    const scoresVals = backend2.data.get(scores.dataId).values;
    const maxOutputSizeVal = maxOutputSize;
    const iouThresholdVal = iouThreshold;
    const scoreThresholdVal = scoreThreshold;
    const softNmsSigmaVal = softNmsSigma;
    const {selectedIndices, selectedScores} = nonMaxSuppressionV5Impl2(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);
    return [
      backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
      backend2.makeTensorInfo([selectedScores.length], "float32", new Float32Array(selectedScores))
    ];
  }
  var nonMaxSuppressionV5Config = {
    kernelName: NonMaxSuppressionV5,
    backendName: "cpu",
    kernelFunc: nonMaxSuppressionV5
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function oneHot2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {indices} = inputs;
    const {depth, onValue, offValue} = attrs;
    assertNotComplex(indices, "oneHot");
    const indicesSize = util_exports.sizeFromShape(indices.shape);
    const res = new Float32Array(indicesSize * depth);
    res.fill(offValue);
    const indicesVal = backend2.data.get(indices.dataId).values;
    for (let event = 0; event < indicesSize; ++event) {
      if (indicesVal[event] >= 0 && indicesVal[event] < depth) {
        res[event * depth + indicesVal[event]] = onValue;
      }
    }
    return backend2.makeTensorInfo([...indices.shape, depth], "int32", res);
  }
  var oneHotConfig = {
    kernelName: OneHot,
    backendName: "cpu",
    kernelFunc: oneHot2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function zerosLike2(args) {
    const {inputs, backend: backend2} = args;
    const {x} = inputs;
    if (x.dtype === "string") {
      throw new Error("zerosLike is not supported for string tensors");
    } else if (x.dtype === "complex64") {
      const realPart = real2({inputs: {input: x}, backend: backend2});
      const r = zerosLike2({inputs: {x: realPart}, backend: backend2});
      const imagPart = imag2({inputs: {input: x}, backend: backend2});
      const i = zerosLike2({inputs: {x: imagPart}, backend: backend2});
      const result = complex2({inputs: {real: r, imag: i}, backend: backend2});
      backend2.disposeIntermediateTensorInfo(realPart);
      backend2.disposeIntermediateTensorInfo(r);
      backend2.disposeIntermediateTensorInfo(imagPart);
      backend2.disposeIntermediateTensorInfo(i);
      return result;
    } else {
      return fill2({backend: backend2, attrs: {shape: x.shape, value: 0, dtype: x.dtype}});
    }
  }
  var zerosLikeConfig = {
    kernelName: ZerosLike,
    backendName: "cpu",
    kernelFunc: zerosLike2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function onesLike2(args) {
    const {inputs, backend: backend2} = args;
    const {x} = inputs;
    if (x.dtype === "string") {
      throw new Error("onesLike is not supported for string tensors");
    } else if (x.dtype === "complex64") {
      const realPart = real2({inputs: {input: x}, backend: backend2});
      const r = onesLike2({inputs: {x: realPart}, backend: backend2});
      const imagPart = imag2({inputs: {input: x}, backend: backend2});
      const i = zerosLike2({inputs: {x: imagPart}, backend: backend2});
      const result = complex2({inputs: {real: r, imag: i}, backend: backend2});
      backend2.disposeIntermediateTensorInfo(realPart);
      backend2.disposeIntermediateTensorInfo(r);
      backend2.disposeIntermediateTensorInfo(imagPart);
      backend2.disposeIntermediateTensorInfo(i);
      return result;
    } else {
      return fill2({backend: backend2, attrs: {shape: x.shape, value: 1, dtype: x.dtype}});
    }
  }
  var onesLikeConfig = {
    kernelName: OnesLike,
    backendName: "cpu",
    kernelFunc: onesLike2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function pack(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {axis} = attrs;
    if (inputs.length === 1) {
      return expandDims3({inputs: {input: inputs[0]}, backend: backend2, attrs: {dim: axis}});
    }
    const shape = inputs[0].shape;
    const dtype = inputs[0].dtype;
    inputs.forEach((t) => {
      util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
      util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes");
    });
    const intermediateTensorInfos = [];
    const expandedTensors = inputs.map((t) => {
      const expandedT = expandDims3({inputs: {input: t}, backend: backend2, attrs: {dim: axis}});
      intermediateTensorInfos.push(expandedT);
      return expandedT;
    });
    const result = concat2({inputs: expandedTensors, backend: backend2, attrs: {axis}});
    intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return result;
  }
  var packConfig = {
    kernelName: Pack,
    backendName: "cpu",
    kernelFunc: pack
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function padV2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {paddings, constantValue} = attrs;
    assertNotComplex(x, "pad");
    const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
    const start = paddings.map((p2) => p2[0]);
    const xVals = backend2.data.get(x.dataId).values;
    const xSize = util_exports.sizeFromShape(x.shape);
    const xRank = x.shape.length;
    const xStrides = util_exports.computeStrides(x.shape);
    const resultSize = util_exports.sizeFromShape(outShape);
    const resultRank = outShape.length;
    const resultStrides = util_exports.computeStrides(outShape);
    const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);
    if (constantValue !== 0) {
      resVals.fill(constantValue);
    }
    for (let i = 0; i < xSize; i++) {
      const coords2 = util_exports.indexToLoc(i, xRank, xStrides);
      const outCoords = coords2.map((c, i2) => c + start[i2]);
      const outIndex = util_exports.locToIndex(outCoords, resultRank, resultStrides);
      resVals[outIndex] = xVals[i];
    }
    const outId = backend2.write(resVals, outShape, x.dtype);
    return {dataId: outId, shape: outShape, dtype: x.dtype};
  }
  var padV2Config = {
    kernelName: PadV2,
    backendName: "cpu",
    kernelFunc: padV2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var powImpl = createSimpleBinaryKernelImpl((a, b) => Math.pow(a, b));
  var pow2 = binaryKernelFunc(Pow, powImpl);
  var powConfig = {
    kernelName: Pow,
    backendName: "cpu",
    kernelFunc: pow2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function range3(args) {
    const {backend: backend2, attrs} = args;
    const {start, stop, dtype, step: step5} = attrs;
    const values = rangeImpl(start, stop, step5, dtype);
    return backend2.makeTensorInfo([values.length], dtype, values);
  }
  var rangeConfig = {
    kernelName: Range,
    backendName: "cpu",
    kernelFunc: range3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var reciprocal2 = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);
  var reciprocalConfig = {
    kernelName: Reciprocal,
    backendName: "cpu",
    kernelFunc: reciprocal2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function resizeBilinear2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {images} = inputs;
    const {alignCorners, halfPixelCenters, size} = attrs;
    assertNotComplex(images, "resizeBilinear");
    const imagesStrides = util_exports.computeStrides(images.shape);
    const [newHeight, newWidth] = size;
    const [batch, oldHeight, oldWidth, numChannels] = images.shape;
    const xValues = backend2.data.get(images.dataId).values;
    const result = new Float32Array(util_exports.sizeFromShape([batch, newHeight, newWidth, numChannels]));
    const effectiveInputSize = [
      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
    ];
    const effectiveOutputSize = [
      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
    ];
    let outputIdx = 0;
    const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
    const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
    for (let b = 0; b < batch; b++) {
      for (let r = 0; r < newHeight; r++) {
        let sourceFracRow;
        if (halfPixelCenters) {
          sourceFracRow = effectiveRowSizeRatio * (r + 0.5) - 0.5;
        } else {
          sourceFracRow = effectiveRowSizeRatio * r;
        }
        const sourceRowFloor = Math.max(0, Math.floor(sourceFracRow));
        const rowFrac = sourceFracRow - sourceRowFloor;
        const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));
        const topRowOffset = b * imagesStrides[0] + sourceRowFloor * imagesStrides[1];
        const botRowOffset = b * imagesStrides[0] + sourceRowCeil * imagesStrides[1];
        for (let c = 0; c < newWidth; c++) {
          let sourceFracCol;
          if (halfPixelCenters) {
            sourceFracCol = effectiveColSizeRatio * (c + 0.5) - 0.5;
          } else {
            sourceFracCol = effectiveColSizeRatio * c;
          }
          const sourceColFloor = Math.max(0, Math.floor(sourceFracCol));
          const colFrac = sourceFracCol - sourceColFloor;
          const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));
          const topLeftOffest = topRowOffset + sourceColFloor * imagesStrides[2];
          const botLeftOffset = botRowOffset + sourceColFloor * imagesStrides[2];
          const topRightOffset = topRowOffset + sourceColCeil * imagesStrides[2];
          const botRightOffest = botRowOffset + sourceColCeil * imagesStrides[2];
          for (let d = 0; d < numChannels; d++) {
            const topLeft = xValues[topLeftOffest + d];
            const bottomLeft = xValues[botLeftOffset + d];
            const topRight = xValues[topRightOffset + d];
            const bottomRight = xValues[botRightOffest + d];
            const top = topLeft + (topRight - topLeft) * colFrac;
            const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;
            const newValue = top + (bottom - top) * rowFrac;
            result[outputIdx++] = newValue;
          }
        }
      }
    }
    return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], "float32", result);
  }
  var resizeBilinearConfig = {
    kernelName: ResizeBilinear,
    backendName: "cpu",
    kernelFunc: resizeBilinear2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function resizeBilinearGrad(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {images, dy} = inputs;
    const {alignCorners} = attrs;
    assertNotComplex([dy, images], "resizeBilinearGrad");
    const imagesStrides = util_exports.computeStrides(images.shape);
    const [batch, xHeight, xWidth, depth] = images.shape;
    const [, yHeight, yWidth] = dy.shape;
    const output = new Float32Array(batch * xHeight * xWidth * depth);
    const effectiveXSize = [
      alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
      alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
    ];
    const effectiveYSize = [
      alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
      alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
    ];
    const heightScale = effectiveXSize[0] / effectiveYSize[0];
    const widthScale = effectiveXSize[1] / effectiveYSize[1];
    const dyValues = backend2.data.get(dy.dataId).values;
    let offset = 0;
    for (let b = 0; b < batch; b++) {
      const bOffset = b * imagesStrides[0];
      for (let r = 0; r < yHeight; r++) {
        const dxR = r * heightScale;
        const topDxRIndex = Math.floor(dxR);
        const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);
        const topDxROffset = bOffset + topDxRIndex * imagesStrides[1];
        const bottomDxROffset = bOffset + bottomDxRIndex * imagesStrides[1];
        const dxRLerp = dxR - topDxRIndex;
        const inverseDxRLerp = 1 - dxRLerp;
        for (let c = 0; c < yWidth; c++) {
          const dxC = c * widthScale;
          const leftDxCIndex = Math.floor(dxC);
          const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);
          const dxCLerp = dxC - leftDxCIndex;
          const inverseDxCLerp = 1 - dxCLerp;
          const topLeftRCOffset = topDxROffset + leftDxCIndex * imagesStrides[2];
          const topRightRCOffset = topDxROffset + rightDxCIndex * imagesStrides[2];
          const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * imagesStrides[2];
          const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * imagesStrides[2];
          const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;
          const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;
          const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;
          const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;
          for (let d = 0; d < depth; d++) {
            const dyVal = dyValues[offset++];
            output[topLeftRCOffset + d] += dyVal * inverseDxRLerpTimesInverseDxCLerp;
            output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;
            output[bottomLeftRCOffset + d] += dyVal * dxRLerpTimesInverseDxCLerp;
            output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;
          }
        }
      }
    }
    return backend2.makeTensorInfo([batch, xWidth, xHeight, depth], "float32", output);
  }
  var resizeBilinearGradConfig2 = {
    kernelName: ResizeBilinearGrad,
    backendName: "cpu",
    kernelFunc: resizeBilinearGrad
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function resizeNearestNeighbor2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {images} = inputs;
    const {alignCorners, halfPixelCenters, size} = attrs;
    assertNotComplex(images, "resizeNearestNeighbor");
    const imagesStrides = util_exports.computeStrides(images.shape);
    const [newHeight, newWidth] = size;
    const [batch, oldHeight, oldWidth, numChannels] = images.shape;
    const xValues = backend2.data.get(images.dataId).values;
    const output = new Float32Array(batch * newHeight * newWidth * numChannels);
    const effectiveInputSize = [
      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
    ];
    const effectiveOutputSize = [
      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
    ];
    const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
    const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
    let outputOffset = 0;
    for (let b = 0; b < batch; b++) {
      const batchOffset = b * imagesStrides[0];
      for (let r = 0; r < newHeight; r++) {
        const sourceFracRow = halfPixelCenters ? effectiveRowSizeRatio * (r + 0.5) : effectiveRowSizeRatio * r;
        let sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
        if (halfPixelCenters) {
          sourceNearestRow = Math.max(0, sourceNearestRow);
        }
        const rowOffset = batchOffset + sourceNearestRow * imagesStrides[1];
        for (let c = 0; c < newWidth; c++) {
          const sourceFracCol = halfPixelCenters ? effectiveColSizeRatio * (c + 0.5) : effectiveColSizeRatio * c;
          let sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));
          if (halfPixelCenters) {
            sourceNearestCol = Math.max(0, sourceNearestCol);
          }
          const colOffset = rowOffset + sourceNearestCol * imagesStrides[2];
          for (let d = 0; d < numChannels; d++) {
            const newVal = xValues[colOffset + d];
            output[outputOffset++] = newVal;
          }
        }
      }
    }
    return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], images.dtype, output);
  }
  var resizeNearestNeighborConfig = {
    kernelName: ResizeNearestNeighbor,
    backendName: "cpu",
    kernelFunc: resizeNearestNeighbor2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function resizeNearestNeighborGrad(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {images, dy} = inputs;
    const {alignCorners} = attrs;
    assertNotComplex([dy, images], "resizeNearestNeighborGrad");
    const imagesStrides = util_exports.computeStrides(images.shape);
    const dyStrides = util_exports.computeStrides(dy.shape);
    const [batch, xHeight, xWidth, depth] = images.shape;
    const [, yHeight, yWidth] = dy.shape;
    const output = new Float32Array(batch * xHeight * xWidth * depth);
    const dyValues = backend2.data.get(dy.dataId).values;
    const effectiveXSize = [
      alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
      alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
    ];
    const effectiveYSize = [
      alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
      alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
    ];
    const heightScale = effectiveXSize[0] / effectiveYSize[0];
    const widthScale = effectiveXSize[1] / effectiveYSize[1];
    const invHeightScale = 1 / heightScale;
    const invWidthScale = 1 / widthScale;
    const winHeight = Math.ceil(invHeightScale) * 2 + 2;
    const winWidth = Math.ceil(invWidthScale) * 2 + 2;
    for (let b = 0; b < batch; b++) {
      const batchOffset = b * imagesStrides[0];
      for (let r = 0; r < xHeight; r++) {
        const rowOffset = batchOffset + r * imagesStrides[1];
        const startRLerp = Math.floor(r * invHeightScale);
        const startDyR = Math.floor(startRLerp - winHeight / 2);
        for (let c = 0; c < xWidth; c++) {
          const colOffset = rowOffset + c * imagesStrides[2];
          const startCLerp = Math.floor(c * invWidthScale);
          const startDyC = Math.floor(startCLerp - winWidth / 2);
          for (let d = 0; d < depth; d++) {
            let accum = 0;
            for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {
              const dyR = dyRIndex + startDyR;
              if (dyR < 0 || dyR >= yHeight) {
                continue;
              }
              const dyROffset = batchOffset + dyR * dyStrides[1];
              const sourceFracRow = dyR * heightScale;
              const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
              if (r !== sourceNearestRow) {
                continue;
              }
              for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {
                const dyC = dyCIndex + startDyC;
                if (dyC < 0 || dyC >= yWidth) {
                  continue;
                }
                const dyCOffset = dyROffset + dyC * dyStrides[2];
                const sourceFracCol = dyC * widthScale;
                const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));
                if (c === sourceNearestCol) {
                  accum += dyValues[dyCOffset + d];
                }
              }
            }
            output[colOffset + d] = accum;
          }
        }
      }
    }
    return backend2.makeTensorInfo(images.shape, images.dtype, output);
  }
  var resizeNearestNeighborGradConfig2 = {
    kernelName: ResizeNearestNeighborGrad,
    backendName: "cpu",
    kernelFunc: resizeNearestNeighborGrad
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function reverse2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {dims} = attrs;
    assertNotComplex(x, "reverse");
    const xRank = x.shape.length;
    const $dims = util_exports.parseAxisParam(dims, x.shape);
    if (xRank === 0) {
      return identity2({inputs: {x}, backend: backend2});
    }
    const outBuf = new TensorBuffer(x.shape, x.dtype);
    const xBuf = backend2.bufferSync(x);
    for (let i = 0; i < outBuf.size; i++) {
      const outLoc = outBuf.indexToLoc(i);
      const inLoc = outLoc.slice();
      $dims.forEach((d) => inLoc[d] = x.shape[d] - 1 - inLoc[d]);
      outBuf.set(xBuf.get(...inLoc), ...outLoc);
    }
    return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
  }
  var reverseConfig = {
    kernelName: Reverse,
    backendName: "cpu",
    kernelFunc: reverse2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var rotateWithOffsetConfig = {
    kernelName: RotateWithOffset,
    backendName: "cpu",
    kernelFunc: ({inputs, attrs, backend: backend2}) => {
      const {image: image3} = inputs;
      const {radians, fillValue, center} = attrs;
      const cpuBackend = backend2;
      const output = util_exports.getTypedArrayFromDType(image3.dtype, util_exports.sizeFromShape(image3.shape));
      const [batch, imageHeight, imageWidth, numChannels] = image3.shape;
      const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);
      const fullOpacityValue = 255;
      const sinFactor = Math.sin(radians);
      const cosFactor = Math.cos(radians);
      const imageVals = cpuBackend.data.get(image3.dataId).values;
      for (let batchIdx = 0; batchIdx < batch; batchIdx++) {
        const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;
        for (let row = 0; row < imageHeight; row++) {
          const rowOffset = row * (imageWidth * numChannels);
          for (let col = 0; col < imageWidth; col++) {
            const colOffset = col * numChannels;
            for (let channel = 0; channel < numChannels; channel++) {
              const coords2 = [batch, row, col, channel];
              const x = coords2[2];
              const y = coords2[1];
              let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;
              let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;
              coordX = Math.round(coordX + centerX);
              coordY = Math.round(coordY + centerY);
              let outputValue = fillValue;
              if (typeof fillValue !== "number") {
                if (channel === 3) {
                  outputValue = fullOpacityValue;
                } else {
                  outputValue = fillValue[channel];
                }
              }
              if (coordX >= 0 && coordX < imageWidth && coordY >= 0 && coordY < imageHeight) {
                const rotatedRowOffset = coordY * (imageWidth * numChannels);
                const rotatedColOffset = coordX * numChannels;
                const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;
                outputValue = imageVals[imageIdx];
              }
              const outIdx = batchOffset + rowOffset + colOffset + channel;
              output[outIdx] = outputValue;
            }
          }
        }
      }
      const dataId = cpuBackend.write(output, image3.shape, image3.dtype);
      return {dataId, shape: image3.shape, dtype: image3.dtype};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var round3 = unaryKernelFunc(Round, (xi) => {
    const base2 = Math.floor(xi);
    if (xi - base2 < 0.5) {
      return Math.floor(xi);
    } else if (xi - base2 > 0.5) {
      return Math.ceil(xi);
    } else {
      if (base2 % 2 === 0) {
        return base2;
      } else {
        return base2 + 1;
      }
    }
  });
  var roundConfig = {
    kernelName: Round,
    backendName: "cpu",
    kernelFunc: round3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function scatterImpl(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {
    const flattenShape = [outputSize / sliceSize, sliceSize];
    const indicesData = indices.values;
    const updatesData = updates.values;
    if (outputSize === 0) {
      return buffer(shape, updates.dtype);
    }
    const outBuf = buffer(flattenShape, updates.dtype);
    outBuf.values.fill(defaultValue);
    for (let i = 0; i < numUpdates; i++) {
      const index = [];
      let flattenIndex = 0;
      for (let j = 0; j < sliceRank; j++) {
        const dim = indicesData[i * sliceRank + j];
        index.push(dim);
        flattenIndex += dim * strides[j];
      }
      if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {
        throw new Error(`Invalid indices: ${index} does not index into ${shape}`);
      }
      for (let k = 0; k < sliceSize; k++) {
        if (sumDupeIndices) {
          outBuf.values[flattenIndex * sliceSize + k] += updatesData[i * sliceSize + k];
        } else {
          outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ? updatesData[0] : updatesData[i * sliceSize + k];
        }
      }
    }
    return outBuf;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function scatterNd(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {indices, updates} = inputs;
    const {shape} = attrs;
    const {sliceRank, numUpdates, sliceSize, strides, outputSize} = backend_util_exports.calculateShapes(updates, indices, shape);
    const sumDupeIndices = true;
    const indicesBuf = backend2.bufferSync(indices);
    const updatesBuf = backend2.bufferSync(updates);
    const outBuf = scatterImpl(indicesBuf, updatesBuf, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, 0, sumDupeIndices);
    return backend2.makeTensorInfo(shape, outBuf.dtype, outBuf.values);
  }
  var scatterNdConfig = {
    kernelName: ScatterNd,
    backendName: "cpu",
    kernelFunc: scatterNd
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function select(args) {
    const {inputs, backend: backend2} = args;
    const {condition, t, e} = inputs;
    assertNotComplex([condition, t, e], "select");
    const conditionRank = condition.shape.length;
    const values = backend2.data.get(condition.dataId).values;
    const tValues = backend2.data.get(t.dataId).values;
    const eValues = backend2.data.get(e.dataId).values;
    const resultDtype = upcastType(t.dtype, e.dtype);
    const newValues = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t.shape), resultDtype);
    let index = 0;
    const offset = conditionRank === 0 || conditionRank > 1 || t.shape.length === 1 ? 1 : util_exports.sizeFromShape(t.shape.slice(1));
    for (let i = 0; i < values.length; i++) {
      for (let j = 0; j < offset; j++) {
        if (values[i] === 1) {
          newValues[index++] = tValues[i];
        } else {
          newValues[index++] = eValues[i];
        }
      }
    }
    return backend2.makeTensorInfo(t.shape, resultDtype, newValues);
  }
  var selectConfig = {
    kernelName: Select,
    backendName: "cpu",
    kernelFunc: select
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var scaleAlpha = backend_util_exports.SELU_SCALEALPHA;
  var scale = backend_util_exports.SELU_SCALE;
  var selu2 = unaryKernelFunc(Selu, (xi) => {
    if (xi >= 0) {
      return scale * xi;
    } else {
      return scaleAlpha * (Math.exp(xi) - 1);
    }
  });
  var seluConfig = {
    kernelName: Selu,
    backendName: "cpu",
    kernelFunc: selu2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var sigmoid2 = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));
  var sigmoidConfig = {
    kernelName: Sigmoid,
    backendName: "cpu",
    kernelFunc: sigmoid2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var sign2 = unaryKernelFunc(Sign, (xi) => {
    if (xi < 0) {
      return -1;
    } else if (xi > 0) {
      return 1;
    } else {
      return 0;
    }
  });
  var signConfig = {
    kernelName: Sign,
    backendName: "cpu",
    kernelFunc: sign2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var sin2 = unaryKernelFunc(Sin, (xi) => Math.sin(xi));
  var sinConfig = {
    kernelName: Sin,
    backendName: "cpu",
    kernelFunc: sin2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var sinh2 = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));
  var sinhConfig = {
    kernelName: Sinh,
    backendName: "cpu",
    kernelFunc: sinh2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var epsilon2 = 11920928955078125e-23;
  var threshold = Math.log(epsilon2) + 2;
  var softplus2 = unaryKernelFunc(Softplus, (xi) => {
    const tooLarge = xi > -threshold;
    const tooSmall = xi < threshold;
    const expX = Math.exp(xi);
    let result;
    if (tooSmall) {
      result = expX;
    } else if (tooLarge) {
      result = xi;
    } else {
      result = Math.log(1 + expX);
    }
    return result;
  });
  var softplusConfig = {
    kernelName: Softplus,
    backendName: "cpu",
    kernelFunc: softplus2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function spaceToBatchND2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {blockShape, paddings} = attrs;
    assertNotComplex([x], "spaceToBatchND");
    const prod5 = util_exports.sizeFromShape(blockShape);
    const completePaddings = [[0, 0]];
    completePaddings.push(...paddings);
    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
      completePaddings.push([0, 0]);
    }
    const paddedX = padV2Config.kernelFunc({
      inputs: {x},
      backend: backend2,
      attrs: {paddings: completePaddings, constantValue: 0}
    });
    const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod5, false);
    const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
    const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod5, false);
    const reshapeInputs = {x: paddedX};
    const reshapeAttrs = {shape: reshapedPaddedShape};
    const paddedXReshaped = reshape3({inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs});
    const transposeInputs = {x: paddedXReshaped};
    const transposeAttrs = {perm: permutedReshapedPaddedPermutation};
    const paddedXT = transpose2({inputs: transposeInputs, backend: backend2, attrs: transposeAttrs});
    const resultReshapeInputs = {x: paddedXT};
    const resultReshapeAttrs = {shape: flattenShape};
    const result = reshape3({inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs});
    backend2.disposeIntermediateTensorInfo(paddedX);
    backend2.disposeIntermediateTensorInfo(paddedXReshaped);
    backend2.disposeIntermediateTensorInfo(paddedXT);
    return result;
  }
  var spaceToBatchNDConfig = {
    kernelName: SpaceToBatchND,
    backendName: "cpu",
    kernelFunc: spaceToBatchND2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sparseToDense2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {sparseIndices, sparseValues, defaultValue} = inputs;
    const {outputShape} = attrs;
    const {sliceRank, numUpdates, sliceSize, strides, outputSize} = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);
    const sumDupeIndices = false;
    const indicesBuf = backend2.bufferSync(sparseIndices);
    const updatesBuf = backend2.bufferSync(sparseValues);
    const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];
    const outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
    return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
  }
  var sparseToDenseConfig = {
    kernelName: SparseToDense,
    backendName: "cpu",
    kernelFunc: sparseToDense2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function splitV(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {numOrSizeSplits, axis} = attrs;
    const $axis = util_exports.parseAxisParam(axis, x.shape)[0];
    const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);
    const begin = new Array(x.shape.length).fill(0);
    const size = x.shape.slice();
    return splitSizes.map((s) => {
      const sliceSize = [...size];
      sliceSize[$axis] = s;
      const sliceT = slice2({inputs: {x}, backend: backend2, attrs: {begin, size: sliceSize}});
      begin[$axis] += s;
      return sliceT;
    });
  }
  var splitVConfig = {
    kernelName: SplitV,
    backendName: "cpu",
    kernelFunc: splitV
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var sqrt2 = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));
  var sqrtConfig = {
    kernelName: Sqrt,
    backendName: "cpu",
    kernelFunc: sqrt2
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var squareConfig = {
    kernelName: Square,
    backendName: "cpu",
    kernelFunc: ({inputs, backend: backend2}) => {
      const {x} = inputs;
      const cpuBackend = backend2;
      assertNotComplex(x, "square");
      const values = cpuBackend.data.get(x.dataId).values;
      const newValues = new Float32Array(values.length);
      for (let i = 0; i < values.length; ++i) {
        const value = values[i];
        newValues[i] = value * value;
      }
      const dataId = cpuBackend.write(newValues, x.shape, x.dtype);
      return {dataId, shape: x.shape, dtype: x.dtype};
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var step2 = unaryKernelFunc(Step, (xi, attrs) => {
    const stepAttrs = attrs;
    if (isNaN(xi)) {
      return NaN;
    } else {
      return xi > 0 ? 1 : stepAttrs.alpha;
    }
  });
  var stepConfig = {
    kernelName: Step,
    backendName: "cpu",
    kernelFunc: step2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function stridedSlice2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask} = attrs;
    assertNotComplex(x, "stridedSlice");
    const {nonStrided, $begin, $strides, size, newShape, outShape} = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
    const $x = reshape3({inputs: {x}, backend: backend2, attrs: {shape: newShape}});
    let result;
    if (nonStrided) {
      const sliced = slice2({inputs: {x: $x}, backend: backend2, attrs: {begin: $begin, size}});
      result = reshape3({inputs: {x: sliced}, backend: backend2, attrs: {shape: outShape}});
      backend2.disposeIntermediateTensorInfo(sliced);
    } else if (outShape.some((axis) => axis === 0)) {
      result = backend2.makeTensorInfo(outShape, x.dtype, []);
    } else {
      const xBuf = backend2.bufferSync($x);
      const outBuf = stridedSliceImpl(outShape, xBuf, $strides, $begin);
      result = backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
    }
    const resultReshaped = reshape3({inputs: {x: result}, backend: backend2, attrs: {shape: outShape}});
    backend2.disposeIntermediateTensorInfo($x);
    backend2.disposeIntermediateTensorInfo(result);
    return resultReshaped;
  }
  var stridedSliceConfig = {
    kernelName: StridedSlice,
    backendName: "cpu",
    kernelFunc: stridedSlice2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var tan2 = unaryKernelFunc(Tan, (xi) => Math.tan(xi));
  var tanConfig = {
    kernelName: Tan,
    backendName: "cpu",
    kernelFunc: tan2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var tanh3 = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));
  var tanhConfig = {
    kernelName: Tanh,
    backendName: "cpu",
    kernelFunc: tanh3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function tile3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {reps} = attrs;
    assertNotComplex(x, "tile");
    const outBuf = tileImpl(backend2.bufferSync(x), reps);
    return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
  }
  var tileConfig = {
    kernelName: Tile,
    backendName: "cpu",
    kernelFunc: tile3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function topK(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {k, sorted} = attrs;
    assertNotComplex(x, "topk");
    const xVals = backend2.data.get(x.dataId).values;
    const [allTopKVals, allTopKIndices] = topKImpl(xVals, x.shape, x.dtype, k, sorted);
    return [
      backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
      backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
    ];
  }
  var topKConfig = {
    kernelName: TopK,
    backendName: "cpu",
    kernelFunc: topK
  };
  /**
   * @license
   * Copyright 2021 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function transform2(args) {
    const {inputs, attrs, backend: backend2} = args;
    const {image: image3, transforms} = inputs;
    const {interpolation, fillMode, fillValue, outputShape} = attrs;
    const [batch, imageHeight, imageWidth, numChannels] = image3.shape;
    const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];
    const outShape = [batch, outHeight, outWidth, numChannels];
    const strides = util_exports.computeStrides(image3.shape);
    const batchStride = strides[0];
    const rowStride = strides[1];
    const colStride = strides[2];
    const outVals = util_exports.getTypedArrayFromDType(image3.dtype, util_exports.sizeFromShape(outShape));
    outVals.fill(fillValue);
    const imageVals = backend2.data.get(image3.dataId).values;
    const transformVals = backend2.data.get(transforms.dataId).values;
    for (let b = 0; b < batch; ++b) {
      const transform4 = transforms.shape[0] === 1 ? transformVals : transformVals.subarray(b * 8, b * 8 + 8);
      for (let outY = 0; outY < outHeight; ++outY) {
        for (let outX = 0; outX < outWidth; ++outX) {
          for (let channel = 0; channel < numChannels; ++channel) {
            let val;
            const projection = transform4[6] * outX + transform4[7] * outY + 1;
            if (projection === 0) {
              continue;
            }
            const inX = (transform4[0] * outX + transform4[1] * outY + transform4[2]) / projection;
            const inY = (transform4[3] * outX + transform4[4] * outY + transform4[5]) / projection;
            const x = mapCoord(inX, imageWidth, fillMode);
            const y = mapCoord(inY, imageHeight, fillMode);
            switch (interpolation) {
              case "nearest":
                val = nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, b, y, x, channel, fillValue);
                break;
              case "bilinear":
                val = bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, b, y, x, channel, fillValue);
                break;
              default:
                throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${interpolation}`);
            }
            const ind = b * batchStride + outY * rowStride + outX * colStride + channel;
            outVals[ind] = val;
          }
        }
      }
      return backend2.makeTensorInfo(outShape, image3.dtype, outVals);
    }
    const dataId = backend2.write(outVals, outShape, image3.dtype);
    return {dataId, shape: image3.shape, dtype: image3.dtype};
  }
  var transformConfig = {
    kernelName: Transform,
    backendName: "cpu",
    kernelFunc: transform2
  };
  function mapCoord(outCoord, len, mode) {
    switch (mode) {
      case "reflect":
        return mapCoordReflect(outCoord, len);
      case "wrap":
        return mapCoordWrap(outCoord, len);
      case "nearest":
        return mapCoordNearest(outCoord, len);
      case "constant":
      default:
        return mapCoordConstant(outCoord, len);
    }
  }
  function mapCoordReflect(outCoord, len) {
    let inCoord = outCoord;
    if (inCoord < 0) {
      if (len <= 1) {
        inCoord = 0;
      } else {
        const sz2 = 2 * len;
        if (inCoord < sz2) {
          inCoord = sz2 * Math.trunc(-inCoord / sz2) + inCoord;
        }
        inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1;
      }
    } else if (inCoord > len - 1) {
      if (len <= 1) {
        inCoord = 0;
      } else {
        const sz2 = 2 * len;
        inCoord -= sz2 * Math.trunc(inCoord / sz2);
        if (inCoord >= len) {
          inCoord = sz2 - inCoord - 1;
        }
      }
    }
    return util_exports.clamp(0, inCoord, len - 1);
  }
  function mapCoordWrap(outCoord, len) {
    let inCoord = outCoord;
    if (inCoord < 0) {
      if (len <= 1) {
        inCoord = 0;
      } else {
        const sz = len - 1;
        inCoord += len * (Math.trunc(-inCoord / sz) + 1);
      }
    } else if (inCoord > len - 1) {
      if (len <= 1) {
        inCoord = 0;
      } else {
        const sz = len - 1;
        inCoord -= len * Math.trunc(inCoord / sz);
      }
    }
    return util_exports.clamp(0, inCoord, len - 1);
  }
  function mapCoordConstant(outCoord, len) {
    return outCoord;
  }
  function mapCoordNearest(outCoord, len) {
    return util_exports.clamp(0, outCoord, len - 1);
  }
  function readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
    const ind = batch * batchStride + y * rowStride + x * colStride + channel;
    if (0 <= y && y < imageHeight && 0 <= x && x < imageWidth) {
      return imageVals[ind];
    } else {
      return fillValue;
    }
  }
  function nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
    const $y = Math.round(y);
    const $x = Math.round(x);
    return readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, $y, $x, channel, fillValue);
  }
  function bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
    const yFloor = Math.floor(y);
    const xFloor = Math.floor(x);
    const yCeil = yFloor + 1;
    const xCeil = xFloor + 1;
    const valueYFloor = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xCeil, channel, fillValue);
    const valueYCeil = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xCeil, channel, fillValue);
    return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function unique3(args) {
    const {inputs, attrs, backend: backend2} = args;
    const {axis} = attrs;
    const {x} = inputs;
    assertNotComplex(x, "unique");
    const values = backend2.data.get(x.dataId).values;
    const {outputValues, outputShape, indices} = uniqueImpl(values, axis, x.shape, x.dtype);
    return [
      backend2.makeTensorInfo(outputShape, x.dtype, outputValues),
      backend2.makeTensorInfo([indices.length], "int32", indices)
    ];
  }
  var uniqueConfig = {
    kernelName: Unique,
    backendName: "cpu",
    kernelFunc: unique3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function unpack(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {value} = inputs;
    let {axis} = attrs;
    if (axis < 0) {
      axis += value.shape.length;
    }
    const valueRank = value.shape.length;
    const num = value.shape[axis];
    const outShape = new Array(valueRank - 1);
    let outIndex = 0;
    for (let i = 0; i < valueRank; i++) {
      if (i !== axis) {
        outShape[outIndex++] = value.shape[i];
      }
    }
    const begin = new Array(valueRank).fill(0);
    const size = value.shape.slice();
    size[axis] = 1;
    const res = new Array(num);
    for (let i = 0; i < res.length; i++) {
      begin[axis] = i;
      const tempRes = slice2({inputs: {x: value}, backend: backend2, attrs: {begin, size}});
      res[i] = reshape3({inputs: {x: tempRes}, backend: backend2, attrs: {shape: outShape}});
      backend2.disposeIntermediateTensorInfo(tempRes);
    }
    return res;
  }
  var unpackConfig = {
    kernelName: Unpack,
    backendName: "cpu",
    kernelFunc: unpack
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function unsortedSegmentSum2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, segmentIds} = inputs;
    const {numSegments} = attrs;
    assertNotComplex(x, "unsortedSegmentSum");
    const xRank = x.shape.length;
    const segmentIdsRank = segmentIds.shape.length;
    const res = [];
    const intermediates = [];
    const numIters = xRank - segmentIdsRank;
    let $segmentIds = segmentIds;
    for (let i = 0; i < numIters; ++i) {
      const expanded = expandDims3({inputs: {input: $segmentIds}, backend: backend2, attrs: {dim: i + 1}});
      $segmentIds = expanded;
      intermediates.push(expanded);
    }
    for (let i = 0; i < numSegments; ++i) {
      const scalarValue = util_exports.createScalarValue(i, "int32");
      const segmentId = backend2.makeTensorInfo([], "int32", scalarValue);
      const mask = equal2({inputs: {a: segmentId, b: $segmentIds}, backend: backend2});
      const maskCasted = cast3({inputs: {x: mask}, backend: backend2, attrs: {dtype: "float32"}});
      const mul2 = multiply2({inputs: {a: maskCasted, b: x}, backend: backend2});
      const sumTensorInfo = sum3({inputs: {x: mul2}, backend: backend2, attrs: {axis: 0, keepDims: false}});
      res.push(sumTensorInfo);
      intermediates.push(segmentId);
      intermediates.push(mask);
      intermediates.push(maskCasted);
      intermediates.push(mul2);
      intermediates.push(sumTensorInfo);
    }
    const result = pack({inputs: res, backend: backend2, attrs: {axis: 0}});
    intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return result;
  }
  var unsortedSegmentSumConfig = {
    kernelName: UnsortedSegmentSum,
    backendName: "cpu",
    kernelFunc: unsortedSegmentSum2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var kernelConfigs = [
    _fusedMatMulConfig,
    absConfig,
    acosConfig,
    acoshConfig,
    addConfig,
    addNConfig,
    allConfig,
    anyConfig,
    argMaxConfig,
    argMinConfig,
    asinConfig,
    asinhConfig,
    atanConfig,
    atan2Config,
    atanhConfig,
    avgPoolConfig,
    avgPool3DConfig,
    avgPool3DGradConfig2,
    avgPoolGradConfig2,
    batchMatMulConfig,
    batchNormConfig,
    batchToSpaceNDConfig,
    bincountConfig,
    castConfig,
    ceilConfig,
    clipConfig,
    complexConfig,
    complexAbsConfig,
    concatConfig,
    conv2DBackpropFilterConfig,
    conv2DBackpropInputConfig,
    conv2DConfig,
    conv3DBackpropFilterV2Config,
    conv3DBackpropInputV2Config,
    conv3DConfig,
    cosConfig,
    coshConfig,
    cropAndResizeConfig,
    cumsumConfig,
    denseBincountConfig,
    depthToSpaceConfig,
    depthwiseConv2dNativeConfig,
    depthwiseConv2dNativeBackpropFilterConfig,
    depthwiseConv2dNativeBackpropInputConfig,
    diagConfig,
    dilation2dConfig,
    dilation2dBackpropInputConfig,
    dilation2dBackpropFilterConfig,
    realDivConfig,
    eluConfig,
    eluGradConfig2,
    equalConfig,
    erfConfig,
    expConfig,
    expandDimsConfig,
    expm1Config,
    fftConfig,
    fillConfig,
    flipLeftRightConfig,
    floorConfig,
    floorDivConfig,
    fusedConv2DConfig,
    fusedDepthwiseConv2DConfig,
    gatherNdConfig,
    gatherV2Config,
    greaterConfig,
    greaterEqualConfig,
    identityConfig,
    ifftConfig,
    imagConfig,
    isFiniteConfig,
    isInfConfig,
    isNaNConfig,
    leakyReluConfig,
    lessConfig,
    lessEqualConfig,
    linSpaceConfig,
    logConfig,
    log1pConfig,
    logicalAndConfig,
    logicalNotConfig,
    logicalOrConfig,
    lRNConfig,
    lRNGradConfig,
    maximumConfig,
    maxPoolConfig,
    maxPool3DConfig,
    maxPool3DGradConfig2,
    maxPoolGradConfig2,
    maxPoolWithArgmaxConfig,
    maxConfig,
    meanConfig,
    minConfig,
    minimumConfig,
    mirrorPadConfig,
    modConfig,
    multinomialConfig,
    multiplyConfig,
    negConfig,
    nonMaxSuppressionV3Config,
    nonMaxSuppressionV4Config,
    nonMaxSuppressionV5Config,
    notEqualConfig,
    oneHotConfig,
    onesLikeConfig,
    packConfig,
    padV2Config,
    powConfig,
    preluConfig,
    prodConfig,
    rangeConfig,
    realConfig,
    reciprocalConfig,
    reluConfig,
    relu6Config,
    reshapeConfig,
    resizeBilinearConfig,
    resizeBilinearGradConfig2,
    resizeNearestNeighborConfig,
    resizeNearestNeighborGradConfig2,
    reverseConfig,
    rotateWithOffsetConfig,
    roundConfig,
    rsqrtConfig,
    scatterNdConfig,
    selectConfig,
    seluConfig,
    sigmoidConfig,
    signConfig,
    sinConfig,
    sinhConfig,
    sliceConfig,
    softmaxConfig,
    softplusConfig,
    spaceToBatchNDConfig,
    sparseToDenseConfig,
    splitVConfig,
    sqrtConfig,
    squareConfig,
    squaredDifferenceConfig,
    stepConfig,
    stridedSliceConfig,
    subConfig,
    sumConfig,
    tanConfig,
    tanhConfig,
    tileConfig,
    topKConfig,
    transposeConfig,
    transformConfig,
    uniqueConfig,
    unpackConfig,
    unsortedSegmentSumConfig,
    zerosLikeConfig
  ];
  for (const kernelConfig of kernelConfigs) {
    registerKernel(kernelConfig);
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var contexts = {};
  var WEBGL_ATTRIBUTES = {
    alpha: false,
    antialias: false,
    premultipliedAlpha: false,
    preserveDrawingBuffer: false,
    depth: false,
    stencil: false,
    failIfMajorPerformanceCaveat: true
  };
  function setWebGLContext(webGLVersion, gl) {
    contexts[webGLVersion] = gl;
  }
  function getWebGLContext(webGLVersion) {
    if (!(webGLVersion in contexts)) {
      const newCtx = getWebGLRenderingContext(webGLVersion);
      if (newCtx !== null) {
        contexts[webGLVersion] = newCtx;
      } else {
        console.log("Could not get context for WebGL version", webGLVersion);
        return null;
      }
    }
    const gl = contexts[webGLVersion];
    if (gl.isContextLost()) {
      delete contexts[webGLVersion];
      return getWebGLContext(webGLVersion);
    }
    gl.disable(gl.DEPTH_TEST);
    gl.disable(gl.STENCIL_TEST);
    gl.disable(gl.BLEND);
    gl.disable(gl.DITHER);
    gl.disable(gl.POLYGON_OFFSET_FILL);
    gl.disable(gl.SAMPLE_COVERAGE);
    gl.enable(gl.SCISSOR_TEST);
    gl.enable(gl.CULL_FACE);
    gl.cullFace(gl.BACK);
    return contexts[webGLVersion];
  }
  function createCanvas(webGLVersion) {
    if (typeof OffscreenCanvas !== "undefined" && webGLVersion === 2) {
      return new OffscreenCanvas(300, 150);
    } else if (typeof document !== "undefined") {
      return document.createElement("canvas");
    } else {
      throw new Error("Cannot create a canvas in this context");
    }
  }
  function getWebGLRenderingContext(webGLVersion) {
    if (webGLVersion !== 1 && webGLVersion !== 2) {
      throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");
    }
    const canvas = createCanvas(webGLVersion);
    canvas.addEventListener("webglcontextlost", (ev) => {
      ev.preventDefault();
      delete contexts[webGLVersion];
    }, false);
    if (webGLVersion === 1) {
      return canvas.getContext("webgl", WEBGL_ATTRIBUTES) || canvas.getContext("experimental-webgl", WEBGL_ATTRIBUTES);
    }
    return canvas.getContext("webgl2", WEBGL_ATTRIBUTES);
  }
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var PackingScheme;
  (function(PackingScheme2) {
    PackingScheme2[PackingScheme2["DENSE"] = 0] = "DENSE";
    PackingScheme2[PackingScheme2["SHARED_BATCH"] = 1] = "SHARED_BATCH";
  })(PackingScheme || (PackingScheme = {}));
  var TextureUsage;
  (function(TextureUsage2) {
    TextureUsage2[TextureUsage2["RENDER"] = 0] = "RENDER";
    TextureUsage2[TextureUsage2["UPLOAD"] = 1] = "UPLOAD";
    TextureUsage2[TextureUsage2["PIXELS"] = 2] = "PIXELS";
    TextureUsage2[TextureUsage2["DOWNLOAD"] = 3] = "DOWNLOAD";
  })(TextureUsage || (TextureUsage = {}));
  var PhysicalTextureType;
  (function(PhysicalTextureType2) {
    PhysicalTextureType2[PhysicalTextureType2["UNPACKED_FLOAT16"] = 0] = "UNPACKED_FLOAT16";
    PhysicalTextureType2[PhysicalTextureType2["UNPACKED_FLOAT32"] = 1] = "UNPACKED_FLOAT32";
    PhysicalTextureType2[PhysicalTextureType2["PACKED_4X1_UNSIGNED_BYTE"] = 2] = "PACKED_4X1_UNSIGNED_BYTE";
    PhysicalTextureType2[PhysicalTextureType2["PACKED_2X2_FLOAT32"] = 3] = "PACKED_2X2_FLOAT32";
    PhysicalTextureType2[PhysicalTextureType2["PACKED_2X2_FLOAT16"] = 4] = "PACKED_2X2_FLOAT16";
  })(PhysicalTextureType || (PhysicalTextureType = {}));
  function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {
    return [columns, rows];
  }
  function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {
    return matrixSize * channelsPerTexture;
  }
  function getDenseTexShape(shape) {
    const size = util_exports.sizeFromShape(shape);
    const texelsNeeded = Math.ceil(size / 4);
    return util_exports.sizeToSquarishShape(texelsNeeded);
  }
  function getPackedMatrixTextureShapeWidthHeight(rows, columns) {
    return [
      Math.max(1, Math.ceil(columns / 2)),
      Math.max(1, Math.ceil(rows / 2))
    ];
  }
  function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {
    const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
    return w * h * 4;
  }
  function getTextureConfig(gl, textureHalfFloatExtension) {
    const glany = gl;
    let internalFormatFloat;
    let internalFormatHalfFloat;
    let internalFormatPackedHalfFloat;
    let internalFormatPackedFloat;
    let textureFormatFloat;
    let downloadTextureFormat;
    let downloadUnpackNumChannels;
    let defaultNumChannels;
    let textureTypeHalfFloat;
    let textureTypeFloat;
    if (env().getNumber("WEBGL_VERSION") === 2) {
      internalFormatFloat = glany.R32F;
      internalFormatHalfFloat = glany.R16F;
      internalFormatPackedHalfFloat = glany.RGBA16F;
      internalFormatPackedFloat = glany.RGBA32F;
      textureFormatFloat = glany.RED;
      downloadUnpackNumChannels = 4;
      defaultNumChannels = 1;
      textureTypeHalfFloat = glany.HALF_FLOAT;
      textureTypeFloat = glany.FLOAT;
    } else {
      internalFormatFloat = gl.RGBA;
      internalFormatHalfFloat = gl.RGBA;
      internalFormatPackedHalfFloat = gl.RGBA;
      internalFormatPackedFloat = glany.RGBA;
      textureFormatFloat = gl.RGBA;
      downloadUnpackNumChannels = 4;
      defaultNumChannels = 4;
      textureTypeHalfFloat = textureHalfFloatExtension != null ? textureHalfFloatExtension.HALF_FLOAT_OES : null;
      textureTypeFloat = gl.FLOAT;
    }
    downloadTextureFormat = gl.RGBA;
    return {
      internalFormatFloat,
      internalFormatHalfFloat,
      internalFormatPackedHalfFloat,
      internalFormatPackedFloat,
      textureFormatFloat,
      downloadTextureFormat,
      downloadUnpackNumChannels,
      defaultNumChannels,
      textureTypeHalfFloat,
      textureTypeFloat
    };
  }
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function callAndCheck(gl, func2) {
    const returnValue = func2();
    if (env().getBool("DEBUG")) {
      checkWebGLError(gl);
    }
    return returnValue;
  }
  function checkWebGLError(gl) {
    const error = gl.getError();
    if (error !== gl.NO_ERROR) {
      throw new Error("WebGL Error: " + getWebGLErrorMessage(gl, error));
    }
  }
  var MIN_FLOAT16 = 596e-10;
  var MAX_FLOAT16 = 65504;
  function canBeRepresented(num) {
    if (env().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || num === 0 || MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16) {
      return true;
    }
    return false;
  }
  function getWebGLErrorMessage(gl, status) {
    switch (status) {
      case gl.NO_ERROR:
        return "NO_ERROR";
      case gl.INVALID_ENUM:
        return "INVALID_ENUM";
      case gl.INVALID_VALUE:
        return "INVALID_VALUE";
      case gl.INVALID_OPERATION:
        return "INVALID_OPERATION";
      case gl.INVALID_FRAMEBUFFER_OPERATION:
        return "INVALID_FRAMEBUFFER_OPERATION";
      case gl.OUT_OF_MEMORY:
        return "OUT_OF_MEMORY";
      case gl.CONTEXT_LOST_WEBGL:
        return "CONTEXT_LOST_WEBGL";
      default:
        return `Unknown error code ${status}`;
    }
  }
  function getExtensionOrThrow(gl, extensionName) {
    return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension "' + extensionName + '" not supported on this browser.');
  }
  function createVertexShader(gl, vertexShaderSource) {
    const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), "Unable to create vertex WebGLShader.");
    callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));
    callAndCheck(gl, () => gl.compileShader(vertexShader));
    if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {
      console.log(gl.getShaderInfoLog(vertexShader));
      throw new Error("Failed to compile vertex shader.");
    }
    return vertexShader;
  }
  function createFragmentShader(gl, fragmentShaderSource) {
    const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), "Unable to create fragment WebGLShader.");
    callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));
    callAndCheck(gl, () => gl.compileShader(fragmentShader));
    if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {
      logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));
      throw new Error("Failed to compile fragment shader.");
    }
    return fragmentShader;
  }
  var lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;
  function logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {
    const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);
    if (lineNumberRegexResult == null) {
      console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);
      console.log(shaderSource);
      return;
    }
    const lineNumber = +lineNumberRegexResult[1];
    const shaderLines = shaderSource.split("\n");
    const pad3 = shaderLines.length.toString().length + 2;
    const linesWithLineNumbers = shaderLines.map((line, lineNumber2) => util_exports.rightPad((lineNumber2 + 1).toString(), pad3) + line);
    let maxLineLength = 0;
    for (let i = 0; i < linesWithLineNumbers.length; i++) {
      maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);
    }
    const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);
    const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);
    const afterErrorLines = linesWithLineNumbers.slice(lineNumber);
    console.log(beforeErrorLines.join("\n"));
    console.log(shaderInfoLog.split("\n")[0]);
    console.log(`%c ${util_exports.rightPad(errorLine[0], maxLineLength)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717");
    console.log(afterErrorLines.join("\n"));
  }
  function createProgram(gl) {
    return throwIfNull(gl, () => gl.createProgram(), "Unable to create WebGLProgram.");
  }
  function linkProgram(gl, program) {
    callAndCheck(gl, () => gl.linkProgram(program));
    if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {
      console.log(gl.getProgramInfoLog(program));
      throw new Error("Failed to link vertex and fragment shaders.");
    }
  }
  function validateProgram(gl, program) {
    callAndCheck(gl, () => gl.validateProgram(program));
    if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {
      console.log(gl.getProgramInfoLog(program));
      throw new Error("Shader program validation failed.");
    }
  }
  function createStaticVertexBuffer(gl, data) {
    const buffer2 = throwIfNull(gl, () => gl.createBuffer(), "Unable to create WebGLBuffer");
    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));
    callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));
    return buffer2;
  }
  function createStaticIndexBuffer(gl, data) {
    const buffer2 = throwIfNull(gl, () => gl.createBuffer(), "Unable to create WebGLBuffer");
    callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer2));
    callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));
    return buffer2;
  }
  function createTexture(gl) {
    return throwIfNull(gl, () => gl.createTexture(), "Unable to create WebGLTexture.");
  }
  function validateTextureSize(width, height) {
    const maxTextureSize = env().getNumber("WEBGL_MAX_TEXTURE_SIZE");
    if (width <= 0 || height <= 0) {
      const requested = `[${width}x${height}]`;
      throw new Error("Requested texture size " + requested + " is invalid.");
    }
    if (width > maxTextureSize || height > maxTextureSize) {
      const requested = `[${width}x${height}]`;
      const max6 = `[${maxTextureSize}x${maxTextureSize}]`;
      throw new Error("Requested texture size " + requested + " greater than WebGL maximum on this browser / GPU " + max6 + ".");
    }
  }
  function createFramebuffer(gl) {
    return throwIfNull(gl, () => gl.createFramebuffer(), "Unable to create WebGLFramebuffer.");
  }
  function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer2, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {
    const loc = gl.getAttribLocation(program, attribute);
    if (loc === -1) {
      return false;
    }
    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));
    callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));
    callAndCheck(gl, () => gl.enableVertexAttribArray(loc));
    return true;
  }
  function bindTextureUnit(gl, texture, textureUnit) {
    validateTextureUnit(gl, textureUnit);
    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));
    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
  }
  function getProgramUniformLocationOrThrow(gl, program, uniformName) {
    return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform "' + uniformName + '" not present in program.');
  }
  function getProgramUniformLocation(gl, program, uniformName) {
    return gl.getUniformLocation(program, uniformName);
  }
  function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {
    callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));
    callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));
  }
  function bindColorTextureToFramebuffer(gl, texture, framebuffer) {
    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));
    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));
  }
  function unbindColorTextureFromFramebuffer(gl, framebuffer) {
    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));
    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));
  }
  function validateFramebuffer(gl) {
    const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);
    if (status !== gl.FRAMEBUFFER_COMPLETE) {
      throw new Error("Error binding framebuffer: " + getFramebufferErrorMessage(gl, status));
    }
  }
  function getFramebufferErrorMessage(gl, status) {
    switch (status) {
      case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
        return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT";
      case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:
        return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";
      case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:
        return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS";
      case gl.FRAMEBUFFER_UNSUPPORTED:
        return "FRAMEBUFFER_UNSUPPORTED";
      default:
        return `unknown error ${status}`;
    }
  }
  function throwIfNull(gl, returnTOrNull, failureMessage) {
    const tOrNull = callAndCheck(gl, () => returnTOrNull());
    if (tOrNull == null) {
      throw new Error(failureMessage);
    }
    return tOrNull;
  }
  function validateTextureUnit(gl, textureUnit) {
    const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;
    const glTextureUnit = textureUnit + gl.TEXTURE0;
    if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {
      const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;
      throw new Error(`textureUnit must be in ${textureUnitRange}.`);
    }
  }
  function getBatchDim(shape, dimsToSkip = 2) {
    return util_exports.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));
  }
  function getRowsCols(shape) {
    if (shape.length === 0) {
      throw Error("Cannot get rows and columns of an empty shape array.");
    }
    return [
      shape.length > 1 ? shape[shape.length - 2] : 1,
      shape[shape.length - 1]
    ];
  }
  function getShapeAs3D(shape) {
    let shapeAs3D = [1, 1, 1];
    const isScalar = shape.length === 0 || shape.length === 1 && shape[0] === 1;
    if (!isScalar) {
      shapeAs3D = [getBatchDim(shape), ...getRowsCols(shape)];
    }
    return shapeAs3D;
  }
  function getTextureShapeFromLogicalShape(logShape, isPacked = false) {
    let maxTexSize = env().getNumber("WEBGL_MAX_TEXTURE_SIZE");
    if (isPacked) {
      maxTexSize = maxTexSize * 2;
      logShape = logShape.map((d, i) => i >= logShape.length - 2 ? util_exports.nearestLargerEven(logShape[i]) : logShape[i]);
      if (logShape.length === 1) {
        logShape = [2, logShape[0]];
      }
    }
    if (logShape.length !== 2) {
      const squeezeResult = util_exports.squeezeShape(logShape);
      logShape = squeezeResult.newShape;
    }
    let size = util_exports.sizeFromShape(logShape);
    if (logShape.length <= 1 && size <= maxTexSize) {
      return [1, size];
    } else if (logShape.length === 2 && logShape[0] <= maxTexSize && logShape[1] <= maxTexSize) {
      return logShape;
    } else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize && logShape[2] <= maxTexSize) {
      return [logShape[0] * logShape[1], logShape[2]];
    } else if (logShape.length === 3 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] <= maxTexSize) {
      return [logShape[0], logShape[1] * logShape[2]];
    } else if (logShape.length === 4 && logShape[0] * logShape[1] * logShape[2] <= maxTexSize && logShape[3] <= maxTexSize) {
      return [logShape[0] * logShape[1] * logShape[2], logShape[3]];
    } else if (logShape.length === 4 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {
      return [logShape[0], logShape[1] * logShape[2] * logShape[3]];
    } else {
      if (isPacked) {
        const batchDim = getBatchDim(logShape);
        let rows = 2, cols = 2;
        if (logShape.length) {
          [rows, cols] = getRowsCols(logShape);
        }
        size = batchDim * (rows / 2) * (cols / 2);
        return util_exports.sizeToSquarishShape(size).map((d) => d * 2);
      }
      return util_exports.sizeToSquarishShape(size);
    }
  }
  function isEven(n) {
    return n % 2 === 0;
  }
  function isReshapeFree(shape1, shape2) {
    shape1 = shape1.slice(-2);
    shape2 = shape2.slice(-2);
    if (util_exports.arraysEqual(shape1, shape2)) {
      return true;
    }
    if (!shape1.length || !shape2.length) {
      return true;
    }
    if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 || shape2[1] === 0) {
      return true;
    }
    if (shape1.length !== shape2.length) {
      const shape1Cols = shape1.slice(-1)[0];
      const shape2Cols = shape2.slice(-1)[0];
      if (shape1Cols === shape2Cols) {
        return true;
      }
      if (isEven(shape1Cols) && isEven(shape2Cols) && (shape1[0] === 1 || shape2[0] === 1)) {
        return true;
      }
    }
    return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);
  }
  var MAX_TEXTURE_SIZE;
  var MAX_TEXTURES_IN_SHADER;
  function getWebGLMaxTextureSize(webGLVersion) {
    if (MAX_TEXTURE_SIZE == null) {
      const gl = getWebGLContext(webGLVersion);
      MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);
    }
    return MAX_TEXTURE_SIZE;
  }
  function getMaxTexturesInShader(webGLVersion) {
    if (MAX_TEXTURES_IN_SHADER == null) {
      const gl = getWebGLContext(webGLVersion);
      MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);
    }
    return Math.min(16, MAX_TEXTURES_IN_SHADER);
  }
  function getWebGLDisjointQueryTimerVersion(webGLVersion) {
    if (webGLVersion === 0) {
      return 0;
    }
    let queryTimerVersion;
    const gl = getWebGLContext(webGLVersion);
    if (hasExtension(gl, "EXT_disjoint_timer_query_webgl2") && webGLVersion === 2) {
      queryTimerVersion = 2;
    } else if (hasExtension(gl, "EXT_disjoint_timer_query")) {
      queryTimerVersion = 1;
    } else {
      queryTimerVersion = 0;
    }
    return queryTimerVersion;
  }
  function hasExtension(gl, extensionName) {
    const ext = gl.getExtension(extensionName);
    return ext != null;
  }
  function isWebGLVersionEnabled(webGLVersion) {
    try {
      const gl = getWebGLContext(webGLVersion);
      if (gl != null) {
        return true;
      }
    } catch (e) {
      console.log("Error when getting WebGL context: ", e);
      return false;
    }
    return false;
  }
  function isCapableOfRenderingToFloatTexture(webGLVersion) {
    if (webGLVersion === 0) {
      return false;
    }
    const gl = getWebGLContext(webGLVersion);
    if (webGLVersion === 1) {
      if (!hasExtension(gl, "OES_texture_float")) {
        return false;
      }
    } else {
      if (!hasExtension(gl, "EXT_color_buffer_float")) {
        return false;
      }
    }
    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
    return isFrameBufferComplete;
  }
  function isDownloadFloatTextureEnabled(webGLVersion) {
    if (webGLVersion === 0) {
      return false;
    }
    const gl = getWebGLContext(webGLVersion);
    if (webGLVersion === 1) {
      if (!hasExtension(gl, "OES_texture_float")) {
        return false;
      }
      if (!hasExtension(gl, "WEBGL_color_buffer_float")) {
        return false;
      }
    } else {
      if (hasExtension(gl, "EXT_color_buffer_float")) {
        return createFloatTextureAndBindToFramebuffer(gl);
      }
      const COLOR_BUFFER_HALF_FLOAT = "EXT_color_buffer_half_float";
      if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {
        const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
        return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);
      }
      return false;
    }
    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
    return isFrameBufferComplete;
  }
  function createFloatTextureAndBindToFramebuffer(gl) {
    const texConfig = getTextureConfig(gl);
    const texture = gl.createTexture();
    gl.bindTexture(gl.TEXTURE_2D, texture);
    const width = 1;
    const height = 1;
    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);
    const frameBuffer = gl.createFramebuffer();
    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
    gl.bindTexture(gl.TEXTURE_2D, null);
    gl.bindFramebuffer(gl.FRAMEBUFFER, null);
    gl.deleteTexture(texture);
    gl.deleteFramebuffer(frameBuffer);
    return isFrameBufferComplete;
  }
  function createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension) {
    const texConfig = getTextureConfig(gl, textureHalfFloatExtension);
    const texture = gl.createTexture();
    gl.bindTexture(gl.TEXTURE_2D, texture);
    const width = 1;
    const height = 1;
    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);
    const frameBuffer = gl.createFramebuffer();
    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
    gl.bindTexture(gl.TEXTURE_2D, null);
    gl.bindFramebuffer(gl.FRAMEBUFFER, null);
    gl.deleteTexture(texture);
    gl.deleteFramebuffer(frameBuffer);
    return isFrameBufferComplete;
  }
  function isWebGLFenceEnabled(webGLVersion) {
    if (webGLVersion !== 2) {
      return false;
    }
    const gl = getWebGLContext(webGLVersion);
    const isEnabled = gl.fenceSync != null;
    return isEnabled;
  }
  function assertNotComplex2(tensor2, opName) {
    if (!Array.isArray(tensor2)) {
      tensor2 = [tensor2];
    }
    tensor2.forEach((t) => {
      if (t != null) {
        util_exports.assert(t.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the WebGL backend.`);
      }
    });
  }
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ENV3 = env();
  ENV3.registerFlag("HAS_WEBGL", () => ENV3.getNumber("WEBGL_VERSION") > 0);
  ENV3.registerFlag("WEBGL_VERSION", () => {
    if (isWebGLVersionEnabled(2)) {
      return 2;
    } else if (isWebGLVersionEnabled(1)) {
      return 1;
    }
    return 0;
  });
  ENV3.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false);
  ENV3.registerFlag("WEBGL_BUFFER_SUPPORTED", () => ENV3.get("WEBGL_VERSION") === 2);
  ENV3.registerFlag("WEBGL_CPU_FORWARD", () => true);
  ENV3.registerFlag("WEBGL_FORCE_F16_TEXTURES", () => false);
  ENV3.registerFlag("WEBGL_PACK", () => ENV3.getBool("HAS_WEBGL"));
  ENV3.registerFlag("WEBGL_PACK_NORMALIZATION", () => ENV3.getBool("WEBGL_PACK"));
  ENV3.registerFlag("WEBGL_PACK_CLIP", () => ENV3.getBool("WEBGL_PACK"));
  ENV3.registerFlag("WEBGL_PACK_DEPTHWISECONV", () => false);
  ENV3.registerFlag("WEBGL_PACK_BINARY_OPERATIONS", () => ENV3.getBool("WEBGL_PACK"));
  ENV3.registerFlag("WEBGL_PACK_UNARY_OPERATIONS", () => ENV3.getBool("WEBGL_PACK"));
  ENV3.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS", () => ENV3.getBool("WEBGL_PACK"));
  ENV3.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => ENV3.getBool("WEBGL_PACK"));
  ENV3.registerFlag("WEBGL_PACK_REDUCE", () => ENV3.getBool("WEBGL_PACK"));
  ENV3.registerFlag("WEBGL_LAZILY_UNPACK", () => ENV3.getBool("WEBGL_PACK"));
  ENV3.registerFlag("WEBGL_CONV_IM2COL", () => ENV3.getBool("WEBGL_PACK"));
  ENV3.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => getWebGLMaxTextureSize(ENV3.getNumber("WEBGL_VERSION")));
  ENV3.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => getMaxTexturesInShader(ENV3.getNumber("WEBGL_VERSION")));
  ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => {
    const webGLVersion = ENV3.getNumber("WEBGL_VERSION");
    if (webGLVersion === 0) {
      return 0;
    }
    return getWebGLDisjointQueryTimerVersion(webGLVersion);
  });
  ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => ENV3.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !device_util_exports.isMobile());
  ENV3.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => isCapableOfRenderingToFloatTexture(ENV3.getNumber("WEBGL_VERSION")));
  ENV3.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => {
    return ENV3.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : ENV3.getBool("WEBGL_RENDER_FLOAT32_CAPABLE");
  });
  ENV3.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => isDownloadFloatTextureEnabled(ENV3.getNumber("WEBGL_VERSION")));
  ENV3.registerFlag("WEBGL_FENCE_API_ENABLED", () => isWebGLFenceEnabled(ENV3.getNumber("WEBGL_VERSION")));
  ENV3.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => {
    const useUniforms = ENV3.getBool("WEBGL_RENDER_FLOAT32_ENABLED");
    return useUniforms ? 4 : 0;
  });
  ENV3.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => {
    return -1;
  }, (threshold2) => {
    if (threshold2 < 0 && threshold2 !== -1) {
      throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold2}.`);
    }
  });
  ENV3.registerFlag("WEBGL_FLUSH_THRESHOLD", () => {
    return device_util_exports.isMobile() && ENV3.getBool("IS_CHROME") ? 1 : -1;
  }, (threshold2) => {
    if (threshold2 < 0 && threshold2 !== -1) {
      throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${threshold2}.`);
    }
  });
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function getGlslDifferences() {
    let version102;
    let attribute;
    let varyingVs;
    let varyingFs;
    let texture2D;
    let output;
    let defineOutput;
    let defineSpecialNaN;
    let defineSpecialInf;
    let defineRound;
    if (env().getNumber("WEBGL_VERSION") === 2) {
      version102 = "#version 300 es";
      attribute = "in";
      varyingVs = "out";
      varyingFs = "in";
      texture2D = "texture";
      output = "outputColor";
      defineOutput = "out vec4 outputColor;";
      defineSpecialNaN = `
      bool isnan_custom(float val) {
        return (val > 0.0 || val < 0.0) ? false : val != 0.0;
      }

      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan_custom(val.x),
          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
      }

      #define isnan(value) isnan_custom(value)
    `;
      defineSpecialInf = ``;
      defineRound = `
      #define round(value) newRound(value)
      int newRound(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 newRound(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `;
    } else {
      version102 = "";
      attribute = "attribute";
      varyingVs = "varying";
      varyingFs = "varying";
      texture2D = "texture2D";
      output = "gl_FragColor";
      defineOutput = "";
      defineSpecialNaN = `
      #define isnan(value) isnan_custom(value)
      bool isnan_custom(float val) {
        return (val > 0. || val < 1. || val == 0.) ? false : true;
      }
      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
      }
    `;
      defineSpecialInf = `
      uniform float INFINITY;

      bool isinf(float val) {
        return abs(val) == INFINITY;
      }
      bvec4 isinf(vec4 val) {
        return equal(abs(val), vec4(INFINITY));
      }
    `;
      defineRound = `
      int round(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 round(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `;
    }
    return {
      version: version102,
      attribute,
      varyingVs,
      varyingFs,
      texture2D,
      output,
      defineOutput,
      defineSpecialNaN,
      defineSpecialInf,
      defineRound
    };
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function getLogicalCoordinatesFromFlatIndex(coords2, shape, index = "index") {
    const strides = util_exports.computeStrides(shape);
    return strides.map((stride, i) => {
      const line1 = `int ${coords2[i]} = ${index} / ${stride}`;
      const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * ${stride}` : `index -= ${coords2[i]} * ${stride}`;
      return `${line1}; ${line2};`;
    }).join("");
  }
  function getFlatIndexFrom3D(shape) {
    const strides = util_exports.computeStrides(shape).map((d) => d.toString());
    return `
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;
  }
`;
  }
  var ENCODE_FLOAT_SNIPPET = `
  const float FLOAT_MAX = 1.70141184e38;
  const float FLOAT_MIN = 1.17549435e-38;

  lowp vec4 encode_float(highp float v) {
    if (isnan(v)) {
      return vec4(255, 255, 255, 255);
    }

    highp float av = abs(v);

    if(av < FLOAT_MIN) {
      return vec4(0.0, 0.0, 0.0, 0.0);
    } else if(v > FLOAT_MAX) {
      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
    } else if(v < -FLOAT_MAX) {
      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
    }

    highp vec4 c = vec4(0,0,0,0);

    highp float e = floor(log2(av));
    highp float m = exp2(fract(log2(av))) - 1.0;

    c[2] = floor(128.0 * m);
    m -= c[2] / 128.0;
    c[1] = floor(32768.0 * m);
    m -= c[1] / 32768.0;
    c[0] = floor(8388608.0 * m);

    highp float ebias = e + 127.0;
    c[3] = floor(ebias / 2.0);
    ebias -= c[3] * 2.0;
    c[2] += floor(ebias) * 128.0;

    c[3] += 128.0 * step(0.0, -v);

    return c / 255.0;
  }
`;
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var DecodeMatrixProgram = class {
    constructor(outputShape) {
      this.variableNames = ["A"];
      this.packedInputs = false;
      this.packedOutput = true;
      this.outPackingScheme = PackingScheme.DENSE;
      const texShape = getDenseTexShape(outputShape);
      const glsl = getGlslDifferences();
      this.outputShape = outputShape;
      this.userCode = `
      ivec3 outCoordsFromFlatIndex(int index) {
        ${getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], outputShape)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx *
          vec2(${texShape[0]}, ${texShape[1]}));
        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getA(rc.x, rc.y, rc.z);
        }

        ${glsl.output} = result;
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var DecodeMatrixPackedProgram = class {
    constructor(outputShape) {
      this.variableNames = ["A"];
      this.packedInputs = true;
      this.packedOutput = true;
      this.outPackingScheme = PackingScheme.DENSE;
      const texShape = getDenseTexShape(outputShape);
      const glsl = getGlslDifferences();
      this.outputShape = outputShape;
      this.userCode = `
      ivec3 outCoordsFromFlatIndex(int index) {
        ${getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], outputShape)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx *
          vec2(${texShape[0]}, ${texShape[1]}));
        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
        }

        ${glsl.output} = result;
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var EncodeFloatProgram = class {
    constructor(outputShape) {
      this.variableNames = ["A"];
      this.outTexUsage = TextureUsage.DOWNLOAD;
      const glsl = getGlslDifferences();
      this.outputShape = outputShape;
      this.userCode = `
      ${ENCODE_FLOAT_SNIPPET}

      void main() {
        float x = getAAtOutCoords();
        ${glsl.output} = encode_float(x);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var EncodeFloatPackedProgram = class {
    constructor(outputShape) {
      this.variableNames = ["A"];
      this.packedInputs = true;
      this.packedOutput = false;
      this.outTexUsage = TextureUsage.DOWNLOAD;
      const glsl = getGlslDifferences();
      this.outputShape = outputShape;
      this.userCode = `
      ${ENCODE_FLOAT_SNIPPET}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${glsl.output} = encode_float(x);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var EncodeMatrixProgram = class {
    constructor(outputShape, texShape, inputIsUnsignedByte = false) {
      this.variableNames = ["A"];
      const glsl = getGlslDifferences();
      const [height, width] = texShape;
      this.outputShape = outputShape;
      let output = `result`;
      if (inputIsUnsignedByte) {
        output = `floor(result * 255. + 0.5)`;
      }
      this.userCode = `
      ${getFlatIndexFrom3D(outputShape)}

      void main() {
        ivec3 coords = getOutputCoords();

        int flatIndex = getFlatIndex(coords);
        int offset = imod(flatIndex, 4);

        flatIndex = idiv(flatIndex, 4, 1.);

        int r = flatIndex / ${width};
        int c = imod(flatIndex, ${width});
        vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);
        vec4 values = ${glsl.texture2D}(A, uv);

        float result;

        if(offset == 0) {
          result = values[0];
        } else if(offset == 1) {
          result = values[1];
        } else if(offset == 2) {
          result = values[2];
        } else {
          result = values[3];
        }

        ${glsl.output} = vec4(${output}, 0., 0., 0.);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var EncodeMatrixPackedProgram = class {
    constructor(outputShape, texShape, inputIsUnsignedByte = false) {
      this.variableNames = ["A"];
      this.packedInputs = false;
      this.packedOutput = true;
      const glsl = getGlslDifferences();
      const [height, width] = texShape;
      this.outputShape = outputShape;
      let mainLoop = "";
      let output = "result";
      if (inputIsUnsignedByte) {
        output = "floor(result * 255. + 0.5)";
      }
      for (let row = 0; row <= 1; row++) {
        for (let col = 0; col <= 1; col++) {
          const channel = row * 2 + col;
          mainLoop += `
          localCoords = coords;
          if(localCoords[2] + ${col} < ${outputShape[2]}) {
            localCoords[2] += ${col};
            if(localCoords[1] + ${row} < ${outputShape[1]}) {
              localCoords[1] += ${row};

              flatIndex = getFlatIndex(localCoords);
              offset = imod(flatIndex, 4);

              flatIndex = idiv(flatIndex, 4, 1.);

              r = flatIndex / ${width};
              c = imod(flatIndex, ${width});
              uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);
              values = ${glsl.texture2D}(A, uv);

              if(offset == 0) {
                result[${channel}] = values[0];
              } else if(offset == 1) {
                result[${channel}] = values[1];
              } else if(offset == 2) {
                result[${channel}] = values[2];
              } else {
                result[${channel}] = values[3];
              }
            }
          }
        `;
        }
      }
      this.userCode = `
      ${getFlatIndexFrom3D(outputShape)}

      void main() {
        ivec3 coords = getOutputCoords();

        vec4 result = vec4(0.);
        int flatIndex, r, c, offset;
        ivec3 localCoords;
        vec2 uv;
        vec4 values;

        ${mainLoop}

        ${glsl.output} = ${output};
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function createVertexShader2(gl) {
    const glsl = getGlslDifferences();
    const vertexShaderSource = `${glsl.version}
    precision highp float;
    ${glsl.attribute} vec3 clipSpacePos;
    ${glsl.attribute} vec2 uv;
    ${glsl.varyingVs} vec2 resultUV;

    void main() {
      gl_Position = vec4(clipSpacePos, 1);
      resultUV = uv;
    }`;
    return createVertexShader(gl, vertexShaderSource);
  }
  function createVertexBuffer(gl) {
    const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);
    return createStaticVertexBuffer(gl, vertexArray);
  }
  function createIndexBuffer(gl) {
    const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);
    return createStaticIndexBuffer(gl, triangleVertexIndices);
  }
  function createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {
    validateTextureSize(width, height);
    const texture = createTexture(gl);
    const tex2d = gl.TEXTURE_2D;
    callAndCheck(gl, () => gl.bindTexture(tex2d, texture));
    callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));
    callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));
    callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));
    callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));
    callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));
    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
    return texture;
  }
  function getInternalFormatForFloat32MatrixTexture(textureConfig) {
    return textureConfig.internalFormatFloat;
  }
  function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {
    const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);
  }
  function getInternalFormatForFloat16MatrixTexture(textureConfig) {
    return textureConfig.internalFormatHalfFloat;
  }
  function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {
    const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);
  }
  function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {
    return textureConfig.downloadTextureFormat;
  }
  function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {
    const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
    return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);
  }
  function getInternalFormatForPackedMatrixTexture(textureConfig) {
    return textureConfig.internalFormatPackedFloat;
  }
  function createPackedMatrixTexture(gl, rows, columns, textureConfig) {
    const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
    return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);
  }
  function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {
    return textureConfig.internalFormatPackedHalfFloat;
  }
  function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {
    const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);
  }
  function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {
    const posOffset = 0;
    const uvOffset = 3 * 4;
    const stride = 3 * 4 + 2 * 4;
    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));
    const success = bindVertexBufferToProgramAttribute(gl, program, "clipSpacePos", vertexBuffer, 3, stride, posOffset);
    return success && bindVertexBufferToProgramAttribute(gl, program, "uv", vertexBuffer, 2, stride, uvOffset);
  }
  function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {
    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
    let dataForUpload, texelDataType, internalFormat;
    if (data instanceof Uint8Array) {
      dataForUpload = new Uint8Array(width * height * 4);
      texelDataType = gl.UNSIGNED_BYTE;
      internalFormat = gl.RGBA;
    } else {
      dataForUpload = new Float32Array(width * height * 4);
      texelDataType = gl.FLOAT;
      internalFormat = textureConfig.internalFormatPackedFloat;
    }
    dataForUpload.set(data);
    callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));
    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
  }
  function uploadPixelDataToTexture(gl, texture, pixels) {
    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
    if (pixels.data instanceof Uint8Array) {
      callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));
    } else {
      callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));
    }
    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
  }
  function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {
    const buffer2 = gl2.createBuffer();
    callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2));
    const bytesPerFloat = 4;
    const valuesPerTexel = 4;
    const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;
    callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));
    callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));
    callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));
    return buffer2;
  }
  function downloadFloat32MatrixFromBuffer(gl, buffer2, size) {
    const gl2 = gl;
    const downloadTarget = new Float32Array(size);
    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);
    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
    return downloadTarget;
  }
  function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {
    const [w, h] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
    const numChannels = 4;
    const downloadTarget = new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));
    callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));
    return new Float32Array(downloadTarget.buffer);
  }
  function downloadPackedMatrixFromBuffer(gl, buffer2, batch, rows, cols, physicalRows, physicalCols, textureConfig) {
    const gl2 = gl;
    const downloadTarget = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));
    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);
    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
    return downloadTarget;
  }
  function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {
    const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);
    callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));
    return packedRGBA;
  }
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var GPGPUContext = class {
    constructor(gl) {
      this.outputTexture = null;
      this.program = null;
      this.disposed = false;
      this.vertexAttrsAreBound = false;
      this.itemsToPoll = [];
      const glVersion = env().getNumber("WEBGL_VERSION");
      if (gl != null) {
        this.gl = gl;
        setWebGLContext(glVersion, gl);
      } else {
        this.gl = getWebGLContext(glVersion);
      }
      let COLOR_BUFFER_FLOAT = "WEBGL_color_buffer_float";
      const COLOR_BUFFER_HALF_FLOAT = "EXT_color_buffer_half_float";
      if (env().getNumber("WEBGL_VERSION") === 1) {
        const TEXTURE_FLOAT = "OES_texture_float";
        const TEXTURE_HALF_FLOAT = "OES_texture_half_float";
        this.textureFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_FLOAT);
        if (hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {
          this.textureHalfFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);
        } else if (env().get("WEBGL_FORCE_F16_TEXTURES")) {
          throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
        }
        this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);
        if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {
          this.colorBufferHalfFloatExtension = getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);
        } else if (env().get("WEBGL_FORCE_F16_TEXTURES")) {
          throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
        }
      } else {
        COLOR_BUFFER_FLOAT = "EXT_color_buffer_float";
        if (hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {
          this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);
        } else if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {
          this.colorBufferHalfFloatExtension = this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
        } else {
          throw new Error("GL context does not support color renderable floats");
        }
      }
      this.vertexBuffer = createVertexBuffer(this.gl);
      this.indexBuffer = createIndexBuffer(this.gl);
      this.framebuffer = createFramebuffer(this.gl);
      this.textureConfig = getTextureConfig(this.gl, this.textureHalfFloatExtension);
    }
    get debug() {
      return env().getBool("DEBUG");
    }
    dispose() {
      if (this.disposed) {
        return;
      }
      if (this.program != null) {
        console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.");
      }
      if (this.outputTexture != null) {
        console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");
      }
      const gl = this.gl;
      callAndCheck(gl, () => gl.finish());
      callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));
      callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));
      callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));
      callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));
      callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));
      this.disposed = true;
    }
    createFloat32MatrixTexture(rows, columns) {
      this.throwIfDisposed();
      return createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);
    }
    createFloat16MatrixTexture(rows, columns) {
      this.throwIfDisposed();
      return createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);
    }
    createUnsignedBytesMatrixTexture(rows, columns) {
      this.throwIfDisposed();
      return createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);
    }
    uploadPixelDataToTexture(texture, pixels) {
      this.throwIfDisposed();
      uploadPixelDataToTexture(this.gl, texture, pixels);
    }
    uploadDenseMatrixToTexture(texture, width, height, data) {
      this.throwIfDisposed();
      uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);
    }
    createFloat16PackedMatrixTexture(rows, columns) {
      this.throwIfDisposed();
      return createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
    }
    createPackedMatrixTexture(rows, columns) {
      this.throwIfDisposed();
      return createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
    }
    deleteMatrixTexture(texture) {
      this.throwIfDisposed();
      if (this.outputTexture === texture) {
        unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
        this.outputTexture = null;
      }
      callAndCheck(this.gl, () => this.gl.deleteTexture(texture));
    }
    downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {
      return this.downloadMatrixDriver(texture, () => downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));
    }
    downloadPackedMatrixFromBuffer(buffer2, batch, rows, columns, physicalRows, physicalCols) {
      return downloadPackedMatrixFromBuffer(this.gl, buffer2, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);
    }
    downloadFloat32MatrixFromBuffer(buffer2, size) {
      return downloadFloat32MatrixFromBuffer(this.gl, buffer2, size);
    }
    createBufferFromTexture(texture, rows, columns) {
      this.bindTextureToFrameBuffer(texture);
      const result = createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);
      this.unbindTextureToFrameBuffer();
      return result;
    }
    createAndWaitForFence() {
      const fenceContext = this.createFence(this.gl);
      return this.pollFence(fenceContext);
    }
    createFence(gl) {
      let query;
      let isFencePassed;
      if (env().getBool("WEBGL_FENCE_API_ENABLED")) {
        const gl2 = gl;
        const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);
        gl.flush();
        isFencePassed = () => {
          const status = gl2.clientWaitSync(sync, 0, 0);
          return status === gl2.ALREADY_SIGNALED || status === gl2.CONDITION_SATISFIED;
        };
        query = sync;
      } else if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0) {
        query = this.beginQuery();
        this.endQuery();
        isFencePassed = () => this.isQueryAvailable(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"));
      } else {
        isFencePassed = () => true;
      }
      return {query, isFencePassed};
    }
    downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {
      return this.downloadMatrixDriver(texture, () => downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));
    }
    createProgram(fragmentShaderSource) {
      this.throwIfDisposed();
      const gl = this.gl;
      const fragmentShader = createFragmentShader(gl, fragmentShaderSource);
      const vertexShader = createVertexShader2(gl);
      const program = createProgram(gl);
      callAndCheck(gl, () => gl.attachShader(program, vertexShader));
      callAndCheck(gl, () => gl.attachShader(program, fragmentShader));
      linkProgram(gl, program);
      if (this.debug) {
        validateProgram(gl, program);
      }
      if (!this.vertexAttrsAreBound) {
        this.setProgram(program);
        this.vertexAttrsAreBound = bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);
      }
      return program;
    }
    deleteProgram(program) {
      this.throwIfDisposed();
      if (program === this.program) {
        this.program = null;
      }
      if (program != null) {
        callAndCheck(this.gl, () => this.gl.deleteProgram(program));
      }
    }
    setProgram(program) {
      this.throwIfDisposed();
      this.program = program;
      if (this.program != null && this.debug) {
        validateProgram(this.gl, this.program);
      }
      callAndCheck(this.gl, () => this.gl.useProgram(program));
    }
    getUniformLocation(program, uniformName, shouldThrow = true) {
      this.throwIfDisposed();
      if (shouldThrow) {
        return getProgramUniformLocationOrThrow(this.gl, program, uniformName);
      } else {
        return getProgramUniformLocation(this.gl, program, uniformName);
      }
    }
    getAttributeLocation(program, attribute) {
      this.throwIfDisposed();
      return callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));
    }
    getUniformLocationNoThrow(program, uniformName) {
      this.throwIfDisposed();
      return this.gl.getUniformLocation(program, uniformName);
    }
    setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {
      this.throwIfDisposed();
      this.throwIfNoProgram();
      bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);
    }
    setOutputMatrixTexture(outputMatrixTexture, rows, columns) {
      this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);
    }
    setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {
      this.throwIfDisposed();
      const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
      this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);
    }
    setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {
      this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);
    }
    setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {
      throw new Error("setOutputPackedMatrixWriteRegion not implemented.");
    }
    debugValidate() {
      if (this.program != null) {
        validateProgram(this.gl, this.program);
      }
      validateFramebuffer(this.gl);
    }
    executeProgram() {
      this.throwIfDisposed();
      this.throwIfNoProgram();
      const gl = this.gl;
      if (this.debug) {
        this.debugValidate();
      }
      callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));
    }
    blockUntilAllProgramsCompleted() {
      this.throwIfDisposed();
      callAndCheck(this.gl, () => this.gl.finish());
    }
    getQueryTimerExtension() {
      if (this.disjointQueryTimerExtension == null) {
        this.disjointQueryTimerExtension = getExtensionOrThrow(this.gl, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query");
      }
      return this.disjointQueryTimerExtension;
    }
    getQueryTimerExtensionWebGL2() {
      return this.getQueryTimerExtension();
    }
    getQueryTimerExtensionWebGL1() {
      return this.getQueryTimerExtension();
    }
    beginQuery() {
      if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
        const gl2 = this.gl;
        const ext2 = this.getQueryTimerExtensionWebGL2();
        const query2 = gl2.createQuery();
        gl2.beginQuery(ext2.TIME_ELAPSED_EXT, query2);
        return query2;
      }
      const ext = this.getQueryTimerExtensionWebGL1();
      const query = ext.createQueryEXT();
      ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);
      return query;
    }
    endQuery() {
      if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
        const gl2 = this.gl;
        const ext2 = this.getQueryTimerExtensionWebGL2();
        gl2.endQuery(ext2.TIME_ELAPSED_EXT);
        return;
      }
      const ext = this.getQueryTimerExtensionWebGL1();
      ext.endQueryEXT(ext.TIME_ELAPSED_EXT);
    }
    async waitForQueryAndGetTime(query) {
      await util_exports.repeatedTry(() => this.disposed || this.isQueryAvailable(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")));
      return this.getQueryTime(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"));
    }
    getQueryTime(query, queryTimerVersion) {
      if (queryTimerVersion === 0) {
        return null;
      }
      if (queryTimerVersion === 2) {
        const gl2 = this.gl;
        const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);
        return timeElapsedNanos / 1e6;
      } else {
        const ext = this.getQueryTimerExtensionWebGL1();
        const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);
        return timeElapsedNanos / 1e6;
      }
    }
    isQueryAvailable(query, queryTimerVersion) {
      if (queryTimerVersion === 0) {
        return true;
      }
      if (queryTimerVersion === 2) {
        const gl2 = this.gl;
        const ext = this.getQueryTimerExtensionWebGL2();
        const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);
        if (this.disjoint == null) {
          this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
        }
        return available && !this.disjoint;
      } else {
        const ext = this.getQueryTimerExtensionWebGL1();
        const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);
        if (this.disjoint == null) {
          this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
        }
        return available && !this.disjoint;
      }
    }
    pollFence(fenceContext) {
      return new Promise((resolve) => {
        this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());
      });
    }
    pollItems() {
      const index = linearSearchLastTrue(this.itemsToPoll.map((x) => x.isDoneFn));
      for (let i = 0; i <= index; ++i) {
        const {resolveFn} = this.itemsToPoll[i];
        resolveFn();
      }
      this.itemsToPoll = this.itemsToPoll.slice(index + 1);
    }
    addItemToPoll(isDoneFn, resolveFn) {
      this.itemsToPoll.push({isDoneFn, resolveFn});
      if (this.itemsToPoll.length > 1) {
        return;
      }
      util_exports.repeatedTry(() => {
        this.pollItems();
        return this.itemsToPoll.length === 0;
      });
    }
    bindTextureToFrameBuffer(texture) {
      this.throwIfDisposed();
      bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);
      if (this.debug) {
        validateFramebuffer(this.gl);
      }
    }
    unbindTextureToFrameBuffer() {
      if (this.outputTexture != null) {
        bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);
        if (this.debug) {
          validateFramebuffer(this.gl);
        }
      } else {
        unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
      }
    }
    downloadMatrixDriver(texture, downloadAndDecode) {
      this.bindTextureToFrameBuffer(texture);
      const result = downloadAndDecode();
      this.unbindTextureToFrameBuffer();
      return result;
    }
    setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {
      this.throwIfDisposed();
      const gl = this.gl;
      bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);
      if (this.debug) {
        validateFramebuffer(gl);
      }
      this.outputTexture = outputMatrixTextureMaybePacked;
      callAndCheck(gl, () => gl.viewport(0, 0, width, height));
      callAndCheck(gl, () => gl.scissor(0, 0, width, height));
    }
    setOutputMatrixWriteRegionDriver(x, y, width, height) {
      this.throwIfDisposed();
      callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));
    }
    throwIfDisposed() {
      if (this.disposed) {
        throw new Error("Attempted to use disposed GPGPUContext.");
      }
    }
    throwIfNoProgram() {
      if (this.program == null) {
        throw new Error("No GPU program is currently set.");
      }
    }
  };
  function linearSearchLastTrue(arr) {
    let i = 0;
    for (; i < arr.length; ++i) {
      const isDone = arr[i]();
      if (!isDone) {
        break;
      }
    }
    return i - 1;
  }
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var {getBroadcastDims: getBroadcastDims2} = backend_util_exports;
  function makeShader(inputsInfo, outputShape, userCode, usesPackedTextures) {
    const prefixSnippets = [];
    inputsInfo.forEach((x) => {
      const size = util_exports.sizeFromShape(x.shapeInfo.logicalShape);
      if (x.shapeInfo.isUniform) {
        prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : ""};`);
      } else {
        prefixSnippets.push(`uniform sampler2D ${x.name};`);
        prefixSnippets.push(`uniform int offset${x.name};`);
      }
    });
    const inputPrefixSnippet = prefixSnippets.join("\n");
    const inputSamplingSnippet = inputsInfo.map((x) => getInputSamplingSnippet(x, outputShape, usesPackedTextures)).join("\n");
    const outTexShape = outputShape.texShape;
    const glsl = getGlslDifferences();
    const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);
    let outputSamplingSnippet;
    let floatTextureSetOutputSnippet;
    let shaderPrefix = getShaderPrefix(glsl);
    if (outputShape.isPacked) {
      outputSamplingSnippet = getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);
      floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);
    } else {
      outputSamplingSnippet = getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);
      floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);
    }
    if (usesPackedTextures) {
      shaderPrefix += SHADER_PACKED_PREFIX;
    }
    const source = [
      shaderPrefix,
      floatTextureSampleSnippet,
      floatTextureSetOutputSnippet,
      inputPrefixSnippet,
      outputSamplingSnippet,
      inputSamplingSnippet,
      userCode
    ].join("\n");
    return source;
  }
  function getSamplerFromInInfo(inInfo) {
    const shape = inInfo.shapeInfo.logicalShape;
    switch (shape.length) {
      case 0:
        return getSamplerScalar(inInfo);
      case 1:
        return getSampler1D(inInfo);
      case 2:
        return getSampler2D(inInfo);
      case 3:
        return getSampler3D(inInfo);
      case 4:
        return getSampler4D(inInfo);
      case 5:
        return getSampler5D(inInfo);
      case 6:
        return getSampler6D(inInfo);
      default:
        throw new Error(`${shape.length}-D input sampling is not yet supported`);
    }
  }
  function getPackedSamplerFromInInfo(inInfo) {
    const shape = inInfo.shapeInfo.logicalShape;
    switch (shape.length) {
      case 0:
        return getPackedSamplerScalar(inInfo);
      case 1:
        return getPackedSampler1D(inInfo);
      case 2:
        return getPackedSampler2D(inInfo);
      case 3:
        return getPackedSampler3D(inInfo);
      default:
        return getPackedSamplerND(inInfo);
    }
  }
  function getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false) {
    let res = "";
    if (usesPackedTextures) {
      res += getPackedSamplerFromInInfo(inInfo);
    } else {
      res += getSamplerFromInInfo(inInfo);
    }
    const inShape = inInfo.shapeInfo.logicalShape;
    const outShape = outShapeInfo.logicalShape;
    if (inShape.length <= outShape.length) {
      if (usesPackedTextures) {
        res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);
      } else {
        res += getSamplerAtOutputCoords(inInfo, outShapeInfo);
      }
    }
    return res;
  }
  function getPackedOutputSamplingSnippet(outShape, outTexShape) {
    switch (outShape.length) {
      case 0:
        return getOutputScalarCoords();
      case 1:
        return getOutputPacked1DCoords(outShape, outTexShape);
      case 2:
        return getOutputPacked2DCoords(outShape, outTexShape);
      case 3:
        return getOutputPacked3DCoords(outShape, outTexShape);
      default:
        return getOutputPackedNDCoords(outShape, outTexShape);
    }
  }
  function getOutputSamplingSnippet(outShape, outTexShape) {
    switch (outShape.length) {
      case 0:
        return getOutputScalarCoords();
      case 1:
        return getOutput1DCoords(outShape, outTexShape);
      case 2:
        return getOutput2DCoords(outShape, outTexShape);
      case 3:
        return getOutput3DCoords(outShape, outTexShape);
      case 4:
        return getOutput4DCoords(outShape, outTexShape);
      case 5:
        return getOutput5DCoords(outShape, outTexShape);
      case 6:
        return getOutput6DCoords(outShape, outTexShape);
      default:
        throw new Error(`${outShape.length}-D output sampling is not yet supported`);
    }
  }
  function getFloatTextureSampleSnippet(glsl) {
    return `
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${glsl.texture2D}(textureSampler, uv).r;
    }
  `;
  }
  function getFloatTextureSetRSnippet(glsl) {
    return `
    void setOutput(float val) {
      ${glsl.output} = vec4(val, 0, 0, 0);
    }
  `;
  }
  function getFloatTextureSetRGBASnippet(glsl) {
    return `
    void setOutput(vec4 val) {
      ${glsl.output} = val;
    }
  `;
  }
  function getShaderPrefix(glsl) {
    const SHADER_PREFIX = `${glsl.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${glsl.varyingFs} vec2 resultUV;
    ${glsl.defineOutput}
    const vec2 halfCR = vec2(0.5, 0.5);

    struct ivec5
    {
      int x;
      int y;
      int z;
      int w;
      int u;
    };

    struct ivec6
    {
      int x;
      int y;
      int z;
      int w;
      int u;
      int v;
    };

    uniform float NAN;
    ${glsl.defineSpecialNaN}
    ${glsl.defineSpecialInf}
    ${glsl.defineRound}

    int imod(int x, int y) {
      return x - y * (x / y);
    }

    int idiv(int a, int b, float sign) {
      int res = a / b;
      int mod = imod(a, b);
      if (sign < 0. && mod != 0) {
        res -= 1;
      }
      return res;
    }

    //Based on the work of Dave Hoskins
    //https://www.shadertoy.com/view/4djSRW
    #define HASHSCALE1 443.8975
    float random(float seed){
      vec2 p = resultUV * seed;
      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);
      p3 += dot(p3, p3.yzx + 19.19);
      return fract((p3.x + p3.y) * p3.z);
    }

    ${SAMPLE_1D_SNIPPET}
    ${SAMPLE_2D_SNIPPET}
    ${SAMPLE_3D_SNIPPET}
  `;
    return SHADER_PREFIX;
  }
  var SAMPLE_1D_SNIPPET = `
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
  int texelIndex = index / 2;
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`;
  var SAMPLE_2D_SNIPPET = `
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
  int texNumC, int row, int col) {
  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`;
  var SAMPLE_3D_SNIPPET = `
vec2 packedUVfrom3D(int texNumR, int texNumC,
    int texelsInBatch, int texelsInLogicalRow, int b,
    int row, int col) {
  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`;
  var SHADER_PACKED_PREFIX = `
  float getChannel(vec4 frag, vec2 innerDims) {
    vec2 modCoord = mod(innerDims, 2.);
    return modCoord.x == 0. ?
      (modCoord.y == 0. ? frag.r : frag.g) :
      (modCoord.y == 0. ? frag.b : frag.a);
  }
  float getChannel(vec4 frag, int dim) {
    float modCoord = mod(float(dim), 2.);
    return modCoord == 0. ? frag.r : frag.g;
  }
`;
  function getOutputScalarCoords() {
    return `
    int getOutputCoords() {
      return 0;
    }
  `;
  }
  function getOutputPacked1DCoords(shape, texShape) {
    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
    if (packedTexShape[0] === 1) {
      return `
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);
      }
    `;
    }
    if (packedTexShape[1] === 1) {
      return `
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);
      }
    `;
    }
    return `
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);
    }
  `;
  }
  function getOutput1DCoords(shape, texShape) {
    if (texShape[0] === 1) {
      return `
      int getOutputCoords() {
        return int(resultUV.x * ${texShape[1]}.0);
      }
    `;
    }
    if (texShape[1] === 1) {
      return `
      int getOutputCoords() {
        return int(resultUV.y * ${texShape[0]}.0);
      }
    `;
    }
    return `
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${texShape[0]}, ${texShape[1]}));
      return resTexRC.x * ${texShape[1]} + resTexRC.y;
    }
  `;
  }
  function getOutputPacked3DCoords(shape, texShape) {
    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
    const texelsInLogicalRow = Math.ceil(shape[2] / 2);
    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);
    return `
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;

      int b = index / ${texelsInBatch};
      index -= b * ${texelsInBatch};

      int r = 2 * (index / ${texelsInLogicalRow});
      int c = imod(index, ${texelsInLogicalRow}) * 2;

      return ivec3(b, r, c);
    }
  `;
  }
  function getOutput3DCoords(shape, texShape) {
    const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], shape);
    return `
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${texShape[0]}, ${texShape[1]}));
      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
      ${coordsFromIndexSnippet}
      return ivec3(r, c, d);
    }
  `;
  }
  function getOutputPackedNDCoords(shape, texShape) {
    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
    const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);
    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);
    let texelsInBatchN = texelsInBatch;
    let batches = ``;
    let coords2 = "b, r, c";
    for (let b = 2; b < shape.length - 1; b++) {
      texelsInBatchN *= shape[shape.length - b - 1];
      batches = `
      int b${b} = index / ${texelsInBatchN};
      index -= b${b} * ${texelsInBatchN};
    ` + batches;
      coords2 = `b${b}, ` + coords2;
    }
    return `
    ivec${shape.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;

      ${batches}

      int b = index / ${texelsInBatch};
      index -= b * ${texelsInBatch};

      int r = 2 * (index / ${texelsInLogicalRow});
      int c = imod(index, ${texelsInLogicalRow}) * 2;

      return ivec${shape.length}(${coords2});
    }
  `;
  }
  function getOutput4DCoords(shape, texShape) {
    const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d", "d2"], shape);
    return `
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${texShape[0]}, ${texShape[1]}));
      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
      ${coordsFromIndexSnippet}
      return ivec4(r, c, d, d2);
    }
  `;
  }
  function getOutput5DCoords(shape, texShape) {
    const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d", "d2", "d3"], shape);
    return `
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},
                             ${texShape[1]}));

      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;

      ${coordsFromIndexSnippet}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `;
  }
  function getOutput6DCoords(shape, texShape) {
    const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d", "d2", "d3", "d4"], shape);
    return `
    ivec6 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${texShape[0]}, ${texShape[1]}));
      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;

      ${coordsFromIndexSnippet}

      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `;
  }
  function getOutputPacked2DCoords(shape, texShape) {
    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
    if (util_exports.arraysEqual(shape, texShape)) {
      return `
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
      }
    `;
    }
    const texelsInLogicalRow = Math.ceil(shape[1] / 2);
    return `
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));

      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
      int r = 2 * (index / ${texelsInLogicalRow});
      int c = imod(index, ${texelsInLogicalRow}) * 2;

      return ivec2(r, c);
    }
  `;
  }
  function getOutput2DCoords(shape, texShape) {
    if (util_exports.arraysEqual(shape, texShape)) {
      return `
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));
      }
    `;
    }
    if (shape[1] === 1) {
      return `
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${texShape[0]}, ${texShape[1]}));
        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `;
    }
    if (shape[0] === 1) {
      return `
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${texShape[0]}, ${texShape[1]}));
        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    `;
    }
    return `
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${texShape[0]}, ${texShape[1]}));
      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
      int r = index / ${shape[1]};
      int c = index - r * ${shape[1]};
      return ivec2(r, c);
    }
  `;
  }
  function getFlatOffsetUniformName(texName) {
    return `offset${texName}`;
  }
  function getPackedSamplerScalar(inputInfo) {
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const glsl = getGlslDifferences();
    return `
    vec4 ${funcName}() {
      return ${glsl.texture2D}(${texName}, halfCR);
    }
  `;
  }
  function getSamplerScalar(inputInfo) {
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    if (inputInfo.shapeInfo.isUniform) {
      return `float ${funcName}() {return ${texName};}`;
    }
    const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;
    if (texNumR === 1 && texNumC === 1) {
      return `
      float ${funcName}() {
        return sampleTexture(${texName}, halfCR);
      }
    `;
    }
    const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;
    const offset = getFlatOffsetUniformName(texName);
    return `
    float ${funcName}() {
      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});
      return sampleTexture(${texName}, uv);
    }
  `;
  }
  function getPackedSampler1D(inputInfo) {
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const texShape = inputInfo.shapeInfo.texShape;
    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
    const glsl = getGlslDifferences();
    return `
    vec4 ${funcName}(int index) {
      vec2 uv = packedUVfrom1D(
        ${packedTexShape[0]}, ${packedTexShape[1]}, index);
      return ${glsl.texture2D}(${texName}, uv);
    }
  `;
  }
  function getSampler1D(inputInfo) {
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    if (inputInfo.shapeInfo.isUniform) {
      return `
      float ${funcName}(int index) {
        ${getUniformSampler(inputInfo)}
      }
    `;
    }
    const texShape = inputInfo.shapeInfo.texShape;
    const tNumR = texShape[0];
    const tNumC = texShape[1];
    if (tNumC === 1 && tNumR === 1) {
      return `
      float ${funcName}(int index) {
        return sampleTexture(${texName}, halfCR);
      }
    `;
    }
    const offset = getFlatOffsetUniformName(texName);
    if (tNumC === 1) {
      return `
      float ${funcName}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;
    }
    if (tNumR === 1) {
      return `
      float ${funcName}(int index) {
        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);
        return sampleTexture(${texName}, uv);
      }
    `;
    }
    return `
    float ${funcName}(int index) {
      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});
      return sampleTexture(${texName}, uv);
    }
  `;
  }
  function getPackedSampler2D(inputInfo) {
    const shape = inputInfo.shapeInfo.logicalShape;
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const texShape = inputInfo.shapeInfo.texShape;
    const texNumR = texShape[0];
    const texNumC = texShape[1];
    const glsl = getGlslDifferences();
    if (texShape != null && util_exports.arraysEqual(shape, texShape)) {
      return `
      vec4 ${funcName}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);

        return ${glsl.texture2D}(${texName}, uv);
      }
    `;
    }
    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
    const valuesPerRow = Math.ceil(shape[1] / 2);
    return `
    vec4 ${funcName}(int row, int col) {
      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);
      return ${glsl.texture2D}(${texName}, uv);
    }
  `;
  }
  function getSampler2D(inputInfo) {
    const shape = inputInfo.shapeInfo.logicalShape;
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const texShape = inputInfo.shapeInfo.texShape;
    if (texShape != null && util_exports.arraysEqual(shape, texShape)) {
      const texNumR2 = texShape[0];
      const texNumC2 = texShape[1];
      return `
    float ${funcName}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC2}.0, ${texNumR2}.0);
      return sampleTexture(${texName}, uv);
    }
  `;
    }
    const {newShape, keptDims} = util_exports.squeezeShape(shape);
    const squeezedShape = newShape;
    if (squeezedShape.length < shape.length) {
      const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
      const params = ["row", "col"];
      return `
      ${getSamplerFromInInfo(newInputInfo)}
      float ${funcName}(int row, int col) {
        return ${funcName}(${getSqueezedParams(params, keptDims)});
      }
    `;
    }
    if (inputInfo.shapeInfo.isUniform) {
      return `
      float ${funcName}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));
        ${getUniformSampler(inputInfo)}
      }
    `;
    }
    const texNumR = texShape[0];
    const texNumC = texShape[1];
    const offset = getFlatOffsetUniformName(texName);
    if (texNumC === 1) {
      return `
    float ${funcName}(int row, int col) {
      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);
      return sampleTexture(${texName}, uv);
    }
  `;
    }
    if (texNumR === 1) {
      return `
    float ${funcName}(int row, int col) {
      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);
      return sampleTexture(${texName}, uv);
    }
  `;
    }
    return `
  float ${funcName}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${shape[1]} + col + ${offset};
    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
    return sampleTexture(${texName}, uv);
  }
`;
  }
  function getPackedSampler3D(inputInfo) {
    const shape = inputInfo.shapeInfo.logicalShape;
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const texShape = inputInfo.shapeInfo.texShape;
    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
    if (shape[0] === 1) {
      const squeezedShape = shape.slice(1);
      const keptDims = [1, 2];
      const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
      const params = ["b", "row", "col"];
      return `
        ${getPackedSamplerFromInInfo(newInputInfo)}
        vec4 ${funcName}(int b, int row, int col) {
          return ${funcName}(${getSqueezedParams(params, keptDims)});
        }
      `;
    }
    const texNumR = packedTexShape[0];
    const texNumC = packedTexShape[1];
    const valuesPerRow = Math.ceil(shape[2] / 2);
    const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);
    const glsl = getGlslDifferences();
    return `
    vec4 ${funcName}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);
      return ${glsl.texture2D}(${texName}, uv);
    }
  `;
  }
  function getSampler3D(inputInfo) {
    const shape = inputInfo.shapeInfo.logicalShape;
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const stride0 = shape[1] * shape[2];
    const stride1 = shape[2];
    const {newShape, keptDims} = util_exports.squeezeShape(shape);
    const squeezedShape = newShape;
    if (squeezedShape.length < shape.length) {
      const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
      const params = ["row", "col", "depth"];
      return `
        ${getSamplerFromInInfo(newInputInfo)}
        float ${funcName}(int row, int col, int depth) {
          return ${funcName}(${getSqueezedParams(params, keptDims)});
        }
      `;
    }
    if (inputInfo.shapeInfo.isUniform) {
      return `
      float ${funcName}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${stride0}, ${stride1}, 1)));
        ${getUniformSampler(inputInfo)}
      }
    `;
    }
    const texShape = inputInfo.shapeInfo.texShape;
    const texNumR = texShape[0];
    const texNumC = texShape[1];
    const flatOffset = inputInfo.shapeInfo.flatOffset;
    if (texNumC === stride0 && flatOffset == null) {
      return `
        float ${funcName}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${texNumC}.0, ${texNumR}.0);
          return sampleTexture(${texName}, uv);
        }
      `;
    }
    if (texNumC === stride1 && flatOffset == null) {
      return `
    float ${funcName}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
      return sampleTexture(${texName}, uv);
    }
  `;
    }
    const offset = getFlatOffsetUniformName(texName);
    return `
      float ${funcName}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};
        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
        return sampleTexture(${texName}, uv);
      }
  `;
  }
  function getPackedSamplerND(inputInfo) {
    const shape = inputInfo.shapeInfo.logicalShape;
    const rank = shape.length;
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const texShape = inputInfo.shapeInfo.texShape;
    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
    const texNumR = packedTexShape[0];
    const texNumC = packedTexShape[1];
    const valuesPerRow = Math.ceil(shape[rank - 1] / 2);
    let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);
    let params = `int b, int row, int col`;
    let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;
    for (let b = 2; b < rank - 1; b++) {
      params = `int b${b}, ` + params;
      texelsInBatch *= shape[rank - b - 1];
      index = `b${b} * ${texelsInBatch} + ` + index;
    }
    const glsl = getGlslDifferences();
    return `
    vec4 ${funcName}(${params}) {
      int index = ${index};
      int texR = index / ${texNumC};
      int texC = index - texR * ${texNumC};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});
      return ${glsl.texture2D}(${texName}, uv);
    }
  `;
  }
  function getSampler4D(inputInfo) {
    const shape = inputInfo.shapeInfo.logicalShape;
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const stride2 = shape[3];
    const stride1 = shape[2] * stride2;
    const stride0 = shape[1] * stride1;
    const {newShape, keptDims} = util_exports.squeezeShape(shape);
    if (newShape.length < shape.length) {
      const newInputInfo = squeezeInputInfo(inputInfo, newShape);
      const params = ["row", "col", "depth", "depth2"];
      return `
      ${getSamplerFromInInfo(newInputInfo)}
      float ${funcName}(int row, int col, int depth, int depth2) {
        return ${funcName}(${getSqueezedParams(params, keptDims)});
      }
    `;
    }
    if (inputInfo.shapeInfo.isUniform) {
      return `
      float ${funcName}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));
        ${getUniformSampler(inputInfo)}
      }
    `;
    }
    const flatOffset = inputInfo.shapeInfo.flatOffset;
    const texShape = inputInfo.shapeInfo.texShape;
    const texNumR = texShape[0];
    const texNumC = texShape[1];
    if (texNumC === stride0 && flatOffset == null) {
      return `
      float ${funcName}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${stride1}, ${stride2}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;
    }
    if (texNumC === stride2 && flatOffset == null) {
      return `
      float ${funcName}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;
    }
    const offset = getFlatOffsetUniformName(texName);
    return `
    float ${funcName}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${stride0} + col * ${stride1} +
          depth * ${stride2} + depth2;
      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});
      return sampleTexture(${texName}, uv);
    }
  `;
  }
  function getSampler5D(inputInfo) {
    const shape = inputInfo.shapeInfo.logicalShape;
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const stride3 = shape[4];
    const stride2 = shape[3] * stride3;
    const stride1 = shape[2] * stride2;
    const stride0 = shape[1] * stride1;
    const {newShape, keptDims} = util_exports.squeezeShape(shape);
    if (newShape.length < shape.length) {
      const newInputInfo = squeezeInputInfo(inputInfo, newShape);
      const params = ["row", "col", "depth", "depth2", "depth3"];
      return `
      ${getSamplerFromInInfo(newInputInfo)}
      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
        return ${funcName}(${getSqueezedParams(params, keptDims)});
      }
    `;
    }
    if (inputInfo.shapeInfo.isUniform) {
      return `
      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
          depth3;
        ${getUniformSampler(inputInfo)}
      }
    `;
    }
    const flatOffset = inputInfo.shapeInfo.flatOffset;
    const texShape = inputInfo.shapeInfo.texShape;
    const texNumR = texShape[0];
    const texNumC = texShape[1];
    if (texNumC === stride0 && flatOffset == null) {
      return `
      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${stride1}, ${stride2}, ${stride3}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;
    }
    if (texNumC === stride3 && flatOffset == null) {
      return `
      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${shape[1] * shape[2] * shape[3]},
               ${shape[2] * shape[3]}, ${shape[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;
    }
    const offset = getFlatOffsetUniformName(texName);
    return `
    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
          depth2 * ${stride3} + depth3 + ${offset};
      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
      return sampleTexture(${texName}, uv);
    }
  `;
  }
  function getSampler6D(inputInfo) {
    const shape = inputInfo.shapeInfo.logicalShape;
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const {newShape, keptDims} = util_exports.squeezeShape(shape);
    if (newShape.length < shape.length) {
      const newInputInfo = squeezeInputInfo(inputInfo, newShape);
      const params = ["row", "col", "depth", "depth2", "depth3", "depth4"];
      return `
      ${getSamplerFromInInfo(newInputInfo)}
      float ${funcName}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${funcName}(${getSqueezedParams(params, keptDims)});
      }
    `;
    }
    const stride4 = shape[5];
    const stride3 = shape[4] * stride4;
    const stride2 = shape[3] * stride3;
    const stride1 = shape[2] * stride2;
    const stride0 = shape[1] * stride1;
    if (inputInfo.shapeInfo.isUniform) {
      return `
      float ${funcName}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
          dot(
            vec2(depth3, depth4),
            vec2(${stride4}, 1)));
        ${getUniformSampler(inputInfo)}
      }
    `;
    }
    const flatOffset = inputInfo.shapeInfo.flatOffset;
    const texShape = inputInfo.shapeInfo.texShape;
    const texNumR = texShape[0];
    const texNumC = texShape[1];
    if (texNumC === stride0 && flatOffset == null) {
      return `
      float ${funcName}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;
    }
    if (texNumC === stride4 && flatOffset == null) {
      return `
      float ${funcName}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},
               ${shape[2] * shape[3] * shape[4]},
               ${shape[3] * shape[4]},
               ${shape[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;
    }
    const offset = getFlatOffsetUniformName(texName);
    return `
    float ${funcName}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};
      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
      return sampleTexture(${texName}, uv);
    }
  `;
  }
  function getUniformSampler(inputInfo) {
    const texName = inputInfo.name;
    const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);
    if (inSize < 2) {
      return `return ${texName};`;
    }
    return `
    for (int i = 0; i < ${inSize}; i++) {
      if (i == index) {
        return ${texName}[i];
      }
    }
  `;
  }
  function getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {
    const texName = inputInfo.name;
    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
    const funcName = "get" + texFuncSnippet + "AtOutCoords";
    const inRank = inputInfo.shapeInfo.logicalShape.length;
    const outRank = outShapeInfo.logicalShape.length;
    const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
    const type = getCoordsDataType(outRank);
    const rankDiff = outRank - inRank;
    let coordsSnippet;
    const fields = ["x", "y", "z", "w", "u", "v"];
    if (inRank === 0) {
      coordsSnippet = "";
    } else if (outRank < 2 && broadcastDims.length >= 1) {
      coordsSnippet = "coords = 0;";
    } else {
      coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join("\n");
    }
    let unpackedCoordsSnippet = "";
    if (outRank < 2 && inRank > 0) {
      unpackedCoordsSnippet = "coords";
    } else {
      unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(", ");
    }
    let output = `return outputValue;`;
    const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);
    const isInputScalar = inSize === 1;
    const outSize = util_exports.sizeFromShape(outShapeInfo.logicalShape);
    const isOutputScalar = outSize === 1;
    if (inRank === 1 && !isInputScalar && !isOutputScalar) {
      output = `
      return vec4(outputValue.xy, outputValue.xy);
    `;
    } else if (isInputScalar && !isOutputScalar) {
      if (outRank === 1) {
        output = `
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `;
      } else {
        output = `
        return vec4(outputValue.x);
      `;
      }
    } else if (broadcastDims.length) {
      const rows = inRank - 2;
      const cols = inRank - 1;
      if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {
        output = `return vec4(outputValue.x);`;
      } else if (broadcastDims.indexOf(rows) > -1) {
        output = `return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);`;
      } else if (broadcastDims.indexOf(cols) > -1) {
        output = `return vec4(outputValue.xx, outputValue.zz);`;
      }
    }
    return `
    vec4 ${funcName}() {
      ${type} coords = getOutputCoords();
      ${coordsSnippet}
      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});
      ${output}
    }
  `;
  }
  function getSamplerAtOutputCoords(inputInfo, outShapeInfo) {
    const texName = inputInfo.name;
    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
    const funcName = "get" + texFuncSnippet + "AtOutCoords";
    const outTexShape = outShapeInfo.texShape;
    const inTexShape = inputInfo.shapeInfo.texShape;
    const inRank = inputInfo.shapeInfo.logicalShape.length;
    const outRank = outShapeInfo.logicalShape.length;
    if (!inputInfo.shapeInfo.isUniform && inRank === outRank && inputInfo.shapeInfo.flatOffset == null && util_exports.arraysEqual(inTexShape, outTexShape)) {
      return `
      float ${funcName}() {
        return sampleTexture(${texName}, resultUV);
      }
    `;
    }
    const type = getCoordsDataType(outRank);
    const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
    const rankDiff = outRank - inRank;
    let coordsSnippet;
    const fields = ["x", "y", "z", "w", "u", "v"];
    if (inRank === 0) {
      coordsSnippet = "";
    } else if (outRank < 2 && broadcastDims.length >= 1) {
      coordsSnippet = "coords = 0;";
    } else {
      coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join("\n");
    }
    let unpackedCoordsSnippet = "";
    if (outRank < 2 && inRank > 0) {
      unpackedCoordsSnippet = "coords";
    } else {
      unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(", ");
    }
    return `
    float ${funcName}() {
      ${type} coords = getOutputCoords();
      ${coordsSnippet}
      return get${texFuncSnippet}(${unpackedCoordsSnippet});
    }
  `;
  }
  function getCoordsDataType(rank) {
    if (rank <= 1) {
      return "int";
    } else if (rank === 2) {
      return "ivec2";
    } else if (rank === 3) {
      return "ivec3";
    } else if (rank === 4) {
      return "ivec4";
    } else if (rank === 5) {
      return "ivec5";
    } else if (rank === 6) {
      return "ivec6";
    } else {
      throw Error(`GPU for rank ${rank} is not yet supported`);
    }
  }
  function squeezeInputInfo(inInfo, squeezedShape) {
    const newInputInfo = JSON.parse(JSON.stringify(inInfo));
    newInputInfo.shapeInfo.logicalShape = squeezedShape;
    return newInputInfo;
  }
  function getSqueezedParams(params, keptDims) {
    return keptDims.map((d) => params[d]).join(", ");
  }
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function compileProgram(gpgpu, program, inputs, output) {
    const userCode = program.userCode;
    const inputInfos = inputs.map((input2, i) => {
      const shapeInfo = {
        logicalShape: input2.shape,
        texShape: input2.isUniform ? null : input2.texData.texShape,
        isUniform: input2.isUniform,
        isPacked: input2.isUniform ? false : input2.texData.isPacked,
        flatOffset: null
      };
      if (input2.texData != null && input2.texData.slice != null && input2.texData.slice.flatOffset > 0) {
        shapeInfo.flatOffset = input2.texData.slice.flatOffset;
      }
      return {name: program.variableNames[i], shapeInfo};
    });
    const inShapeInfos = inputInfos.map((x) => x.shapeInfo);
    const outShapeInfo = {
      logicalShape: output.shape,
      texShape: output.texData.texShape,
      isUniform: false,
      isPacked: output.texData.isPacked,
      flatOffset: null
    };
    const source = makeShader(inputInfos, outShapeInfo, userCode, program.packedInputs);
    const webGLProgram = gpgpu.createProgram(source);
    let infLoc = null;
    const nanLoc = gpgpu.getUniformLocation(webGLProgram, "NAN", false);
    if (env().getNumber("WEBGL_VERSION") === 1) {
      infLoc = gpgpu.getUniformLocation(webGLProgram, "INFINITY", false);
    }
    const uniformLocations = {};
    for (let i = 0; i < program.variableNames.length; i++) {
      const varName = program.variableNames[i];
      const shouldThrow = false;
      uniformLocations[varName] = gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);
      uniformLocations[`offset${varName}`] = gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);
    }
    return {
      program,
      source,
      webGLProgram,
      uniformLocations,
      inShapeInfos,
      outShapeInfo,
      infLoc,
      nanLoc
    };
  }
  function validateBinaryAndProgram(shapeInfos, inputs) {
    if (shapeInfos.length !== inputs.length) {
      throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);
    }
    shapeInfos.forEach((s, i) => {
      const shapeA = s.logicalShape;
      const input2 = inputs[i];
      const shapeB = input2.shape;
      if (!util_exports.arraysEqual(shapeA, shapeB)) {
        throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);
      }
      if (s.isUniform && input2.isUniform) {
        return;
      }
      const texShapeA = s.texShape;
      const texShapeB = input2.isUniform ? null : input2.texData.texShape;
      if (!util_exports.arraysEqual(texShapeA, texShapeB)) {
        throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`);
      }
    });
  }
  function runProgram(gpgpu, binary, inputs, output, customSetup) {
    validateBinaryAndProgram(binary.inShapeInfos, inputs);
    validateBinaryAndProgram([binary.outShapeInfo], [output]);
    const outTex = output.texData.texture;
    const outTexShape = output.texData.texShape;
    if (output.texData.isPacked) {
      gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);
    } else {
      gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);
    }
    gpgpu.setProgram(binary.webGLProgram);
    if (env().getNumber("WEBGL_VERSION") === 1) {
      if (binary.infLoc !== null) {
        gpgpu.gl.uniform1f(binary.infLoc, Infinity);
      }
    }
    if (binary.nanLoc !== null) {
      gpgpu.gl.uniform1f(binary.nanLoc, NaN);
    }
    inputs.forEach((input2, i) => {
      const varName = binary.program.variableNames[i];
      const varLoc = binary.uniformLocations[varName];
      const varOffsetLoc = binary.uniformLocations[`offset${varName}`];
      if (varLoc == null) {
        return;
      }
      if (input2.isUniform) {
        if (util_exports.sizeFromShape(input2.shape) < 2) {
          gpgpu.gl.uniform1f(varLoc, input2.uniformValues[0]);
        } else {
          let vals = input2.uniformValues;
          if (!(vals instanceof Float32Array)) {
            vals = new Float32Array(vals);
          }
          gpgpu.gl.uniform1fv(varLoc, vals);
        }
        return;
      }
      if (input2.texData.slice != null && varOffsetLoc != null) {
        gpgpu.gl.uniform1i(varOffsetLoc, input2.texData.slice.flatOffset);
      }
      gpgpu.setInputMatrixTexture(input2.texData.texture, varLoc, i);
    });
    if (customSetup != null) {
      customSetup(gpgpu, binary.webGLProgram);
    }
    gpgpu.executeProgram();
  }
  function makeShaderKey(program, inputs, output) {
    let keyInputs = "";
    inputs.concat(output).forEach((x) => {
      const hasOffset = x.texData != null && x.texData.slice != null && x.texData.slice.flatOffset > 0;
      const texShape = x.isUniform ? "uniform" : x.texData.texShape;
      keyInputs += `${x.shape}_${texShape}_${hasOffset}`;
    });
    const keyUserCode = program.userCode;
    let key = program.constructor.name;
    key += "_" + keyInputs + "_" + keyUserCode;
    return key;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var {addImpl: addImplCPU, bincountImpl: bincountImplCPU, bincountReduceImpl: bincountReduceImplCPU, ceilImpl: ceilImplCPU, concatImpl: concatImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, gatherV2Impl: gatherV2ImplCPU, greaterImpl: greaterImplCPU, lessImpl: lessImplCPU, linSpaceImpl: linSpaceImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, maximumImpl: maximumImplCPU, minimumImpl: minimumImplCPU, multiplyImpl: multiplyImplCPU, negImpl: negImplCPU, prodImpl: prodImplCPU, rangeImpl: rangeImplCPU, rsqrtImpl: rsqrtImplCPU, simpleAbsImpl: simpleAbsImplCPU, sliceImpl: sliceImplCPU, stridedSliceImpl: stridedSliceImplCPU, subImpl: subImplCPU, tileImpl: tileImplCPU, topKImpl: topKImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU} = shared_exports;
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function getVecChannels(name, rank) {
    return ["x", "y", "z", "w", "u", "v"].slice(0, rank).map((d) => `${name}.${d}`);
  }
  function getChannels(name, rank) {
    if (rank === 1) {
      return [name];
    }
    return getVecChannels(name, rank);
  }
  function getSourceCoords(rank, dims) {
    if (rank === 1) {
      return "rc";
    }
    let coords2 = "";
    for (let i = 0; i < rank; i++) {
      coords2 += dims[i];
      if (i < rank - 1) {
        coords2 += ",";
      }
    }
    return coords2;
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var PackProgram = class {
    constructor(outputShape) {
      this.variableNames = ["A"];
      this.packedInputs = false;
      this.packedOutput = true;
      this.outputShape = outputShape;
      const rank = outputShape.length;
      if (rank === 0) {
        this.userCode = `
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;
      } else {
        const channels = getChannels("rc", rank);
        const dtype = getCoordsDataType(rank);
        const outOfBoundsCondition = getOutOfBoundsCondition(rank, outputShape, channels);
        const setup42 = getSetup(rank, outputShape[outputShape.length - 1], outputShape[outputShape.length - 2], channels);
        const output = getOutput(outputShape, channels);
        this.userCode = `
        void main() {
          ${dtype} rc = getOutputCoords();

          if(${outOfBoundsCondition}) {
            setOutput(vec4(0));
          } else {
            ${setup42}

            setOutput(vec4(${output}));
          }
        }
      `;
      }
    }
  };
  function getSourceCoordsArr(rank, dims) {
    const coords2 = [];
    for (let row = 0; row <= 1; row++) {
      for (let col = 0; col <= 1; col++) {
        let coord = `${row === 0 ? "r" : "rp1"}, ${col === 0 ? "c" : "cp1"}`;
        for (let d = 2; d < rank; d++) {
          coord = `${dims[dims.length - 1 - d]},` + coord;
        }
        coords2.push(coord);
      }
    }
    return coords2;
  }
  function getOutOfBoundsCondition(rank, shape, dims) {
    if (rank === 1) {
      return `rc > ${shape[0]}`;
    }
    let cond = "";
    for (let i = rank - 2; i < rank; i++) {
      cond += `${dims[i]} >= ${shape[i]}`;
      if (i < rank - 1) {
        cond += "||";
      }
    }
    return cond;
  }
  function getSetup(rank, cols, rows, dims) {
    if (rank === 1) {
      return "";
    }
    const innerDims = dims.slice(-2);
    return `
    int r = ${innerDims[0]};
    int c = ${innerDims[1]};
    int rp1 = r + 1;
    int cp1 = c + 1;

    bool cEdge = cp1 >= ${cols};
    bool rEdge = rp1 >= ${rows};
  `;
  }
  function getOutput(shape, dims) {
    const rank = shape.length;
    const sourceCoords = getSourceCoordsArr(rank, dims);
    if (rank === 1) {
      return `getA(rc),
            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),
            0, 0`;
    }
    return `getA(${sourceCoords[0]}),
          cEdge ? 0. : getA(${sourceCoords[1]}),
          rEdge ? 0. : getA(${sourceCoords[2]}),
          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ReshapePackedProgram = class {
    constructor(outputShape, inputShape) {
      this.variableNames = ["A"];
      this.packedInputs = true;
      this.packedOutput = true;
      this.outputShape = outputShape;
      let mainLoop = ``;
      for (let i = 0; i < 4; i++) {
        let thisRC = `thisRC = rc;`;
        if (i % 2 === 1) {
          thisRC += `thisRC.z += 1;`;
        }
        if (i > 1) {
          thisRC += `thisRC.y += 1;`;
        }
        mainLoop += `
        ${thisRC}
        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ""}
          int flatIndex = getFlatIndex(thisRC);

          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));

          result[${i}] =
            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
        ${i > 0 ? "}" : ""}
      `;
      }
      this.userCode = `
      ${getReshapedInputCoords(inputShape)}
      ${getFlatIndexFrom3D(outputShape)}

      void main() {
        ivec3 rc = getOutputCoords();

        vec4 result = vec4(0.);

        ivec3 thisRC;
        int rows = ${outputShape[1]};
        int cols = ${outputShape[2]};

        ${mainLoop}

        setOutput(result);
      }
    `;
    }
  };
  function getReshapedInputCoords(shape) {
    const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], shape);
    return `
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${coordsFromIndexSnippet}
      return ivec3(r, c, d);
    }
  `;
  }
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var TextureManager = class {
    constructor(gpgpu) {
      this.gpgpu = gpgpu;
      this.numUsedTextures = 0;
      this.numFreeTextures = 0;
      this._numBytesAllocated = 0;
      this._numBytesFree = 0;
      this.freeTextures = {};
      this.logEnabled = false;
      this.usedTextures = {};
    }
    acquireTexture(shapeRC, usage, isPacked) {
      const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);
      const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);
      if (!(shapeKey in this.freeTextures)) {
        this.freeTextures[shapeKey] = [];
      }
      if (!(shapeKey in this.usedTextures)) {
        this.usedTextures[shapeKey] = [];
      }
      const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);
      if (this.freeTextures[shapeKey].length > 0) {
        this.numFreeTextures--;
        this.numUsedTextures++;
        this._numBytesFree -= texBytes;
        this.log();
        const newTexture2 = this.freeTextures[shapeKey].shift();
        this.usedTextures[shapeKey].push(newTexture2);
        return newTexture2;
      }
      let newTexture;
      if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {
        newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);
      } else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {
        newTexture = this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);
      } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {
        newTexture = this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);
      } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {
        newTexture = this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);
      } else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {
        newTexture = this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);
      }
      this.usedTextures[shapeKey].push(newTexture);
      this.numUsedTextures++;
      this._numBytesAllocated += texBytes;
      this.log();
      return newTexture;
    }
    releaseTexture(texture, shape, logicalTexType, isPacked) {
      if (this.freeTextures == null) {
        return;
      }
      const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);
      const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);
      if (!(shapeKey in this.freeTextures)) {
        this.freeTextures[shapeKey] = [];
      }
      const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);
      const deleteTexThreshold = env().get("WEBGL_DELETE_TEXTURE_THRESHOLD");
      if (deleteTexThreshold !== -1 && this._numBytesAllocated > deleteTexThreshold) {
        this.gpgpu.deleteMatrixTexture(texture);
        this._numBytesAllocated -= texBytes;
      } else {
        this.freeTextures[shapeKey].push(texture);
        this.numFreeTextures++;
        this._numBytesFree += texBytes;
      }
      this.numUsedTextures--;
      const texList = this.usedTextures[shapeKey];
      const texIndex = texList.indexOf(texture);
      if (texIndex < 0) {
        throw new Error("Cannot release a texture that was never provided by this texture manager");
      }
      texList.splice(texIndex, 1);
      this.log();
    }
    log() {
      if (!this.logEnabled) {
        return;
      }
      const total = this.numFreeTextures + this.numUsedTextures;
      console.log("Free/Used", `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);
      const freeRatio = this._numBytesFree / this._numBytesAllocated;
      console.log(`Bytes allocated: ${this._numBytesAllocated}`);
      console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);
    }
    get numBytesAllocated() {
      return this._numBytesAllocated;
    }
    get numBytesFree() {
      return this._numBytesFree;
    }
    getNumUsedTextures() {
      return this.numUsedTextures;
    }
    getNumFreeTextures() {
      return this.numFreeTextures;
    }
    dispose() {
      if (this.freeTextures == null) {
        return;
      }
      for (const texShape in this.freeTextures) {
        this.freeTextures[texShape].forEach((tex) => {
          this.gpgpu.deleteMatrixTexture(tex);
        });
      }
      for (const texShape in this.usedTextures) {
        this.usedTextures[texShape].forEach((tex) => {
          this.gpgpu.deleteMatrixTexture(tex);
        });
      }
      this.freeTextures = null;
      this.usedTextures = null;
      this.numUsedTextures = 0;
      this.numFreeTextures = 0;
      this._numBytesAllocated = 0;
      this._numBytesFree = 0;
    }
  };
  function numBytesForInternalFormat(gl, internalFormat) {
    const glany = gl;
    if (internalFormat === glany.R32F) {
      return 4;
    } else if (internalFormat === glany.R16F) {
      return 2;
    } else if (internalFormat === glany.RGBA32F) {
      return 16;
    } else if (internalFormat === gl.RGBA) {
      return 16;
    } else if (internalFormat === glany.RGBA16F) {
      return 8;
    }
    throw new Error(`Unknown internal format ${internalFormat}`);
  }
  function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {
    const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);
    let numElements;
    if (isPacked) {
      const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);
      numElements = packedWidth * packedHeight;
    } else {
      const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);
      numElements = width * height;
    }
    const bytesPerElement2 = numBytesForInternalFormat(gl, internalFormat);
    return numElements * bytesPerElement2;
  }
  function internalFormatForPhysicalTexType(physicalTexType, textureConfig) {
    switch (physicalTexType) {
      case PhysicalTextureType.PACKED_2X2_FLOAT32:
        return getInternalFormatForPackedMatrixTexture(textureConfig);
      case PhysicalTextureType.PACKED_2X2_FLOAT16:
        return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);
      case PhysicalTextureType.UNPACKED_FLOAT32:
        return getInternalFormatForFloat32MatrixTexture(textureConfig);
      case PhysicalTextureType.UNPACKED_FLOAT16:
        return getInternalFormatForFloat16MatrixTexture(textureConfig);
      case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:
        return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);
      default:
        throw new Error(`Unknown physical texture type ${physicalTexType}`);
    }
  }
  function getPhysicalTextureForRendering(isPacked) {
    if (env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")) {
      if (isPacked) {
        return PhysicalTextureType.PACKED_2X2_FLOAT32;
      }
      return PhysicalTextureType.UNPACKED_FLOAT32;
    }
    if (isPacked) {
      return PhysicalTextureType.PACKED_2X2_FLOAT16;
    }
    return PhysicalTextureType.UNPACKED_FLOAT16;
  }
  function getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {
    if (logicalTexType === TextureUsage.UPLOAD) {
      return PhysicalTextureType.PACKED_2X2_FLOAT32;
    } else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {
      return getPhysicalTextureForRendering(isPacked);
    } else if (logicalTexType === TextureUsage.DOWNLOAD || logicalTexType === TextureUsage.PIXELS) {
      return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;
    }
    throw new Error(`Unknown logical texture type ${logicalTexType}`);
  }
  function getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {
    return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;
  }
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var UnaryOpProgram = class {
    constructor(aShape, opSnippet) {
      this.variableNames = ["A"];
      this.outputShape = aShape;
      this.userCode = `
      float unaryOperation(float x) {
        ${opSnippet}
      }

      void main() {
        float x = getAAtOutCoords();
        float y = unaryOperation(x);

        setOutput(y);
      }
    `;
    }
  };
  var CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;
  var LINEAR = `return x;`;
  var ABS = `return abs(x);`;
  var ELU2 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;
  var RELU = CHECK_NAN_SNIPPET + `
  return (x < 0.0) ? 0.0 : x;
`;
  var RELU6 = CHECK_NAN_SNIPPET + `
  return (x < 0.0) ? 0.0 : min(6.0, x);
`;
  var CLONE = "return x;";
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var LINEAR2 = `return x;`;
  var ELU3 = `
  vec4 result;

  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);

  return result;
`;
  var RELU2 = `
  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`;
  var RELU62 = `
  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`;
  var UnaryOpPackedProgram = class {
    constructor(aShape, opSnippet) {
      this.variableNames = ["A"];
      this.packedInputs = true;
      this.packedOutput = true;
      this.outputShape = aShape;
      this.userCode = `
      vec4 unaryOperation(vec4 x) {
        ${opSnippet}
      }

      void main() {
        vec4 x = getAAtOutCoords();
        vec4 y = unaryOperation(x);

        setOutput(y);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var UnpackProgram = class {
    constructor(outputShape) {
      this.variableNames = ["A"];
      this.packedInputs = true;
      this.packedOutput = false;
      this.outputShape = outputShape;
      const rank = outputShape.length;
      const channels = getChannels("rc", rank);
      const dtype = getCoordsDataType(rank);
      const sourceCoords = getSourceCoords(rank, channels);
      const innerDims = channels.slice(-2);
      const coords2 = rank <= 1 ? "rc" : `vec2(${innerDims.join(",")})`;
      this.userCode = `
      void main() {
        ${dtype} rc = getOutputCoords();
        vec4 packedInput = getA(${sourceCoords});

        setOutput(getChannel(packedInput, ${coords2}));
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var whereImpl3 = kernel_impls_exports.whereImpl;
  var EPSILON_FLOAT322 = 1e-7;
  var EPSILON_FLOAT162 = 1e-4;
  var binaryCaches = {};
  function getBinaryCache(webGLVersion) {
    if (webGLVersion in binaryCaches) {
      return binaryCaches[webGLVersion];
    }
    binaryCaches[webGLVersion] = {};
    return binaryCaches[webGLVersion];
  }
  var CPU_HANDOFF_SIZE_THRESHOLD = 128;
  var BEFORE_PAGING_CONSTANT = 600;
  function numMBBeforeWarning() {
    if (env().global.screen == null) {
      return 1024;
    }
    return env().global.screen.height * env().global.screen.width * window.devicePixelRatio * BEFORE_PAGING_CONSTANT / 1024 / 1024;
  }
  var MathBackendWebGL = class extends KernelBackend {
    constructor(gpgpu) {
      super();
      this.pendingRead = new WeakMap();
      this.pendingDisposal = new WeakSet();
      this.dataRefCount = new WeakMap();
      this.numBytesInGPU = 0;
      this.uploadWaitMs = 0;
      this.downloadWaitMs = 0;
      this.lastGlFlushTime = 0;
      this.warnedAboutMemory = false;
      this.warnedAboutCPUBackend = false;
      this.pendingDeletes = 0;
      this.disposed = false;
      if (!env().getBool("HAS_WEBGL")) {
        throw new Error("WebGL is not supported on this device");
      }
      if (gpgpu == null) {
        const gl = getWebGLContext(env().getNumber("WEBGL_VERSION"));
        this.binaryCache = getBinaryCache(env().getNumber("WEBGL_VERSION"));
        this.gpgpu = new GPGPUContext(gl);
        this.canvas = gl.canvas;
        this.gpgpuCreatedLocally = true;
      } else {
        this.gpgpu = gpgpu;
        this.binaryCache = {};
        this.gpgpuCreatedLocally = false;
        this.canvas = gpgpu.gl.canvas;
      }
      this.textureManager = new TextureManager(this.gpgpu);
      this.numMBBeforeWarning = numMBBeforeWarning();
      this.texData = new DataStorage(this, engine());
    }
    nextDataId() {
      return MathBackendWebGL.nextDataId++;
    }
    numDataIds() {
      return this.texData.numDataIds() + (this.cpuBackend ? this.cpuBackend.numDataIds() : 0) - this.pendingDeletes;
    }
    write(values, shape, dtype) {
      if (env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || env().getBool("DEBUG")) {
        this.checkNumericalProblems(values);
      }
      if (dtype === "complex64" && values != null) {
        throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);
      }
      const dataId = {id: this.nextDataId()};
      this.texData.set(dataId, {shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1});
      return dataId;
    }
    refCount(dataId) {
      if (this.texData.has(dataId)) {
        const tensorData = this.texData.get(dataId);
        return tensorData.refCount;
      }
      return 0;
    }
    incRef(dataId) {
      const texData = this.texData.get(dataId);
      texData.refCount++;
    }
    decRef(dataId) {
      if (this.texData.has(dataId)) {
        const texData = this.texData.get(dataId);
        texData.refCount--;
      }
    }
    move(dataId, values, shape, dtype, refCount) {
      if (env().getBool("DEBUG")) {
        this.checkNumericalProblems(values);
      }
      if (dtype === "complex64") {
        throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);
      }
      this.texData.set(dataId, {shape, dtype, values, usage: TextureUsage.UPLOAD, refCount});
    }
    disposeIntermediateTensorInfo(tensorInfo) {
      this.disposeData(tensorInfo.dataId);
    }
    readSync(dataId) {
      const texData = this.texData.get(dataId);
      const {values, dtype, complexTensorInfos, slice: slice5, shape, isPacked} = texData;
      if (slice5 != null) {
        let program;
        if (isPacked) {
          program = new UnaryOpPackedProgram(shape, CLONE);
        } else {
          program = new UnaryOpProgram(shape, CLONE);
        }
        const res = this.runWebGLProgram(program, [{dataId, shape, dtype}], dtype);
        const data = this.readSync(res.dataId);
        this.disposeIntermediateTensorInfo(res);
        return data;
      }
      if (values != null) {
        return this.convertAndCacheOnCPU(dataId);
      }
      if (dtype === "string") {
        return values;
      }
      const shouldTimeProgram = this.activeTimers != null;
      let start;
      if (shouldTimeProgram) {
        start = util_exports.now();
      }
      let result;
      if (dtype === "complex64") {
        const realValues = this.readSync(complexTensorInfos.real.dataId);
        const imagValues = this.readSync(complexTensorInfos.imag.dataId);
        result = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);
      } else {
        result = this.getValuesFromTexture(dataId);
      }
      if (shouldTimeProgram) {
        this.downloadWaitMs += util_exports.now() - start;
      }
      return this.convertAndCacheOnCPU(dataId, result);
    }
    async read(dataId) {
      if (this.pendingRead.has(dataId)) {
        const subscribers2 = this.pendingRead.get(dataId);
        return new Promise((resolve) => subscribers2.push(resolve));
      }
      const texData = this.texData.get(dataId);
      const {values, shape, slice: slice5, dtype, complexTensorInfos, isPacked} = texData;
      if (slice5 != null) {
        let program;
        if (isPacked) {
          program = new UnaryOpPackedProgram(shape, CLONE);
        } else {
          program = new UnaryOpProgram(shape, CLONE);
        }
        const res = this.runWebGLProgram(program, [{dataId, shape, dtype}], dtype);
        const data = this.read(res.dataId);
        this.disposeIntermediateTensorInfo(res);
        return data;
      }
      if (values != null) {
        return this.convertAndCacheOnCPU(dataId);
      }
      if (!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && env().getNumber("WEBGL_VERSION") === 2) {
        throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.`);
      }
      let buffer2 = null;
      let tmpDownloadTarget;
      if (dtype !== "complex64" && env().get("WEBGL_BUFFER_SUPPORTED")) {
        tmpDownloadTarget = this.decode(dataId);
        const tmpData = this.texData.get(tmpDownloadTarget.dataId);
        buffer2 = this.gpgpu.createBufferFromTexture(tmpData.texture, ...getDenseTexShape(shape));
      }
      this.pendingRead.set(dataId, []);
      if (dtype !== "complex64") {
        await this.gpgpu.createAndWaitForFence();
      }
      let vals;
      if (dtype === "complex64") {
        const ps = await Promise.all([
          this.read(complexTensorInfos.real.dataId),
          this.read(complexTensorInfos.imag.dataId)
        ]);
        const realValues = ps[0];
        const imagValues = ps[1];
        vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);
      } else if (buffer2 == null) {
        vals = this.getValuesFromTexture(dataId);
      } else {
        const size = util_exports.sizeFromShape(shape);
        vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2, size);
      }
      if (tmpDownloadTarget != null) {
        this.disposeIntermediateTensorInfo(tmpDownloadTarget);
      }
      const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);
      const subscribers = this.pendingRead.get(dataId);
      this.pendingRead.delete(dataId);
      subscribers.forEach((resolve) => resolve(dTypeVals));
      if (this.pendingDisposal.has(dataId)) {
        this.pendingDisposal.delete(dataId);
        if (this.disposeData(dataId)) {
          engine().removeDataId(dataId, this);
        }
        this.pendingDeletes--;
      }
      return dTypeVals;
    }
    bufferSync(t) {
      const data = this.readSync(t.dataId);
      let decodedData = data;
      if (t.dtype === "string") {
        try {
          decodedData = data.map((d) => util_exports.decodeString(d));
        } catch (_a) {
          throw new Error("Failed to decode encoded string bytes into utf-8");
        }
      }
      return buffer(t.shape, t.dtype, decodedData);
    }
    checkNumericalProblems(values) {
      if (values == null) {
        return;
      }
      for (let i = 0; i < values.length; i++) {
        const num = values[i];
        if (!canBeRepresented(num)) {
          if (env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")) {
            throw Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);
          }
          throw Error(`The value ${num} cannot be represented on this device.`);
        }
      }
    }
    getValuesFromTexture(dataId) {
      const {shape, dtype, isPacked} = this.texData.get(dataId);
      const size = util_exports.sizeFromShape(shape);
      if (env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) {
        const tmpTarget = this.decode(dataId);
        const tmpData2 = this.texData.get(tmpTarget.dataId);
        const vals2 = this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture, ...getDenseTexShape(shape)).subarray(0, size);
        this.disposeIntermediateTensorInfo(tmpTarget);
        return vals2;
      }
      const shouldUsePackedProgram = env().getBool("WEBGL_PACK") && isPacked === true;
      const outputShape = shouldUsePackedProgram ? getShapeAs3D(shape) : shape;
      const program = shouldUsePackedProgram ? new EncodeFloatPackedProgram(outputShape) : new EncodeFloatProgram(outputShape);
      const output = this.runWebGLProgram(program, [{shape: outputShape, dtype, dataId}], "float32");
      const tmpData = this.texData.get(output.dataId);
      const vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture, tmpData.texShape[0], tmpData.texShape[1]).subarray(0, size);
      this.disposeIntermediateTensorInfo(output);
      return vals;
    }
    timerAvailable() {
      return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0;
    }
    async time(f) {
      const oldActiveTimers = this.activeTimers;
      const newActiveTimers = [];
      let outerMostTime = false;
      if (this.programTimersStack == null) {
        this.programTimersStack = newActiveTimers;
        outerMostTime = true;
      } else {
        this.activeTimers.push(newActiveTimers);
      }
      this.activeTimers = newActiveTimers;
      f();
      const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null);
      const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null);
      this.activeTimers = oldActiveTimers;
      if (outerMostTime) {
        this.programTimersStack = null;
      }
      const res = {
        uploadWaitMs: this.uploadWaitMs,
        downloadWaitMs: this.downloadWaitMs,
        kernelMs: null,
        wallMs: null
      };
      if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
        const kernelMs = await Promise.all(flattenedActiveTimerQueries);
        res["kernelMs"] = util_exports.sum(kernelMs);
        res["getExtraProfileInfo"] = () => kernelMs.map((d, i) => ({name: flattenedActiveTimerNames[i], ms: d})).map((d) => `${d.name}: ${d.ms}`).join(", ");
      } else {
        res["kernelMs"] = {
          error: "WebGL query timers are not supported in this environment."
        };
      }
      this.uploadWaitMs = 0;
      this.downloadWaitMs = 0;
      return res;
    }
    memory() {
      return {
        unreliable: false,
        numBytesInGPU: this.numBytesInGPU,
        numBytesInGPUAllocated: this.textureManager.numBytesAllocated,
        numBytesInGPUFree: this.textureManager.numBytesFree
      };
    }
    startTimer() {
      if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
        return this.gpgpu.beginQuery();
      }
      return {startMs: util_exports.now(), endMs: null};
    }
    endTimer(query) {
      if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
        this.gpgpu.endQuery();
        return query;
      }
      query.endMs = util_exports.now();
      return query;
    }
    async getQueryTime(query) {
      if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
        return this.gpgpu.waitForQueryAndGetTime(query);
      }
      const timerQuery = query;
      return timerQuery.endMs - timerQuery.startMs;
    }
    disposeData(dataId, force = false) {
      if (this.pendingDisposal.has(dataId)) {
        return false;
      }
      if (!this.texData.has(dataId)) {
        return true;
      }
      if (force) {
        this.texData.get(dataId).refCount = 0;
      } else {
        this.texData.get(dataId).refCount--;
      }
      if (!force && this.texData.get(dataId).refCount > 0) {
        return false;
      }
      if (this.pendingRead.has(dataId)) {
        this.pendingDisposal.add(dataId);
        this.pendingDeletes++;
        return false;
      }
      this.releaseGPUData(dataId);
      const {complexTensorInfos} = this.texData.get(dataId);
      if (complexTensorInfos != null) {
        this.disposeData(complexTensorInfos.real.dataId, force);
        this.disposeData(complexTensorInfos.imag.dataId, force);
      }
      this.texData.delete(dataId);
      return true;
    }
    releaseGPUData(dataId) {
      const {texture, dtype, texShape, usage, isPacked, slice: slice5} = this.texData.get(dataId);
      const key = slice5 && slice5.origDataId || dataId;
      const refCount = this.dataRefCount.get(key);
      if (refCount > 1) {
        this.dataRefCount.set(key, refCount - 1);
      } else {
        this.dataRefCount.delete(key);
        if (texture != null) {
          this.numBytesInGPU -= this.computeBytes(texShape, dtype);
          this.textureManager.releaseTexture(texture, texShape, usage, isPacked);
        }
      }
      const texData = this.texData.get(dataId);
      texData.texture = null;
      texData.texShape = null;
      texData.isPacked = false;
      texData.slice = null;
    }
    getTexture(dataId) {
      this.uploadToGPU(dataId);
      return this.texData.get(dataId).texture;
    }
    getDataInfo(dataId) {
      return this.texData.get(dataId);
    }
    getCPUBackend() {
      if (!env().getBool("WEBGL_CPU_FORWARD")) {
        return null;
      }
      if (this.cpuBackend == null) {
        this.cpuBackend = engine().findBackend("cpu");
      }
      return this.cpuBackend;
    }
    shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {
      const cpuBackend = this.getCPUBackend();
      if (!env().getBool("IS_TEST") && !this.warnedAboutCPUBackend && cpuBackend == null) {
        console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance.");
        this.warnedAboutCPUBackend = true;
      }
      return cpuBackend != null && inputs.every((input2) => this.texData.get(input2.dataId).texture == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold);
    }
    getGPGPUContext() {
      return this.gpgpu;
    }
    where(condition) {
      backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");
      const condVals = condition.dataSync();
      return whereImpl3(condition.shape, condVals);
    }
    packedUnaryOp(x, op2, dtype) {
      const program = new UnaryOpPackedProgram(x.shape, op2);
      const outInfo = this.compileAndRun(program, [x], dtype);
      return engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);
    }
    abs(x) {
      if (this.shouldExecuteOnCPU([x]) && x.dtype !== "complex64") {
        const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);
        return this.makeOutput(x.shape, x.dtype, outValues);
      }
      if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) {
        return this.packedUnaryOp(x, ABS, x.dtype);
      }
      const program = new UnaryOpProgram(x.shape, ABS);
      const outInfo = this.compileAndRun(program, [x]);
      return engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);
    }
    makeTensorInfo(shape, dtype, values) {
      let dataId;
      if (dtype === "string" && values != null && values.length > 0 && util_exports.isString(values[0])) {
        const encodedValues = values.map((d) => util_exports.encodeString(d));
        dataId = this.write(encodedValues, shape, dtype);
      } else {
        dataId = this.write(values, shape, dtype);
      }
      this.texData.get(dataId).usage = null;
      return {dataId, shape, dtype};
    }
    makeOutput(shape, dtype, values) {
      const {dataId} = this.makeTensorInfo(shape, dtype, values);
      return engine().makeTensorFromDataId(dataId, shape, dtype, this);
    }
    unpackTensor(input2) {
      const program = new UnpackProgram(input2.shape);
      return this.runWebGLProgram(program, [input2], input2.dtype);
    }
    packTensor(input2) {
      const program = new PackProgram(input2.shape);
      const preventEagerUnpackingOutput = true;
      return this.runWebGLProgram(program, [input2], input2.dtype, null, preventEagerUnpackingOutput);
    }
    packedReshape(input2, afterShape) {
      const input3DShape = [
        getBatchDim(input2.shape),
        ...getRowsCols(input2.shape)
      ];
      const input3D = {
        dtype: input2.dtype,
        shape: input3DShape,
        dataId: input2.dataId
      };
      const afterShapeAs3D = [
        getBatchDim(afterShape),
        ...getRowsCols(afterShape)
      ];
      const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);
      const preventEagerUnpackingOfOutput = true;
      const output = this.runWebGLProgram(program, [input3D], input2.dtype, null, preventEagerUnpackingOfOutput);
      return {dataId: output.dataId, shape: afterShape, dtype: output.dtype};
    }
    decode(dataId) {
      const texData = this.texData.get(dataId);
      const {isPacked, shape, dtype} = texData;
      const shapeAs3D = getShapeAs3D(shape);
      let program;
      if (isPacked) {
        program = new DecodeMatrixPackedProgram(shapeAs3D);
      } else {
        program = new DecodeMatrixProgram(shapeAs3D);
      }
      const preventEagerUnpackingOfOutput = true;
      const out = this.runWebGLProgram(program, [{shape: shapeAs3D, dtype, dataId}], dtype, null, preventEagerUnpackingOfOutput);
      return {dtype, shape, dataId: out.dataId};
    }
    runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {
      const output = this.makeTensorInfo(program.outputShape, outputDtype);
      const outData = this.texData.get(output.dataId);
      if (program.packedOutput) {
        outData.isPacked = true;
      }
      if (program.outPackingScheme === PackingScheme.DENSE) {
        const texelShape = getDenseTexShape(program.outputShape);
        outData.texShape = texelShape.map((d) => d * 2);
      }
      if (program.outTexUsage != null) {
        outData.usage = program.outTexUsage;
      }
      if (util_exports.sizeFromShape(output.shape) === 0) {
        outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0);
        return output;
      }
      const dataToDispose = [];
      const inputsData = inputs.map((input2) => {
        if (input2.dtype === "complex64") {
          throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`);
        }
        let texData = this.texData.get(input2.dataId);
        if (texData.texture == null) {
          if (!program.packedInputs && util_exports.sizeFromShape(input2.shape) <= env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM")) {
            return {
              shape: input2.shape,
              texData: null,
              isUniform: true,
              uniformValues: texData.values
            };
          }
          if (program.packedInputs) {
            texData.isPacked = true;
            texData.shape = input2.shape;
          }
        } else if (!!texData.isPacked !== !!program.packedInputs) {
          input2 = texData.isPacked ? this.unpackTensor(input2) : this.packTensor(input2);
          dataToDispose.push(input2);
          texData = this.texData.get(input2.dataId);
        } else if (texData.isPacked && !isReshapeFree(texData.shape, input2.shape)) {
          const savedInput = input2;
          const targetShape = input2.shape;
          input2.shape = texData.shape;
          input2 = this.packedReshape(input2, targetShape);
          dataToDispose.push(input2);
          texData = this.texData.get(input2.dataId);
          savedInput.shape = targetShape;
        }
        this.uploadToGPU(input2.dataId);
        return {shape: input2.shape, texData, isUniform: false};
      });
      this.uploadToGPU(output.dataId);
      const outputData = {shape: output.shape, texData: outData, isUniform: false};
      const key = makeShaderKey(program, inputsData, outputData);
      const binary = this.getAndSaveBinary(key, () => {
        return compileProgram(this.gpgpu, program, inputsData, outputData);
      });
      const shouldTimeProgram = this.activeTimers != null;
      let query;
      if (shouldTimeProgram) {
        query = this.startTimer();
      }
      runProgram(this.gpgpu, binary, inputsData, outputData, customSetup);
      dataToDispose.forEach((info) => this.disposeIntermediateTensorInfo(info));
      if (shouldTimeProgram) {
        query = this.endTimer(query);
        this.activeTimers.push({name: program.constructor.name, query: this.getQueryTime(query)});
      }
      const glFlushThreshold = env().get("WEBGL_FLUSH_THRESHOLD");
      if (glFlushThreshold > 0) {
        const time2 = util_exports.now();
        if (time2 - this.lastGlFlushTime > glFlushThreshold) {
          this.gpgpu.gl.flush();
          this.lastGlFlushTime = time2;
        }
      }
      if (!env().getBool("WEBGL_LAZILY_UNPACK") && outData.isPacked && preventEagerUnpackingOfOutput === false) {
        const unpacked = this.unpackTensor(output);
        this.disposeIntermediateTensorInfo(output);
        return unpacked;
      }
      return output;
    }
    compileAndRun(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {
      outputDtype = outputDtype || inputs[0].dtype;
      const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput);
      return outInfo;
    }
    getAndSaveBinary(key, getBinary) {
      if (!(key in this.binaryCache)) {
        this.binaryCache[key] = getBinary();
      }
      return this.binaryCache[key];
    }
    getTextureManager() {
      return this.textureManager;
    }
    dispose() {
      if (this.disposed) {
        return;
      }
      if (!env().getBool("IS_TEST")) {
        const allKeys = Object.keys(this.binaryCache);
        allKeys.forEach((key) => {
          this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);
          delete this.binaryCache[key];
        });
      }
      this.textureManager.dispose();
      if (this.canvas != null && (typeof HTMLCanvasElement !== "undefined" && this.canvas instanceof HTMLCanvasElement)) {
        this.canvas.remove();
      } else {
        this.canvas = null;
      }
      if (this.gpgpuCreatedLocally) {
        this.gpgpu.program = null;
        this.gpgpu.dispose();
      }
      this.disposed = true;
    }
    floatPrecision() {
      if (this.floatPrecisionValue == null) {
        this.floatPrecisionValue = tidy(() => {
          if (!env().get("WEBGL_RENDER_FLOAT32_ENABLED")) {
            const debugFlag = env().getBool("DEBUG");
            env().set("DEBUG", false);
            const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];
            env().set("DEBUG", debugFlag);
            if (underflowCheckValue > 0) {
              return 32;
            }
          }
          return 16;
        });
      }
      return this.floatPrecisionValue;
    }
    epsilon() {
      return this.floatPrecision() === 32 ? EPSILON_FLOAT322 : EPSILON_FLOAT162;
    }
    uploadToGPU(dataId) {
      const texData = this.texData.get(dataId);
      const {shape, dtype, values, texture, usage, isPacked} = texData;
      if (texture != null) {
        return;
      }
      const shouldTimeProgram = this.activeTimers != null;
      let start;
      if (shouldTimeProgram) {
        start = util_exports.now();
      }
      let texShape = texData.texShape;
      if (texShape == null) {
        texShape = getTextureShapeFromLogicalShape(shape, isPacked);
        texData.texShape = texShape;
      }
      if (values != null) {
        const shapeAs3D = getShapeAs3D(shape);
        let program;
        let width = texShape[1], height = texShape[0];
        const isByteArray = values instanceof Uint8Array;
        if (isPacked) {
          [width, height] = getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);
          program = new EncodeMatrixPackedProgram(shapeAs3D, [height, width], isByteArray);
        } else {
          program = new EncodeMatrixProgram(shapeAs3D, [height, width], isByteArray);
        }
        const tempDenseInputHandle = this.makeTensorInfo([height, width], dtype);
        if (isByteArray) {
          this.texData.get(tempDenseInputHandle.dataId).usage = TextureUsage.PIXELS;
        } else {
          this.texData.get(tempDenseInputHandle.dataId).usage = TextureUsage.UPLOAD;
        }
        this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);
        const preventEagerUnpacking = true;
        const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, null, preventEagerUnpacking);
        const outputTexData = this.texData.get(encodedOutputTarget.dataId);
        texData.texture = outputTexData.texture;
        texData.texShape = outputTexData.texShape;
        texData.isPacked = outputTexData.isPacked;
        texData.usage = outputTexData.usage;
        this.disposeIntermediateTensorInfo(tempDenseInputHandle);
        this.texData.delete(encodedOutputTarget.dataId);
        texData.values = null;
        if (shouldTimeProgram) {
          this.uploadWaitMs += util_exports.now() - start;
        }
      } else {
        const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);
        texData.texture = newTexture;
      }
    }
    convertAndCacheOnCPU(dataId, float32Values) {
      const texData = this.texData.get(dataId);
      const {dtype} = texData;
      this.releaseGPUData(dataId);
      if (float32Values != null) {
        texData.values = float32ToTypedArray(float32Values, dtype);
      }
      return texData.values;
    }
    acquireTexture(texShape, texType, dtype, isPacked) {
      this.numBytesInGPU += this.computeBytes(texShape, dtype);
      if (!this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {
        const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);
        this.warnedAboutMemory = true;
        console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`);
      }
      return this.textureManager.acquireTexture(texShape, texType, isPacked);
    }
    computeBytes(shape, dtype) {
      return shape[0] * shape[1] * util_exports.bytesPerElement(dtype);
    }
  };
  MathBackendWebGL.nextDataId = 0;
  function float32ToTypedArray(a, dtype) {
    if (dtype === "float32" || dtype === "complex64") {
      return a;
    } else if (dtype === "int32" || dtype === "bool") {
      const result = dtype === "int32" ? new Int32Array(a.length) : new Uint8Array(a.length);
      for (let i = 0; i < result.length; ++i) {
        result[i] = Math.round(a[i]);
      }
      return result;
    } else {
      throw new Error(`Unknown dtype ${dtype}`);
    }
  }
  /** @license See the LICENSE file. */
  var version6 = "3.3.0";
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  if (device_util_exports.isBrowser()) {
    registerBackend("webgl", () => new MathBackendWebGL(), 2);
  }
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var CHECK_NAN_SNIPPET2 = `
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`;
  var BinaryOpProgram = class {
    constructor(op2, aShape, bShape) {
      this.variableNames = ["A", "B"];
      this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
      this.userCode = `
      float binaryOperation(float a, float b) {
        ${op2}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var CHECK_NAN_SNIPPET3 = `
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`;
  var BinaryOpPackedProgram = class {
    constructor(op2, aShape, bShape, checkOutOfBounds = false) {
      this.variableNames = ["A", "B"];
      this.supportsBroadcasting = true;
      this.packedInputs = true;
      this.packedOutput = true;
      this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
      const rank = this.outputShape.length;
      let checkOutOfBoundsString = "";
      if (checkOutOfBounds) {
        if (rank === 0 || util_exports.sizeFromShape(this.outputShape) === 1) {
          checkOutOfBoundsString = `
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;
        } else {
          const dtype = getCoordsDataType(rank);
          checkOutOfBoundsString = `
          ${dtype} coords = getOutputCoords();
        `;
          if (rank === 1) {
            checkOutOfBoundsString += `
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;
          } else {
            const channels = getChannels("coords", rank);
            checkOutOfBoundsString += `
            bool nextRowOutOfBounds =
              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};
            bool nextColOutOfBounds =
              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `;
          }
        }
      }
      this.userCode = `
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${op2}
      }

      void main() {
        vec4 a = getAAtOutCoords();
        vec4 b = getBAtOutCoords();

        vec4 result = binaryOperation(a, b);
        ${checkOutOfBoundsString}

        setOutput(result);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function identity3(args) {
    const {inputs, backend: backend2} = args;
    const {x} = inputs;
    backend2.incRef(x.dataId);
    return {dataId: x.dataId, shape: x.shape, dtype: x.dtype};
  }
  var identityConfig2 = {
    kernelName: Identity,
    backendName: "webgl",
    kernelFunc: identity3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function complex3(args) {
    const {inputs, backend: backend2} = args;
    const {real: real4, imag: imag4} = inputs;
    const complexInfo = backend2.makeTensorInfo(real4.shape, "complex64");
    const complex4 = backend2.texData.get(complexInfo.dataId);
    const realTensorInfo = identity3({inputs: {x: real4}, backend: backend2});
    const imagTensorInfo = identity3({inputs: {x: imag4}, backend: backend2});
    complex4.complexTensorInfos = {real: realTensorInfo, imag: imagTensorInfo};
    return complexInfo;
  }
  var complexConfig2 = {
    kernelName: Complex,
    backendName: "webgl",
    kernelFunc: complex3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var LEAKYRELU = `return (a < 0.) ? b * a : a;`;
  var LEAKYRELU_PACKED = `
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
  function leakyRelu3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {alpha} = attrs;
    const $alpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(alpha, "float32"));
    const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(LEAKYRELU_PACKED, x.shape, $alpha.shape) : new BinaryOpProgram(LEAKYRELU, x.shape, $alpha.shape);
    const result = backend2.runWebGLProgram(program, [x, $alpha], x.dtype);
    backend2.disposeIntermediateTensorInfo($alpha);
    return result;
  }
  var leakyReluConfig2 = {
    kernelName: LeakyRelu,
    backendName: "webgl",
    kernelFunc: leakyRelu3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var PRELU = `return (a < 0.) ? b * a : a;`;
  var PRELU_PACKED = `
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
  function prelu4(args) {
    const {inputs, backend: backend2} = args;
    const {x, alpha} = inputs;
    const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(PRELU_PACKED, x.shape, alpha.shape) : new BinaryOpProgram(PRELU, x.shape, alpha.shape);
    return backend2.runWebGLProgram(program, [x, alpha], x.dtype);
  }
  var preluConfig2 = {
    kernelName: Prelu,
    backendName: "webgl",
    kernelFunc: prelu4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;
  var CHECK_NAN_SNIPPET_BINARY = `
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`;
  var CHECK_NAN_SNIPPET_BINARY_PACKED = `
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`;
  function unaryKernelFunc2({opSnippet, packedOpSnippet, cpuKernelImpl, dtype}) {
    return ({inputs, backend: backend2}) => {
      const {x} = inputs;
      const webglBackend = backend2;
      const $dtype = dtype || x.dtype;
      if (webglBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {
        const xData = webglBackend.texData.get(x.dataId);
        const outValues = cpuKernelImpl(xData.values, $dtype);
        return webglBackend.makeTensorInfo(x.shape, $dtype, outValues);
      }
      const shouldUsePackedProgram = env().getBool("WEBGL_PACK_UNARY_OPERATIONS") && packedOpSnippet != null;
      let program;
      if (shouldUsePackedProgram) {
        program = new UnaryOpPackedProgram(x.shape, packedOpSnippet);
      } else {
        program = new UnaryOpProgram(x.shape, opSnippet);
      }
      return webglBackend.runWebGLProgram(program, [x], $dtype);
    };
  }
  function binaryKernelFunc2({opSnippet, packedOpSnippet, checkOutOfBounds = false, supportsComplex = false, cpuKernelImpl, dtype}) {
    return ({inputs, backend: backend2}) => {
      const {a, b} = inputs;
      const webglBackend = backend2;
      if (supportsComplex && a.dtype === "complex64") {
        const aData = webglBackend.texData.get(a.dataId);
        const bData = webglBackend.texData.get(b.dataId);
        const [real4, imag4] = [
          [aData.complexTensorInfos.real, bData.complexTensorInfos.real],
          [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]
        ].map((complexParts) => {
          const [aPart, bPart] = complexParts;
          const aHandle = {
            dataId: aPart.dataId,
            dtype: aPart.dtype,
            shape: a.shape
          };
          const bHandle = {
            dataId: bPart.dataId,
            dtype: bPart.dtype,
            shape: b.shape
          };
          const program2 = new BinaryOpProgram(opSnippet, a.shape, b.shape);
          return webglBackend.runWebGLProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));
        });
        const complexOutput = complex3({inputs: {real: real4, imag: imag4}, backend: webglBackend});
        webglBackend.disposeIntermediateTensorInfo(real4);
        webglBackend.disposeIntermediateTensorInfo(imag4);
        return complexOutput;
      }
      const $dtype = dtype || upcastType(a.dtype, b.dtype);
      if (webglBackend.shouldExecuteOnCPU([a, b]) && cpuKernelImpl != null) {
        const aData = webglBackend.texData.get(a.dataId);
        const bData = webglBackend.texData.get(b.dataId);
        const [outValues, outShape] = cpuKernelImpl(a.shape, b.shape, aData.values, bData.values, $dtype);
        const out = webglBackend.makeTensorInfo(outShape, $dtype);
        const outData = webglBackend.texData.get(out.dataId);
        outData.values = outValues;
        return out;
      }
      const shouldUsePackedProgram = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") && packedOpSnippet != null;
      let program;
      if (shouldUsePackedProgram) {
        program = new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, checkOutOfBounds);
      } else {
        program = new BinaryOpProgram(opSnippet, a.shape, b.shape);
      }
      return webglBackend.runWebGLProgram(program, [a, b], $dtype);
    };
  }
  function mapActivationToShaderProgram(activation2, packed = false) {
    if (activation2 === "linear") {
      if (packed) {
        return LINEAR2;
      }
      return LINEAR;
    } else if (activation2 === "relu") {
      if (packed) {
        return RELU2;
      }
      return RELU;
    } else if (activation2 === "elu") {
      if (packed) {
        return ELU3;
      }
      return ELU2;
    } else if (activation2 === "relu6") {
      if (packed) {
        return RELU62;
      }
      return RELU6;
    } else if (activation2 === "prelu") {
      if (packed) {
        return PRELU_PACKED;
      }
      return PRELU;
    } else if (activation2 === "leakyrelu") {
      if (packed) {
        return LEAKYRELU_PACKED;
      }
      return LEAKYRELU;
    }
    throw new Error(`Activation ${activation2} has not been implemented for the WebGL backend.`);
  }
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var MatMulPackedProgram = class {
    constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyreluActivation = false) {
      this.variableNames = ["matrixA", "matrixB"];
      this.packedInputs = true;
      this.packedOutput = true;
      this.outputShape = outputShape;
      const sharedDim = transposeA ? aShape[1] : aShape[2];
      const sharedDimensionPacked = Math.ceil(sharedDim / 2);
      const aSample = transposeA ? "i * 2, rc.y" : "rc.y, i * 2";
      const bSample = transposeB ? "rc.z, i * 2" : "i * 2, rc.z";
      const aSwizzle = transposeA ? ["a.xxyy", "a.zzww"] : ["a.xxzz", "a.yyww"];
      const bSwizzle = transposeB ? ["b.xzxz", "b.ywyw"] : ["b.xyxy", "b.zwzw"];
      let activationSnippet = "", applyActivationSnippet = "";
      if (activation2) {
        if (hasPreluActivation) {
          activationSnippet = `vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${activation2}
        }`;
        } else if (hasLeakyreluActivation) {
          activationSnippet = `vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${activation2}
        }`;
        } else {
          activationSnippet = `vec4 activation(vec4 x) {
          ${activation2}
        }`;
        }
        applyActivationSnippet = `result = activation(result);`;
      }
      const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
      if (addBias) {
        this.variableNames.push("bias");
      }
      if (hasPreluActivation) {
        this.variableNames.push("preluActivationWeights");
      }
      if (hasLeakyreluActivation) {
        this.variableNames.push("leakyreluAlpha");
      }
      let batchASnippet = "rc.x";
      let batchBSnippet = "rc.x";
      if (aShape[0] < bShape[0]) {
        batchASnippet = `int(min(float(rc.x), ${aShape[0] - 1}.))`;
      } else if (bShape[0] < aShape[0]) {
        batchBSnippet = `int(min(float(rc.x), ${bShape[0] - 1}.))`;
      }
      this.userCode = `
      ${activationSnippet}

      const float sharedDimension = ${sharedDimensionPacked}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        for (int i = 0; i < ${sharedDimensionPacked}; i++) {
          int batchA = ${batchASnippet};
          int batchB = ${batchBSnippet};
          vec4 a = getMatrixA(batchA, ${aSample});
          vec4 b = getMatrixB(batchB, ${bSample});

          // These swizzled products need to be separately added.
          // See: https://github.com/tensorflow/tfjs/issues/1735
          result += (${aSwizzle[0]} * ${bSwizzle[0]});
          result += (${aSwizzle[1]} * ${bSwizzle[1]});
        }
        return result;
      }

      void main() {
        ivec3 rc = getOutputCoords();
        vec4 result = dot2x2ARowBCol(rc);

        ${addBiasSnippet}

        ${applyActivationSnippet}

        setOutput(result);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var COMPLEX_MULTIPLY = {
    REAL: "return areal * breal - aimag * bimag;",
    IMAG: "return areal * bimag + aimag * breal;"
  };
  var BinaryOpComplexProgram = class {
    constructor(op2, aShape, bShape) {
      this.variableNames = ["AReal", "AImag", "BReal", "BImag"];
      this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
      this.userCode = `
      float binaryOpComplex(
          float areal, float aimag, float breal, float bimag) {
        ${op2}
      }

      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var MUL = "return a * b;";
  function multiply3(args) {
    const {inputs, backend: backend2} = args;
    const {a, b} = inputs;
    const dtype = backend_util_exports.upcastType(a.dtype, b.dtype);
    if (a.dtype === "complex64") {
      const aData = backend2.texData.get(a.dataId);
      const bData = backend2.texData.get(b.dataId);
      const realProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL, a.shape, b.shape);
      const imagProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);
      const inputs2 = [
        {
          dataId: aData.complexTensorInfos.real.dataId,
          dtype: aData.complexTensorInfos.real.dtype,
          shape: a.shape
        },
        {
          dataId: aData.complexTensorInfos.imag.dataId,
          dtype: aData.complexTensorInfos.imag.dtype,
          shape: a.shape
        },
        {
          dataId: bData.complexTensorInfos.real.dataId,
          dtype: bData.complexTensorInfos.real.dtype,
          shape: b.shape
        },
        {
          dataId: bData.complexTensorInfos.imag.dataId,
          dtype: bData.complexTensorInfos.imag.dtype,
          shape: b.shape
        }
      ];
      const realPart = backend2.runWebGLProgram(realProgram, inputs2, "float32");
      const imagPart = backend2.runWebGLProgram(imagProgram, inputs2, "float32");
      const complexOutput = complex3({inputs: {real: realPart, imag: imagPart}, backend: backend2});
      backend2.disposeIntermediateTensorInfo(realPart);
      backend2.disposeIntermediateTensorInfo(imagPart);
      return complexOutput;
    }
    if (backend2.shouldExecuteOnCPU([a, b])) {
      const aData = backend2.texData.get(a.dataId);
      const bData = backend2.texData.get(b.dataId);
      const [outValues, outShape] = multiplyImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);
      const out = backend2.makeTensorInfo(outShape, dtype);
      const outData = backend2.texData.get(out.dataId);
      outData.values = outValues;
      return out;
    }
    let program;
    if (env().getBool("WEBGL_PACK_BINARY_OPERATIONS")) {
      program = new BinaryOpPackedProgram(MUL, a.shape, b.shape);
    } else {
      program = new BinaryOpProgram(MUL, a.shape, b.shape);
    }
    return backend2.runWebGLProgram(program, [a, b], dtype);
  }
  var multiplyConfig2 = {
    kernelName: Multiply,
    backendName: "webgl",
    kernelFunc: multiply3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function packedReshape(input2, afterShape, backend2) {
    const input3DShape = [
      getBatchDim(input2.shape),
      ...getRowsCols(input2.shape)
    ];
    const input3D = {
      dtype: input2.dtype,
      shape: input3DShape,
      dataId: input2.dataId
    };
    const afterShapeAs3D = [
      getBatchDim(afterShape),
      ...getRowsCols(afterShape)
    ];
    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);
    const preventEagerUnpackingOfOutput = true;
    const output = backend2.runWebGLProgram(program, [input3D], input2.dtype, null, preventEagerUnpackingOfOutput);
    return {dataId: output.dataId, shape: afterShape, dtype: output.dtype};
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function reshape4(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {shape} = attrs;
    const webglBackend = backend2;
    const xSize = util_exports.sizeFromShape(x.shape);
    const $shape = util_exports.inferFromImplicitShape(shape, xSize);
    const $xSize = util_exports.sizeFromShape($shape);
    util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);
    const xTexData = webglBackend.texData.get(x.dataId);
    if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) && !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {
      return packedReshape(x, $shape, webglBackend);
    }
    webglBackend.incRef(x.dataId);
    return {dataId: x.dataId, shape: $shape, dtype: x.dtype};
  }
  var reshapeConfig2 = {
    kernelName: Reshape,
    backendName: "webgl",
    kernelFunc: reshape4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var MeanProgram = class {
    constructor(reduceInfo, divisor) {
      this.variableNames = ["x"];
      const {windowSize, batchSize, inSize, outSize} = reduceInfo;
      this.outputShape = [batchSize, outSize];
      const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
      const windowSizeVec4Remainder = windowSize % 4;
      let updateSnippet = `sumValue += dot(values, ones);`;
      if (divisor != null) {
        const denominator = 1 / divisor;
        updateSnippet = `sumValue += dot(values * ${util_exports.isInt(denominator) ? denominator.toPrecision(2) : denominator}, ones);`;
      }
      let checkOutOfBounds = "";
      if (inSize % windowSize > 0) {
        checkOutOfBounds = `
        if (inIdx < 0 || inIdx >= ${inSize}) {
          return 0.0;
        }
      `;
      }
      this.userCode = `
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${checkOutOfBounds}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${windowSize};

        float sumValue = 0.0;

        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${updateSnippet}
        }

        int inIdx = inOffset + ${windowSizeNearestVec4};
        if (${windowSizeVec4Remainder === 1}) {
          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder === 2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1), 0.0, 0.0);

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder === 3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2), 0.0);

          ${updateSnippet}
        }
        setOutput(sumValue);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ReduceProgram = class {
    constructor(reduceInfo, reduceType) {
      this.variableNames = ["x"];
      const {windowSize, batchSize, inSize, outSize} = reduceInfo;
      this.outputShape = [batchSize, outSize];
      let initializationValue = "0.0";
      let compareOp = ``;
      if (reduceType === "prod") {
        initializationValue = "1.0";
      } else if (reduceType === "min") {
        initializationValue = "1.0 / 1e-20";
        compareOp = `min`;
      } else if (reduceType === "max") {
        initializationValue = "-1.0 / 1e-20";
        compareOp = `max`;
      }
      let returnValue = `${reduceType}(${reduceType}(${reduceType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
      if (reduceType === "sum") {
        returnValue = `sumValue`;
      } else if (reduceType === "prod") {
        returnValue = `prodValue`;
      } else if (reduceType === "all") {
        returnValue = `allValue`;
      } else if (reduceType === "any") {
        returnValue = `anyValue`;
      }
      const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
      const windowSizeVec4Remainder = windowSize % 4;
      let updateSnippet = `
      if (${reduceType === "sum"}) {
        sumValue += dot(values, ones);
      } else if (${reduceType === "prod"}) {
        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
        prodValue *= tmp[0] * tmp[1];
      } else {
        minMaxValue = ${compareOp}(values, minMaxValue);
      }
    `;
      let vecType = `vec4`;
      if (reduceType === "all") {
        initializationValue = "1.0";
        updateSnippet = `
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `;
        vecType = `bvec4`;
      } else if (reduceType === "any") {
        initializationValue = "0.0";
        updateSnippet = `
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `;
        vecType = `bvec4`;
      }
      let checkOutOfBounds = "";
      if (inSize % windowSize > 0) {
        checkOutOfBounds = `
        if (inIdx < 0 || inIdx >= ${inSize}) {
          return initializationValue;
        }
      `;
      }
      this.userCode = `
      const float initializationValue = ${initializationValue};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${checkOutOfBounds}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${windowSize};

        vec4 minMaxValue = vec4(${initializationValue});
        float prodValue = 1.0;
        float sumValue = 0.0;
        float allValue = 1.0;
        float anyValue = 0.0;

        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
          int inIdx = inOffset + i;
          ${vecType} values = ${vecType}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${updateSnippet}
        }

        int inIdx = inOffset + ${windowSizeNearestVec4};
        if (${windowSizeVec4Remainder === 1}) {
          ${vecType} values = ${vecType}(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder === 2}) {
          ${vecType} values = ${vecType}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder === 3}) {
          ${vecType} values = ${vecType}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          ${updateSnippet}
        }
        setOutput(${returnValue});
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function getReductionStages(inShape) {
    const stages = [];
    while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {
      const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];
      const windowSize = backend_util_exports.computeOptimalWindowSize(outSize);
      stages.push({
        inSize: outSize,
        windowSize,
        outSize: Math.ceil(outSize / windowSize)
      });
    }
    return stages;
  }
  function reduce(x, dtype, reductionType, backend2) {
    const reductionStages = getReductionStages(x.shape);
    let result = x;
    for (let i = 0; i < reductionStages.length; i++) {
      const {inSize, windowSize, outSize} = reductionStages[i];
      let program;
      let previousResult;
      if (reductionType === "mean") {
        program = i === 0 ? new MeanProgram({windowSize, inSize, batchSize: x.shape[0], outSize}, inSize) : new MeanProgram({windowSize, inSize, batchSize: x.shape[0], outSize});
      } else {
        program = new ReduceProgram({windowSize, inSize, batchSize: x.shape[0], outSize}, reductionType);
      }
      previousResult = result;
      result = backend2.runWebGLProgram(program, [result], dtype);
      if (previousResult.dataId !== x.dataId) {
        backend2.disposeIntermediateTensorInfo(previousResult);
      }
    }
    return result;
  }
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var TransposeProgram = class {
    constructor(aShape, newDim) {
      this.variableNames = ["A"];
      const outputShape = new Array(aShape.length);
      for (let i = 0; i < outputShape.length; i++) {
        outputShape[i] = aShape[newDim[i]];
      }
      this.outputShape = outputShape;
      this.rank = outputShape.length;
      const dtype = getCoordsDataType(this.rank);
      const switched = getSwitchedCoords(newDim);
      this.userCode = `
    void main() {
      ${dtype} resRC = getOutputCoords();
      setOutput(getA(${switched}));
    }
    `;
    }
  };
  function getSwitchedCoords(newDim) {
    const rank = newDim.length;
    if (rank > 6) {
      throw Error(`Transpose for rank ${rank} is not yet supported`);
    }
    const originalOrder = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"];
    const switchedCoords = new Array(rank);
    for (let i = 0; i < newDim.length; i++) {
      switchedCoords[newDim[i]] = originalOrder[i];
    }
    return switchedCoords.join();
  }
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var TransposePackedProgram = class {
    constructor(aShape, newDim) {
      this.variableNames = ["A"];
      this.packedInputs = true;
      this.packedOutput = true;
      const outputShape = new Array(aShape.length);
      for (let i = 0; i < outputShape.length; i++) {
        outputShape[i] = aShape[newDim[i]];
      }
      this.outputShape = outputShape;
      this.rank = outputShape.length;
      if (this.rank > 6) {
        throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);
      }
      const dtype = getCoordsDataType(this.rank);
      const outputOrder = getVecChannels("rc", this.rank);
      const switchedOrder = new Array(this.rank);
      for (let i = 0; i < newDim.length; i++) {
        switchedOrder[newDim[i]] = outputOrder[i];
      }
      const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;
      const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;
      const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;
      this.userCode = `
    void main() {
      ${dtype} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${getc};
      if(${nextColumn}) {
        result[1] = ${getc};
      }
      --${outputOrder[this.rank - 1]};
      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {
        result[2] = ${getc};
        if(${nextColumn}) {
          result[3] = ${getc};
        }
      }
      setOutput(result);
    }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function transposeImpl2(x, perm, backend2) {
    const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new TransposePackedProgram(x.shape, perm) : new TransposeProgram(x.shape, perm);
    return backend2.runWebGLProgram(program, [x], x.dtype);
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sumImpl(x, axis, keepDims, backend2) {
    const reductionIndices = axis;
    const xRank = x.shape.length;
    const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);
    let axes = origAxes;
    const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
    const sumInputIsTransposed = permutedAxes != null;
    let sumInput = x;
    if (sumInputIsTransposed) {
      sumInput = transposeImpl2(x, permutedAxes, backend2);
      axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
    }
    backend_util_exports.assertAxesAreInnerMostDims("sum", axes, xRank);
    const [sumOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(sumInput.shape, axes);
    let outShape = sumOutShape;
    if (keepDims) {
      outShape = backend_util_exports.expandShapeToKeepDim(sumOutShape, origAxes);
    }
    const inSize = util_exports.sizeFromShape(reduceShape);
    const xSize = util_exports.sizeFromShape(x.shape);
    const batchSize = xSize / inSize;
    const reshapedInput = reshape4({inputs: {x: sumInput}, attrs: {shape: [batchSize, inSize]}, backend: backend2});
    const outType = sumOutType(x.dtype);
    const reduced = reduce(reshapedInput, outType, "sum", backend2);
    const out = reshape4({inputs: {x: reduced}, attrs: {shape: outShape}, backend: backend2});
    backend2.disposeIntermediateTensorInfo(reshapedInput);
    backend2.disposeIntermediateTensorInfo(reduced);
    if (sumInputIsTransposed) {
      backend2.disposeIntermediateTensorInfo(sumInput);
    }
    return out;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sum4(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis, keepDims} = attrs;
    return sumImpl(x, axis, keepDims, backend2);
  }
  var sumConfig2 = {
    kernelName: Sum,
    backendName: "webgl",
    kernelFunc: sum4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function transpose3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {perm} = attrs;
    const webglBackend = backend2;
    const xRank = x.shape.length;
    const newShape = new Array(xRank);
    for (let i = 0; i < newShape.length; i++) {
      newShape[i] = x.shape[perm[i]];
    }
    let out;
    if (webglBackend.shouldExecuteOnCPU([x])) {
      const xTexData = webglBackend.texData.get(x.dataId);
      const values = xTexData.values;
      const outValues = transposeImplCPU(values, x.shape, x.dtype, perm, newShape);
      out = webglBackend.makeTensorInfo(newShape, x.dtype);
      const outData = webglBackend.texData.get(out.dataId);
      outData.values = outValues;
    } else {
      out = transposeImpl2(x, perm, webglBackend);
    }
    return out;
  }
  var transposeConfig2 = {
    kernelName: Transpose,
    backendName: "webgl",
    kernelFunc: transpose3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var MATMUL_SHARED_DIM_THRESHOLD = 1e3;
  function batchMatMulImpl({a, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null}) {
    const aRank = a.shape.length;
    const bRank = b.shape.length;
    const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];
    const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
    const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];
    const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
    const outerDimsA = a.shape.slice(0, -2);
    const outerDimsB = b.shape.slice(0, -2);
    const batchDimA = util_exports.sizeFromShape(outerDimsA);
    const batchDimB = util_exports.sizeFromShape(outerDimsB);
    const batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;
    util_exports.assert(aRank >= 2 && bRank >= 2 && batchDimsCompatible, () => `Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);
    const outShapeOuterDims = batchDimA > batchDimB ? a.shape.slice(0, -2) : b.shape.slice(0, -2);
    const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
    util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
    const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];
    const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];
    const a3d = reshape4({inputs: {x: a}, backend: backend2, attrs: {shape: a3dShape}});
    const b3d = reshape4({inputs: {x: b}, backend: backend2, attrs: {shape: b3dShape}});
    const intermediates = [a3d, b3d];
    const batchDim = Math.max(batchDimA, batchDimB);
    const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];
    const hasBias = bias != null;
    const hasPreluActivationWeights = preluActivationWeights != null;
    const hasLeakyreluAlpha = activation2 === "leakyrelu";
    const fusedActivation = activation2 != null ? mapActivationToShaderProgram(activation2, true) : null;
    const containsFusedOps = hasBias || hasPreluActivationWeights || hasLeakyreluAlpha || fusedActivation != null;
    let out;
    if ((outerShapeA === 1 || outerShapeB === 1) && sharedDim > MATMUL_SHARED_DIM_THRESHOLD && containsFusedOps === false) {
      let aVec = a3d;
      let bVec = b3d;
      if (transposeA) {
        aVec = transpose3({inputs: {x: a3d}, backend: backend2, attrs: {perm: [0, 2, 1]}});
        intermediates.push(aVec);
      }
      if (transposeB) {
        bVec = transpose3({inputs: {x: b3d}, backend: backend2, attrs: {perm: [0, 2, 1]}});
        intermediates.push(bVec);
      }
      const shouldReshapeA = outerShapeB !== 1;
      const shouldReshapeB = outerShapeB === 1;
      let aVec3d = aVec;
      if (shouldReshapeA) {
        aVec3d = reshape4({
          inputs: {x: aVec},
          backend: backend2,
          attrs: {shape: [batchDim, sharedDim, 1]}
        });
        intermediates.push(aVec3d);
      }
      const axis = outerShapeB === 1 ? 2 : 1;
      let bVec3d = bVec;
      if (shouldReshapeB) {
        bVec3d = reshape4({
          inputs: {x: bVec},
          backend: backend2,
          attrs: {shape: [batchDim, 1, sharedDim]}
        });
        intermediates.push(bVec3d);
      }
      const product = multiply3({inputs: {a: aVec3d, b: bVec3d}, backend: backend2});
      out = sum4({inputs: {x: product}, backend: backend2, attrs: {axis, keepDims: true}});
      intermediates.push(product);
    } else {
      const dtype = upcastType(a.dtype, b.dtype);
      const program = new MatMulPackedProgram(a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
      const inputs = [a3d, b3d];
      if (bias != null) {
        inputs.push(bias);
      }
      if (hasPreluActivationWeights) {
        inputs.push(preluActivationWeights);
      }
      if (hasLeakyreluAlpha) {
        const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
        inputs.push($leakyreluAlpha);
        intermediates.push($leakyreluAlpha);
      }
      out = backend2.runWebGLProgram(program, inputs, dtype);
    }
    const outReshaped = reshape4({inputs: {x: out}, backend: backend2, attrs: {shape: outShape}});
    intermediates.push(out);
    for (const i of intermediates) {
      backend2.disposeIntermediateTensorInfo(i);
    }
    return outReshaped;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function _fusedMatMul2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {a, b, bias, preluActivationWeights} = inputs;
    const {transposeA, transposeB, activation: activation2, leakyreluAlpha} = attrs;
    return batchMatMulImpl({
      a,
      b,
      transposeA,
      transposeB,
      backend: backend2,
      bias,
      preluActivationWeights,
      leakyreluAlpha,
      activation: activation2
    });
  }
  var _fusedMatMulConfig2 = {
    kernelName: _FusedMatMul,
    backendName: "webgl",
    kernelFunc: _fusedMatMul2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ABS2 = `return abs(x);`;
  function abs3(args) {
    const {inputs, backend: backend2} = args;
    const {x} = inputs;
    if (backend2.shouldExecuteOnCPU([x]) && x.dtype !== "complex64") {
      const xData = backend2.texData.get(x.dataId);
      const outValues = simpleAbsImplCPU(xData.values);
      return backend2.makeTensorInfo(x.shape, x.dtype, outValues);
    }
    let program;
    if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) {
      program = new UnaryOpPackedProgram(x.shape, ABS2);
    } else {
      program = new UnaryOpProgram(x.shape, ABS2);
    }
    return backend2.runWebGLProgram(program, [x], x.dtype);
  }
  var absConfig2 = {
    kernelName: Abs,
    backendName: "webgl",
    kernelFunc: abs3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ACOS = CHECK_NAN_SNIPPET + `
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`;
  var acos3 = unaryKernelFunc2({opSnippet: ACOS});
  var acosConfig2 = {
    kernelName: Acos,
    backendName: "webgl",
    kernelFunc: acos3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ACOSH = CHECK_NAN_SNIPPET + `
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`;
  var acosh3 = unaryKernelFunc2({opSnippet: ACOSH});
  var acoshConfig2 = {
    kernelName: Acosh,
    backendName: "webgl",
    kernelFunc: acosh3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ADD = "return a + b;";
  var addKernelFunc = binaryKernelFunc2({
    opSnippet: ADD,
    packedOpSnippet: ADD,
    supportsComplex: true,
    cpuKernelImpl: addImplCPU
  });
  var addConfig2 = {
    kernelName: Add,
    backendName: "webgl",
    kernelFunc: addKernelFunc
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var AddNProgram = class {
    constructor(outputShape, shapes) {
      this.outputShape = [];
      this.outputShape = outputShape;
      this.variableNames = shapes.map((_, i) => `T${i}`);
      const snippets = [];
      this.variableNames.forEach((variable2) => {
        snippets.push(`float v${variable2} = get${variable2}AtOutCoords();`);
      });
      const operation = this.variableNames.map((variable2) => {
        return `v${variable2}`;
      }).join(" + ");
      this.userCode = `
      void main() {
        ${snippets.join("\n        ")}

        float result = ${operation};
        setOutput(result);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var AddNPackedProgram = class {
    constructor(outputShape, shapes) {
      this.outputShape = [];
      this.packedInputs = true;
      this.packedOutput = true;
      this.outputShape = outputShape;
      this.variableNames = shapes.map((_, i) => `T${i}`);
      const snippets = [];
      this.variableNames.forEach((variable2) => {
        snippets.push(`vec4 v${variable2} = get${variable2}AtOutCoords();`);
      });
      const operation = this.variableNames.map((variable2) => {
        return `v${variable2}`;
      }).join(" + ");
      this.userCode = `
      void main() {
        ${snippets.join("\n        ")}

        vec4 result = ${operation};
        setOutput(result);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function addN3(args) {
    const {inputs, backend: backend2} = args;
    const tensors = inputs;
    if (tensors.length === 1) {
      return identity3({inputs: {x: tensors[0]}, backend: backend2});
    }
    if (tensors.length > env().get("WEBGL_MAX_TEXTURES_IN_SHADER")) {
      const midIndex = Math.floor(tensors.length / 2);
      const leftSide = addN3({inputs: tensors.slice(0, midIndex), backend: backend2});
      const rightSide = addN3({inputs: tensors.slice(midIndex), backend: backend2});
      return addN3({inputs: [leftSide, rightSide], backend: backend2});
    }
    const dtype = tensors.map((t) => t.dtype).reduce((d1, d2) => upcastType(d1, d2));
    const shapes = tensors.map((t) => t.shape);
    const usePackedOp = env().getBool("WEBGL_PACK");
    const program = usePackedOp ? new AddNPackedProgram(tensors[0].shape, shapes) : new AddNProgram(tensors[0].shape, shapes);
    return backend2.runWebGLProgram(program, tensors, dtype);
  }
  var addNConfig2 = {
    kernelName: AddN,
    backendName: "webgl",
    kernelFunc: addN3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function all3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis, keepDims} = attrs;
    const xRank = x.shape.length;
    const origAxes = util_exports.parseAxisParam(axis, x.shape);
    let axes = origAxes;
    const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
    let permutedX = x;
    if (permutedAxes != null) {
      permutedX = transpose3({inputs: {x}, backend: backend2, attrs: {perm: permutedAxes}});
      axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
    }
    backend_util_exports.assertAxesAreInnerMostDims("all", axes, xRank);
    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
    const inSize = util_exports.sizeFromShape(reduceShape);
    const a2D = reshape4({inputs: {x: permutedX}, backend: backend2, attrs: {shape: [-1, inSize]}});
    const reduced = reduce(a2D, a2D.dtype, "all", backend2);
    let res;
    if (keepDims) {
      const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
      res = reshape4({inputs: {x: reduced}, backend: backend2, attrs: {shape: newShape}});
    } else {
      res = reshape4({inputs: {x: reduced}, backend: backend2, attrs: {shape: outShape}});
    }
    backend2.disposeIntermediateTensorInfo(a2D);
    backend2.disposeIntermediateTensorInfo(reduced);
    if (permutedAxes != null) {
      backend2.disposeIntermediateTensorInfo(permutedX);
    }
    return res;
  }
  var allConfig2 = {
    kernelName: All,
    backendName: "webgl",
    kernelFunc: all3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function any3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis, keepDims} = attrs;
    const xRank = x.shape.length;
    const origAxes = util_exports.parseAxisParam(axis, x.shape);
    let axes = origAxes;
    const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
    let permutedX = x;
    if (permutedAxes != null) {
      permutedX = transpose3({inputs: {x}, backend: backend2, attrs: {perm: permutedAxes}});
      axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
    }
    backend_util_exports.assertAxesAreInnerMostDims("any", axes, xRank);
    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
    const inSize = util_exports.sizeFromShape(reduceShape);
    const a2D = reshape4({inputs: {x: permutedX}, backend: backend2, attrs: {shape: [-1, inSize]}});
    const reduced = reduce(a2D, a2D.dtype, "any", backend2);
    let res;
    if (keepDims) {
      const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
      res = reshape4({inputs: {x: reduced}, backend: backend2, attrs: {shape: newShape}});
    } else {
      res = reshape4({inputs: {x: reduced}, backend: backend2, attrs: {shape: outShape}});
    }
    backend2.disposeIntermediateTensorInfo(a2D);
    backend2.disposeIntermediateTensorInfo(reduced);
    if (permutedAxes != null) {
      backend2.disposeIntermediateTensorInfo(permutedX);
    }
    return res;
  }
  var anyConfig2 = {
    kernelName: Any,
    backendName: "webgl",
    kernelFunc: any3
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ArgMinMaxProgram = class {
    constructor(reduceInfo, op2, firstPass) {
      this.variableNames = ["A"];
      const {windowSize, batchSize, outSize} = reduceInfo;
      if (!firstPass) {
        this.variableNames.push("bestIndicesA");
      }
      this.outputShape = [batchSize, outSize];
      const compOp = op2 === "max" ? ">" : "<";
      const indexSnippet = firstPass ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));";
      this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${windowSize};

        int bestIndex = inOffset;
        float bestValue = getA(batch, bestIndex);

        for (int i = 0; i < ${windowSize}; i++) {
          int inIdx = ${indexSnippet};
          float candidate = getA(batch, inIdx);
          if (candidate ${compOp} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ArgMinMaxPackedProgram = class {
    constructor(shape, windowSize, op2, firstPass) {
      this.variableNames = ["A"];
      this.packedInputs = true;
      this.packedOutput = true;
      util_exports.assert(shape.length > 2, () => `Packed arg${op2.charAt(0).toUpperCase() + op2.slice(1)} supports only inputs with rank above 2.`);
      const inSize = shape[shape.length - 1];
      const outSize = Math.ceil(inSize / windowSize);
      this.outputShape = shape.slice(0, -1);
      if (outSize > 1) {
        this.outputShape.push(outSize);
      }
      if (!firstPass) {
        this.variableNames.push("bestIndicesA");
      }
      const outShape = this.outputShape;
      const rank = outShape.length;
      const dtype = getCoordsDataType(rank);
      const coords2 = getChannels("coords", rank);
      let sourceLocSetup;
      let sourceRank;
      if (outSize === 1) {
        sourceRank = rank + 1;
        const sourceLocDType = getCoordsDataType(sourceRank);
        sourceLocSetup = `
        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords2.join()}, 0);
        ++${coords2[rank - 1]};
        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords2.join()}, 0);
        ++${coords2[rank - 2]};
        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords2.join()}, 0);
        --${coords2[rank - 1]};
        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords2.join()}, 0);
        --${coords2[rank - 2]};`;
      } else {
        sourceRank = rank;
        sourceLocSetup = `
        ${dtype} sourceLocR = coords;
        ++${coords2[rank - 1]};
        ${dtype} sourceLocG = coords;
        ++${coords2[rank - 2]};
        ${dtype} sourceLocA = coords;
        --${coords2[rank - 1]};
        ${dtype} sourceLocB = coords;
        --${coords2[rank - 2]};`;
      }
      const channels = ["x", "y", "z", "w", "u", "v"].slice(0, sourceRank);
      const inChannel = "." + channels[sourceRank - 1];
      const intChannels = channels.map((x) => "int " + x);
      const srcRCoords = getChannels("sourceLocR", sourceRank - 1).concat("inIdx.r");
      const srcGCoords = getChannels("sourceLocG", sourceRank - 1).concat("inIdx.g");
      const srcBCoords = getChannels("sourceLocB", sourceRank - 1).concat("inIdx.b");
      const srcACoords = getChannels("sourceLocA", sourceRank - 1).concat("inIdx.a");
      const compOp = op2 === "max" ? "greaterThan" : "lessThan";
      const fetchCandidateIdx = firstPass ? "" : `
          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),
                             getBestIndicesAChannel(${srcGCoords.join()}),
                             getBestIndicesAChannel(${srcBCoords.join()}),
                             getBestIndicesAChannel(${srcACoords.join()})));`;
      const fetchValue = `vec4(
            getAChannel(${srcRCoords.join()}),
            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,
            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;
      const getBestIndicesAChannelSnippet = firstPass ? "" : `
      float getBestIndicesAChannel(${intChannels.join()}) {
        return getChannel(getBestIndicesA(${channels.join()}),
                                          vec2(${channels.slice(-2).join()}));
      }`;
      this.userCode = `
      float getAChannel(${intChannels.join()}) {
        return getChannel(getA(${channels.join()}),
                               vec2(${channels.slice(-2).join()}));
      }
      ${getBestIndicesAChannelSnippet}
      void main() {
        ${dtype} coords = getOutputCoords();
        bool hasNextCol = ${coords2[rank - 1]} < ${outShape[rank - 1] - 1};
        bool hasNextRow = ${coords2[rank - 2]} < ${outShape[rank - 2] - 1};
        ${sourceLocSetup}
        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},
          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${fetchValue};

        for (int i = 0; i < ${windowSize}; i++) {
          inIdx = srcIdx;
          ${fetchCandidateIdx}
          vec4 candidate = ${fetchValue};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function argReduce(backend2, x, reduceType, bestIndicesA = null) {
    let batchSize = x.shape[0];
    let inSize = x.shape[1];
    if (bestIndicesA != null) {
      batchSize = bestIndicesA.shape[0];
      inSize = bestIndicesA.shape[1];
    }
    const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);
    const reduceInfo = {windowSize, inSize, batchSize, outSize: Math.ceil(inSize / windowSize)};
    const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);
    const inputs = [x];
    if (bestIndicesA != null) {
      inputs.push(bestIndicesA);
    }
    const output = backend2.runWebGLProgram(program, inputs, "int32");
    if (output.shape[1] === 1) {
      return output;
    }
    const result = argReduce(backend2, x, reduceType, output);
    backend2.disposeIntermediateTensorInfo(output);
    return result;
  }
  function argReducePacked(backend2, x, reduceType, bestIndicesA = null) {
    const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;
    const inSize = inShape[inShape.length - 1];
    const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);
    const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);
    const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];
    const output = backend2.runWebGLProgram(program, inputs, "int32");
    if (output.shape.length === x.shape.length) {
      const result = argReducePacked(backend2, x, reduceType, output);
      backend2.disposeIntermediateTensorInfo(output);
      return result;
    }
    return output;
  }
  function argMinMaxReduce(backend2, x, axis, reduceType) {
    const axes = [axis];
    backend_util_exports.assertAxesAreInnerMostDims("arg" + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.shape.length);
    if (!env().getBool("WEBGL_PACK_REDUCE") || x.shape.length <= 2) {
      const intermediateTensorInfos = [];
      const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(x.shape, axes);
      const inSize = util_exports.sizeFromShape(reduceShape);
      const a2D = reshape4({inputs: {x}, backend: backend2, attrs: {shape: [-1, inSize]}});
      intermediateTensorInfos.push(a2D);
      const reduced = argReduce(backend2, a2D, reduceType);
      intermediateTensorInfos.push(reduced);
      const reshaped = reshape4({inputs: {x: reduced}, backend: backend2, attrs: {shape: outShape}});
      intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
      return reshaped;
    }
    return argReducePacked(backend2, x, reduceType);
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function argMax3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis} = attrs;
    let axes = util_exports.parseAxisParam(axis, x.shape);
    const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
    let $x = x;
    const intermediateTensorInfos = [];
    if (permutedAxes != null) {
      $x = transpose3({inputs: {x}, backend: backend2, attrs: {perm: permutedAxes}});
      intermediateTensorInfos.push($x);
      axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
    }
    backend_util_exports.assertAxesAreInnerMostDims("argMax", [axes[0]], $x.shape.length);
    const out = argMinMaxReduce(backend2, $x, axes[0], "max");
    intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return out;
  }
  var argMaxConfig2 = {
    kernelName: ArgMax,
    backendName: "webgl",
    kernelFunc: argMax3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function argMin3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis} = attrs;
    let axes = util_exports.parseAxisParam(axis, x.shape);
    const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
    let $x = x;
    const intermediateTensorInfos = [];
    if (permutedAxes != null) {
      $x = transpose3({inputs: {x}, backend: backend2, attrs: {perm: permutedAxes}});
      intermediateTensorInfos.push($x);
      axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
    }
    backend_util_exports.assertAxesAreInnerMostDims("argMin", [axes[0]], $x.shape.length);
    const out = argMinMaxReduce(backend2, $x, axes[0], "min");
    intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return out;
  }
  var argMinConfig2 = {
    kernelName: ArgMin,
    backendName: "webgl",
    kernelFunc: argMin3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ASIN = CHECK_NAN_SNIPPET + `
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`;
  var asin3 = unaryKernelFunc2({opSnippet: ASIN});
  var asinConfig2 = {
    kernelName: Asin,
    backendName: "webgl",
    kernelFunc: asin3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;
  var asinh3 = unaryKernelFunc2({opSnippet: ASINH});
  var asinhConfig2 = {
    kernelName: Asinh,
    backendName: "webgl",
    kernelFunc: asinh3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ATAN = CHECK_NAN_SNIPPET + `
  return atan(x);
`;
  var atan4 = unaryKernelFunc2({opSnippet: ATAN});
  var atanConfig2 = {
    kernelName: Atan,
    backendName: "webgl",
    kernelFunc: atan4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ATAN2 = CHECK_NAN_SNIPPET_BINARY + `
  return atan(a, b);
`;
  var ATAN2_PACKED = `
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  ` + CHECK_NAN_SNIPPET_BINARY_PACKED + `
  return result;
`;
  var atan23 = binaryKernelFunc2({opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED});
  var atan2Config2 = {
    kernelName: Atan2,
    backendName: "webgl",
    kernelFunc: atan23
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ATANH = CHECK_NAN_SNIPPET + `
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
  var atanh3 = unaryKernelFunc2({opSnippet: ATANH});
  var atanhConfig2 = {
    kernelName: Atanh,
    backendName: "webgl",
    kernelFunc: atanh3
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var Pool2DProgram = class {
    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {
      this.variableNames = ["x"];
      if (poolType === "avg" && computePositions) {
        throw new Error("Cannot compute positions for average pool.");
      }
      const filterWidth = convInfo.filterWidth;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const dilationHeight = convInfo.dilationHeight;
      const dilationWidth = convInfo.dilationWidth;
      const effectiveFilterHeight = convInfo.effectiveFilterHeight;
      const effectiveFilterWidth = convInfo.effectiveFilterWidth;
      const padTop = convInfo.padInfo.top;
      const padLeft = convInfo.padInfo.left;
      this.outputShape = convInfo.outShape;
      const isAvgPool = poolType === "avg";
      const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;
      const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;
      let initializationValue = "0.0";
      if (!isAvgPool) {
        initializationValue = "-1.0 / 1e-20";
      }
      if (computePositions) {
        const compareOp2 = ">=";
        this.userCode = `
        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
        const ivec2 pads = ivec2(${padTop}, ${padLeft});

        void main() {
          ivec4 coords = getOutputCoords();
          int batch = coords[0];
          int d = coords[3];

          ivec2 xRCCorner = coords.yz * strides - pads;
          int xRCorner = xRCCorner.x;
          int xCCorner = xRCCorner.y;

          // max/min x(?, ?, d) to get y(yR, yC, d).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;
          float avgValue = 0.0;

          for (int wR = 0; wR < ${effectiveFilterHeight};
              wR += ${dilationHeight}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${convInfo.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${effectiveFilterWidth};
                wC += ${dilationWidth}) {
              int xC = xCCorner + wC;

              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                continue;
              }

              float value = getX(batch, xR, xC, d);

              // If a min / max value has already been found, use it. If not,
              // use the current value.
              float currMinMaxValue = mix(
                  value, minMaxValue, minMaxValueFound);
              if (value ${compareOp2} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${flattenPositions ? includeBatchInIndex ? batchFlattenPositionStr : flattenPositionStr : `wR * ${effectiveFilterWidth} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;
        return;
      }
      const compareOp = "max";
      let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
      if (poolType === "avg") {
        returnValue = `avgValue / count`;
      }
      const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
      const filterWidthVec4Remainder = filterWidth % 4;
      const updateSnippet = `
      if (${isAvgPool}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${compareOp}(values, minMaxValue);
      }
    `;
      this.userCode = `
      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
      const ivec2 pads = ivec2(${padTop}, ${padLeft});
      const float initializationValue = ${initializationValue};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xR, int xC, int d) {
        if (xC < 0 || xC >= ${convInfo.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xR, xC, d);
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d = coords[3];

        ivec2 xRCCorner = coords.yz * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // max/min x(?, ?, d) to get y(yR, yC, d).
        // ? = to be determined
        vec4 minMaxValue = vec4(${initializationValue});
        float avgValue = 0.0;
        count = 0.0;

        for (int wR = 0; wR < ${effectiveFilterHeight};
            wR += ${dilationHeight}) {
          int xR = xRCorner + wR;

          if (xR < 0 || xR >= ${convInfo.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
            int xC = xCCorner + wC * ${dilationWidth};

            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${dilationWidth}, d),
              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)
            );

            ${updateSnippet}
          }

          int xC = xCCorner + ${filterWidthNearestVec4};
          if (${filterWidthVec4Remainder === 1}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              initializationValue,
              initializationValue,
              initializationValue
            );

            ${updateSnippet}
          } else if (${filterWidthVec4Remainder === 2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${dilationWidth}, d),
              initializationValue,
              initializationValue
            );

            ${updateSnippet}
          } else if (${filterWidthVec4Remainder === 3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${dilationWidth}, d),
              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
              initializationValue
            );

            ${updateSnippet}
          }
        }
        setOutput(${returnValue});
      }
    `;
    }
  };
  var Pool3DProgram = class {
    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {
      this.variableNames = ["x"];
      if (poolType === "avg" && computePositions) {
        throw new Error("Cannot compute positions for average pool.");
      }
      const filterWidth = convInfo.filterWidth;
      const strideDepth = convInfo.strideDepth;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const dilationDepth = convInfo.dilationDepth;
      const dilationHeight = convInfo.dilationHeight;
      const dilationWidth = convInfo.dilationWidth;
      const effectiveFilterDepth = convInfo.effectiveFilterDepth;
      const effectiveFilterHeight = convInfo.effectiveFilterHeight;
      const effectiveFilterWidth = convInfo.effectiveFilterWidth;
      const padFront = convInfo.padInfo.front;
      const padTop = convInfo.padInfo.top;
      const padLeft = convInfo.padInfo.left;
      this.outputShape = convInfo.outShape;
      const isAvgPool = poolType === "avg";
      let initializationValue = "0.0";
      if (!isAvgPool) {
        initializationValue = "-1.0 / 1e-20";
      }
      if (computePositions) {
        const compareOp2 = ">=";
        this.userCode = `
        const ivec3 strides =
            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});

        void main() {
          ivec5 coords = getOutputCoords();
          int batch = coords.x;
          int ch = coords.u;

          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
          int xDCorner = xCorner.x;
          int xRCorner = xCorner.y;
          int xCCorner = xCorner.z;

          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;

          for (int wD = 0; wD < ${effectiveFilterDepth};
              wD += ${dilationDepth}) {
            int xD = xDCorner + wD;

            if (xD < 0 || xD >= ${convInfo.inDepth}) {
              continue;
            }

            for (int wR = 0; wR < ${effectiveFilterHeight};
                wR += ${dilationHeight}) {
              int xR = xRCorner + wR;

              if (xR < 0 || xR >= ${convInfo.inHeight}) {
                continue;
              }

              for (int wC = 0; wC < ${effectiveFilterWidth};
                  wC += ${dilationWidth}) {
                int xC = xCCorner + wC;

                if (xC < 0 || xC >= ${convInfo.inWidth}) {
                  continue;
                }

                float value = getX(batch, xD, xR, xC, ch);

                // If a min / max value has already been found, use it. If not,
                // use the current value.
                float currMinMaxValue = mix(
                    value, minMaxValue, minMaxValueFound);
                if (value ${compareOp2} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${flattenPositions ? includeBatchInIndex ? `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
                      wR * ${effectiveFilterWidth} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;
        return;
      }
      const compareOp = "max";
      let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
      if (poolType === "avg") {
        returnValue = `avgValue / count`;
      }
      const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
      const filterWidthVec4Remainder = filterWidth % 4;
      const updateSnippet = `
      if (${isAvgPool}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${compareOp}(values, minMaxValue);
      }
    `;
      this.userCode = `
      const ivec3 strides =
        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
      const float initializationValue = ${initializationValue};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xD, int xR, int xC, int ch) {
        if (xC < 0 || xC >= ${convInfo.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xD, xR, xC, ch);
      }

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xDCorner = xCorner.x;
        int xRCorner = xCorner.y;
        int xCCorner = xCorner.z;

        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
        // ? = to be determined
        vec4 minMaxValue = vec4(${initializationValue});
        float avgValue = 0.0;
        count = 0.0;

        for (int wD = 0; wD < ${effectiveFilterDepth};
            wD += ${dilationDepth}) {
          int xD = xDCorner + wD;

          if (xD < 0 || xD >= ${convInfo.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${effectiveFilterHeight};
            wR += ${dilationHeight}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${convInfo.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
              int xC = xCCorner + wC * ${dilationWidth};

              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)
              );

              ${updateSnippet}
            }

            int xC = xCCorner + ${filterWidthNearestVec4};
            if (${filterWidthVec4Remainder === 1}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                initializationValue,
                initializationValue,
                initializationValue
              );

              ${updateSnippet}
            } else if (${filterWidthVec4Remainder === 2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
                initializationValue,
                initializationValue
              );

              ${updateSnippet}
            } else if (${filterWidthVec4Remainder === 3}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
                initializationValue
              );

              ${updateSnippet}
            }
          }
          setOutput(${returnValue});
        }
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function avgPool3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    assertNotComplex2(x, "avgPool");
    const {filterSize, strides, pad: pad3, dimRoundingMode} = attrs;
    const dilations = 1;
    util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);
    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
      return identity3({inputs: {x}, backend: backend2});
    }
    const avgPoolProgram = new Pool2DProgram(convInfo, "avg", false);
    return backend2.runWebGLProgram(avgPoolProgram, [x], "float32");
  }
  var avgPoolConfig2 = {
    kernelName: AvgPool,
    backendName: "webgl",
    kernelFunc: avgPool3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function avgPool3D2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {filterSize, strides, pad: pad3, dimRoundingMode, dataFormat} = attrs;
    const dilations = [1, 1, 1];
    const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat);
    const avgPoolProgram = new Pool3DProgram(convInfo, "avg", false);
    return backend2.runWebGLProgram(avgPoolProgram, [x], "float32");
  }
  var avgPool3DConfig2 = {
    kernelName: AvgPool3D,
    backendName: "webgl",
    kernelFunc: avgPool3D2
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var AvgPool2DBackpropProgram = class {
    constructor(convInfo) {
      this.variableNames = ["dy"];
      this.outputShape = convInfo.inShape;
      const filterHeight = convInfo.filterHeight;
      const filterWidth = convInfo.filterWidth;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const dilationHeight = convInfo.dilationHeight;
      const dilationWidth = convInfo.dilationWidth;
      const effectiveFilterHeight = convInfo.effectiveFilterHeight;
      const effectiveFilterWidth = convInfo.effectiveFilterWidth;
      const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
      const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
      const avgMultiplier = 1 / (filterHeight * filterWidth);
      this.userCode = `
      const ivec2 pads = ivec2(${padTop}, ${padLeft});
      const float avgMultiplier = float(${avgMultiplier});

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${effectiveFilterHeight};
            wR += ${dilationHeight}) {
          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${effectiveFilterWidth};
            wC+= ${dilationWidth}) {
            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);

            dotProd += dyValue * avgMultiplier;
          }
        }
        setOutput(dotProd);
      }
    `;
    }
  };
  var AvgPool3DBackpropProgram = class {
    constructor(convInfo) {
      this.variableNames = ["dy"];
      this.outputShape = convInfo.inShape;
      const filterDepth = convInfo.filterDepth;
      const filterHeight = convInfo.filterHeight;
      const filterWidth = convInfo.filterWidth;
      const strideDepth = convInfo.strideDepth;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const dilationDepth = convInfo.dilationDepth;
      const dilationHeight = convInfo.dilationHeight;
      const dilationWidth = convInfo.dilationWidth;
      const effectiveFilterDepth = convInfo.effectiveFilterDepth;
      const effectiveFilterHeight = convInfo.effectiveFilterHeight;
      const effectiveFilterWidth = convInfo.effectiveFilterWidth;
      const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
      const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
      const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
      const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
      this.userCode = `
      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
      const float avgMultiplier = float(${avgMultiplier});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${effectiveFilterDepth};
            wD += ${dilationDepth}) {
          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;

          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${effectiveFilterHeight};
              wR += ${dilationHeight}) {
            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${effectiveFilterWidth};
                wC += ${dilationWidth}) {
              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);

              dotProd += dyValue * avgMultiplier;
            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function avgPool3DGrad2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {dy, input: input2} = inputs;
    const x = input2;
    const {filterSize, strides, pad: pad3, dimRoundingMode} = attrs;
    const dilations = [1, 1, 1];
    const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);
    const avgPoolBackpropProgram = new AvgPool3DBackpropProgram(convInfo);
    return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);
  }
  var avgPoolGrad3DConfig = {
    kernelName: AvgPool3DGrad,
    backendName: "webgl",
    kernelFunc: avgPool3DGrad2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function avgPoolGrad3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {dy, input: input2} = inputs;
    const x = input2;
    assertNotComplex2([dy, input2], "avgPoolGrad");
    const {filterSize, strides, pad: pad3} = attrs;
    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3);
    const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);
    return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);
  }
  var avgPoolGradConfig3 = {
    kernelName: AvgPoolGrad,
    backendName: "webgl",
    kernelFunc: avgPoolGrad3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function batchMatMul2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {a, b} = inputs;
    const {transposeA, transposeB} = attrs;
    return batchMatMulImpl({a, b, transposeA, transposeB, backend: backend2});
  }
  var batchMatMulConfig2 = {
    kernelName: BatchMatMul,
    backendName: "webgl",
    kernelFunc: batchMatMul2
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var BatchNormProgram = class {
    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
      this.outputShape = [];
      this.variableNames = ["x", "mean", "variance"];
      backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);
      backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);
      let offsetSnippet = "0.0";
      if (offsetShape != null) {
        backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);
        this.variableNames.push("offset");
        offsetSnippet = "getOffsetAtOutCoords()";
      }
      let scaleSnippet = "1.0";
      if (scaleShape != null) {
        backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);
        this.variableNames.push("scale");
        scaleSnippet = "getScaleAtOutCoords()";
      }
      this.outputShape = xShape;
      this.userCode = `
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${offsetSnippet};
        float scale = ${scaleSnippet};
        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var BatchNormPackedProgram = class {
    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
      this.packedInputs = true;
      this.packedOutput = true;
      this.variableNames = ["x", "mean", "variance"];
      backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);
      backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);
      let offsetSnippet = "vec4(0.0)";
      if (offsetShape != null) {
        backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);
        this.variableNames.push("offset");
        offsetSnippet = "getOffsetAtOutCoords()";
      }
      let scaleSnippet = "vec4(1.0)";
      if (scaleShape != null) {
        backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);
        this.variableNames.push("scale");
        scaleSnippet = "getScaleAtOutCoords()";
      }
      this.outputShape = xShape;
      this.userCode = `
      void main() {
        vec4 offset = ${offsetSnippet};
        vec4 scale = ${scaleSnippet};

        vec4 x = getXAtOutCoords();
        vec4 mean = getMeanAtOutCoords();
        vec4 variance = getVarianceAtOutCoords();

        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));

        setOutput((x - mean) * inv + offset);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var batchNorm3 = ({inputs, backend: backend2, attrs}) => {
    const {x, mean: mean4, variance, offset, scale: scale22} = inputs;
    util_exports.assert(mean4.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
    util_exports.assert(offset == null || mean4.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
    util_exports.assert(scale22 == null || mean4.shape.length === scale22.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
    let {varianceEpsilon} = attrs;
    if (varianceEpsilon == null) {
      varianceEpsilon = 1e-3;
    }
    const finalInputs = [x, mean4, variance];
    let offsetShape = null;
    if (offset != null) {
      offsetShape = offset.shape;
      finalInputs.push(offset);
    }
    let scaleShape = null;
    if (scale22 != null) {
      scaleShape = scale22.shape;
      finalInputs.push(scale22);
    }
    const program = env().getBool("WEBGL_PACK_NORMALIZATION") ? new BatchNormPackedProgram(x.shape, mean4.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) : new BatchNormProgram(x.shape, mean4.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);
    const output = backend2.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);
    return output;
  };
  var batchNormConfig2 = {
    kernelName: FusedBatchNorm,
    backendName: "webgl",
    kernelFunc: batchNorm3
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SliceProgram = class {
    constructor(destSize) {
      this.variableNames = ["source"];
      this.outputShape = destSize;
      this.rank = destSize.length;
      const dtype = getCoordsDataType(this.rank);
      const uniformPart = `uniform int start[${this.rank}];`;
      const sourceCoords = getCoords(this.rank);
      let body;
      const coordSum = destSize.map((_, i) => {
        return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;
      });
      body = `
        ${dtype} sourceLoc;
        ${dtype} coords = getOutputCoords();
        ${coordSum.join("\n")}
      `;
      this.userCode = `
      ${uniformPart}
      void main() {
        ${body}
        setOutput(getSource(${sourceCoords}));
      }
    `;
    }
    getCustomSetupFunc(start) {
      if (start.length !== this.rank) {
        throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);
      }
      return (gpgpu, webGLProgram) => {
        if (this.startLoc == null) {
          this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, "start");
          if (this.startLoc == null) {
            return;
          }
        }
        gpgpu.gl.uniform1iv(this.startLoc, start);
      };
    }
  };
  var coords = ["x", "y", "z", "w", "u", "v"];
  function getCoords(rank) {
    if (rank === 1) {
      return "sourceLoc";
    } else if (rank <= 6) {
      return coords.slice(0, rank).map((x) => "sourceLoc." + x).join(",");
    } else {
      throw Error(`Slicing for rank ${rank} is not yet supported`);
    }
  }
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SlicePackedProgram = class {
    constructor(destSize) {
      this.variableNames = ["source"];
      this.packedInputs = true;
      this.packedOutput = true;
      this.outputShape = destSize;
      this.rank = destSize.length;
      const dtype = getCoordsDataType(this.rank);
      const coords2 = getChannels("coords", this.rank);
      const sourceLoc = getChannels("sourceLoc", this.rank);
      const innerDims = this.rank === 1 ? "sourceLoc" : `vec2(${sourceLoc.slice(-2).join()})`;
      const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;
      const upperRow = `
      result.x = ${getChannel};
      if (++${coords2[this.rank - 1]} < ${destSize[this.rank - 1]}) {
        ++${sourceLoc[this.rank - 1]};
        result.y = ${getChannel};
        --${sourceLoc[this.rank - 1]};
      }
    `;
      const lowerRow = this.rank === 1 ? "" : `
      --${coords2[this.rank - 1]};
      if (++${coords2[this.rank - 2]} < ${destSize[this.rank - 2]}) {
        ++${sourceLoc[this.rank - 2]};
        result.z = ${getChannel};
        if (++${coords2[this.rank - 1]} < ${destSize[this.rank - 1]}) {
          ++${sourceLoc[this.rank - 1]};
          result.w = ${getChannel};
        }
      }
    `;
      const sourceLocSetup = this.rank <= 4 ? `sourceLoc = coords +
            ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` : destSize.map((_, i) => `${sourceLoc[i]} = ${coords2[i]} + start[${i}];`).join("\n");
      this.userCode = `
      uniform int start[${this.rank}];
      void main() {
        ${dtype} coords = getOutputCoords();
        ${dtype} sourceLoc;
        ${sourceLocSetup}
        vec4 result = vec4(0.);
        ${upperRow}
        ${lowerRow}
        setOutput(result);
      }
    `;
    }
    getCustomSetupFunc(start) {
      if (start.length !== this.rank) {
        throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);
      }
      return (gpgpu, webGLProgram) => {
        if (this.startLoc == null) {
          this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, "start");
          if (this.startLoc == null) {
            return;
          }
        }
        gpgpu.gl.uniform1iv(this.startLoc, start);
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function shallowSlice(x, begin, size, backend2) {
    const xTexData = backend2.texData.get(x.dataId);
    const t = backend2.makeTensorInfo(size, x.dtype);
    const newTexData = backend2.texData.get(t.dataId);
    Object.assign(newTexData, xTexData);
    newTexData.refCount = 1;
    newTexData.shape = size;
    newTexData.dtype = x.dtype;
    let flatOffset = slice_util_exports.computeFlatOffset(begin, util_exports.computeStrides(x.shape));
    if (xTexData.slice) {
      flatOffset += xTexData.slice.flatOffset;
    }
    newTexData.slice = {
      flatOffset,
      origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId
    };
    const refCount = backend2.dataRefCount.get(newTexData.slice.origDataId) || 1;
    backend2.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);
    return t;
  }
  function slice3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {begin, size} = attrs;
    const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);
    slice_util_exports.assertParamsValid(x, $begin, $size);
    if (util_exports.sizeFromShape($size) === 0) {
      return backend2.makeTensorInfo($size, x.dtype, []);
    }
    if (backend2.shouldExecuteOnCPU([x]) || x.dtype === "string") {
      const xTexData = backend2.texData.get(x.dataId);
      const outValues = sliceImplCPU(xTexData.values, $begin, $size, x.shape, x.dtype);
      return backend2.makeTensorInfo($size, x.dtype, outValues);
    }
    const {isPacked} = backend2.texData.get(x.dataId);
    const isContinous = slice_util_exports.isSliceContinous(x.shape, $begin, $size);
    if (isPacked || !isContinous) {
      const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new SlicePackedProgram($size) : new SliceProgram($size);
      const customSetup = program.getCustomSetupFunc($begin);
      return backend2.runWebGLProgram(program, [x], x.dtype, customSetup);
    }
    backend2.uploadToGPU(x.dataId);
    return shallowSlice(x, $begin, $size, backend2);
  }
  var sliceConfig2 = {
    kernelName: Slice,
    backendName: "webgl",
    kernelFunc: slice3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var batchToSpaceND3 = (args) => {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {blockShape, crops} = attrs;
    util_exports.assert(x.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");
    const prod5 = blockShape.reduce((a, b) => a * b);
    const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod5);
    const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);
    const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod5);
    const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);
    const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);
    const toDispose = [];
    const reshapedIntermediate = reshape4({inputs: {x}, backend: backend2, attrs: {shape: reshaped}});
    const transposedIntermediate = transpose3({inputs: {x: reshapedIntermediate}, backend: backend2, attrs: {perm: permuted}});
    const reshapedIntermediate2 = reshape4({
      inputs: {x: transposedIntermediate},
      backend: backend2,
      attrs: {shape: reshapedPermuted}
    });
    const sliced = slice3({
      inputs: {x: reshapedIntermediate2},
      backend: backend2,
      attrs: {begin: sliceBeginCoords, size: sliceSize}
    });
    toDispose.push(reshapedIntermediate);
    toDispose.push(transposedIntermediate);
    toDispose.push(reshapedIntermediate2);
    toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return sliced;
  };
  var batchToSpaceNDConfig2 = {
    kernelName: BatchToSpaceND,
    backendName: "webgl",
    kernelFunc: batchToSpaceND3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function bincount3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, weights} = inputs;
    const {size} = attrs;
    const xVals = backend2.readSync(x.dataId);
    const weightsVals = backend2.readSync(weights.dataId);
    const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);
    return backend2.makeTensorInfo([size], weights.dtype, outVals);
  }
  var bincountConfig2 = {
    kernelName: Bincount,
    backendName: "webgl",
    kernelFunc: bincount3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var NOT_EQUAL = `return float(a != b);`;
  var notEqual3 = binaryKernelFunc2({opSnippet: NOT_EQUAL, dtype: "bool"});
  var notEqualConfig2 = {
    kernelName: NotEqual,
    backendName: "webgl",
    kernelFunc: notEqual3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function real3(args) {
    const {inputs, backend: backend2} = args;
    const {input: input2} = inputs;
    const inputData = backend2.texData.get(input2.dataId);
    return identity3({inputs: {x: inputData.complexTensorInfos.real}, backend: backend2});
  }
  var realConfig2 = {
    kernelName: Real,
    backendName: "webgl",
    kernelFunc: real3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var TO_INT = `return float(int(x));`;
  function int(input2, backend2) {
    const program = new UnaryOpProgram(input2.shape, TO_INT);
    const output = backend2.runWebGLProgram(program, [input2], "int32");
    return {dataId: output.dataId, shape: output.shape, dtype: output.dtype};
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function cast4(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {dtype} = attrs;
    if (dtype === "complex64") {
      if (x.dtype === "complex64") {
        return identity3({inputs: {x}, backend: backend2});
      }
      const zerosTensor = zeros(x.shape);
      const floatX = cast4({inputs: {x}, backend: backend2, attrs: {dtype: "float32"}});
      const result = complex3({inputs: {real: floatX, imag: zerosTensor}, backend: backend2});
      zerosTensor.dispose();
      backend2.disposeIntermediateTensorInfo(floatX);
      return result;
    }
    if (x.dtype === "complex64") {
      const realPart = real3({inputs: {input: x}, backend: backend2});
      const result = cast4({inputs: {x: realPart}, backend: backend2, attrs: {dtype}});
      backend2.disposeIntermediateTensorInfo(realPart);
      return result;
    }
    if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {
      const result = identity3({inputs: {x}, backend: backend2});
      return {dataId: result.dataId, shape: result.shape, dtype};
    }
    if (dtype === "int32") {
      return int(x, backend2);
    }
    if (dtype === "bool") {
      const zerosTensorInfo = backend2.makeTensorInfo([], "bool", util_exports.getTypedArrayFromDType("bool", 1));
      const binaryInputs = {a: x, b: zerosTensorInfo};
      const result = notEqual3({inputs: binaryInputs, backend: backend2});
      backend2.disposeIntermediateTensorInfo(zerosTensorInfo);
      return result;
    }
    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
  }
  var castConfig2 = {
    kernelName: Cast,
    backendName: "webgl",
    kernelFunc: cast4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var CEIL = `return ceil(x);`;
  var ceil3 = unaryKernelFunc2({opSnippet: CEIL, packedOpSnippet: CEIL, cpuKernelImpl: ceilImplCPU});
  var ceilConfig2 = {
    kernelName: Ceil,
    backendName: "webgl",
    kernelFunc: ceil3
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ClipProgram = class {
    constructor(aShape) {
      this.variableNames = ["A"];
      this.outputShape = aShape;
      this.userCode = `
      uniform float minVal;
      uniform float maxVal;

      void main() {
        float value = getAAtOutCoords();
        if (isnan(value)) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, minVal, maxVal));
      }
    `;
    }
    getCustomSetupFunc(min6, max6) {
      return (gpgpu, webGLProgram) => {
        if (this.minLoc == null) {
          this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, "minVal");
          this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, "maxVal");
        }
        gpgpu.gl.uniform1f(this.minLoc, min6);
        gpgpu.gl.uniform1f(this.maxLoc, max6);
      };
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ClipPackedProgram = class {
    constructor(aShape) {
      this.variableNames = ["A"];
      this.packedInputs = true;
      this.packedOutput = true;
      this.outputShape = aShape;
      this.userCode = `
      uniform float minVal;
      uniform float maxVal;

      void main() {
        vec4 value = getAAtOutCoords();

        if (any(isnan(value))) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
      }
    `;
    }
    getCustomSetupFunc(min6, max6) {
      return (gpgpu, webGLProgram) => {
        if (this.minLoc == null) {
          this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, "minVal");
          this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, "maxVal");
        }
        gpgpu.gl.uniform1f(this.minLoc, min6);
        gpgpu.gl.uniform1f(this.maxLoc, max6);
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function clipByValue2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {clipValueMin, clipValueMax} = attrs;
    let program;
    if (env().getBool("WEBGL_PACK_CLIP")) {
      program = new ClipPackedProgram(x.shape);
    } else {
      program = new ClipProgram(x.shape);
    }
    const customSetup = program.getCustomSetupFunc(clipValueMin, clipValueMax);
    return backend2.runWebGLProgram(program, [x], x.dtype, customSetup);
  }
  var clipByValueConfig = {
    kernelName: ClipByValue,
    backendName: "webgl",
    kernelFunc: clipByValue2
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ComplexAbsProgram = class {
    constructor(shape) {
      this.variableNames = ["real", "imag"];
      this.outputShape = shape;
      this.userCode = `
      void main() {
        float re = abs(getRealAtOutCoords());
        float im = abs(getImagAtOutCoords());
        float mx = max(re, im);

        // sadly the length function in glsl is not underflow-safe
        // (at least not on Intel GPUs). So the safe solution is
        // to ensure underflow-safety in all cases.
        setOutput(
          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
        );
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function makeComplexComponentTensorInfo(complexTensor, complexPart) {
    return {
      dataId: complexPart.dataId,
      dtype: complexPart.dtype,
      shape: complexTensor.shape
    };
  }
  function complexAbs2(args) {
    const {inputs, backend: backend2} = args;
    const {x} = inputs;
    const xData = backend2.texData.get(x.dataId);
    const program = new ComplexAbsProgram(x.shape);
    const programInputs = [
      makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),
      makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag)
    ];
    return backend2.runWebGLProgram(program, programInputs, programInputs[0].dtype);
  }
  var complexAbsConfig2 = {
    kernelName: ComplexAbs,
    backendName: "webgl",
    kernelFunc: complexAbs2
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ConcatProgram = class {
    constructor(shapes) {
      this.outputShape = [];
      this.outputShape = backend_util_exports.computeOutShape(shapes, 1);
      this.variableNames = shapes.map((_, i) => `T${i}`);
      const offsets = new Array(shapes.length - 1);
      offsets[0] = shapes[0][1];
      for (let i = 1; i < offsets.length; i++) {
        offsets[i] = offsets[i - 1] + shapes[i][1];
      }
      const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];
      for (let i = 1; i < offsets.length; i++) {
        const shift = offsets[i - 1];
        snippets.push(`else if (yC < ${offsets[i]}) setOutput(getT${i}(yR, yC-${shift}));`);
      }
      const lastIndex = offsets.length;
      const lastShift = offsets[offsets.length - 1];
      snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);
      this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${snippets.join("\n        ")}
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ConcatPackedProgram = class {
    constructor(shapes, axis) {
      this.packedInputs = true;
      this.packedOutput = true;
      this.outputShape = [];
      this.outputShape = backend_util_exports.computeOutShape(shapes, axis);
      const shape = this.outputShape;
      const rank = shape.length;
      const dtype = getCoordsDataType(rank);
      const coords2 = getChannels("coords", rank);
      const channels = ["x", "y", "z", "w", "u", "v"].slice(0, rank);
      this.variableNames = shapes.map((_, i) => `T${i}`);
      const offsets = new Array(shapes.length - 1);
      offsets[0] = shapes[0][axis];
      for (let i = 1; i < offsets.length; i++) {
        offsets[i] = offsets[i - 1] + shapes[i][axis];
      }
      const channel = channels[axis];
      const lastChannels = channels.slice(-2);
      const allChannels = channels.join();
      let getValueSnippet = `if (${channel} < ${offsets[0]}) {
        return getChannel(
            getT0(${allChannels}), vec2(${lastChannels.join()}));
        }`;
      for (let i = 1; i < offsets.length; i++) {
        const shift2 = offsets[i - 1];
        getValueSnippet += `
        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {
          return getChannel(
            getT${i}(${shiftedChannels(channels, channel, shift2)}),
            vec2(${shiftedChannels(lastChannels, channel, shift2)}));
        }`;
      }
      const lastIndex = offsets.length;
      const shift = offsets[offsets.length - 1];
      getValueSnippet += `
        return getChannel(
          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),
          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;
      this.userCode = `
      float getValue(${channels.map((x) => "int " + x)}) {
        ${getValueSnippet}
      }

      void main() {
        ${dtype} coords = getOutputCoords();
        vec4 result = vec4(getValue(${coords2}), 0., 0., 0.);

        ${coords2[rank - 1]} = ${coords2[rank - 1]} + 1;
        if (${coords2[rank - 1]} < ${shape[rank - 1]}) {
          result.g = getValue(${coords2});
        }

        ${coords2[rank - 2]} = ${coords2[rank - 2]} + 1;
        if (${coords2[rank - 2]} < ${shape[rank - 2]}) {
          result.a = getValue(${coords2});
        }

        ${coords2[rank - 1]} = ${coords2[rank - 1]} - 1;
        if (${coords2[rank - 2]} < ${shape[rank - 2]} &&
            ${coords2[rank - 1]} < ${shape[rank - 1]}) {
          result.b = getValue(${coords2});
        }
        setOutput(result);
      }
    `;
    }
  };
  function shiftedChannels(channels, channel, shift) {
    const channelIdx = channels.indexOf(channel);
    const res = channels.map((c, idx) => {
      if (idx === channelIdx) {
        return `${c} - ${shift}`;
      } else {
        return c;
      }
    });
    return res.join();
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function imag3(args) {
    const {inputs, backend: backend2} = args;
    const {input: input2} = inputs;
    const inputData = backend2.texData.get(input2.dataId);
    return identity3({inputs: {x: inputData.complexTensorInfos.imag}, backend: backend2});
  }
  var imagConfig2 = {
    kernelName: Imag,
    backendName: "webgl",
    kernelFunc: imag3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function concatImpl2(inputs, axis, backend2) {
    const dtype = inputs[0].dtype;
    if (dtype === "complex64") {
      const reals = inputs.map((t) => real3({inputs: {input: t}, backend: backend2}));
      const imags = inputs.map((t) => imag3({inputs: {input: t}, backend: backend2}));
      const realConcated = concatImpl2(reals, axis, backend2);
      const imagConcated = concatImpl2(imags, axis, backend2);
      const result2 = complex3({inputs: {real: realConcated, imag: imagConcated}, backend: backend2});
      reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r));
      imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i));
      backend2.disposeIntermediateTensorInfo(realConcated);
      backend2.disposeIntermediateTensorInfo(imagConcated);
      return result2;
    }
    if (dtype === "string") {
      const {tensors2D: tensors2D2, outShape: outShape2} = computeTensors2D(inputs, axis, backend2);
      const inputsValShapes = tensors2D2.map((t) => {
        return {vals: backend2.readSync(t.dataId), shape: t.shape};
      });
      const simplyConcat = tensors2D2[0].shape[0] === 1;
      const outVals = concatImplCPU(inputsValShapes, outShape2, dtype, simplyConcat);
      const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);
      const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals);
      tensors2D2.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
      return outInfo;
    }
    if (inputs.length > env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")) {
      const midIndex = Math.floor(inputs.length / 2);
      const leftSide = concatImpl2(inputs.slice(0, midIndex), axis, backend2);
      const rightSide = concatImpl2(inputs.slice(midIndex), axis, backend2);
      const result2 = concatImpl2([leftSide, rightSide], axis, backend2);
      backend2.disposeIntermediateTensorInfo(leftSide);
      backend2.disposeIntermediateTensorInfo(rightSide);
      return result2;
    }
    if (env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && inputs[0].shape.length > 1) {
      const program2 = new ConcatPackedProgram(inputs.map((t) => t.shape), axis);
      return backend2.runWebGLProgram(program2, inputs, dtype);
    }
    const {tensors2D, outShape} = computeTensors2D(inputs, axis, backend2);
    const program = new ConcatProgram(tensors2D.map((t) => t.shape));
    const result = backend2.runWebGLProgram(program, tensors2D, dtype);
    tensors2D.forEach((r) => backend2.disposeIntermediateTensorInfo(r));
    const reshapedResult = reshape4({inputs: {x: result}, attrs: {shape: outShape}, backend: backend2});
    backend2.disposeIntermediateTensorInfo(result);
    return reshapedResult;
  }
  function computeTensors2D(inputs, axis, backend2) {
    const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);
    const tensors2D = inputs.map((x) => reshape4({
      inputs: {x},
      attrs: {shape: [-1, util_exports.sizeFromShape(x.shape.slice(axis))]},
      backend: backend2
    }));
    return {tensors2D, outShape};
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function concat3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {axis} = attrs;
    const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];
    const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis);
    if (util_exports.sizeFromShape(outShape) === 0) {
      return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);
    }
    const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);
    if ($inputs.length === 1) {
      return identity3({inputs: {x: $inputs[0]}, backend: backend2});
    }
    const shapes = $inputs.map((t) => t.shape);
    backend_util_exports.assertParamsConsistent(shapes, $axis);
    return concatImpl2($inputs, $axis, backend2);
  }
  var concatConfig2 = {
    kernelName: Concat,
    backendName: "webgl",
    kernelFunc: concat3
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var Conv2DProgram = class {
    constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false, hasLeakyreluAlpha = false) {
      this.variableNames = ["x", "W"];
      this.outputShape = convInfo.outShape;
      const padTop = convInfo.padInfo.top;
      const padLeft = convInfo.padInfo.left;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const dilationHeight = convInfo.dilationHeight;
      const dilationWidth = convInfo.dilationWidth;
      const filterHeight = convInfo.filterHeight;
      const filterWidth = convInfo.filterWidth;
      const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
      const inputDepthVec4Remainder = convInfo.inChannels % 4;
      const isChannelsLast = convInfo.dataFormat === "channelsLast";
      const rowDim = isChannelsLast ? 1 : 2;
      const colDim = isChannelsLast ? 2 : 3;
      const channelDim = isChannelsLast ? 3 : 1;
      let activationSnippet = "", applyActivationSnippet = "";
      if (activation2) {
        if (hasPreluActivationWeights) {
          activationSnippet = `float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${activation2}
        }`;
        } else if (hasLeakyreluAlpha) {
          activationSnippet = `float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${activation2}
        }`;
        } else {
          activationSnippet = `
          float activation(float x) {
            ${activation2}
          }
        `;
        }
        applyActivationSnippet = `result = activation(result);`;
      }
      const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
      if (addBias) {
        this.variableNames.push("bias");
      }
      if (hasPreluActivationWeights) {
        this.variableNames.push("preluActivationWeights");
      }
      if (hasLeakyreluAlpha) {
        this.variableNames.push("leakyreluAlpha");
      }
      this.userCode = `
      ${activationSnippet}

      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d2 = coords[${channelDim}];

        ivec2 xRCCorner =
            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${filterHeight}; wR++) {
          int xR = xRCorner + wR * ${dilationHeight};

          if (xR < 0 || xR >= ${convInfo.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${filterWidth}; wC++) {
            int xC = xCCorner + wC * ${dilationWidth};

            if (xC < 0 || xC >= ${convInfo.inWidth}) {
              continue;
            }

            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
              vec4 wValues = vec4(
                getW(wR, wC, d1, d2),
                getW(wR, wC, d1 + 1, d2),
                getW(wR, wC, d1 + 2, d2),
                getW(wR, wC, d1 + 3, d2)
              );

              if (${isChannelsLast}) {
                vec4 xValues = vec4(
                  getX(batch, xR, xC, d1),
                  getX(batch, xR, xC, d1 + 1),
                  getX(batch, xR, xC, d1 + 2),
                  getX(batch, xR, xC, d1 + 3)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec4 xValues = vec4(
                  getX(batch, d1, xR, xC),
                  getX(batch, d1 + 1, xR, xC),
                  getX(batch, d1 + 2, xR, xC),
                  getX(batch, d1 + 3, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }
            }

            if (${inputDepthVec4Remainder === 1}) {

              if (${isChannelsLast}) {
                dotProd +=
                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *
                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
              } else {
                dotProd +=
                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *
                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
              }

            } else if (${inputDepthVec4Remainder === 2}) {
              vec2 wValues = vec2(
                getW(wR, wC, ${inputDepthNearestVec4}, d2),
                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)
              );

              if (${isChannelsLast}) {
                vec2 xValues = vec2(
                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec2 xValues = vec2(
                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            } else if (${inputDepthVec4Remainder === 3}) {
              vec3 wValues = vec3(
                getW(wR, wC, ${inputDepthNearestVec4}, d2),
                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),
                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)
              );

              if (${isChannelsLast}) {
                vec3 xValues = vec3(
                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),
                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec3 xValues = vec3(
                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),
                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            }
          }
        }

        float result = dotProd;
        ${addBiasSnippet}
        ${applyActivationSnippet}
        setOutput(result);
      }
    `;
    }
  };
  var Conv3DProgram = class {
    constructor(convInfo) {
      this.variableNames = ["x", "W"];
      this.outputShape = convInfo.outShape;
      const padFront = convInfo.padInfo.front;
      const padTop = convInfo.padInfo.top;
      const padLeft = convInfo.padInfo.left;
      const strideDepth = convInfo.strideDepth;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const dilationDepth = convInfo.dilationDepth;
      const dilationHeight = convInfo.dilationHeight;
      const dilationWidth = convInfo.dilationWidth;
      const filterDepth = convInfo.filterDepth;
      const filterHeight = convInfo.filterHeight;
      const filterWidth = convInfo.filterWidth;
      const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
      const inputDepthVec4Remainder = convInfo.inChannels % 4;
      this.userCode = `
      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d2 = coords.u;

        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xFCorner = xFRCCorner.x;
        int xRCorner = xFRCCorner.y;
        int xCCorner = xFRCCorner.z;

        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
        // y(yF, yR, yC, d2). ? = to be determined. : = across all
        // values in that axis.
        float dotProd = 0.0;
        for (int wF = 0; wF < ${filterDepth}; wF++) {
          int xF = xFCorner + wF * ${dilationDepth};

          if (xF < 0 || xF >= ${convInfo.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${filterHeight}; wR++) {
            int xR = xRCorner + wR * ${dilationHeight};

            if (xR < 0 || xR >= ${convInfo.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${filterWidth}; wC++) {
              int xC = xCCorner + wC * ${dilationWidth};

              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                continue;
              }

              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
                vec4 xValues = vec4(
                  getX(batch, xF, xR, xC, d1),
                  getX(batch, xF, xR, xC, d1 + 1),
                  getX(batch, xF, xR, xC, d1 + 2),
                  getX(batch, xF, xR, xC, d1 + 3)
                );
                vec4 wValues = vec4(
                  getW(wF, wR, wC, d1, d2),
                  getW(wF, wR, wC, d1 + 1, d2),
                  getW(wF, wR, wC, d1 + 2, d2),
                  getW(wF, wR, wC, d1 + 3, d2)
                );

                dotProd += dot(xValues, wValues);
              }

              if (${inputDepthVec4Remainder === 1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *
                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);
              } else if (${inputDepthVec4Remainder === 2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${inputDepthVec4Remainder === 3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),
                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var Im2ColPackedProgram = class {
    constructor(outputShape, inputShape, convInfo) {
      this.variableNames = ["A"];
      this.packedInputs = true;
      this.packedOutput = true;
      this.outputShape = outputShape;
      const {filterWidth, inChannels, strideWidth, strideHeight, padInfo, outWidth, dilationWidth, dilationHeight, dataFormat} = convInfo;
      const {left, top} = padInfo;
      const itemsPerBlockRow = inChannels * filterWidth;
      const glsl = getGlslDifferences();
      const isChannelsLast = dataFormat === "channelsLast";
      const rowDim = isChannelsLast ? 0 : 1;
      const colDim = isChannelsLast ? 1 : 2;
      let unrolled = ``;
      for (let row = 0; row <= 1; row++) {
        for (let col = 0; col <= 1; col++) {
          unrolled += `
          blockIndex = rc.y + ${col};
          pos = rc.x + ${row};

          if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {
            offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${top};
            d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});

            if(d0 < ${inputShape[rowDim]} && d0 >= 0) {

              offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${strideWidth}. - ${left}.);
              d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${itemsPerBlockRow}.) / ${inChannels}.));

              if(d1 < ${inputShape[colDim]} && d1 >= 0) {

                ch = int(mod(float(pos), ${inChannels}.));

                if (${isChannelsLast}) {
                  innerDims = vec2(d1, ch);
                  result[${row * 2 + col}] = getChannel(
                    getA(d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${row * 2 + col}] = getChannel(
                    getA(ch, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                }
              }
            }
          }
        `;
        }
      }
      this.userCode = `
      void main() {
        ivec2 rc = getOutputCoords();

        vec4 result = vec4(0);

        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
        vec2 innerDims;

        ${unrolled}

        ${glsl.output} = result;
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv2dByMatMul({x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null}) {
    const xShape = x.shape;
    const xTexData = backend2.texData.get(x.dataId);
    const sharedMatMulDim = convInfo.inChannels;
    const outerShapeX = xShape[0] * xShape[1] * xShape[2];
    const outerShapeFilter = convInfo.outChannels;
    const isChannelsLast = convInfo.dataFormat === "channelsLast";
    const transposeA = false;
    const transposeB = false;
    let out;
    const intermediates = [];
    const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) && sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;
    const reshapeWillBeExpensive = xShape[2] % 2 !== 0 && !!xTexData.isPacked;
    if (batchMatMulWillBeUnpacked || !env().getBool("WEBGL_LAZILY_UNPACK") || !env().getBool("WEBGL_PACK_BINARY_OPERATIONS") || !reshapeWillBeExpensive) {
      const targetShape = isChannelsLast ? xShape[0] * xShape[1] * xShape[2] : xShape[0] * xShape[2] * xShape[3];
      const xReshaped = reshape4({
        inputs: {x},
        backend: backend2,
        attrs: {shape: [1, targetShape, convInfo.inChannels]}
      });
      const filterReshaped = reshape4({
        inputs: {x: filter},
        backend: backend2,
        attrs: {shape: [1, convInfo.inChannels, convInfo.outChannels]}
      });
      const result = batchMatMulImpl({
        a: xReshaped,
        b: filterReshaped,
        transposeA,
        transposeB,
        backend: backend2,
        bias,
        activation: activation2,
        preluActivationWeights,
        leakyreluAlpha
      });
      out = reshape4({inputs: {x: result}, backend: backend2, attrs: {shape: convInfo.outShape}});
      intermediates.push(xReshaped);
      intermediates.push(filterReshaped);
      intermediates.push(result);
    } else {
      const targetShape = isChannelsLast ? xShape[0] * xShape[1] * (xShape[2] + 1) : xShape[0] * xShape[2] * (xShape[3] + 1);
      const xReshaped = {
        dataId: x.dataId,
        shape: [1, targetShape, convInfo.inChannels],
        dtype: x.dtype
      };
      const originalXTexDataShape = xTexData.shape;
      xTexData.shape = xTexData.shape.slice();
      xTexData.shape[xTexData.shape.length - 2]++;
      util_exports.assert(isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);
      const filterReshaped = reshape4({
        inputs: {x: filter},
        backend: backend2,
        attrs: {shape: [1, convInfo.inChannels, convInfo.outChannels]}
      });
      intermediates.push(filterReshaped);
      const pointwiseConv = batchMatMulImpl({
        a: xReshaped,
        b: filterReshaped,
        backend: backend2,
        transposeA,
        transposeB,
        bias,
        activation: activation2,
        preluActivationWeights,
        leakyreluAlpha
      });
      const pointwiseConvTexData = backend2.texData.get(pointwiseConv.dataId);
      util_exports.assert(pointwiseConvTexData.isPacked, () => "batchMatMul result is expected to be packed");
      xTexData.shape = originalXTexDataShape;
      pointwiseConvTexData.shape = convInfo.outShape;
      out = identity3({inputs: {x: pointwiseConv}, backend: backend2});
      out.shape = convInfo.outShape;
      intermediates.push(pointwiseConv);
    }
    for (const i of intermediates) {
      backend2.disposeIntermediateTensorInfo(i);
    }
    return out;
  }
  function conv2dWithIm2Row({x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null}) {
    const {filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat} = convInfo;
    const isChannelsLast = dataFormat === "channelsLast";
    const sharedDim = filterWidth * filterHeight * inChannels;
    const numCols = outHeight * outWidth;
    const x2ColShape = [sharedDim, numCols];
    const transposeA = true;
    const transposeB = false;
    const intermediates = [];
    const xSqueezed = reshape4({inputs: {x}, backend: backend2, attrs: {shape: x.shape.slice(1)}});
    const w2Row = reshape4({
      inputs: {x: filter},
      backend: backend2,
      attrs: {shape: [1, sharedDim, util_exports.sizeFromShape(filter.shape) / sharedDim]}
    });
    intermediates.push(xSqueezed);
    intermediates.push(w2Row);
    const im2ColProgram = new Im2ColPackedProgram(x2ColShape, xSqueezed.shape, convInfo);
    const im2Col = backend2.runWebGLProgram(im2ColProgram, [xSqueezed], "float32");
    const im2ColReshaped = reshape4({
      inputs: {x: im2Col},
      backend: backend2,
      attrs: {shape: [1, x2ColShape[0], x2ColShape[1]]}
    });
    intermediates.push(im2Col);
    intermediates.push(im2ColReshaped);
    const hasBias = bias != null;
    const hasPreluActivationWeights = preluActivationWeights != null;
    const hasLeakyreluAlpha = activation2 === "leakyrelu";
    const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, true) : null;
    const matmulProgram = new MatMulPackedProgram(im2ColReshaped.shape, w2Row.shape, [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
    const inputs = [im2ColReshaped, w2Row];
    if (bias) {
      inputs.push(bias);
    }
    if (hasPreluActivationWeights) {
      inputs.push(preluActivationWeights);
    }
    if (hasLeakyreluAlpha) {
      const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
      inputs.push($leakyreluAlpha);
      intermediates.push($leakyreluAlpha);
    }
    const product = backend2.runWebGLProgram(matmulProgram, inputs, "float32");
    const outShape = isChannelsLast ? [1, outHeight, outWidth, convInfo.outChannels] : [1, convInfo.outChannels, outHeight, outWidth];
    const out = reshape4({inputs: {x: product}, backend: backend2, attrs: {shape: outShape}});
    intermediates.push(product);
    for (const i of intermediates) {
      backend2.disposeIntermediateTensorInfo(i);
    }
    return out;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv2d4(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, filter} = inputs;
    const {strides, pad: pad3, dataFormat, dilations, dimRoundingMode} = attrs;
    const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
    const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);
    let out;
    if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) {
      out = conv2dByMatMul({x, filter, convInfo, backend: backend2});
    } else if (env().getBool("WEBGL_CONV_IM2COL") && x.shape[0] === 1) {
      out = conv2dWithIm2Row({x, filter, convInfo, backend: backend2});
    } else {
      const program = new Conv2DProgram(convInfo);
      out = backend2.runWebGLProgram(program, [x, filter], "float32");
    }
    const outReshaped = reshape4({inputs: {x: out}, backend: backend2, attrs: {shape: convInfo.outShape}});
    backend2.disposeIntermediateTensorInfo(out);
    return outReshaped;
  }
  var conv2DConfig2 = {
    kernelName: Conv2D,
    backendName: "webgl",
    kernelFunc: conv2d4
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var Conv2DDerFilterProgram = class {
    constructor(convInfo) {
      this.variableNames = ["x", "dy"];
      this.outputShape = convInfo.filterShape;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const padTop = convInfo.padInfo.top;
      const padLeft = convInfo.padInfo.left;
      const isChannelsLast = convInfo.dataFormat === "channelsLast";
      this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int d2 = coords.w;

        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int b = 0; b < ${convInfo.batchSize}; b++) {
          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
            int xR = wR + yR * ${strideHeight} - ${padTop};

            if (xR < 0 || xR >= ${convInfo.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
              int xC = wC + yC * ${strideWidth} - ${padLeft};

              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                continue;
              }

              if (${isChannelsLast}) {
                float dyValue = getDy(b, yR, yC, d2);
                float xValue = getX(b, xR, xC, d1);
                dotProd += (xValue * dyValue);
              } else {
                float dyValue = getDy(b, d2, yR, yC);
                float xValue = getX(b, d1, xR, xC);
                dotProd += (xValue * dyValue);
              }

            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
  };
  var Conv2DDerInputProgram = class {
    constructor(convInfo) {
      this.variableNames = ["dy", "W"];
      this.outputShape = convInfo.inShape;
      const filterHeight = convInfo.filterHeight;
      const filterWidth = convInfo.filterWidth;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const isChannelsLast = convInfo.dataFormat === "channelsLast";
      const padTop = filterHeight - 1 - convInfo.padInfo.top;
      const padLeft = filterWidth - 1 - convInfo.padInfo.left;
      const rowDim = isChannelsLast ? 1 : 2;
      const colDim = isChannelsLast ? 2 : 3;
      const channelDim = isChannelsLast ? 3 : 1;
      this.userCode = `
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[${channelDim}];

        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${filterHeight}; wR++) {
          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${filterHeight} - 1 - wR;

          for (int wC = 0; wC < ${filterWidth}; wC++) {
            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${filterWidth} - 1 - wC;

            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {

              if (${isChannelsLast}) {
                float xValue = getDy(batch, idyR, idyC, d2);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              } else {
                float xValue = getDy(batch, d2, idyR, idyC);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }

            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
  };
  var Conv3DDerFilterProgram = class {
    constructor(convInfo) {
      this.variableNames = ["x", "dy"];
      this.outputShape = convInfo.filterShape;
      const strideDepth = convInfo.strideDepth;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const padFront = convInfo.padInfo.front;
      const padTop = convInfo.padInfo.top;
      const padLeft = convInfo.padInfo.left;
      this.userCode = `
      void main() {
        ivec5 coords = getOutputCoords();
        int wF = coords.x;
        int wR = coords.y;
        int wC = coords.z;
        int d1 = coords.w;
        int d2 = coords.u;

        float dotProd = 0.0;

        for (int b = 0; b < ${convInfo.batchSize}; b++) {
          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {
            int xF = wF + yF * ${strideDepth} - ${padFront};

            if (xF < 0 || xF >= ${convInfo.inDepth}) {
              continue;
            }

            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
              int xR = wR + yR * ${strideHeight} - ${padTop};

              if (xR < 0 || xR >= ${convInfo.inHeight}) {
                continue;
              }

              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
                int xC = wC + yC * ${strideWidth} - ${padLeft};

                if (xC < 0 || xC >= ${convInfo.inWidth}) {
                  continue;
                }

                float dyValue = getDy(b, yF, yR, yC, d2);
                float xValue = getX(b, xF, xR, xC, d1);
                dotProd += (xValue * dyValue);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
  };
  var Conv3DDerInputProgram = class {
    constructor(convInfo) {
      this.variableNames = ["dy", "W"];
      this.outputShape = convInfo.inShape;
      const filterDepth = convInfo.filterDepth;
      const filterHeight = convInfo.filterHeight;
      const filterWidth = convInfo.filterWidth;
      const strideDepth = convInfo.strideDepth;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const padFront = filterDepth - 1 - convInfo.padInfo.front;
      const padTop = filterHeight - 1 - convInfo.padInfo.top;
      const padLeft = filterWidth - 1 - convInfo.padInfo.left;
      this.userCode = `
      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.u;


        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyFCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        float dotProd = 0.0;
        for (int wF = 0; wF < ${filterDepth}; wF++) {
          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;

          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {
            continue;
          }
          int idyF = int(dyF);

          int wFPerm = ${filterDepth} - 1 - wF;

          for (int wR = 0; wR < ${filterHeight}; wR++) {
            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
              fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            int wRPerm = ${filterHeight} - 1 - wR;

            for (int wC = 0; wC < ${filterWidth}; wC++) {
              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              int wCPerm = ${filterWidth} - 1 - wC;

              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv2DBackpropFilter3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, dy} = inputs;
    const {strides, pad: pad3, dataFormat, dimRoundingMode, filterShape} = attrs;
    const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
    const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);
    const program = new Conv2DDerFilterProgram(convInfo);
    return backend2.runWebGLProgram(program, [x, dy], "float32");
  }
  var conv2DBackpropFilterConfig2 = {
    kernelName: Conv2DBackpropFilter,
    backendName: "webgl",
    kernelFunc: conv2DBackpropFilter3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv2DBackpropInput3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {dy, filter} = inputs;
    const {inputShape, strides, pad: pad3, dataFormat, dimRoundingMode} = attrs;
    const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
    const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);
    const program = new Conv2DDerInputProgram(convInfo);
    return backend2.runWebGLProgram(program, [dy, filter], "float32");
  }
  var conv2DBackpropInputConfig2 = {
    kernelName: Conv2DBackpropInput,
    backendName: "webgl",
    kernelFunc: conv2DBackpropInput3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv3D2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, filter} = inputs;
    const {strides, pad: pad3, dilations} = attrs;
    const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3);
    const program = new Conv3DProgram(convInfo);
    return backend2.runWebGLProgram(program, [x, filter], "float32");
  }
  var conv3DConfig2 = {
    kernelName: Conv3D,
    backendName: "webgl",
    kernelFunc: conv3D2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv3DBackpropFilterV22(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, dy} = inputs;
    const {strides, pad: pad3, filterShape} = attrs;
    const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3);
    const program = new Conv3DDerFilterProgram(convInfo);
    return backend2.runWebGLProgram(program, [x, dy], "float32");
  }
  var conv3DBackpropFilterV2Config2 = {
    kernelName: Conv3DBackpropFilterV2,
    backendName: "webgl",
    kernelFunc: conv3DBackpropFilterV22
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function conv3DBackpropInput2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {dy, filter} = inputs;
    const {pad: pad3, strides, inputShape} = attrs;
    const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3);
    const program = new Conv3DDerInputProgram(convInfo);
    return backend2.runWebGLProgram(program, [dy, filter], "float32");
  }
  var conv3DBackpropInputConfig = {
    kernelName: Conv3DBackpropInputV2,
    backendName: "webgl",
    kernelFunc: conv3DBackpropInput2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var COS = CHECK_NAN_SNIPPET_UNARY + `
  return cos(x);
`;
  var cos3 = unaryKernelFunc2({opSnippet: COS});
  var cosConfig2 = {
    kernelName: Cos,
    backendName: "webgl",
    kernelFunc: cos3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var COSH = `
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`;
  var cosh3 = unaryKernelFunc2({opSnippet: COSH});
  var coshConfig2 = {
    kernelName: Cosh,
    backendName: "webgl",
    kernelFunc: cosh3
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var CropAndResizeProgram = class {
    constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {
      this.variableNames = ["Image", "Boxes", "BoxInd"];
      this.outputShape = [];
      const [batch, imageHeight, imageWidth, depth] = imageShape;
      const [numBoxes] = boxShape;
      const [cropHeight, cropWidth] = cropSize;
      this.outputShape = [numBoxes, cropHeight, cropWidth, depth];
      const methodId = method === "bilinear" ? 1 : 0;
      const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];
      const [heightRatio, heightScale, inY] = cropHeight > 1 ? [
        `${(imageHeight - 1) / (cropHeight - 1)}`,
        "(y2-y1) * height_ratio",
        `y1*${inputHeightFloat} + float(y)*(height_scale)`
      ] : [
        "0.0",
        "0.0",
        `0.5 * (y1+y2) * ${inputHeightFloat}`
      ];
      const [widthRatio, widthScale, inX] = cropWidth > 1 ? [
        `${(imageWidth - 1) / (cropWidth - 1)}`,
        "(x2-x1) * width_ratio",
        `x1*${inputWidthFloat} + float(x)*(width_scale)`
      ] : [
        "0.0",
        "0.0",
        `0.5 * (x1+x2) * ${inputWidthFloat}`
      ];
      this.userCode = `
      const float height_ratio = float(${heightRatio});
      const float width_ratio = float(${widthRatio});
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int y = coords[1];
        int x = coords[2];
        int d = coords[3];

        // get box vals
        float y1 = getBoxes(b,0);
        float x1 = getBoxes(b,1);
        float y2 = getBoxes(b,2);
        float x2 = getBoxes(b,3);

        // get image in batch index
        int bInd = round(getBoxInd(b));
        if(bInd < 0 || bInd >= ${batch}) {
          return;
        }

        float height_scale = ${heightScale};
        float width_scale = ${widthScale};

        float in_y = ${inY};
        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {
          setOutput(float(${extrapolationValue}));
          return;
        }
        float in_x = ${inX};
        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {
          setOutput(float(${extrapolationValue}));
          return;
        }

        vec2 sourceFracIndexCR = vec2(in_x,in_y);
        if(${methodId} == 1) {
          // Compute the four integer indices.
          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));

          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);

          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);

          float top = topLeft + (topRight - topLeft) * fracCR.x;
          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          float newValue = top + (bottom - top) * fracCR.y;
          setOutput(newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          ivec2 sourceNearestCR = ivec2(floor(
            sourceFracIndexCR + vec2(0.5,0.5)));
          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutput(newValue);
        }
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var cropAndResize3 = (args) => {
    const {inputs, backend: backend2, attrs} = args;
    const {image: image3, boxes, boxInd} = inputs;
    const {cropSize, method, extrapolationValue} = attrs;
    const program = new CropAndResizeProgram(image3.shape, boxes.shape, cropSize, method, extrapolationValue);
    return backend2.runWebGLProgram(program, [image3, boxes, boxInd], "float32");
  };
  var cropAndResizeConfig2 = {
    kernelName: CropAndResize,
    backendName: "webgl",
    kernelFunc: cropAndResize3
  };
  var CumSumProgram = class {
    constructor(shape, exclusive, reverse5) {
      this.variableNames = ["x"];
      this.outputShape = shape;
      const rank = shape.length;
      const val = exclusive ? "0.0" : `getX(${getCoords2(rank, "coords")})`;
      const length = shape[shape.length - 1];
      let condition = "";
      let idxString = "";
      if (exclusive) {
        condition = reverse5 ? `end != ${length - 1}` : "end != 0";
        idxString = reverse5 ? "end + 1" : "end - 1";
      } else {
        condition = reverse5 ? `end + pow2 < ${length}` : "end >= pow2";
        idxString = reverse5 ? "end + pow2" : "end - pow2";
      }
      this.userCode = `
      uniform float index;
      void main() {
        ${getCoordsDataType(rank)} coords = getOutputCoords();
        int end = ${getFinalCoord(rank, "coords")};
        float val = ${val};
        int pow2 = int(pow(2.0, index));
        if (${condition}) {
          int idx = ${idxString};
          ${getFinalCoord(rank, "coords")} = idx;
          val += getX(${getCoords2(rank, "coords")});
        }
        setOutput(val);
      }
    `;
    }
    getCustomSetupFunc(index) {
      return (gpgpu, webGLProgram) => {
        if (this.index == null) {
          this.index = gpgpu.getUniformLocation(webGLProgram, "index");
        }
        gpgpu.gl.uniform1f(this.index, index);
      };
    }
  };
  function getCoords2(rank, name) {
    if (rank === 1) {
      return `${name}`;
    } else if (rank === 2) {
      return `${name}.x, ${name}.y`;
    } else if (rank === 3) {
      return `${name}.x, ${name}.y, ${name}.z`;
    } else if (rank === 4) {
      return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;
    } else {
      throw Error(`Cumulative sum for rank ${rank} is not yet supported`);
    }
  }
  function getFinalCoord(rank, name) {
    if (rank === 1) {
      return `${name}`;
    } else if (rank === 2) {
      return `${name}.y`;
    } else if (rank === 3) {
      return `${name}.z`;
    } else if (rank === 4) {
      return `${name}.w`;
    } else {
      throw Error(`Cumulative sum for rank ${rank} is not yet supported`);
    }
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function cumsum3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis, exclusive, reverse: reverse5} = attrs;
    const xRank = x.shape.length;
    const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
    let permutedX = x;
    if (permutation != null) {
      permutedX = transpose3({inputs: {x}, backend: backend2, attrs: {perm: permutation}});
    }
    const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
    if (permutedAxis !== xRank - 1) {
      throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.shape.length - 1} but got axis=${axis}`);
    }
    const size = permutedX.shape[permutedAxis];
    let result = identity3({inputs: {x: permutedX}, backend: backend2});
    for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {
      const program = new CumSumProgram(permutedX.shape, false, reverse5);
      const customSetup = program.getCustomSetupFunc(i);
      const prevResult = result;
      result = backend2.runWebGLProgram(program, [result], result.dtype, customSetup);
      backend2.disposeIntermediateTensorInfo(prevResult);
    }
    if (exclusive) {
      const program = new CumSumProgram(permutedX.shape, exclusive, reverse5);
      const prevResult = result;
      result = backend2.runWebGLProgram(program, [result], result.dtype);
      backend2.disposeIntermediateTensorInfo(prevResult);
    }
    if (permutation != null) {
      const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);
      const reverseTransposedResult = transpose3({inputs: {x: result}, backend: backend2, attrs: {perm: reversePermutation}});
      backend2.disposeIntermediateTensorInfo(result);
      backend2.disposeIntermediateTensorInfo(permutedX);
      return reverseTransposedResult;
    }
    return result;
  }
  var cumsumConfig2 = {
    kernelName: Cumsum,
    backendName: "webgl",
    kernelFunc: cumsum3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function denseBincount3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, weights} = inputs;
    const {size, binaryOutput} = attrs;
    if (x.shape.length === 1) {
      const xVals = backend2.readSync(x.dataId);
      const weightsVals = backend2.readSync(weights.dataId);
      const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);
      return backend2.makeTensorInfo([size], weights.dtype, outVals);
    } else if (x.shape.length === 2) {
      const xBuf = backend2.bufferSync(x);
      const weightsBuf = backend2.bufferSync(weights);
      const outBuf = bincountReduceImplCPU(xBuf, weightsBuf, size, binaryOutput);
      return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);
    }
    throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);
  }
  var denseBincountConfig2 = {
    kernelName: DenseBincount,
    backendName: "webgl",
    kernelFunc: denseBincount3
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var DepthToSpaceProgram = class {
    constructor(outputShape, blockSize, dataFormat) {
      this.variableNames = ["x"];
      this.outputShape = [];
      this.outputShape = outputShape;
      this.blockSize = blockSize;
      this.dataFormat = dataFormat;
      this.userCode = `
    void main() {
      ivec4 coords = getOutputCoords();
      int b = coords[0];
      int h = ${this.getHeightCoordString()};
      int w = ${this.getWidthCoordString()};
      int d = ${this.getDepthCoordString()};

      int in_h = h / ${blockSize};
      int offset_h = imod(h, ${blockSize});
      int in_w = w / ${blockSize};
      int offset_w = imod(w, ${blockSize});
      int offset_d = (offset_h * ${blockSize} + offset_w) *
        ${this.getOutputDepthSize()};
      int in_d = d + offset_d;

      float result = ${this.getInputSamplingString()};
      setOutput(result);
    }
  `;
    }
    getHeightCoordString() {
      if (this.dataFormat === "NHWC") {
        return `coords[1]`;
      } else {
        return `coords[2]`;
      }
    }
    getWidthCoordString() {
      if (this.dataFormat === "NHWC") {
        return `coords[2]`;
      } else {
        return `coords[3]`;
      }
    }
    getDepthCoordString() {
      if (this.dataFormat === "NHWC") {
        return `coords[3]`;
      } else {
        return `coords[1]`;
      }
    }
    getOutputDepthSize() {
      if (this.dataFormat === "NHWC") {
        return this.outputShape[3];
      } else {
        return this.outputShape[1];
      }
    }
    getInputSamplingString() {
      if (this.dataFormat === "NHWC") {
        return `getX(b, in_h, in_w, in_d)`;
      } else {
        return `getX(b, in_d, in_h, in_w)`;
      }
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function depthToSpace3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {blockSize, dataFormat} = attrs;
    util_exports.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);
    const batchSize = x.shape[0];
    const inputHeight = dataFormat === "NHWC" ? x.shape[1] : x.shape[2];
    const inputWidth = dataFormat === "NHWC" ? x.shape[2] : x.shape[3];
    const inputDepth = dataFormat === "NHWC" ? x.shape[3] : x.shape[1];
    const outputHeight = inputHeight * blockSize;
    const outputWidth = inputWidth * blockSize;
    const outputDepth = inputDepth / (blockSize * blockSize);
    const outputShape = dataFormat === "NHWC" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];
    const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);
    return backend2.runWebGLProgram(program, [x], x.dtype);
  }
  var depthToSpaceConfig2 = {
    kernelName: DepthToSpace,
    backendName: "webgl",
    kernelFunc: depthToSpace3
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var DepthwiseConv2DProgram = class {
    constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {
      this.variableNames = ["x", "W"];
      this.outputShape = convInfo.outShape;
      const xNumRows = convInfo.inHeight;
      const xNumCols = convInfo.inWidth;
      const padTop = convInfo.padInfo.top;
      const padLeft = convInfo.padInfo.left;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const dilationHeight = convInfo.dilationHeight;
      const dilationWidth = convInfo.dilationWidth;
      const filterHeight = convInfo.filterHeight;
      const filterWidth = convInfo.filterWidth;
      const channelMul = convInfo.outChannels / convInfo.inChannels;
      let activationSnippet = "", applyActivationSnippet = "";
      if (activation2) {
        if (hasPreluActivation) {
          activationSnippet = `float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${activation2}
        }`;
        } else if (hasLeakyReluAlpha) {
          activationSnippet = `float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${activation2}
        }`;
        } else {
          activationSnippet = `
          float activation(float x) {
            ${activation2}
          }
        `;
        }
        applyActivationSnippet = `result = activation(result);`;
      }
      const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
      if (addBias) {
        this.variableNames.push("bias");
      }
      if (hasPreluActivation) {
        this.variableNames.push("preluActivationWeights");
      }
      if (hasLeakyReluAlpha) {
        this.variableNames.push("leakyreluAlpha");
      }
      this.userCode = `
      ${activationSnippet}

      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${channelMul};
        int q = d2 - d1 * ${channelMul};

        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
        for (int wR = 0; wR < ${filterHeight}; wR++) {
          int xR = xRCorner + wR * ${dilationHeight};

          if (xR < 0 || xR >= ${xNumRows}) {
            continue;
          }

          for (int wC = 0; wC < ${filterWidth}; wC++) {
            int xC = xCCorner + wC * ${dilationWidth};

            if (xC < 0 || xC >= ${xNumCols}) {
              continue;
            }

            float xVal = getX(batch, xR, xC, d1);
            float wVal = getW(wR, wC, d1, q);
            dotProd += xVal * wVal;
          }
        }

        float result = dotProd;
        ${addBiasSnippet}
        ${applyActivationSnippet}
        setOutput(result);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var DepthwiseConvPacked2DProgram = class {
    constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {
      this.variableNames = ["x", "W"];
      this.packedInputs = true;
      this.packedOutput = true;
      this.outputShape = convInfo.outShape;
      const xNumRows = convInfo.inHeight;
      const xNumCols = convInfo.inWidth;
      const padTop = convInfo.padInfo.top;
      const padLeft = convInfo.padInfo.left;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const dilationHeight = convInfo.dilationHeight;
      const dilationWidth = convInfo.dilationWidth;
      const filterHeight = convInfo.filterHeight;
      const filterWidth = convInfo.filterWidth;
      const texelsAcross = filterWidth;
      let mainLoop = `int xR; int xC; int xCOffset;`;
      for (let r = 0; r < filterHeight; r++) {
        for (let c = 0; c < filterWidth; c++) {
          mainLoop += `
          vec4 xTexelR${r}C${c * 2} = vec4(0.);
          vec4 wR${r}C${c} = vec4(0.);
          vec4 xR${r}C${c} = vec4(0.);`;
        }
      }
      for (let r = 0; r < filterHeight; r++) {
        for (let texelC = 0; texelC < texelsAcross; texelC++) {
          const c = texelC * 2;
          mainLoop += `
          xR = xRCorner + ${r * dilationHeight};
          xC = xCCorner + ${c * dilationWidth};
        `;
          if (strideWidth === 1) {
            if (c < filterWidth) {
              if (padLeft % 2 === 1) {
                mainLoop += `
                xCOffset = xC + 1;
                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {
                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if(xCOffset + 1 >= ${xNumCols}) {
                    xTexelR${r}C${c}.zw = vec2(0.);
                  }
                } else {
                  xTexelR${r}C${c} = vec4(0.);
                }

                xCOffset = xC + 1 - 2;
                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {
                  vec4 previous = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if(xCOffset + 1 >= ${xNumCols}) {
                    previous.zw = vec2(0.);
                  }

                  xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);
                } else {
                  xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);
                }
              `;
              } else {
                mainLoop += `
                if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {
                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);
                } else {
                  xTexelR${r}C${c} = vec4(0.);
                }

                xR${r}C${c} = xTexelR${r}C${c};
              `;
              }
              if (c + 1 < filterWidth) {
                const nextTexelOffset = padLeft % 2 === 0 ? util_exports.nearestLargerEven(dilationWidth) : dilationWidth;
                if (dilationWidth % 2 === 0 && padLeft % 2 === 1 || dilationWidth % 2 !== 0 && padLeft % 2 !== 1) {
                  mainLoop += `
                  xCOffset = xC + ${padLeft % 2} + ${nextTexelOffset};

                  if(xR >= 0 && xR < ${xNumRows} &&
                    xCOffset >= 0 && xCOffset < ${xNumCols}) {
                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);
                  }
                `;
                  if (dilationWidth > 1) {
                    mainLoop += `
                    xCOffset -= 2;
                    if(xR >= 0 && xR < ${xNumRows} &&
                      xCOffset >= 0 && xCOffset < ${xNumCols}) {
                      xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
                    } else {
                      xTexelR${r}C${c} = vec4(0.);
                    }
                  `;
                  }
                  mainLoop += `
                  xR${r}C${c + 1} = vec4(
                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.xy);
                `;
                } else {
                  mainLoop += `
                  xCOffset = xC + ${nextTexelOffset};

                  if(xR >= 0 && xR < ${xNumRows} &&
                    xCOffset >= 0 && xCOffset < ${xNumCols}) {
                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);
                  }

                  xR${r}C${c + 1} = xTexelR${r}C${c + 2};
                `;
                }
              }
            }
          } else {
            if (c < filterWidth) {
              mainLoop += `
              if(xR >= 0 && xR < ${xNumRows}) {
            `;
              if (padLeft % 2 === 1) {
                mainLoop += `
                xCOffset = xC + 1 - ${strideWidth};
                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
                } else {
                  xTexelR${r}C${c} = vec4(0.);
                }

                if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {
                  xTexelR${r}C${c + 2} = getX(batch, xR, xC + 1, d1);
                } else {
                  xTexelR${r}C${c + 2} = vec4(0.);
                }

                xR${r}C${c} = vec4(
                  xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);
              `;
                if (c + 1 < filterWidth) {
                  mainLoop += `
                  vec4 final = vec4(0.);
                  xCOffset = xC + 1 + ${strideWidth};
                  if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xR${r}C${c + 1} = vec4(xTexelR${r}C${c + 2}.xy, final.xy);
                `;
                }
              } else {
                mainLoop += `
                if(xC >= 0 && xC < ${xNumCols}) {
                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);
                } else {
                  xTexelR${r}C${c} = vec4(0.);
                }

                xCOffset = xC + ${strideWidth};
                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
                  xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);
                } else {
                  xTexelR${r}C${c + 2} = vec4(0.);
                }

                xR${r}C${c} = vec4(
                  xTexelR${r}C${c}.xy, xTexelR${r}C${c + 2}.xy);
              `;
                if (c + 1 < filterWidth) {
                  mainLoop += `
                  xR${r}C${c + 1} = vec4(
                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);
                `;
                }
              }
              mainLoop += `}`;
            }
          }
          if (c < filterWidth) {
            mainLoop += `
            vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);
            wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);
          `;
            if (c + 1 < filterWidth) {
              mainLoop += `
              vec4 wTexelR${r}C${c + 1} = getW(${r}, ${c + 1}, d1, q);
              wR${r}C${c + 1} =
                vec4(wTexelR${r}C${c + 1}.xz, wTexelR${r}C${c + 1}.xz);`;
            }
          }
        }
      }
      for (let r = 0; r < filterHeight; r++) {
        for (let c = 0; c < filterWidth; c++) {
          mainLoop += `dotProd += xR${r}C${c} * wR${r}C${c};`;
        }
      }
      let activationSnippet = "", applyActivationSnippet = "";
      if (activation2) {
        if (hasPreluActivation) {
          activationSnippet = `vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${activation2}
        }`;
        } else if (hasLeakyReluAlpha) {
          activationSnippet = `vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${activation2}
        }`;
        } else {
          activationSnippet = `vec4 activation(vec4 x) {
          ${activation2}
        }`;
        }
        applyActivationSnippet = `result = activation(result);`;
      }
      const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
      if (addBias) {
        this.variableNames.push("bias");
      }
      if (hasPreluActivation) {
        this.variableNames.push("preluActivationWeights");
      }
      if (hasLeakyReluAlpha) {
        this.variableNames.push("leakyreluAlpha");
      }
      this.userCode = `
      ${activationSnippet}

      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {

        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2;
        int q = 0;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        vec4 dotProd = vec4(0.);

        ${mainLoop}

        vec4 result = dotProd;
        ${addBiasSnippet}
        ${applyActivationSnippet}
        setOutput(result);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function depthwiseConv2dNative2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, filter} = inputs;
    const {strides, pad: pad3, dilations, dimRoundingMode} = attrs;
    let $dilations = dilations;
    if ($dilations == null) {
      $dilations = [1, 1];
    }
    util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);
    const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);
    let program;
    if (env().getBool("WEBGL_PACK_DEPTHWISECONV") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1) {
      program = new DepthwiseConvPacked2DProgram(convInfo);
    } else {
      program = new DepthwiseConv2DProgram(convInfo);
    }
    return backend2.runWebGLProgram(program, [x, filter], "float32");
  }
  var depthwiseConv2dNativeConfig2 = {
    kernelName: DepthwiseConv2dNative,
    backendName: "webgl",
    kernelFunc: depthwiseConv2dNative2
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var DepthwiseConv2DDerFilterProgram = class {
    constructor(convInfo) {
      this.variableNames = ["x", "dy"];
      this.outputShape = convInfo.filterShape;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const padTop = convInfo.padInfo.top;
      const padLeft = convInfo.padInfo.left;
      const channelMul = convInfo.outChannels / convInfo.inChannels;
      this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int dm = coords.w;
        int d2 = d1 * ${channelMul} + dm;

        float dotProd = 0.0;

        // TO DO: Vec4 over the batch size
        for (int b = 0; b < ${convInfo.batchSize}; b++) {
          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
            int xR = wR + yR * ${strideHeight} - ${padTop};

            if (xR < 0 || xR >= ${convInfo.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
              int xC = wC + yC * ${strideWidth} - ${padLeft};

              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                continue;
              }

              float dyValue = getDy(b, yR, yC, d2);
              float xValue = getX(b, xR, xC, d1);
              dotProd += (xValue * dyValue);
            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
  };
  var DepthwiseConv2DDerInputProgram = class {
    constructor(convInfo) {
      this.variableNames = ["dy", "W"];
      this.outputShape = convInfo.inShape;
      const filterHeight = convInfo.filterHeight;
      const filterWidth = convInfo.filterWidth;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const padTop = filterHeight - 1 - convInfo.padInfo.top;
      const padLeft = filterWidth - 1 - convInfo.padInfo.left;
      const channelMul = convInfo.outChannels / convInfo.inChannels;
      this.userCode = `
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[3];
        ivec2 dyCorner = coords.yz - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        float dotProd = 0.0;

        for (int wR = 0; wR < ${filterHeight}; wR++) {
          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${filterHeight} - 1 - wR;

          for (int wC = 0; wC < ${filterWidth}; wC++) {
            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${filterWidth} - 1 - wC;

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${channelMul}; dm++) {
              int d2 = d1 * ${channelMul} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function depthwiseConv2dNativeBackpropFilter3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, dy} = inputs;
    const {strides, dilations, pad: pad3, dimRoundingMode, filterShape} = attrs;
    const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad3, dimRoundingMode, true);
    const program = new DepthwiseConv2DDerFilterProgram(convInfo);
    return backend2.runWebGLProgram(program, [x, dy], "float32");
  }
  var depthwiseConv2dNativeBackpropFilterConfig2 = {
    kernelName: DepthwiseConv2dNativeBackpropFilter,
    backendName: "webgl",
    kernelFunc: depthwiseConv2dNativeBackpropFilter3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function depthwiseConv2dNativeBackpropInput3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {dy, filter} = inputs;
    const {strides, dilations, pad: pad3, dimRoundingMode, inputShape} = attrs;
    const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, true);
    const program = new DepthwiseConv2DDerInputProgram(convInfo);
    return backend2.runWebGLProgram(program, [dy, filter], "float32");
  }
  var depthwiseConv2dNativeBackpropInputConfig2 = {
    kernelName: DepthwiseConv2dNativeBackpropInput,
    backendName: "webgl",
    kernelFunc: depthwiseConv2dNativeBackpropInput3
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var DiagProgram = class {
    constructor(size) {
      this.variableNames = ["X"];
      this.outputShape = [size, size];
      this.userCode = `
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function diag3(args) {
    const {inputs, backend: backend2} = args;
    const {x} = inputs;
    const outShape = [...x.shape, ...x.shape];
    const xSize = util_exports.sizeFromShape(x.shape);
    const flat = reshape4({inputs: {x}, backend: backend2, attrs: {shape: [xSize]}});
    const program = new DiagProgram(xSize);
    const res = backend2.runWebGLProgram(program, [flat], flat.dtype);
    const out = reshape4({inputs: {x: res}, backend: backend2, attrs: {shape: outShape}});
    backend2.disposeIntermediateTensorInfo(flat);
    backend2.disposeIntermediateTensorInfo(res);
    return out;
  }
  var diagConfig2 = {
    kernelName: Diag,
    backendName: "webgl",
    kernelFunc: diag3
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var Dilation2DProgram = class {
    constructor(convInfo) {
      this.variableNames = ["x", "W"];
      this.outputShape = convInfo.outShape;
      const {inHeight, inWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth} = convInfo;
      const {top: padTop, left: padLeft} = padInfo;
      this.userCode = `
      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
      const ivec2 pads = ivec2(${padTop}, ${padLeft});
      const float neg_infinity = -3.4e38;

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.w;
        ivec2 outTopLeftCorner =
            coords.yz * strides - pads;
        int hBeg = outTopLeftCorner.x;
        int wBeg = outTopLeftCorner.y;

        float curVal = neg_infinity;
        for (int h = 0; h < ${filterHeight}; h++) {
          int hIn = hBeg + h * ${dilationHeight};

          if (hIn >= 0 && hIn < ${inHeight}) {
            for (int w = 0; w < ${filterWidth}; w++) {
              int wIn = wBeg + w * ${dilationWidth};

              if (wIn >= 0 && wIn < ${inWidth}) {
                float xVal = getX(batch, hIn, wIn, d1);
                float wVal = getW(h, w, d1);

                float val = xVal + wVal;
                if (val > curVal) {
                  curVal = val;
                }
              }
            }
          }
        }

        float result = curVal;
        setOutput(result);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function dilation2D(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, filter} = inputs;
    const {strides, pad: pad3, dilations} = attrs;
    const convInfo = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations);
    let out;
    const program = new Dilation2DProgram(convInfo);
    out = backend2.runWebGLProgram(program, [x, filter], "float32");
    const outReshaped = reshape4({inputs: {x: out}, backend: backend2, attrs: {shape: convInfo.outShape}});
    backend2.disposeIntermediateTensorInfo(out);
    return outReshaped;
  }
  var dilation2DConfig = {
    kernelName: Dilation2D,
    backendName: "webgl",
    kernelFunc: dilation2D
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ELU4 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;
  var ELU_PACKED = `
  vec4 result;

  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);

  return result;
`;
  var elu5 = unaryKernelFunc2({opSnippet: ELU4, packedOpSnippet: ELU_PACKED});
  var eluConfig2 = {
    kernelName: Elu,
    backendName: "webgl",
    kernelFunc: elu5
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;
  var ELU_DER_PACKED = `
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`;
  var eluGrad2 = (args) => {
    const {inputs, backend: backend2} = args;
    const {dy, y} = inputs;
    const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(ELU_DER_PACKED, dy.shape, y.shape) : new BinaryOpProgram(ELU_DER, dy.shape, y.shape);
    return backend2.runWebGLProgram(program, [dy, y], dy.dtype);
  };
  var eluGradConfig3 = {
    kernelName: EluGrad,
    backendName: "webgl",
    kernelFunc: eluGrad2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var PACKED_EQUAL = `
  return vec4(equal(a, b));
`;
  var EQUAL = `return float(a == b);`;
  var equal3 = binaryKernelFunc2({opSnippet: EQUAL, packedOpSnippet: PACKED_EQUAL, dtype: "bool"});
  var equalConfig2 = {
    kernelName: Equal,
    backendName: "webgl",
    kernelFunc: equal3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ERF = `
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${backend_util_exports.ERF_P};
  float a1 = ${backend_util_exports.ERF_A1};
  float a2 = ${backend_util_exports.ERF_A2};
  float a3 = ${backend_util_exports.ERF_A3};
  float a4 = ${backend_util_exports.ERF_A4};
  float a5 = ${backend_util_exports.ERF_A5};

  float sign = sign(x);
  x = abs(x);
  float t = 1.0 / (1.0 + p * x);
  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`;
  var erf3 = unaryKernelFunc2({opSnippet: ERF});
  var erfConfig2 = {
    kernelName: Erf,
    backendName: "webgl",
    kernelFunc: erf3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var EXP = `return exp(x);`;
  var exp3 = unaryKernelFunc2({opSnippet: EXP, packedOpSnippet: EXP, cpuKernelImpl: expImplCPU});
  var expConfig2 = {
    kernelName: Exp,
    backendName: "webgl",
    kernelFunc: exp3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function expandDims4(args) {
    const {inputs, attrs, backend: backend2} = args;
    const {dim} = attrs;
    const {input: input2} = inputs;
    const inputRank = input2.shape.length;
    const newShape = input2.shape.slice();
    let $dim = dim;
    if (dim < 0) {
      util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);
      $dim = inputRank + dim + 1;
    }
    newShape.splice($dim, 0, 1);
    return reshape4({inputs: {x: input2}, backend: backend2, attrs: {shape: newShape}});
  }
  var expandDimsConfig2 = {
    kernelName: ExpandDims,
    backendName: "webgl",
    kernelFunc: expandDims4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var EXPM1 = `return exp(x) - 1.0;`;
  var expm13 = unaryKernelFunc2({opSnippet: EXPM1, packedOpSnippet: EXPM1, cpuKernelImpl: expm1ImplCPU});
  var expm1Config2 = {
    kernelName: Expm1,
    backendName: "webgl",
    kernelFunc: expm13
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var FFTProgram = class {
    constructor(component, inputShape, inverse) {
      this.variableNames = ["real", "imag"];
      const innerDim = inputShape[1];
      this.outputShape = inputShape;
      const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;
      const resultDenominator = inverse ? `${innerDim}.0` : "1.0";
      let opString;
      if (component === "real") {
        opString = "return real * expR - imag * expI;";
      } else if (component === "imag") {
        opString = "return real * expI + imag * expR;";
      } else {
        throw new Error(`FFT component must be either "real" or "imag", got ${component}.`);
      }
      this.userCode = `
      const float exponentMultiplier = ${exponentMultiplierSnippet};

      float unaryOpComplex(float real, float expR, float imag, float expI) {
        ${opString}
      }

      float mulMatDFT(int batch, int index) {
        float indexRatio = float(index) / float(${innerDim});
        float exponentMultiplierTimesIndexRatio =
            exponentMultiplier * indexRatio;

        float result = 0.0;

        for (int i = 0; i < ${innerDim}; i++) {
          // x = (-2|2 * PI / N) * index * i;
          float x = exponentMultiplierTimesIndexRatio * float(i);
          float expR = cos(x);
          float expI = sin(x);
          float real = getReal(batch, i);
          float imag = getImag(batch, i);

          result +=
              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};
        }

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fftImpl2(x, inverse, backend2) {
    const xData = backend2.texData.get(x.dataId);
    const inputSize = util_exports.sizeFromShape(x.shape);
    const innerDimensionSize = x.shape[x.shape.length - 1];
    const batch = inputSize / innerDimensionSize;
    const input2D = reshape4({inputs: {x}, backend: backend2, attrs: {shape: [batch, innerDimensionSize]}});
    const xShape = input2D.shape;
    const realProgram = new FFTProgram("real", xShape, inverse);
    const imagProgram = new FFTProgram("imag", xShape, inverse);
    const inputs = [
      {
        dataId: xData.complexTensorInfos.real.dataId,
        dtype: xData.complexTensorInfos.real.dtype,
        shape: xShape
      },
      {
        dataId: xData.complexTensorInfos.imag.dataId,
        dtype: xData.complexTensorInfos.imag.dtype,
        shape: xShape
      }
    ];
    const realPart = backend2.runWebGLProgram(realProgram, inputs, "float32");
    const imagPart = backend2.runWebGLProgram(imagProgram, inputs, "float32");
    const complexOutput = complex3({inputs: {real: realPart, imag: imagPart}, backend: backend2});
    backend2.disposeIntermediateTensorInfo(realPart);
    backend2.disposeIntermediateTensorInfo(imagPart);
    const complexOutputReshaped = reshape4({inputs: {x: complexOutput}, backend: backend2, attrs: {shape: x.shape}});
    backend2.disposeIntermediateTensorInfo(input2D);
    backend2.disposeIntermediateTensorInfo(complexOutput);
    return complexOutputReshaped;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fft3(args) {
    const {inputs, backend: backend2} = args;
    const {input: input2} = inputs;
    return fftImpl2(input2, false, backend2);
  }
  var fftConfig2 = {
    kernelName: FFT,
    backendName: "webgl",
    kernelFunc: fft3
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var FillProgram = class {
    constructor(shape, value) {
      this.outputShape = [];
      this.variableNames = ["x"];
      this.outputShape = shape;
      this.userCode = `
      uniform float value;
      void main() {
        // Input can be obtained from uniform value.
        setOutput(value);
      }
    `;
    }
    getCustomSetupFunc(value) {
      return (gpgpu, webGLProgram) => {
        if (this.valueLoc == null) {
          this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, "value");
        }
        gpgpu.gl.uniform1f(this.valueLoc, value);
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fill3(args) {
    const {backend: backend2, attrs} = args;
    const {shape, value} = attrs;
    let {dtype} = attrs;
    dtype = dtype || util_exports.inferDtype(value);
    if (dtype === "string") {
      const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape));
      values.fill(value);
      return backend2.makeTensorInfo(shape, dtype, values);
    } else {
      const program = new FillProgram(shape, value);
      const customSetup = program.getCustomSetupFunc(value);
      return backend2.runWebGLProgram(program, [], dtype, customSetup);
    }
  }
  var fillConfig2 = {
    kernelName: Fill,
    backendName: "webgl",
    kernelFunc: fill3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var FlipLeftRightProgram = class {
    constructor(imageShape) {
      this.variableNames = ["Image"];
      this.outputShape = [];
      const imageWidth = imageShape[2];
      this.outputShape = imageShape;
      this.userCode = `
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];

          int coordX = ${imageWidth} - x;
          float outputValue;
          if(coordX >= 0 && coordX < ${imageWidth}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var flipLeftRightConfig2 = {
    kernelName: FlipLeftRight,
    backendName: "webgl",
    kernelFunc: ({inputs, backend: backend2}) => {
      const {image: image3} = inputs;
      const webglBackend = backend2;
      const program = new FlipLeftRightProgram(image3.shape);
      const output = webglBackend.runWebGLProgram(program, [image3], image3.dtype);
      return output;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var FLOOR = `return floor(x);`;
  var floor3 = unaryKernelFunc2({opSnippet: FLOOR, packedOpSnippet: FLOOR, cpuKernelImpl: floorImplCPU});
  var floorConfig2 = {
    kernelName: Floor,
    backendName: "webgl",
    kernelFunc: floor3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var INT_DIV = `
  float s = sign(a) * sign(b);
  int ia = round(a);
  int ib = round(b);
  if (ib != 0) {
    // Windows (D3D) wants guaranteed non-zero int division at compile-time.
    return float(idiv(ia, ib, s));
  } else {
    return NAN;
  }
`;
  var INT_DIV_PACKED = `
  ivec4 ia = round(a);
  ivec4 ib = round(b);
  bvec4 cond = notEqual(ib, ivec4(0));
  ivec4 result = ivec4(0);
  vec4 s = sign(a) * sign(b);

  // Windows (D3D) wants guaranteed non-zero int division at compile-time.
  if (cond[0]) {
    result[0] = idiv(ia[0], ib[0], s[0]);
  }
  if (cond[1]) {
    result[1] = idiv(ia[1], ib[1], s[1]);
  }
  if (cond[2]) {
    result[2] = idiv(ia[2], ib[2], s[2]);
  }
  if (cond[3]) {
    result[3] = idiv(ia[3], ib[3], s[3]);
  }
  return vec4(result);
`;
  var floorDiv3 = binaryKernelFunc2({opSnippet: INT_DIV, packedOpSnippet: INT_DIV_PACKED, dtype: "int32"});
  var floorDivConfig2 = {
    kernelName: FloorDiv,
    backendName: "webgl",
    kernelFunc: floorDiv3
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var FromPixelsProgram = class {
    constructor(outputShape) {
      this.variableNames = ["A"];
      const glsl = getGlslDifferences();
      const [height, width] = outputShape;
      this.outputShape = outputShape;
      this.userCode = `
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);

        vec4 values = ${glsl.texture2D}(A, uv);
        float value;
        if (depth == 0) {
          value = values.r;
        } else if (depth == 1) {
          value = values.g;
        } else if (depth == 2) {
          value = values.b;
        } else if (depth == 3) {
          value = values.a;
        }

        setOutput(floor(value * 255.0 + 0.5));
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var FromPixelsPackedProgram = class {
    constructor(outputShape) {
      this.variableNames = ["A"];
      this.packedInputs = false;
      this.packedOutput = true;
      const glsl = getGlslDifferences();
      const [height, width] = outputShape;
      this.outputShape = outputShape;
      this.userCode = `
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];

        vec4 result = vec4(0.);

        for(int row=0; row<=1; row++) {
          for(int col=0; col<=1; col++) {
            texC = coords[1] + row;
            depth = coords[2] + col;

            vec2 uv = (vec2(texC, texR) + halfCR) /
                       vec2(${width}.0, ${height}.0);
            vec4 values = ${glsl.texture2D}(A, uv);
            float value;
            if (depth == 0) {
              value = values.r;
            } else if (depth == 1) {
              value = values.g;
            } else if (depth == 2) {
              value = values.b;
            } else if (depth == 3) {
              value = values.a;
            }

            result[row * 2 + col] = floor(value * 255.0 + 0.5);
          }
        }

        ${glsl.output} = result;
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var fromPixelsConfig = {
    kernelName: FromPixels,
    backendName: "webgl",
    kernelFunc: fromPixels2
  };
  var fromPixels2DContext2;
  function fromPixels2(args) {
    const {inputs, backend: backend2, attrs} = args;
    let {pixels} = inputs;
    const {numChannels} = attrs;
    const isVideo = typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement;
    const isImage = typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement;
    const [width, height] = isVideo ? [
      pixels.videoWidth,
      pixels.videoHeight
    ] : [pixels.width, pixels.height];
    const texShape = [height, width];
    const outShape = [height, width, numChannels];
    if (isImage || isVideo) {
      if (fromPixels2DContext2 == null) {
        fromPixels2DContext2 = document.createElement("canvas").getContext("2d");
      }
      fromPixels2DContext2.canvas.width = width;
      fromPixels2DContext2.canvas.height = height;
      fromPixels2DContext2.drawImage(pixels, 0, 0, width, height);
      pixels = fromPixels2DContext2.canvas;
    }
    const tempPixelHandle = backend2.makeTensorInfo(texShape, "int32");
    backend2.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;
    backend2.gpgpu.uploadPixelDataToTexture(backend2.getTexture(tempPixelHandle.dataId), pixels);
    const program = env().getBool("WEBGL_PACK") ? new FromPixelsPackedProgram(outShape) : new FromPixelsProgram(outShape);
    const res = backend2.runWebGLProgram(program, [tempPixelHandle], "int32");
    backend2.disposeData(tempPixelHandle.dataId);
    return res;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fusedConv2d(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, filter, bias, preluActivationWeights} = inputs;
    const {strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha} = attrs;
    const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
    const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);
    let out;
    const intermediates = [];
    if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) {
      out = conv2dByMatMul({
        x,
        filter,
        convInfo,
        backend: backend2,
        bias,
        activation: activation2,
        preluActivationWeights,
        leakyreluAlpha
      });
    } else if (env().getBool("WEBGL_CONV_IM2COL") && x.shape[0] === 1) {
      out = conv2dWithIm2Row({
        x,
        filter,
        convInfo,
        backend: backend2,
        bias,
        activation: activation2,
        preluActivationWeights,
        leakyreluAlpha
      });
    } else {
      const hasBias = bias != null;
      const hasPreluActivationWeights = preluActivationWeights != null;
      const hasLeakyreluAlpha = activation2 === "leakyrelu";
      const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, false) : null;
      const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
      const inputs2 = [x, filter];
      if (bias) {
        inputs2.push(bias);
      }
      if (preluActivationWeights) {
        inputs2.push(preluActivationWeights);
      }
      if (hasLeakyreluAlpha) {
        const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
        inputs2.push($leakyreluAlpha);
        intermediates.push($leakyreluAlpha);
      }
      out = backend2.runWebGLProgram(program, inputs2, "float32");
    }
    const outReshaped = reshape4({inputs: {x: out}, backend: backend2, attrs: {shape: convInfo.outShape}});
    intermediates.push(out);
    intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return outReshaped;
  }
  var fusedConv2DConfig2 = {
    kernelName: FusedConv2D,
    backendName: "webgl",
    kernelFunc: fusedConv2d
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fusedDepthwiseConv2D2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, filter, bias, preluActivationWeights} = inputs;
    const {strides, pad: pad3, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha} = attrs;
    const intermediates = [];
    let $dilations = dilations;
    if ($dilations == null) {
      $dilations = [1, 1];
    }
    util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);
    const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);
    const shouldPackDepthwiseConv = env().getBool("WEBGL_PACK_DEPTHWISECONV") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1;
    const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, shouldPackDepthwiseConv) : null;
    const programInputs = [x, filter];
    const hasBias = bias != null;
    const hasPreluActivationWeights = preluActivationWeights != null;
    const hasLeakyreluAlpha = activation2 === "leakyrelu";
    if (hasBias) {
      programInputs.push(bias);
    }
    if (hasPreluActivationWeights) {
      programInputs.push(preluActivationWeights);
    }
    if (hasLeakyreluAlpha) {
      const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
      programInputs.push($leakyreluAlpha);
      intermediates.push($leakyreluAlpha);
    }
    let program;
    if (shouldPackDepthwiseConv) {
      program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
    } else {
      program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
    }
    const result = backend2.runWebGLProgram(program, programInputs, "float32");
    intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return result;
  }
  var fusedDepthwiseConv2DConfig2 = {
    kernelName: FusedDepthwiseConv2D,
    backendName: "webgl",
    kernelFunc: fusedDepthwiseConv2D2
  };
  var GatherNDProgram = class {
    constructor(sliceDim, strides, shape) {
      this.sliceDim = sliceDim;
      this.strides = strides;
      this.variableNames = ["x", "indices"];
      this.outputShape = shape;
      const stridesType = getCoordsDataType(strides.length);
      const dtype = getCoordsDataType(shape.length);
      const strideString = this.sliceDim > 1 ? "strides[j]" : "strides";
      this.userCode = `
        ${stridesType} strides = ${stridesType}(${this.strides});
         void main() {
          ${dtype} coords = getOutputCoords();
          int flattenIndex = 0;
          for (int j = 0; j < ${this.sliceDim}; j++) {
            int index = round(getIndices(coords[0], j));
            flattenIndex += index * ${strideString};
          }
          setOutput(getX(flattenIndex, coords[1]));
        }
      `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function gatherNd2(args) {
    const {inputs, backend: backend2} = args;
    const {params, indices} = inputs;
    const indicesShape = indices.shape;
    const sliceRank = indicesShape[indicesShape.length - 1];
    const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);
    const flattenIndices = reshape4({inputs: {x: indices}, backend: backend2, attrs: {shape: [numSlices, sliceRank]}});
    const flattenX = reshape4({
      inputs: {x: params},
      backend: backend2,
      attrs: {shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize]}
    });
    const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);
    const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices], flattenX.dtype);
    const reshaped = reshape4({inputs: {x: res}, backend: backend2, attrs: {shape: resultShape}});
    backend2.disposeIntermediateTensorInfo(flattenIndices);
    backend2.disposeIntermediateTensorInfo(flattenX);
    backend2.disposeIntermediateTensorInfo(res);
    return reshaped;
  }
  var gatherNdConfig2 = {
    kernelName: GatherNd,
    backendName: "webgl",
    kernelFunc: gatherNd2
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var GatherProgram = class {
    constructor(aShape, outputShape) {
      this.variableNames = ["A", "indices"];
      this.outputShape = outputShape;
      this.rank = outputShape.length;
      const dtype = getCoordsDataType(this.rank);
      const sourceCoords = getSourceCoords2(aShape, 2);
      this.userCode = `
      void main() {
        ${dtype} resRC = getOutputCoords();
        setOutput(getA(${sourceCoords}));
      }
    `;
    }
  };
  function getSourceCoords2(aShape, axis) {
    const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
    const sourceCoords = [];
    for (let i = 0; i < aShape.length; i++) {
      if (i === 2) {
        sourceCoords.push("int(getIndices(resRC.x, resRC.z))");
      } else {
        sourceCoords.push(`${currentCoords[i]}`);
      }
    }
    return sourceCoords.join();
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function gatherV22(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, indices} = inputs;
    const {axis, batchDims} = attrs;
    const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
    const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);
    const indicesSize = util_exports.sizeFromShape(indices.shape);
    const toDispose = [];
    const flattenX = reshape4({
      inputs: {x},
      backend: backend2,
      attrs: {
        shape: [
          shapeInfo.batchSize,
          shapeInfo.outerSize,
          shapeInfo.dimSize,
          shapeInfo.sliceSize
        ]
      }
    });
    const flattenIndex = reshape4({
      inputs: {x: indices},
      backend: backend2,
      attrs: {shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize]}
    });
    toDispose.push(flattenX);
    toDispose.push(flattenIndex);
    const flattenOutputShape = [
      shapeInfo.batchSize,
      shapeInfo.outerSize,
      indicesSize / shapeInfo.batchSize,
      shapeInfo.sliceSize
    ];
    if (backend2.shouldExecuteOnCPU([x, indices]) || x.dtype === "string") {
      const indicesBuf = backend2.bufferSync(flattenIndex);
      const xBuf = backend2.bufferSync(flattenX);
      const outBuf = gatherV2ImplCPU(xBuf, indicesBuf, flattenOutputShape);
      toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
      return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
    }
    const program = new GatherProgram(flattenX.shape, flattenOutputShape);
    const res = backend2.runWebGLProgram(program, [flattenX, flattenIndex], flattenX.dtype);
    toDispose.push(res);
    const reshaped = reshape4({inputs: {x: res}, backend: backend2, attrs: {shape: shapeInfo.outputShape}});
    toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return reshaped;
  }
  var gatherV2Config2 = {
    kernelName: GatherV2,
    backendName: "webgl",
    kernelFunc: gatherV22
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var GREATER = `return float(a > b);`;
  var GREATER_PACKED = `
  return vec4(greaterThan(a, b));
`;
  var greater4 = binaryKernelFunc2({
    opSnippet: GREATER,
    packedOpSnippet: GREATER_PACKED,
    cpuKernelImpl: greaterImplCPU,
    dtype: "bool"
  });
  var greaterConfig2 = {
    kernelName: Greater,
    backendName: "webgl",
    kernelFunc: greater4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var GREATER_EQUAL = `return float(a >= b);`;
  var GREATER_EQUAL_PACKED = `
  return vec4(greaterThanEqual(a, b));
`;
  var greaterEqual3 = binaryKernelFunc2({
    opSnippet: GREATER_EQUAL,
    packedOpSnippet: GREATER_EQUAL_PACKED,
    dtype: "bool"
  });
  var greaterEqualConfig2 = {
    kernelName: GreaterEqual,
    backendName: "webgl",
    kernelFunc: greaterEqual3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function ifft3(args) {
    const {inputs, backend: backend2} = args;
    const {input: input2} = inputs;
    return fftImpl2(input2, true, backend2);
  }
  var ifftConfig2 = {
    kernelName: IFFT,
    backendName: "webgl",
    kernelFunc: ifft3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var IS_FINITE = `return float(!isnan(x) && !isinf(x));`;
  var isFinite4 = unaryKernelFunc2({opSnippet: IS_FINITE, dtype: "bool"});
  var isFiniteConfig2 = {
    kernelName: IsFinite,
    backendName: "webgl",
    kernelFunc: isFinite4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var IS_INF = `return float(isinf(x));`;
  var isInf3 = unaryKernelFunc2({opSnippet: IS_INF, dtype: "bool"});
  var isInfConfig2 = {
    kernelName: IsInf,
    backendName: "webgl",
    kernelFunc: isInf3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var IS_NAN = `return float(isnan(x));`;
  var isNaN4 = unaryKernelFunc2({opSnippet: IS_NAN, dtype: "bool"});
  var isNaNConfig2 = {
    kernelName: IsNan,
    backendName: "webgl",
    kernelFunc: isNaN4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var LESS = `return float(a < b);`;
  var LESS_PACKED = `
  return vec4(lessThan(a, b));
`;
  var less4 = binaryKernelFunc2({
    opSnippet: LESS,
    packedOpSnippet: LESS_PACKED,
    cpuKernelImpl: lessImplCPU,
    dtype: "bool"
  });
  var lessConfig2 = {
    kernelName: Less,
    backendName: "webgl",
    kernelFunc: less4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var LESS_EQUAL = `return float(a <= b);`;
  var LESS_EQUAL_PACKED = `
  return vec4(lessThanEqual(a, b));
`;
  var lessEqual3 = binaryKernelFunc2({opSnippet: LESS_EQUAL, packedOpSnippet: LESS_EQUAL_PACKED, dtype: "bool"});
  var lessEqualConfig2 = {
    kernelName: LessEqual,
    backendName: "webgl",
    kernelFunc: lessEqual3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function linSpace2(args) {
    const {backend: backend2, attrs} = args;
    const {start, stop, num} = attrs;
    const outVals = linSpaceImplCPU(start, stop, num);
    return backend2.makeTensorInfo([outVals.length], "float32", outVals);
  }
  var linSpaceConfig2 = {
    kernelName: LinSpace,
    backendName: "webgl",
    kernelFunc: linSpace2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var LOG = `if (x < 0.0) return NAN;
  return log(x);`;
  var LOG_PACKED = `
  vec4 result = log(x);
  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
  result.r = isNaN.r == 1.0 ? NAN : result.r;
  result.g = isNaN.g == 1.0 ? NAN : result.g;
  result.b = isNaN.b == 1.0 ? NAN : result.b;
  result.a = isNaN.a == 1.0 ? NAN : result.a;

  return result;
`;
  var log4 = unaryKernelFunc2({opSnippet: LOG, packedOpSnippet: LOG_PACKED, cpuKernelImpl: logImplCPU});
  var logConfig2 = {
    kernelName: Log,
    backendName: "webgl",
    kernelFunc: log4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var LOG1P = `return log(1.0 + x);`;
  var log1p3 = unaryKernelFunc2({opSnippet: LOG1P});
  var log1pConfig2 = {
    kernelName: Log1p,
    backendName: "webgl",
    kernelFunc: log1p3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;
  var LOGICAL_AND_PACKED = `
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`;
  var logicalAnd3 = binaryKernelFunc2({
    opSnippet: LOGICAL_AND,
    packedOpSnippet: LOGICAL_AND_PACKED,
    dtype: "bool"
  });
  var logicalAndConfig2 = {
    kernelName: LogicalAnd,
    backendName: "webgl",
    kernelFunc: logicalAnd3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var LOGICAL_NOT = `return float(!(x >= 1.0));`;
  var logicalNot3 = unaryKernelFunc2({opSnippet: LOGICAL_NOT});
  var logicalNotConfig2 = {
    kernelName: LogicalNot,
    backendName: "webgl",
    kernelFunc: logicalNot3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;
  var LOGICAL_OR_PACKED = `
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`;
  var logicalOr3 = binaryKernelFunc2({opSnippet: LOGICAL_OR, packedOpSnippet: LOGICAL_OR_PACKED, dtype: "bool"});
  var logicalOrConfig2 = {
    kernelName: LogicalOr,
    backendName: "webgl",
    kernelFunc: logicalOr3
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var LRNProgram = class {
    constructor(xShape, radius, bias, alpha, beta) {
      this.variableNames = ["x"];
      this.outputShape = [];
      const rad = radius;
      const maxD = xShape[3] - 1;
      this.outputShape = xShape;
      let powOperator;
      const basis = `float(${bias}) + float(${alpha}) * sum`;
      if (beta === 0.5) {
        powOperator = `inversesqrt(${basis})`;
      } else if (beta === 1) {
        powOperator = `1.0/(${basis})`;
      } else {
        powOperator = `exp(log(${basis}) * float(-${beta}));`;
      }
      this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${rad}; j <= ${rad}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${maxD}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${powOperator};
        setOutput(val);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var LRNPackedProgram = class {
    constructor(xShape, radius, bias, alpha, beta) {
      this.variableNames = ["x"];
      this.outputShape = [];
      this.packedInputs = true;
      this.packedOutput = true;
      const rad = radius;
      const maxD = xShape[3] - 1;
      this.outputShape = xShape;
      let powOperator;
      const basis = `float(${bias}) + float(${alpha}) * sum`;
      if (beta === 0.5) {
        powOperator = `inversesqrt(${basis})`;
      } else if (beta === 1) {
        powOperator = `1.0/(${basis})`;
      } else {
        powOperator = `exp(log(${basis}) * float(-${beta}));`;
      }
      this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords.x;
        int r = coords.y;
        int c = coords.z;
        int d = coords.w;

        bool hasNextCol = d < ${this.outputShape[3]};
        bool hasNextRow = c < ${this.outputShape[2]};

        vec4 sum = vec4(0.);
        vec4 xFragAtOutputCoords = getX(b, r, c, d);

        vec4 xAtOutputCoords = vec4(
          getChannel(xFragAtOutputCoords, vec2(c, d)),
          hasNextCol ?
            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
          hasNextRow ?
            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
          (hasNextRow && hasNextCol) ?
            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
        );

        int firstChannel = d - ${rad};
        vec2 cache = vec2(0.);
        if(firstChannel >= 0){
          vec4 firstChannelFrag = getX(b, r, c, firstChannel);
          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
            if(hasNextRow){
              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
            }
        }

        ivec2 depth = ivec2(d, d + 1);
        for (int j = - ${rad}; j <= ${rad}; j++) {
          ivec2 idx = depth + j;
          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));

          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;

          if(depthInRange || depthPlusOneInRange){
            vec4 z = vec4(0.);
            vec4 xFragAtCurrentDepth;
            z.xz = cache.xy;
            if(depthPlusOneInRange && hasNextCol){
              xFragAtCurrentDepth = idx.y != d ?
                getX(b, r, c, idx.y) : xFragAtOutputCoords;
              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
              if(hasNextRow){
                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
              }
            }
            cache.xy = z.yw;
            sum += z * z;
          }
        }
        vec4 result = xAtOutputCoords * ${powOperator};
        setOutput(result);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var lrn = (args) => {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {depthRadius, bias, alpha, beta} = attrs;
    const program = env().getBool("WEBGL_PACK_NORMALIZATION") ? new LRNPackedProgram(x.shape, depthRadius, bias, alpha, beta) : new LRNProgram(x.shape, depthRadius, bias, alpha, beta);
    return backend2.runWebGLProgram(program, [x], x.dtype);
  };
  var LRNConfig = {
    kernelName: LRN,
    backendName: "webgl",
    kernelFunc: lrn
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var LRNGradProgram = class {
    constructor(inputShape, depthRadius, bias, alpha, beta) {
      this.variableNames = ["inputImage", "outputImage", "dy"];
      this.outputShape = [];
      this.outputShape = inputShape;
      this.depth = inputShape[3];
      this.depthRadius = depthRadius;
      this.bias = bias;
      this.alpha = alpha;
      this.beta = beta;
      this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];

        float result = 0.0;
        for (int d = 0; d < ${this.depth}; ++d) {
          int depthBegin = int(max(0.0, float(d - ${depthRadius})));
          int depthEnd = int(min(float(${this.depth}),
              float(d + ${depthRadius} + 1)));

          const int MIN_DEPTH_BEGIN = 0;
          const int MAX_DEPTH_END = ${this.depth};

          float norm = 0.0;
          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd) {
              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
            }
            else {
              break;
            }
          }

          norm = float(${alpha}) * norm + float(${bias});

          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd){
              float dyi = -2.0 * float(${alpha})
                * float(${beta})
                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${beta});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var lrnGrad = (args) => {
    const {inputs, backend: backend2, attrs} = args;
    const {x, y, dy} = inputs;
    const {depthRadius, bias, alpha, beta} = attrs;
    const program = new LRNGradProgram(x.shape, depthRadius, bias, alpha, beta);
    return backend2.runWebGLProgram(program, [x, y, dy], x.dtype);
  };
  var LRNGradConfig = {
    kernelName: LRNGrad,
    backendName: "webgl",
    kernelFunc: lrnGrad
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxImpl2(x, reduceShape, outShape, backend2) {
    const inSize = util_exports.sizeFromShape(reduceShape);
    const xSize = util_exports.sizeFromShape(x.shape);
    const batchSize = xSize / inSize;
    const reshapedInput = reshape4({inputs: {x}, attrs: {shape: [batchSize, inSize]}, backend: backend2});
    const reduced = reduce(reshapedInput, x.dtype, "max", backend2);
    const reshapedOutput = reshape4({inputs: {x: reduced}, attrs: {shape: outShape}, backend: backend2});
    backend2.disposeIntermediateTensorInfo(reshapedInput);
    backend2.disposeIntermediateTensorInfo(reduced);
    return reshapedOutput;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function max4(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {reductionIndices, keepDims} = attrs;
    const xRank = x.shape.length;
    const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);
    let axes = origAxes;
    const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
    const maxInputIsTransposed = permutedAxes != null;
    const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);
    let maxInput = x;
    if (maxInputIsTransposed) {
      if (shouldExecuteOnCPU) {
        const xTexData = backend2.texData.get(maxInput.dataId);
        const values = xTexData.values;
        const newShape = new Array(xRank);
        for (let i = 0; i < newShape.length; i++) {
          newShape[i] = x.shape[permutedAxes[i]];
        }
        const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
        maxInput = backend2.makeTensorInfo(newShape, x.dtype);
        const maxInputData = backend2.texData.get(maxInput.dataId);
        maxInputData.values = maxInputValues;
      } else {
        maxInput = transposeImpl2(x, permutedAxes, backend2);
      }
      axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
    }
    backend_util_exports.assertAxesAreInnerMostDims("max", axes, xRank);
    const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(maxInput.shape, axes);
    let outShape = maxOutShape;
    if (keepDims) {
      outShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);
    }
    let out;
    if (shouldExecuteOnCPU) {
      const xTexData = backend2.texData.get(maxInput.dataId);
      const values = xTexData.values;
      const outValues = maxImplCPU(values, util_exports.sizeFromShape(reduceShape), outShape, x.dtype);
      out = backend2.makeTensorInfo(outShape, x.dtype);
      const outData = backend2.texData.get(out.dataId);
      outData.values = outValues;
    } else {
      out = maxImpl2(maxInput, reduceShape, outShape, backend2);
    }
    if (maxInputIsTransposed) {
      backend2.disposeIntermediateTensorInfo(maxInput);
    }
    return out;
  }
  var maxConfig2 = {
    kernelName: Max,
    backendName: "webgl",
    kernelFunc: max4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var MAXIMUM = CHECK_NAN_SNIPPET2 + `
  return max(a, b);
`;
  var MAXIMUM_PACKED = `
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  ` + CHECK_NAN_SNIPPET3 + `
  return result;
`;
  var maximum4 = binaryKernelFunc2({
    opSnippet: MAXIMUM,
    packedOpSnippet: MAXIMUM_PACKED,
    cpuKernelImpl: maximumImplCPU
  });
  var maximumConfig2 = {
    kernelName: Maximum,
    backendName: "webgl",
    kernelFunc: maximum4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxPool3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    assertNotComplex2(x, "maxPool");
    const {filterSize, strides, pad: pad3, dimRoundingMode} = attrs;
    const dilations = 1;
    util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);
    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
      return identity3({inputs: {x}, backend: backend2});
    }
    const maxPoolProgram = new Pool2DProgram(convInfo, "max", false);
    return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);
  }
  var maxPoolConfig2 = {
    kernelName: MaxPool,
    backendName: "webgl",
    kernelFunc: maxPool3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxPool3d2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {filterSize, strides, pad: pad3, dataFormat, dimRoundingMode} = attrs;
    const dilations = [1, 1, 1];
    const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat);
    const maxPoolProgram = new Pool3DProgram(convInfo, "max", false);
    return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);
  }
  var maxPool3DConfig2 = {
    kernelName: MaxPool3D,
    backendName: "webgl",
    kernelFunc: maxPool3d2
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var MaxPool2DBackpropProgram = class {
    constructor(convInfo) {
      this.variableNames = ["dy", "maxPos"];
      this.outputShape = convInfo.inShape;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const dilationHeight = convInfo.dilationHeight;
      const effectiveFilterHeight = convInfo.effectiveFilterHeight;
      const effectiveFilterWidth = convInfo.effectiveFilterWidth;
      const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
      const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
      const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;
      this.userCode = `
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${effectiveFilterHeight};
          wR += ${dilationHeight}) {
          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {
            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);
            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));

            // Get the current value, check it against the value from the
            // position matrix.
            int curPosValue = wR * ${effectiveFilterWidth} + wC;
            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

            dotProd += dyValue * mask;
          }
        }
        setOutput(dotProd);
      }
    `;
    }
  };
  var MaxPool3DBackpropProgram = class {
    constructor(convInfo) {
      this.variableNames = ["dy", "maxPos"];
      this.outputShape = convInfo.inShape;
      const strideDepth = convInfo.strideDepth;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const dilationDepth = convInfo.dilationDepth;
      const dilationHeight = convInfo.dilationHeight;
      const dilationWidth = convInfo.dilationWidth;
      const effectiveFilterDepth = convInfo.effectiveFilterDepth;
      const effectiveFilterHeight = convInfo.effectiveFilterHeight;
      const effectiveFilterWidth = convInfo.effectiveFilterWidth;
      const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
      const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
      const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
      const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;
      this.userCode = `
      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${effectiveFilterDepth};
           wD += ${dilationDepth}) {
          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;

          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${effectiveFilterHeight};
              wR += ${dilationHeight}) {
            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${effectiveFilterWidth};
                wC += ${dilationWidth}) {
              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
              int maxPosValue = ${lastIndex} -
                  int(getMaxPos(batch, idyD, idyR, idyC, ch));

              // Get the current value, check it against the value from the
              // position matrix.
              int curPosValue =
                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
                  wR * ${effectiveFilterWidth} + wC;
              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxPool3DGrad2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {dy, input: input2} = inputs;
    const x = input2;
    const {filterSize, strides, pad: pad3, dimRoundingMode} = attrs;
    const dilations = [1, 1, 1];
    const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);
    const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, "max", true);
    const maxPool3dPositions2 = backend2.runWebGLProgram(maxPool3dPositionsProgram, [x], x.dtype);
    const maxPoolBackpropProgram = new MaxPool3DBackpropProgram(convInfo);
    const result = backend2.runWebGLProgram(maxPoolBackpropProgram, [dy, maxPool3dPositions2], x.dtype);
    backend2.disposeIntermediateTensorInfo(maxPool3dPositions2);
    return result;
  }
  var maxPoolGrad3DConfig = {
    kernelName: MaxPool3DGrad,
    backendName: "webgl",
    kernelFunc: maxPool3DGrad2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxPoolGrad3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {dy, input: input2, output} = inputs;
    const x = input2;
    assertNotComplex2([input2, output], "maxPoolGrad");
    const {filterSize, strides, pad: pad3, dimRoundingMode} = attrs;
    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);
    const getPositions = true;
    const maxPoolPositionsProgram = new Pool2DProgram(convInfo, "max", getPositions);
    const maxPoolPositions2 = backend2.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);
    const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);
    const result = backend2.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions2], x.dtype);
    backend2.disposeIntermediateTensorInfo(maxPoolPositions2);
    return result;
  }
  var maxPoolGradConfig3 = {
    kernelName: MaxPoolGrad,
    backendName: "webgl",
    kernelFunc: maxPoolGrad3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, backend2) {
    let program = new Pool2DProgram(convInfo, "max", false);
    const poolOutput = backend2.runWebGLProgram(program, [x], "float32");
    program = new Pool2DProgram(convInfo, "max", true, true, includeBatchInIndex);
    const indexOutput = backend2.runWebGLProgram(program, [x], "float32");
    return [poolOutput, indexOutput];
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var maxPoolWithArgmaxConfig2 = {
    kernelName: MaxPoolWithArgmax,
    backendName: "webgl",
    kernelFunc: ({inputs, attrs, backend: backend2}) => {
      const {x} = inputs;
      const {filterSize, strides, pad: pad3, includeBatchInIndex} = attrs;
      const webglBackend = backend2;
      util_exports.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);
      const dilations = [1, 1];
      util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
      const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3);
      const [result, indexes] = maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, webglBackend);
      return [result, indexes];
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function meanImpl(x, reduceShape, outShape, backend2) {
    const inSize = util_exports.sizeFromShape(reduceShape);
    const xSize = util_exports.sizeFromShape(x.shape);
    const batchSize = xSize / inSize;
    const reshapedInput = reshape4({inputs: {x}, attrs: {shape: [batchSize, inSize]}, backend: backend2});
    const reduced = reduce(reshapedInput, "float32", "mean", backend2);
    const reshapedOutput = reshape4({inputs: {x: reduced}, attrs: {shape: outShape}, backend: backend2});
    backend2.disposeIntermediateTensorInfo(reshapedInput);
    backend2.disposeIntermediateTensorInfo(reduced);
    return reshapedOutput;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var meanConfig2 = {
    kernelName: Mean,
    backendName: "webgl",
    kernelFunc: ({inputs, attrs, backend: backend2}) => {
      const {x} = inputs;
      const {keepDims, axis} = attrs;
      const webglBackend = backend2;
      const xRank = x.shape.length;
      const origAxes = util_exports.parseAxisParam(axis, x.shape);
      let axes = origAxes;
      const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
      const meanInputIsTransposed = permutedAxes != null;
      const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);
      const intermediates = [];
      let meanInput = x;
      if (meanInputIsTransposed) {
        if (shouldExecuteOnCPU) {
          const xTexData = webglBackend.texData.get(meanInput.dataId);
          const values = xTexData.values;
          const newShape = new Array(xRank);
          for (let i = 0; i < newShape.length; i++) {
            newShape[i] = x.shape[permutedAxes[i]];
          }
          const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
          meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);
          const meanInputData = webglBackend.texData.get(meanInput.dataId);
          meanInputData.values = meanInputValues;
        } else {
          meanInput = transposeImpl2(x, permutedAxes, webglBackend);
        }
        intermediates.push(meanInput);
        axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
      }
      backend_util_exports.assertAxesAreInnerMostDims("sum", axes, xRank);
      const [meanOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(meanInput.shape, axes);
      let outShape = meanOutShape;
      if (keepDims) {
        outShape = backend_util_exports.expandShapeToKeepDim(meanOutShape, origAxes);
      }
      const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);
      for (const i of intermediates) {
        webglBackend.disposeIntermediateTensorInfo(i);
      }
      return out;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function min4(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis, keepDims} = attrs;
    const xRank = x.shape.length;
    const origAxes = util_exports.parseAxisParam(axis, x.shape);
    let axes = origAxes;
    const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
    let permutedX = x;
    if (permutedAxes != null) {
      permutedX = transpose3({inputs: {x}, backend: backend2, attrs: {perm: permutedAxes}});
      axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
    }
    backend_util_exports.assertAxesAreInnerMostDims("min", axes, xRank);
    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
    const inSize = util_exports.sizeFromShape(reduceShape);
    const a2D = reshape4({inputs: {x: permutedX}, backend: backend2, attrs: {shape: [-1, inSize]}});
    const reduced = reduce(a2D, a2D.dtype, "min", backend2);
    let res;
    if (keepDims) {
      const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
      res = reshape4({inputs: {x: reduced}, backend: backend2, attrs: {shape: newShape}});
    } else {
      res = reshape4({inputs: {x: reduced}, backend: backend2, attrs: {shape: outShape}});
    }
    backend2.disposeIntermediateTensorInfo(a2D);
    backend2.disposeIntermediateTensorInfo(reduced);
    if (permutedAxes != null) {
      backend2.disposeIntermediateTensorInfo(permutedX);
    }
    return res;
  }
  var minConfig2 = {
    kernelName: Min,
    backendName: "webgl",
    kernelFunc: min4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var MINIMUM = CHECK_NAN_SNIPPET2 + `
  return min(a, b);
`;
  var MINIMUM_PACKED = `
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  ` + CHECK_NAN_SNIPPET3 + `
  return result;
`;
  var minimum4 = binaryKernelFunc2({
    opSnippet: MINIMUM,
    packedOpSnippet: MINIMUM_PACKED,
    cpuKernelImpl: minimumImplCPU
  });
  var minimumConfig2 = {
    kernelName: Minimum,
    backendName: "webgl",
    kernelFunc: minimum4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var MirrorPadProgram = class {
    constructor(xShape, paddings, mode) {
      this.variableNames = ["x"];
      this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
      const rank = xShape.length;
      const dtype = getCoordsDataType(rank);
      const start = paddings.map((p2) => p2[0]).join(",");
      const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
      const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank);
      const offset = mode === "reflect" ? 0 : 1;
      if (rank === 1) {
        this.userCode = `
        int start = ${start};
        int end = ${end};

        void main() {
          int outC = getOutputCoords();
          if (outC < start) {
            outC = start * 2 - outC - ${offset};
          } else if(outC >= end) {
            outC = (end - 1) * 2 - outC + ${offset};
          }
          setOutput(getX(outC - start));
        }
      `;
        return;
      }
      this.userCode = `
      ${dtype} start = ${dtype}(${start});
      ${dtype} end = ${dtype}(${end});

      void main() {
        ${dtype} outC = getOutputCoords();
        for (int i = 0; i < ${rank}; i++) {
          if (outC[i] < start[i]) {
            outC[i] = start[i] * 2 - outC[i] - ${offset};
          } else if(outC[i] >= end[i]) {
            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};
          }
        }
        ${dtype} coords = outC - start;
        setOutput(getX(${unpackedCoords}));
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var MirrorPadPackedProgram = class {
    constructor(xShape, paddings, mode) {
      this.variableNames = ["x"];
      this.packedInputs = true;
      this.packedOutput = true;
      this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
      const rank = xShape.length;
      const dtype = getCoordsDataType(rank);
      const start = paddings.map((p2) => p2[0]).join(",");
      const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
      const coords2 = getChannels("rc", rank);
      const source = getChannels("source", rank);
      const cLimit = `${coords2[rank - 1]} < ${this.outputShape[rank - 1]}`;
      const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`;
      const offset = mode === "reflect" ? 0 : 1;
      let mainLoop = "";
      if (rank === 1) {
        const padSetup = `
        ${dtype} source = rc;
        if (source < start) {
          source = start * 2 - source - ${offset};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${offset};
        }
        source -= start;
      `;
        mainLoop = `
        ${dtype} rc = outputLoc;
        ${padSetup}
        result[0] = getChannel(getX(${source.join()}), ${innerDims});
        ${coords2[rank - 1]} += 1;
        if(${cLimit}) {
          ${padSetup}
          result[1] = getChannel(getX(${source.join()}), ${innerDims});
        }
      `;
      } else {
        const padSetup = `
        ${dtype} source = rc;
        ${dtype} lt = ${dtype}(lessThan(source, start));
        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));
        ${dtype} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${offset}) +
                gte * ((end - 1) * 2 - source + ${offset});
        source -= start;
      `;
        mainLoop = `
        ${dtype} rc = outputLoc;
        ${padSetup}
        result[0] = getChannel(getX(${source.join()}), ${innerDims});
        ${coords2[rank - 1]} += 1;
        if(${cLimit}) {
          ${padSetup}
          result[1] = getChannel(getX(${source.join()}), ${innerDims});
        }
        rc = outputLoc;
        ${coords2[rank - 2]} += 1;
        if(${coords2[rank - 2]} < ${this.outputShape[rank - 2]}) {
          ${padSetup}
          result[2] = getChannel(getX(${source.join()}), ${innerDims});
          ${coords2[rank - 1]} += 1;
          if(${cLimit}) {
            ${padSetup}
            result[3] = getChannel(getX(${source.join()}), ${innerDims});
          }
        }
      `;
      }
      this.userCode = `
      const ${dtype} start = ${dtype}(${start});
      const ${dtype} end = ${dtype}(${end});

      void main() {
        ${dtype} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${mainLoop}
        setOutput(result);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var mirrorPadKernelFunc = ({inputs, backend: backend2, attrs}) => {
    const {x} = inputs;
    const {paddings, mode} = attrs;
    const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new MirrorPadPackedProgram(x.shape, paddings, mode) : new MirrorPadProgram(x.shape, paddings, mode);
    const output = backend2.runWebGLProgram(program, [x], x.dtype);
    return output;
  };
  var mirrorPadConfig2 = {
    kernelName: MirrorPad,
    backendName: "webgl",
    kernelFunc: mirrorPadKernelFunc
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var MOD = `if (b == 0.0) return NAN;
  return mod(a, b);`;
  var MOD_PACKED = `
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  ` + CHECK_NAN_SNIPPET3 + `
  return result;
`;
  var mod3 = binaryKernelFunc2({
    opSnippet: MOD,
    packedOpSnippet: MOD_PACKED
  });
  var modConfig2 = {
    kernelName: Mod,
    backendName: "webgl",
    kernelFunc: mod3
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var MultinomialProgram = class {
    constructor(batchSize, numOutcomes, numSamples) {
      this.variableNames = ["probs"];
      this.outputShape = [batchSize, numSamples];
      this.userCode = `
      uniform float seed;

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];

        float r = random(seed);
        float cdf = 0.0;

        for (int i = 0; i < ${numOutcomes - 1}; i++) {
          cdf += getProbs(batch, i);

          if (r < cdf) {
            setOutput(float(i));
            return;
          }
        }

        // If no other event happened, last event happened.
        setOutput(float(${numOutcomes - 1}));
      }
    `;
    }
    getCustomSetupFunc(seed) {
      return (gpgpu, webGLProgram) => {
        if (this.seedLoc == null) {
          this.seedLoc = gpgpu.getUniformLocation(webGLProgram, "seed");
        }
        gpgpu.gl.uniform1f(this.seedLoc, seed);
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var DIV = `
if (a == b) {
  return 1.0;
};
return a / b;`;
  var DIV_PACKED = `
  // vec4 one = vec4(equal(a, b));
  // return one + (vec4(1.0) - one) * a / b;
  vec4 result = a / b;
  if(a.x == b.x) {
    result.x = 1.;
  }
  if(a.y == b.y) {
    result.y = 1.;
  }
  if(a.z == b.z) {
    result.z = 1.;
  }
  if(a.w == b.w) {
    result.w = 1.;
  }

  return result;
`;
  var realDiv = binaryKernelFunc2({opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true});
  var realDivConfig2 = {
    kernelName: RealDiv,
    backendName: "webgl",
    kernelFunc: realDiv
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SUB = "return a - b;";
  var sub3 = binaryKernelFunc2({
    opSnippet: SUB,
    packedOpSnippet: SUB,
    supportsComplex: true,
    cpuKernelImpl: subImplCPU
  });
  var subConfig2 = {
    kernelName: Sub,
    backendName: "webgl",
    kernelFunc: sub3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function softmax4(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {logits} = inputs;
    const {dim} = attrs;
    const axes = util_exports.parseAxisParam([dim], logits.shape);
    const maxLogit = max4({
      inputs: {x: logits},
      backend: backend2,
      attrs: {reductionIndices: axes, keepDims: false}
    });
    const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);
    const maxLogitsReshaped = reshape4({inputs: {x: maxLogit}, backend: backend2, attrs: {shape: expandedShape}});
    const a = sub3({inputs: {a: logits, b: maxLogitsReshaped}, backend: backend2});
    const b = exp3({inputs: {x: a}, backend: backend2});
    const sumExp = sum4({inputs: {x: b}, backend: backend2, attrs: {axis: axes, keepDims: false}});
    const sumExpReshaped = reshape4({inputs: {x: sumExp}, backend: backend2, attrs: {shape: expandedShape}});
    const res = realDiv({inputs: {a: b, b: sumExpReshaped}, backend: backend2});
    backend2.disposeIntermediateTensorInfo(maxLogit);
    backend2.disposeIntermediateTensorInfo(maxLogitsReshaped);
    backend2.disposeIntermediateTensorInfo(a);
    backend2.disposeIntermediateTensorInfo(b);
    backend2.disposeIntermediateTensorInfo(sumExp);
    backend2.disposeIntermediateTensorInfo(sumExpReshaped);
    return res;
  }
  var softmaxConfig2 = {
    kernelName: Softmax,
    backendName: "webgl",
    kernelFunc: softmax4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function multinomial3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {logits} = inputs;
    const {numSamples, seed, normalized} = attrs;
    const probs = normalized ? logits : softmax4({inputs: {logits}, backend: backend2, attrs: {dim: logits.shape.length - 1}});
    const batchSize = probs.shape[0];
    const numOutcomes = probs.shape[1];
    const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);
    const customSetup = program.getCustomSetupFunc(seed);
    const res = backend2.runWebGLProgram(program, [probs], "int32", customSetup);
    if (!normalized) {
      backend2.disposeIntermediateTensorInfo(probs);
    }
    return res;
  }
  var multinomialConfig2 = {
    kernelName: Multinomial,
    backendName: "webgl",
    kernelFunc: multinomial3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var NEG = `return -x;`;
  function neg3(args) {
    const {inputs, backend: backend2} = args;
    const {x} = inputs;
    if (backend2.shouldExecuteOnCPU([x])) {
      const xData = backend2.texData.get(x.dataId);
      const [outValues, newShape] = negImplCPU(xData.values, x.shape, x.dtype);
      return backend2.makeTensorInfo(newShape, x.dtype, outValues);
    }
    let program;
    if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) {
      program = new UnaryOpPackedProgram(x.shape, NEG);
    } else {
      program = new UnaryOpProgram(x.shape, NEG);
    }
    return backend2.runWebGLProgram(program, [x], x.dtype);
  }
  var negConfig2 = {
    kernelName: Neg,
    backendName: "webgl",
    kernelFunc: neg3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var nonMaxSuppressionV3Impl3 = kernel_impls_exports.nonMaxSuppressionV3Impl;
  function nonMaxSuppressionV32(args) {
    backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
    const {inputs, backend: backend2, attrs} = args;
    const {boxes, scores} = inputs;
    const {maxOutputSize, iouThreshold, scoreThreshold} = attrs;
    const boxesVals = backend2.readSync(boxes.dataId);
    const scoresVals = backend2.readSync(scores.dataId);
    const {selectedIndices} = nonMaxSuppressionV3Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
    return backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices));
  }
  var nonMaxSuppressionV3Config2 = {
    kernelName: NonMaxSuppressionV3,
    backendName: "webgl",
    kernelFunc: nonMaxSuppressionV32
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var nonMaxSuppressionV4Impl3 = kernel_impls_exports.nonMaxSuppressionV4Impl;
  function nonMaxSuppressionV42(args) {
    backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
    const {inputs, backend: backend2, attrs} = args;
    const {boxes, scores} = inputs;
    const {maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize} = attrs;
    const boxesVals = backend2.readSync(boxes.dataId);
    const scoresVals = backend2.readSync(scores.dataId);
    const {selectedIndices, validOutputs} = nonMaxSuppressionV4Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
    return [
      backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
      backend2.makeTensorInfo([], "int32", new Int32Array([validOutputs]))
    ];
  }
  var nonMaxSuppressionV4Config2 = {
    kernelName: NonMaxSuppressionV4,
    backendName: "webgl",
    kernelFunc: nonMaxSuppressionV42
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var nonMaxSuppressionV5Impl3 = kernel_impls_exports.nonMaxSuppressionV5Impl;
  function nonMaxSuppressionV52(args) {
    backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
    const {inputs, backend: backend2, attrs} = args;
    const {boxes, scores} = inputs;
    const {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma} = attrs;
    const boxesVals = backend2.readSync(boxes.dataId);
    const scoresVals = backend2.readSync(scores.dataId);
    const maxOutputSizeVal = maxOutputSize;
    const iouThresholdVal = iouThreshold;
    const scoreThresholdVal = scoreThreshold;
    const softNmsSigmaVal = softNmsSigma;
    const {selectedIndices, selectedScores} = nonMaxSuppressionV5Impl3(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);
    return [
      backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
      backend2.makeTensorInfo([selectedScores.length], "float32", new Float32Array(selectedScores))
    ];
  }
  var nonMaxSuppressionV5Config2 = {
    kernelName: NonMaxSuppressionV5,
    backendName: "webgl",
    kernelFunc: nonMaxSuppressionV52
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var OneHotProgram = class {
    constructor(numIndices, depth, onValue, offValue) {
      this.variableNames = ["indices"];
      this.outputShape = [numIndices, depth];
      this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${offValue}), float(${onValue}),
                      float(index == coords.y)));
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var oneHot3 = (args) => {
    const {inputs, backend: backend2, attrs} = args;
    const {indices} = inputs;
    const {depth, onValue, offValue} = attrs;
    const indicesSize = util_exports.sizeFromShape(indices.shape);
    const program = new OneHotProgram(indicesSize, depth, onValue, offValue);
    const reshaped = reshape4({inputs: {x: indices}, backend: backend2, attrs: {shape: [indicesSize]}});
    const result = backend2.runWebGLProgram(program, [reshaped], indices.dtype);
    backend2.disposeIntermediateTensorInfo(reshaped);
    const outShape = [...indices.shape, depth];
    const out = reshape4({inputs: {x: result}, backend: backend2, attrs: {shape: outShape}});
    backend2.disposeIntermediateTensorInfo(result);
    return out;
  };
  var oneHotConfig2 = {
    kernelName: OneHot,
    backendName: "webgl",
    kernelFunc: oneHot3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function zerosLike3(args) {
    const {inputs, backend: backend2} = args;
    const {x} = inputs;
    if (x.dtype === "complex64") {
      const realPart = real3({inputs: {input: x}, backend: backend2});
      const r = zerosLike3({inputs: {x: realPart}, backend: backend2});
      const imagPart = imag3({inputs: {input: x}, backend: backend2});
      const i = zerosLike3({inputs: {x: imagPart}, backend: backend2});
      const result = complex3({inputs: {real: r, imag: i}, backend: backend2});
      backend2.disposeIntermediateTensorInfo(realPart);
      backend2.disposeIntermediateTensorInfo(r);
      backend2.disposeIntermediateTensorInfo(imagPart);
      backend2.disposeIntermediateTensorInfo(i);
      return result;
    } else {
      return fill3({
        attrs: {
          shape: x.shape,
          dtype: x.dtype,
          value: x.dtype === "string" ? "" : 0
        },
        backend: backend2
      });
    }
  }
  var zerosLikeConfig2 = {
    kernelName: ZerosLike,
    backendName: "webgl",
    kernelFunc: zerosLike3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function onesLike3(args) {
    const {inputs, backend: backend2} = args;
    const {x} = inputs;
    if (x.dtype === "string") {
      throw new Error("onesLike is not supported under string dtype");
    } else if (x.dtype === "complex64") {
      const realPart = real3({inputs: {input: x}, backend: backend2});
      const r = onesLike3({inputs: {x: realPart}, backend: backend2});
      const imagPart = imag3({inputs: {input: x}, backend: backend2});
      const i = zerosLike3({inputs: {x: imagPart}, backend: backend2});
      const result = complex3({inputs: {real: r, imag: i}, backend: backend2});
      backend2.disposeIntermediateTensorInfo(realPart);
      backend2.disposeIntermediateTensorInfo(r);
      backend2.disposeIntermediateTensorInfo(imagPart);
      backend2.disposeIntermediateTensorInfo(i);
      return result;
    } else {
      return fill3({attrs: {shape: x.shape, dtype: x.dtype, value: 1}, backend: backend2});
    }
  }
  var onesLikeConfig2 = {
    kernelName: OnesLike,
    backendName: "webgl",
    kernelFunc: onesLike3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function pack2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {axis} = attrs;
    if (inputs.length === 1) {
      return expandDims4({inputs: {input: inputs[0]}, backend: backend2, attrs: {dim: axis}});
    }
    const shape = inputs[0].shape;
    const dtype = inputs[0].dtype;
    inputs.forEach((t) => {
      util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
      util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes");
    });
    const intermediateTensorInfos = [];
    const expandedTensors = inputs.map((t) => {
      const expandedT = expandDims4({inputs: {input: t}, backend: backend2, attrs: {dim: axis}});
      intermediateTensorInfos.push(expandedT);
      return expandedT;
    });
    const result = concat3({inputs: expandedTensors, backend: backend2, attrs: {axis}});
    intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return result;
  }
  var packConfig2 = {
    kernelName: Pack,
    backendName: "webgl",
    kernelFunc: pack2
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var PadProgram = class {
    constructor(xShape, paddings, constantValue) {
      this.variableNames = ["x"];
      this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
      const rank = xShape.length;
      const type = getCoordsDataType(rank);
      const start = paddings.map((p2) => p2[0]).join(",");
      const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
      const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank);
      if (rank === 1) {
        this.userCode = `
        int start = ${start};
        int end = ${end};
        uniform float value;

        void main() {
          int outC = getOutputCoords();
          if (outC < start || outC >= end) {
            setOutput(value);
          } else {
            setOutput(getX(outC - start));
          }
        }
      `;
        return;
      }
      this.userCode = `
      ${type} start = ${type}(${start});
      ${type} end = ${type}(${end});
      uniform float value;

      void main() {
        ${type} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(value);
        } else {
          ${type} coords = outC - start;
          setOutput(getX(${unpackedCoords}));
        }
      }
    `;
    }
    getCustomSetupFunc(value) {
      return (gpgpu, webGLProgram) => {
        if (this.valueLoc == null) {
          this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, "value");
        }
        gpgpu.gl.uniform1f(this.valueLoc, value);
      };
    }
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var PadPackedProgram = class {
    constructor(xShape, paddings, constantValue) {
      this.variableNames = ["x"];
      this.packedInputs = true;
      this.packedOutput = true;
      this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
      const rank = xShape.length;
      const dtype = getCoordsDataType(rank);
      const start = paddings.map((p2) => p2[0]).join(",");
      const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
      const coords2 = getChannels("rc", rank);
      const source = getChannels("source", rank);
      const cLimit = `${coords2[rank - 1]} < ${this.outputShape[rank - 1]}`;
      const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`;
      const componentSetup = [
        `${dtype} rc = outputLoc;`,
        `${coords2[rank - 1]} += 1;
       if(${cLimit}) {
      `,
        rank === 1 ? "" : `}
       rc = outputLoc;
       ${coords2[rank - 2]} += 1;
       if(${coords2[rank - 2]} < ${this.outputShape[rank - 2]}) {`,
        rank === 1 ? "" : `  ${coords2[rank - 1]} += 1;
         if(${cLimit}) {`
      ];
      const paddingArea = rank === 1 ? "rc < start || rc >= end" : "any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";
      let mainLoop = "";
      for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {
        mainLoop += `
        ${componentSetup[i]}
        if (${paddingArea}) {
          result[${i}] = float(value);
        } else {
          ${dtype} source = rc - start;
          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});
        }
      `;
      }
      mainLoop += rank === 1 ? `} ` : `}}`;
      this.userCode = `
      const ${dtype} start = ${dtype}(${start});
      const ${dtype} end = ${dtype}(${end});
      uniform float value;

      void main() {
        ${dtype} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${mainLoop}
        setOutput(result);
      }
    `;
    }
    getCustomSetupFunc(value) {
      return (gpgpu, webGLProgram) => {
        if (this.valueLoc == null) {
          this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, "value");
        }
        gpgpu.gl.uniform1f(this.valueLoc, value);
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var padV22 = (args) => {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {paddings, constantValue} = attrs;
    const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new PadPackedProgram(x.shape, paddings, constantValue) : new PadProgram(x.shape, paddings, constantValue);
    const customSetup = program.getCustomSetupFunc(constantValue);
    return backend2.runWebGLProgram(program, [x], x.dtype, customSetup);
  };
  var padV2Config2 = {
    kernelName: PadV2,
    backendName: "webgl",
    kernelFunc: padV22
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var POW = `
  if(a < 0.0 && floor(b) < b){
    return NAN;
  }
  if (b == 0.0) {
    return 1.0;
  }
  return (round(mod(b, 2.0)) != 1) ?
      pow(abs(a), b) : sign(a) * pow(abs(a), b);
`;
  var POW_PACKED = `
  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
  vec4 result = multiplier * pow(abs(a), b);

  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
  bvec4 isExpZero = equal(b, vec4(0.0));
  result.r = isExpZero.r ? 1.0 : result.r;
  result.g = isExpZero.g ? 1.0 : result.g;
  result.b = isExpZero.b ? 1.0 : result.b;
  result.a = isExpZero.a ? 1.0 : result.a;

  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
  ` + CHECK_NAN_SNIPPET3 + `
  return result;
`;
  var pow3 = binaryKernelFunc2({opSnippet: POW, packedOpSnippet: POW_PACKED});
  var powConfig2 = {
    kernelName: Pow,
    backendName: "webgl",
    kernelFunc: pow3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function prod3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis, keepDims} = attrs;
    const xRank = x.shape.length;
    const toDispose = [];
    const origAxes = util_exports.parseAxisParam(axis, x.shape);
    let axes = origAxes;
    const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
    let permutedX = x;
    if (permutedAxes != null) {
      permutedX = transpose3({inputs: {x}, backend: backend2, attrs: {perm: permutedAxes}});
      axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
      toDispose.push(permutedX);
    }
    backend_util_exports.assertAxesAreInnerMostDims("prod", axes, xRank);
    let res;
    if (backend2.shouldExecuteOnCPU([permutedX])) {
      const xVals = backend2.texData.get(permutedX.dataId).values;
      const {outVals, outShape, outDtype} = prodImplCPU(permutedX.shape, permutedX.dtype, xVals, axes);
      res = backend2.makeTensorInfo(outShape, outDtype, outVals);
    } else {
      const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
      const inSize = util_exports.sizeFromShape(reduceShape);
      const a2D = reshape4({inputs: {x: permutedX}, backend: backend2, attrs: {shape: [-1, inSize]}});
      const outputDType = sumOutType(x.dtype);
      const reduced = reduce(a2D, outputDType, "prod", backend2);
      res = reshape4({inputs: {x: reduced}, backend: backend2, attrs: {shape: outShape}});
      toDispose.push(a2D);
      toDispose.push(reduced);
    }
    if (keepDims) {
      toDispose.push(res);
      const newShape = backend_util_exports.expandShapeToKeepDim(res.shape, origAxes);
      res = reshape4({inputs: {x: res}, backend: backend2, attrs: {shape: newShape}});
    }
    toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return res;
  }
  var prodConfig2 = {
    kernelName: Prod,
    backendName: "webgl",
    kernelFunc: prod3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var range4 = (args) => {
    const {backend: backend2, attrs} = args;
    const {start, stop, step: step5, dtype} = attrs;
    const values = rangeImplCPU(start, stop, step5, dtype);
    return backend2.makeTensorInfo([values.length], dtype, values);
  };
  var rangeConfig2 = {
    kernelName: Range,
    backendName: "webgl",
    kernelFunc: range4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var RECIPROCAL = `return 1.0 / x;`;
  var reciprocal3 = unaryKernelFunc2({opSnippet: RECIPROCAL});
  var reciprocalConfig2 = {
    kernelName: Reciprocal,
    backendName: "webgl",
    kernelFunc: reciprocal3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var RELU3 = CHECK_NAN_SNIPPET + `
  return (x < 0.0) ? 0.0 : x;
`;
  var RELU_PACKED = `
  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`;
  var relu3 = unaryKernelFunc2({opSnippet: RELU3, packedOpSnippet: RELU_PACKED});
  var reluConfig2 = {
    kernelName: Relu,
    backendName: "webgl",
    kernelFunc: relu3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var RELU63 = CHECK_NAN_SNIPPET + `
  return (x < 0.0) ? 0.0 : min(6.0, x);
`;
  var RELU6_PACKED = `
  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`;
  var relu63 = unaryKernelFunc2({opSnippet: RELU63, packedOpSnippet: RELU6_PACKED});
  var relu6Config2 = {
    kernelName: Relu6,
    backendName: "webgl",
    kernelFunc: relu63
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ResizeBilinearProgram = class {
    constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
      this.variableNames = ["A"];
      this.outputShape = [];
      const [batch, oldHeight, oldWidth, depth] = inputShape;
      this.outputShape = [batch, newHeight, newWidth, depth];
      const effectiveInSize = [
        alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
        alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
      ];
      const effectiveOutSize = [
        alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
        alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
      ];
      let sourceFracIndexRC;
      if (halfPixelCenters) {
        sourceFracIndexRC = `(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)`;
      } else {
        sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;
      }
      this.userCode = `
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${effectiveInSize[0] / effectiveOutSize[0]},
          ${effectiveInSize[1] / effectiveOutSize[1]});
      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = ${sourceFracIndexRC};

        // Compute the four integer indices.
        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
        ivec2 sourceCeilRC = ivec2(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);

        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);

        float top = topLeft + (topRight - topLeft) * fracRC.y;
        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
        float newValue = top + (bottom - top) * fracRC.x;

        setOutput(newValue);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ResizeBilinearPackedProgram = class {
    constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
      this.variableNames = ["A"];
      this.packedInputs = true;
      this.packedOutput = true;
      this.outputShape = [];
      const [batch, oldHeight, oldWidth, depth] = inputShape;
      this.outputShape = [batch, newHeight, newWidth, depth];
      const effectiveInSize = [
        alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
        alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
      ];
      const effectiveOutSize = [
        alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
        alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
      ];
      let sourceFracIndexRC;
      if (halfPixelCenters) {
        sourceFracIndexRC = `(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)`;
      } else {
        sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;
      }
      this.userCode = `
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${effectiveInSize[0] / effectiveOutSize[0]},
          ${effectiveInSize[1] / effectiveOutSize[1]},
          ${effectiveInSize[1] / effectiveOutSize[1]});
      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,
                                     ${oldWidth}.0);

      float getAValue(int b, int r, int c, int d) {
        return getChannel(getA(b, r, c, d), vec2(c, d));
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        // Calculate values for next column in yRC.z.
        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);

        // Fractional source index.
        vec3 sourceFracIndexRC = ${sourceFracIndexRC};

        // Compute the four integer indices.
        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
        ivec3 sourceCeilRC = ivec3(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${depth - 1};
        bool hasNextRow = coords.z < ${newWidth - 1};

        // In parallel, construct four corners for all four components in
        // packed 2x2 cell.
        vec4 topLeft = vec4(
          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 bottomLeft = vec4(
          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 topRight = vec4(
          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec4 bottomRight = vec4(
          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
        vec4 newValue = mix(top, bottom, fracRC.x);

        setOutput(newValue);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function resizeBilinear3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {images} = inputs;
    const {alignCorners, halfPixelCenters, size} = attrs;
    const [newHeight, newWidth] = size;
    const program = env().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new ResizeBilinearPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeBilinearProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);
    return backend2.runWebGLProgram(program, [images], "float32");
  }
  var resizeBilinearConfig2 = {
    kernelName: ResizeBilinear,
    backendName: "webgl",
    kernelFunc: resizeBilinear3
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ResizeBilinearBackpropProgram = class {
    constructor(dyShape, inputShape, alignCorners) {
      this.variableNames = ["dy"];
      this.outputShape = [];
      this.outputShape = inputShape;
      const [, xHeight, xWidth] = inputShape;
      const [, yHeight, yWidth] = dyShape;
      const effectiveXSize = [
        alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
        alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
      ];
      const effectiveYSize = [
        alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
        alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
      ];
      const heightScale = effectiveXSize[0] / effectiveYSize[0];
      const widthScale = effectiveXSize[1] / effectiveYSize[1];
      const invHeightScale = 1 / heightScale;
      const invWidthScale = 1 / widthScale;
      const winHeight = Math.ceil(invHeightScale) * 2 + 2;
      const winWidth = Math.ceil(invWidthScale) * 2 + 2;
      this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${heightScale});
        const float widthScale = float(${widthScale});

        const float invHeightScale = float(${invHeightScale});
        const float invWidthScale = float(${invWidthScale});

        const int winHeight = int(${winHeight});
        const int winWidth = int(${winWidth});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(startRLerp - float(winHeight / 2));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(startCLerp - float(winWidth / 2));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${yHeight}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${yWidth}) {
              continue;
            }

            float dxR = float(dyR) * heightScale;
            int topDxRIndex = int(floor(dxR));
            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));
            float dxRLerp = dxR - float(topDxRIndex);
            float inverseDxRLerp = 1.0 - dxRLerp;

            float dxC = float(dyC) * widthScale;
            int leftDxCIndex = int(floor(dxC));
            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));
            float dxCLerp = dxC - float(leftDxCIndex);
            float inverseDxCLerp = 1.0 - dxCLerp;

            if (r == topDxRIndex && c == leftDxCIndex) {
              // topLeft
              accumulator +=
                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
            }

            if (r == topDxRIndex && c == rightDxCIndex) {
              // topRight
              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
            }

            if (r == bottomDxRIndex && c == leftDxCIndex) {
              // bottomLeft
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
            }

            if (r == bottomDxRIndex && c == rightDxCIndex) {
              // bottomRight
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function resizeBilinearGrad2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {images, dy} = inputs;
    const {alignCorners} = attrs;
    const program = new ResizeBilinearBackpropProgram(dy.shape, images.shape, alignCorners);
    return backend2.runWebGLProgram(program, [dy], dy.dtype);
  }
  var resizeBilinearGradConfig3 = {
    kernelName: ResizeBilinearGrad,
    backendName: "webgl",
    kernelFunc: resizeBilinearGrad2
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ResizeNearestNeighborProgram = class {
    constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
      this.variableNames = ["A"];
      this.outputShape = [];
      const [batch, oldHeight, oldWidth, depth] = inputShape;
      this.outputShape = [batch, newHeight, newWidth, depth];
      const effectiveInSize = [
        alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
        alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
      ];
      const effectiveOutSize = [
        alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
        alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
      ];
      const roundBase = alignCorners ? "0.5" : "0.0";
      let sourceFracIndexRC;
      if (halfPixelCenters) {
        sourceFracIndexRC = `max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))`;
      } else {
        sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;
      }
      this.userCode = `
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${effectiveInSize[0] / effectiveOutSize[0]},
          ${effectiveInSize[1] / effectiveOutSize[1]});
      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = ${sourceFracIndexRC};

        // Compute the coordinators of nearest neighbor point.
        ivec2 sourceNearestRC = ivec2(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));
        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);

        setOutput(newValue);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function resizeNearestNeighbor3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {images} = inputs;
    const {alignCorners, halfPixelCenters, size} = attrs;
    const [newHeight, newWidth] = size;
    const program = new ResizeNearestNeighborProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);
    return backend2.runWebGLProgram(program, [images], images.dtype);
  }
  var resizeNearestNeighborConfig2 = {
    kernelName: ResizeNearestNeighbor,
    backendName: "webgl",
    kernelFunc: resizeNearestNeighbor3
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ResizeNearestNeigborBackpropProgram = class {
    constructor(dyShape, inputShape, alignCorners) {
      this.variableNames = ["dy"];
      this.outputShape = [];
      this.outputShape = inputShape;
      const [, xHeight, xWidth] = inputShape;
      const [, yHeight, yWidth] = dyShape;
      const effectiveXSize = [
        alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
        alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
      ];
      const effectiveYSize = [
        alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
        alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
      ];
      const heightScale = effectiveXSize[0] / effectiveYSize[0];
      const widthScale = effectiveXSize[1] / effectiveYSize[1];
      const invHeightScale = 1 / heightScale;
      const invWidthScale = 1 / widthScale;
      const winHeight = Math.ceil(invHeightScale) * 2 + 2;
      const winWidth = Math.ceil(invWidthScale) * 2 + 2;
      this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${heightScale});
        const float widthScale = float(${widthScale});

        const float invHeightScale = float(${invHeightScale});
        const float invWidthScale = float(${invWidthScale});

        const int winHeight = int(${winHeight});
        const int winWidth = int(${winWidth});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(floor(startRLerp - float(winHeight / 2)));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(floor(startCLerp - float(winWidth / 2)));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${yHeight}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${yWidth}) {
              continue;
            }

            float sourceFracRow =
              float(${effectiveXSize[0]}) *
                (float(dyR) / float(${effectiveYSize[0]}));

            float sourceFracCol =
                float(${effectiveXSize[1]}) *
                  (float(dyC) / float(${effectiveYSize[1]}));

            int sourceNearestRow = int(min(
                float(int(${xHeight}) - 1),
                ${alignCorners} ? float(round(sourceFracRow)) :
                                  float(floor(sourceFracRow))));

            int sourceNearestCol = int(min(
                float(int(${xWidth}) - 1),
                ${alignCorners} ? float(round(sourceFracCol)) :
                                  float(floor(sourceFracCol))));

            if (r == sourceNearestRow && c == sourceNearestCol) {
              accumulator += getDy(b, dyR, dyC, d);
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function resizeNearestNeighborGrad2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {images, dy} = inputs;
    const {alignCorners} = attrs;
    const program = new ResizeNearestNeigborBackpropProgram(dy.shape, images.shape, alignCorners);
    return backend2.runWebGLProgram(program, [dy], dy.dtype);
  }
  var resizeNearestNeighborGradConfig3 = {
    kernelName: ResizeNearestNeighborGrad,
    backendName: "webgl",
    kernelFunc: resizeNearestNeighborGrad2
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ReverseProgram = class {
    constructor(xShape, axis) {
      this.variableNames = ["x"];
      const rank = xShape.length;
      if (rank > 4) {
        throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);
      }
      this.outputShape = xShape;
      if (rank === 1) {
        this.userCode = `
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${xShape[0]} - coord - 1));
        }
      `;
        return;
      }
      const getInCoord = (i) => {
        if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
          return `${xShape[i]} - coords[${i}] - 1`;
        }
        return `coords[${i}]`;
      };
      const inCoords = xShape.map((_, i) => getInCoord(i)).join(",");
      const type = getCoordsDataType(rank);
      this.userCode = `
      void main() {
        ${type} coords = getOutputCoords();
        setOutput(getX(${inCoords}));
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ReversePackedProgram = class {
    constructor(xShape, axis) {
      this.variableNames = ["x"];
      this.packedInputs = true;
      this.packedOutput = true;
      const rank = xShape.length;
      if (rank > 4) {
        throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);
      }
      this.outputShape = xShape;
      const channels = getChannels("rc", rank);
      const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;
      const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;
      const type = getCoordsDataType(rank);
      if (rank === 1) {
        this.userCode = `
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${xShape[0]} - rc - 1),
            ${xShape[0]} - rc - 1);
          if(${nextColumn}){
              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),
                ${xShape[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `;
      } else {
        this.userCode = `
        void main() {
          ${type} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${getR(channels.slice())};
          if(${nextColumn}){
            result.g = ${getG(channels.slice())};
          }
          if(${nextRow}) {
            result.b = ${getB(channels.slice())};
            if(${nextColumn}) {
              result.a = ${getA(channels.slice())};
            }
          }
          setOutput(result);
        }
    `;
      }
      function getR(channels2) {
        return getChannel(channels2);
      }
      function getG(channels2) {
        channels2[rank - 1] = "(" + channels2[rank - 1] + ` + 1)`;
        return getChannel(channels2);
      }
      function getB(channels2) {
        channels2[rank - 2] = "(" + channels2[rank - 2] + ` + 1)`;
        return getChannel(channels2);
      }
      function getA(channels2) {
        channels2[rank - 1] = "(" + channels2[rank - 1] + ` + 1)`;
        channels2[rank - 2] = "(" + channels2[rank - 2] + ` + 1)`;
        return getChannel(channels2);
      }
      function getChannel(channels2) {
        const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels2));
        const inCoords = inCoordsArray.join(",");
        const innerDims = inCoordsArray.slice(-2).join(",");
        return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;
      }
      function getInCoord(i, channels1) {
        if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
          return `${xShape[i]} - ${channels1[i]} - 1`;
        } else {
          return `${channels1[i]}`;
        }
      }
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function reverse3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {dims} = attrs;
    const xRank = x.shape.length;
    const $dims = util_exports.parseAxisParam(dims, x.shape);
    if (xRank === 0) {
      return identity3({inputs: {x}, backend: backend2});
    }
    const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new ReversePackedProgram(x.shape, $dims) : new ReverseProgram(x.shape, $dims);
    return backend2.runWebGLProgram(program, [x], x.dtype);
  }
  var reverseConfig2 = {
    kernelName: Reverse,
    backendName: "webgl",
    kernelFunc: reverse3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var RotateProgram = class {
    constructor(imageShape, fillValue) {
      this.variableNames = ["Image"];
      this.outputShape = [];
      const imageHeight = imageShape[1];
      const imageWidth = imageShape[2];
      this.outputShape = imageShape;
      let fillSnippet = "";
      if (typeof fillValue === "number") {
        fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;
      } else {
        fillSnippet = `
        vec3 fill = vec3(${fillValue.join(",")});
        float outputValue = fill[coords[3]];`;
      }
      this.userCode = `
        uniform vec4 params;
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
          int y = coords[1];
          float coordXFloat = (float(x) - params[0]) * params[3] -
            (float(y) - params[1]) * params[2];
          float coordYFloat = (float(x) - params[0]) * params[2] +
            (float(y) - params[1]) * params[3];
          int coordX = int(round(coordXFloat + params[0]));
          int coordY = int(round(coordYFloat + params[1]));
          ${fillSnippet}
          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `;
    }
    getCustomSetupFunc(centerX, centerY, sinFactor, cosFactor) {
      return (gpgpu, webGLProgram) => {
        if (this.paramsLoc == null) {
          this.paramsLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, "params");
        }
        gpgpu.gl.uniform4f(this.paramsLoc, centerX, centerY, sinFactor, cosFactor);
      };
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var rotateWithOffsetConfig2 = {
    kernelName: RotateWithOffset,
    backendName: "webgl",
    kernelFunc: ({inputs, attrs, backend: backend2}) => {
      const {image: image3} = inputs;
      const {radians, fillValue, center} = attrs;
      const webglBackend = backend2;
      const program = new RotateProgram(image3.shape, fillValue);
      const [centerX, centerY] = backend_util_exports.getImageCenter(center, image3.shape[1], image3.shape[2]);
      const customSetup = program.getCustomSetupFunc(centerX, centerY, Math.sin(radians), Math.cos(radians));
      const output = webglBackend.runWebGLProgram(program, [image3], image3.dtype, customSetup);
      return output;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ROUND = `
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`;
  var round4 = unaryKernelFunc2({opSnippet: ROUND});
  var roundConfig2 = {
    kernelName: Round,
    backendName: "webgl",
    kernelFunc: round4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var RSQRT = `return inversesqrt(x);`;
  var rsqrt3 = unaryKernelFunc2({opSnippet: RSQRT, cpuKernelImpl: rsqrtImplCPU});
  var rsqrtConfig2 = {
    kernelName: Rsqrt,
    backendName: "webgl",
    kernelFunc: rsqrt3
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ScatterProgram = class {
    constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {
      this.variableNames = ["updates", "indices", "defaultValue"];
      this.outputShape = shape;
      const stridesType = getCoordsDataType(strides.length);
      const dtype = getCoordsDataType(shape.length);
      let indicesString = "";
      if (indicesRank === 1) {
        indicesString = "i";
      } else if (indicesRank === 2) {
        indicesString = "i, j";
      }
      const indicesSnippet = `getIndices(${indicesString})`;
      let updatesString = "";
      if (updatesRank === 1) {
        updatesString = "i";
      } else if (updatesRank === 2) {
        updatesString = "i, coords[1]";
      }
      const updatesSnippet = `getUpdates(${updatesString})`;
      const strideString = sliceDim > 1 ? "strides[j]" : "strides";
      this.userCode = `
        ${stridesType} strides = ${stridesType}(${strides});

        void main() {
          ${dtype} coords = getOutputCoords();
          float sum = 0.0;
          bool found = false;
          for (int i = 0; i < ${updateSize}; i++) {
            int flattenedIndex = 0;
            for (int j = 0; j < ${sliceDim}; j++) {
              int index = round(${indicesSnippet});
              flattenedIndex += index * ${strideString};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${updatesSnippet};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function scatterNd2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {indices, updates} = inputs;
    const {shape} = attrs;
    const {sliceRank, numUpdates, sliceSize, strides, outputSize} = backend_util_exports.calculateShapes(updates, indices, shape);
    const flattenShape = [outputSize / sliceSize, sliceSize];
    if (outputSize === 0) {
      return backend2.makeTensorInfo(shape, indices.dtype);
    }
    const flattenIndices = reshape4({inputs: {x: indices}, backend: backend2, attrs: {shape: [numUpdates, sliceRank]}});
    const flattenX = reshape4({inputs: {x: updates}, backend: backend2, attrs: {shape: [numUpdates, sliceSize]}});
    const defaultValue = backend2.makeTensorInfo([], "float32", new Float32Array([0]));
    const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape);
    const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices, defaultValue], flattenX.dtype);
    const reshaped = reshape4({inputs: {x: res}, backend: backend2, attrs: {shape}});
    backend2.disposeIntermediateTensorInfo(flattenIndices);
    backend2.disposeIntermediateTensorInfo(flattenX);
    backend2.disposeIntermediateTensorInfo(res);
    backend2.disposeIntermediateTensorInfo(defaultValue);
    return reshaped;
  }
  var scatterNdConfig2 = {
    kernelName: ScatterNd,
    backendName: "webgl",
    kernelFunc: scatterNd2
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SelectProgram = class {
    constructor(cRank, shape, rank) {
      this.variableNames = ["c", "a", "b"];
      this.outputShape = shape;
      let cCoords;
      let abCoords;
      if (rank > 4) {
        throw Error(`Where for rank ${rank} is not yet supported`);
      }
      if (rank === 1) {
        abCoords = `resRC`;
        cCoords = `resRC`;
      } else {
        const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
        const cCoordVars = [];
        const abCoordVars = [];
        for (let i = 0; i < shape.length; i++) {
          abCoordVars.push(`${currentCoords[i]}`);
          if (i < cRank) {
            cCoordVars.push(`${currentCoords[i]}`);
          }
        }
        cCoords = cCoordVars.join();
        abCoords = abCoordVars.join();
      }
      const dtype = getCoordsDataType(rank);
      this.userCode = `
      void main() {
        ${dtype} resRC = getOutputCoords();
        float cVal = getC(${cCoords});
        if (cVal >= 1.0) {
          setOutput(getA(${abCoords}));
        } else {
          setOutput(getB(${abCoords}));
        }
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function select2(args) {
    const {inputs, backend: backend2} = args;
    const {condition, t, e} = inputs;
    const program = new SelectProgram(condition.shape.length, t.shape, t.shape.length);
    return backend2.runWebGLProgram(program, [condition, t, e], upcastType(t.dtype, e.dtype));
  }
  var selectConfig2 = {
    kernelName: Select,
    backendName: "webgl",
    kernelFunc: select2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SELU = `
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${backend_util_exports.SELU_SCALEALPHA};
  float scale = ${backend_util_exports.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;
  var selu3 = unaryKernelFunc2({opSnippet: SELU});
  var seluConfig2 = {
    kernelName: Selu,
    backendName: "webgl",
    kernelFunc: selu3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;
  var sigmoid3 = unaryKernelFunc2({opSnippet: SIGMOID});
  var sigmoidConfig2 = {
    kernelName: Sigmoid,
    backendName: "webgl",
    kernelFunc: sigmoid3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SIGN = `
  if (isnan(x)) { return 0.0; }
  return sign(x);
`;
  var sign3 = unaryKernelFunc2({opSnippet: SIGN});
  var signConfig2 = {
    kernelName: Sign,
    backendName: "webgl",
    kernelFunc: sign3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SIN = CHECK_NAN_SNIPPET_UNARY + `
  return sin(x);
`;
  var sin3 = unaryKernelFunc2({opSnippet: SIN});
  var sinConfig2 = {
    kernelName: Sin,
    backendName: "webgl",
    kernelFunc: sin3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SINH = `
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`;
  var sinh3 = unaryKernelFunc2({opSnippet: SINH});
  var sinhConfig2 = {
    kernelName: Sinh,
    backendName: "webgl",
    kernelFunc: sinh3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SOFTPLUS = `
  float epsilon = 1.1920928955078125e-7;
  float threshold = log(epsilon) + 2.0;

  bool too_large = x > -threshold;
  bool too_small = x < threshold;

  float result;
  float exp_x = exp(x);

  if (too_large){
    result = x;
  }
  else if (too_small){
    result = exp_x;
  }
  else{
    result = log(exp_x + 1.0);
  }
  return result;
`;
  var softplus3 = unaryKernelFunc2({opSnippet: SOFTPLUS});
  var softplusConfig2 = {
    kernelName: Softplus,
    backendName: "webgl",
    kernelFunc: softplus3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var spaceToBatchND3 = (args) => {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {blockShape, paddings} = attrs;
    util_exports.assert(x.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");
    const prod5 = blockShape.reduce((a, b) => a * b);
    const completePaddings = [[0, 0]];
    completePaddings.push(...paddings);
    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
      completePaddings.push([0, 0]);
    }
    const toDispose = [];
    const paddedX = padV22({
      inputs: {x},
      backend: backend2,
      attrs: {paddings: completePaddings, constantValue: 0}
    });
    const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod5, false);
    const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
    const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod5, false);
    const reshapedPaddedX = reshape4({inputs: {x: paddedX}, backend: backend2, attrs: {shape: reshapedPaddedShape}});
    const paddedXT = transpose3({
      inputs: {x: reshapedPaddedX},
      backend: backend2,
      attrs: {perm: permutedReshapedPaddedPermutation}
    });
    const result = reshape4({inputs: {x: paddedXT}, backend: backend2, attrs: {shape: flattenShape}});
    toDispose.push(paddedX);
    toDispose.push(reshapedPaddedX);
    toDispose.push(paddedXT);
    toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return result;
  };
  var spaceToBatchNDConfig2 = {
    kernelName: SpaceToBatchND,
    backendName: "webgl",
    kernelFunc: spaceToBatchND3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sparseToDense3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {sparseIndices, sparseValues, defaultValue} = inputs;
    const {outputShape} = attrs;
    const {sliceRank, numUpdates, strides, outputSize} = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);
    const sumDupeIndices = false;
    const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides, [outputSize, 1], sumDupeIndices);
    const res = backend2.runWebGLProgram(program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype);
    const reshaped = reshape4({inputs: {x: res}, backend: backend2, attrs: {shape: outputShape}});
    backend2.disposeIntermediateTensorInfo(res);
    return reshaped;
  }
  var sparseToDenseConfig2 = {
    kernelName: SparseToDense,
    backendName: "webgl",
    kernelFunc: sparseToDense3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function splitV2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {numOrSizeSplits, axis} = attrs;
    const $axis = util_exports.parseAxisParam(axis, x.shape)[0];
    const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);
    const xRank = x.shape.length;
    const begin = new Array(xRank).fill(0);
    const size = x.shape.slice();
    return splitSizes.map((s) => {
      const sliceSize = [...size];
      sliceSize[$axis] = s;
      const sliceT = slice3({inputs: {x}, backend: backend2, attrs: {begin, size: sliceSize}});
      begin[$axis] += s;
      return sliceT;
    });
  }
  var splitVConfig2 = {
    kernelName: SplitV,
    backendName: "webgl",
    kernelFunc: splitV2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SQRT = `return sqrt(x);`;
  var sqrt3 = unaryKernelFunc2({opSnippet: SQRT});
  var sqrtConfig2 = {
    kernelName: Sqrt,
    backendName: "webgl",
    kernelFunc: sqrt3
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SQUARE = `return x * x;`;
  var square3 = unaryKernelFunc2({opSnippet: SQUARE});
  var squareConfig2 = {
    kernelName: Square,
    backendName: "webgl",
    kernelFunc: square3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SQUARED_DIFFERENCE = "return (a - b) * (a - b);";
  var squaredDifference3 = binaryKernelFunc2({opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE});
  var squaredDifferenceConfig2 = {
    kernelName: SquaredDifference,
    backendName: "webgl",
    kernelFunc: squaredDifference3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function step3({inputs, attrs, backend: backend2}) {
    const {x} = inputs;
    const opSnippet = CHECK_NAN_SNIPPET + `
    return x > 0.0 ? 1.0 : float(${attrs.alpha});
  `;
    const program = new UnaryOpProgram(x.shape, opSnippet);
    return backend2.runWebGLProgram(program, [x], x.dtype);
  }
  var stepConfig2 = {
    kernelName: Step,
    backendName: "webgl",
    kernelFunc: step3
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var StridedSliceProgram = class {
    constructor(begin, strides, size) {
      this.variableNames = ["x"];
      this.outputShape = size;
      const rank = size.length;
      const inputDtype = getCoordsDataType(size.length);
      const dtype = getCoordsDataType(size.length);
      let newCoords = "";
      if (rank === 1) {
        newCoords = "coords * strides + begin";
      } else {
        let outputAxis = 0;
        newCoords = size.map((_, i) => {
          outputAxis++;
          return size.length === 1 ? `coords * strides[${i}] + begin[${i}]` : `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;
        }).join(",");
      }
      this.userCode = `
      ${inputDtype} begin = ${inputDtype}(${begin});
      ${inputDtype} strides = ${inputDtype}(${strides});

      void main() {
        ${dtype} coords = getOutputCoords();
        setOutput(getX(${newCoords}));
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function stridedSlice3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask} = attrs;
    const {nonStrided, $begin, $strides, size, newShape, outShape} = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
    const $x = reshape4({inputs: {x}, backend: backend2, attrs: {shape: newShape}});
    let result;
    if (nonStrided) {
      const sliced = slice3({inputs: {x: $x}, backend: backend2, attrs: {begin: $begin, size}});
      result = reshape4({inputs: {x: sliced}, backend: backend2, attrs: {shape: outShape}});
      backend2.disposeIntermediateTensorInfo(sliced);
    } else if (outShape.some((axis) => axis === 0)) {
      result = backend2.makeTensorInfo(outShape, x.dtype, []);
    } else {
      const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([$x]);
      if (shouldExecuteOnCPU) {
        const xTexData = backend2.texData.get($x.dataId);
        const values = xTexData.values;
        const xBuf = buffer($x.shape, $x.dtype, values);
        const resultValues = stridedSliceImplCPU(outShape, xBuf, $strides, $begin);
        result = backend2.makeTensorInfo(outShape, $x.dtype, resultValues.values);
      } else {
        const program = new StridedSliceProgram($begin, $strides, outShape);
        result = backend2.runWebGLProgram(program, [$x], $x.dtype);
      }
    }
    const resultReshaped = reshape4({inputs: {x: result}, backend: backend2, attrs: {shape: outShape}});
    backend2.disposeIntermediateTensorInfo($x);
    backend2.disposeIntermediateTensorInfo(result);
    return resultReshaped;
  }
  var stridedSliceConfig2 = {
    kernelName: StridedSlice,
    backendName: "webgl",
    kernelFunc: stridedSlice3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var TAN = `return tan(x);`;
  var tan3 = unaryKernelFunc2({opSnippet: TAN});
  var tanConfig2 = {
    kernelName: Tan,
    backendName: "webgl",
    kernelFunc: tan3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var TANH = `
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`;
  var tanh4 = unaryKernelFunc2({opSnippet: TANH});
  var tanhConfig2 = {
    kernelName: Tanh,
    backendName: "webgl",
    kernelFunc: tanh4
  };
  /**
   * @license
   * Copyright 2017 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var TileProgram = class {
    constructor(aShape, reps) {
      this.variableNames = ["A"];
      const outputShape = new Array(aShape.length);
      for (let i = 0; i < outputShape.length; i++) {
        outputShape[i] = aShape[i] * reps[i];
      }
      this.outputShape = outputShape;
      this.rank = outputShape.length;
      const dtype = getCoordsDataType(this.rank);
      const sourceCoords = getSourceCoords3(aShape);
      this.userCode = `
      void main() {
        ${dtype} resRC = getOutputCoords();
        setOutput(getA(${sourceCoords}));
      }
    `;
    }
  };
  function getSourceCoords3(aShape) {
    const rank = aShape.length;
    if (rank > 5) {
      throw Error(`Tile for rank ${rank} is not yet supported`);
    }
    if (rank === 1) {
      return `imod(resRC, ${aShape[0]})`;
    }
    const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"];
    const sourceCoords = [];
    for (let i = 0; i < aShape.length; i++) {
      sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);
    }
    return sourceCoords.join();
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function tile4(params) {
    const {inputs, backend: backend2, attrs} = params;
    const {x} = inputs;
    const {reps} = attrs;
    if (x.dtype === "string") {
      const data = backend2.readSync(x.dataId);
      const decodedData = data.map((d) => util_exports.decodeString(d));
      const buf = buffer(x.shape, x.dtype, decodedData);
      const outBuf = tileImplCPU(buf, reps);
      return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
    }
    const program = new TileProgram(x.shape, reps);
    const output = backend2.runWebGLProgram(program, [x], x.dtype);
    return output;
  }
  var tileConfig2 = {
    kernelName: Tile,
    backendName: "webgl",
    kernelFunc: tile4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function topK2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {k, sorted} = attrs;
    const xVals = backend2.readSync(x.dataId);
    const [allTopKVals, allTopKIndices] = topKImplCPU(xVals, x.shape, x.dtype, k, sorted);
    return [
      backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
      backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
    ];
  }
  var topKConfig2 = {
    kernelName: TopK,
    backendName: "webgl",
    kernelFunc: topK2
  };
  /**
   * @license
   * Copyright 2021 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var TransformProgram = class {
    constructor(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape) {
      this.variableNames = ["Image", "Transforms"];
      this.outputShape = outShape;
      const interpolationModeId = interpolation === "nearest" ? 1 : 2;
      let fillModeId;
      switch (fillMode) {
        case "constant":
          fillModeId = 1;
          break;
        case "reflect":
          fillModeId = 2;
          break;
        case "wrap":
          fillModeId = 3;
          break;
        case "nearest":
          fillModeId = 4;
          break;
        default:
          fillModeId = 1;
          break;
      }
      this.userCode = `
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${fillModeId} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${fillModeId} == 3) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord -= len * float(int(float(inCoord / sz)));
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${fillModeId} == 4) {
                return clamp(outCoord, 0.0, len - 1.0);
              } else {
                return outCoord;
              }
            }

            float readWithFillValue(int batch, int coordY, int coordX,
              int channel) {
              float outputValue;
              if (0 <= coordY && coordY < ${imageHeight} && 0 <= coordX && coordX < ${imageWidth}) {
                  outputValue = getImage(batch, coordY, coordX, channel);
              } else {
                outputValue = float(${fillValue});
              }
              return outputValue;
            }

            void main() {
              ivec4 coords = getOutputCoords();
              float outputValue;
              int batch = coords[0];
              int x = coords[2];
              int y = coords[1];
              int channel = coords[3];
              float xf = float(x);
              float yf = float(y);
              float a1 = getTransforms(batch, 0);
              float a2 = getTransforms(batch, 1);
              float a3 = getTransforms(batch, 2);
              float b1 = getTransforms(batch, 3);
              float b2 = getTransforms(batch, 4);
              float b3 = getTransforms(batch, 5);
              float c1 = getTransforms(batch, 6);
              float c2 = getTransforms(batch, 7);
              float projection = c1 * xf + c2 * yf + 1.0;
              if (projection == 0.0) {
                outputValue = float(${fillValue});
              } else {
                float inX = (a1 * xf + a2 * yf + a3) / projection;
                float inY = (b1 * xf + b2 * yf + b3) / projection;
                float mapX = mapCoord(inX, float(${imageWidth}));
                float mapY = mapCoord(inY, float(${imageHeight}));

                if (${interpolationModeId} == 1) {
                  int coordY = int(round(mapY));
                  int coordX = int(round(mapX));
                  outputValue = readWithFillValue(batch, coordY, coordX,
                    channel);
                } else {
                  float yFloor = floor(mapY);
                  float xFloor = floor(mapX);
                  float yCeil = yFloor + 1.0;
                  float xCeil = xFloor + 1.0;
                  float valueYFloor = (xCeil - mapX) *
                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);
                  float valueYCeil = (xCeil - mapX) *
                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);
                  outputValue = (yCeil - mapY) * valueYFloor +
                  (mapY - yFloor) * valueYCeil;
                }
              }
              setOutput(outputValue);
            }
        `;
    }
  };
  /**
   * @license
   * Copyright 2021 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function transform3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {image: image3, transforms} = inputs;
    const {interpolation, fillMode, fillValue, outputShape} = attrs;
    const [batch, imageHeight, imageWidth, numChannels] = image3.shape;
    const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];
    const outShape = [
      batch,
      outHeight,
      outWidth,
      numChannels
    ];
    const program = new TransformProgram(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape);
    return backend2.runWebGLProgram(program, [image3, transforms], "float32");
  }
  var transformConfig2 = {
    kernelName: Transform,
    backendName: "webgl",
    kernelFunc: transform3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the License);
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an AS IS BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function unique4(args) {
    const {inputs, attrs, backend: backend2} = args;
    const {axis} = attrs;
    const {x} = inputs;
    assertNotComplex2(x, "unique");
    console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
    const values = backend2.readSync(x.dataId);
    const {outputValues, outputShape, indices} = uniqueImplCPU(values, axis, x.shape, x.dtype);
    return [
      backend2.makeTensorInfo(outputShape, x.dtype, outputValues),
      backend2.makeTensorInfo([indices.length], "int32", indices)
    ];
  }
  var uniqueConfig2 = {
    kernelName: Unique,
    backendName: "webgl",
    kernelFunc: unique4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function unpack2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {value} = inputs;
    let {axis} = attrs;
    if (axis < 0) {
      axis += value.shape.length;
    }
    const x = value;
    const xRank = x.shape.length;
    const num = value.shape[axis];
    const outShape = new Array(xRank - 1);
    let outIndex = 0;
    for (let i = 0; i < xRank; i++) {
      if (i !== axis) {
        outShape[outIndex++] = x.shape[i];
      }
    }
    const toDispose = [];
    const begin = new Array(xRank).fill(0);
    const size = x.shape.slice();
    size[axis] = 1;
    const res = new Array(num);
    for (let i = 0; i < res.length; i++) {
      begin[axis] = i;
      const sliced = slice3({inputs: {x}, backend: backend2, attrs: {begin, size}});
      const reshaped = reshape4({inputs: {x: sliced}, backend: backend2, attrs: {shape: outShape}});
      res[i] = reshaped;
      toDispose.push(sliced);
    }
    toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return res;
  }
  var unpackConfig2 = {
    kernelName: Unpack,
    backendName: "webgl",
    kernelFunc: unpack2
  };
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var SegmentOpProgram = class {
    constructor(segOpInfo, segOpType) {
      this.variableNames = ["x", "segmentIds"];
      const windowSize = segOpInfo.windowSize;
      const batchSize = segOpInfo.batchSize;
      const inSize = segOpInfo.inSize;
      const numSegments = segOpInfo.numSegments;
      const outSize = numSegments * Math.ceil(inSize / windowSize);
      this.outputShape = [batchSize, outSize];
      const initializationValue = "0.0";
      const returnValue = `sumValue`;
      const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
      const windowSizeVec4Remainder = windowSize % 4;
      const updateSnippet = `
        sumValue += dot(values, segFilter);
    `;
      let checkValueOutOfBounds = "";
      if (inSize % windowSize > 0) {
        checkValueOutOfBounds = `
        if (inIdx < 0 || inIdx >= ${inSize}) {
          return initializationValue;
        }
      `;
      }
      let checkSegmentIdOutOfBounds = "";
      if (inSize % windowSize > 0) {
        checkSegmentIdOutOfBounds = `
        if (inIdx < 0 || inIdx >= ${inSize}) {
          return -1.0;
        }
      `;
      }
      this.userCode = `
      const float initializationValue = ${initializationValue};

      float getValue(int batch, int inIdx) {
        ${checkValueOutOfBounds}
        return getX(batch, inIdx);
      }

      float getSegmentIdAtIndex(int inIdx) {
        ${checkSegmentIdOutOfBounds}
        return getSegmentIds(inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = int(floor(float(outIdx) / float(
          ${numSegments})) * float(${windowSize}));
        int currentSeg = int(mod(float(outIdx), float(${numSegments})));

        float sumValue = 0.0;

        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
          );

          ${updateSnippet}
        }

        int inIdx = inOffset + ${windowSizeNearestVec4};
        if (${windowSizeVec4Remainder === 1}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            0,
            0,
            0
          );

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder === 2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
              0,
              0
          );

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder === 3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            0
          );

          ${updateSnippet}
        }
        setOutput(${returnValue});
      }
    `;
    }
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function unsortedSegmentSum3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x, segmentIds} = inputs;
    const {numSegments} = attrs;
    const xRank = x.shape.length;
    const toDispose = [];
    let axis = 0;
    const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
    let permutedX = x;
    if (permutation != null) {
      permutedX = transpose3({inputs: {x}, backend: backend2, attrs: {perm: permutation}});
      toDispose.push(permutedX);
      axis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
    }
    const outShape = backend_util_exports.segment_util.computeOutShape(permutedX.shape, axis, numSegments);
    const inSize = util_exports.sizeFromShape([permutedX.shape[axis]]);
    const a2D = reshape4({inputs: {x: permutedX}, backend: backend2, attrs: {shape: [-1, inSize]}});
    toDispose.push(a2D);
    const outputDType = sumOutType(x.dtype);
    const segOpCompute = (x2, segOpType, segmentIds2, dtype, numSegments2) => {
      const batchSize = x2.shape[0];
      const inSize2 = x2.shape[1];
      const windowSize = backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2, numSegments2);
      const segOpInfo = {windowSize, inSize: inSize2, batchSize, numSegments: numSegments2};
      const program = new SegmentOpProgram(segOpInfo, segOpType);
      const output = backend2.compileAndRun(program, [x2, segmentIds2], dtype);
      toDispose.push(output);
      if (output.shape[1] === numSegments2) {
        return output;
      }
      const rangeInfo = range4({
        backend: backend2,
        attrs: {start: 0, stop: numSegments2, step: 1, dtype: "float32"}
      });
      const tileInfo = tile4({
        inputs: {x: rangeInfo},
        backend: backend2,
        attrs: {reps: [inSize2 / windowSize]}
      });
      toDispose.push(rangeInfo);
      toDispose.push(tileInfo);
      const result2 = segOpCompute(output, segOpType, tileInfo, dtype, numSegments2);
      return result2;
    };
    const segOpResult = segOpCompute(a2D, "unsortedSegmentSum", segmentIds, outputDType, numSegments);
    const reshaped = reshape4({inputs: {x: segOpResult}, backend: backend2, attrs: {shape: outShape}});
    let result = reshaped;
    if (permutation != null) {
      toDispose.push(reshaped);
      const perm = backend_util_exports.getUndoAxesPermutation(permutation);
      result = transpose3({inputs: {x: result}, backend: backend2, attrs: {perm}});
    }
    toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
    return result;
  }
  var unsortedSegmentSumConfig2 = {
    kernelName: UnsortedSegmentSum,
    backendName: "webgl",
    kernelFunc: unsortedSegmentSum3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var kernelConfigs2 = [
    LRNConfig,
    LRNGradConfig,
    _fusedMatMulConfig2,
    absConfig2,
    acosConfig2,
    acoshConfig2,
    addConfig2,
    addNConfig2,
    allConfig2,
    anyConfig2,
    argMaxConfig2,
    argMinConfig2,
    asinConfig2,
    asinhConfig2,
    atan2Config2,
    atanConfig2,
    atanhConfig2,
    avgPool3DConfig2,
    avgPoolConfig2,
    avgPoolGrad3DConfig,
    avgPoolGradConfig3,
    batchMatMulConfig2,
    batchNormConfig2,
    batchToSpaceNDConfig2,
    bincountConfig2,
    castConfig2,
    ceilConfig2,
    clipByValueConfig,
    complexAbsConfig2,
    complexConfig2,
    concatConfig2,
    conv2DBackpropFilterConfig2,
    conv2DBackpropInputConfig2,
    conv2DConfig2,
    conv3DBackpropFilterV2Config2,
    conv3DBackpropInputConfig,
    conv3DConfig2,
    cosConfig2,
    coshConfig2,
    cropAndResizeConfig2,
    cumsumConfig2,
    denseBincountConfig2,
    depthToSpaceConfig2,
    depthwiseConv2dNativeBackpropFilterConfig2,
    depthwiseConv2dNativeBackpropInputConfig2,
    depthwiseConv2dNativeConfig2,
    diagConfig2,
    dilation2DConfig,
    eluConfig2,
    eluGradConfig3,
    equalConfig2,
    erfConfig2,
    expConfig2,
    expandDimsConfig2,
    expm1Config2,
    fftConfig2,
    fillConfig2,
    flipLeftRightConfig2,
    floorConfig2,
    floorDivConfig2,
    fromPixelsConfig,
    fusedConv2DConfig2,
    fusedDepthwiseConv2DConfig2,
    gatherNdConfig2,
    gatherV2Config2,
    greaterConfig2,
    greaterEqualConfig2,
    identityConfig2,
    ifftConfig2,
    imagConfig2,
    isFiniteConfig2,
    isInfConfig2,
    isNaNConfig2,
    leakyReluConfig2,
    lessConfig2,
    lessEqualConfig2,
    linSpaceConfig2,
    log1pConfig2,
    logConfig2,
    logicalAndConfig2,
    logicalNotConfig2,
    logicalOrConfig2,
    maxConfig2,
    maxPool3DConfig2,
    maxPoolConfig2,
    maxPoolGrad3DConfig,
    maxPoolGradConfig3,
    maxPoolWithArgmaxConfig2,
    maximumConfig2,
    meanConfig2,
    minConfig2,
    minimumConfig2,
    mirrorPadConfig2,
    modConfig2,
    multinomialConfig2,
    multiplyConfig2,
    negConfig2,
    nonMaxSuppressionV3Config2,
    nonMaxSuppressionV4Config2,
    nonMaxSuppressionV5Config2,
    notEqualConfig2,
    oneHotConfig2,
    onesLikeConfig2,
    packConfig2,
    padV2Config2,
    powConfig2,
    preluConfig2,
    prodConfig2,
    rangeConfig2,
    realConfig2,
    realDivConfig2,
    reciprocalConfig2,
    relu6Config2,
    reluConfig2,
    reshapeConfig2,
    resizeBilinearConfig2,
    resizeBilinearGradConfig3,
    resizeNearestNeighborConfig2,
    resizeNearestNeighborGradConfig3,
    reverseConfig2,
    rotateWithOffsetConfig2,
    roundConfig2,
    rsqrtConfig2,
    scatterNdConfig2,
    selectConfig2,
    seluConfig2,
    sigmoidConfig2,
    signConfig2,
    sinConfig2,
    sinhConfig2,
    sliceConfig2,
    softmaxConfig2,
    softplusConfig2,
    spaceToBatchNDConfig2,
    sparseToDenseConfig2,
    splitVConfig2,
    sqrtConfig2,
    squareConfig2,
    squaredDifferenceConfig2,
    stepConfig2,
    stridedSliceConfig2,
    subConfig2,
    sumConfig2,
    tanConfig2,
    tanhConfig2,
    tileConfig2,
    topKConfig2,
    transformConfig2,
    transposeConfig2,
    uniqueConfig2,
    unpackConfig2,
    unsortedSegmentSumConfig2,
    zerosLikeConfig2
  ];
  for (const kernelConfig of kernelConfigs2) {
    registerKernel(kernelConfig);
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  /** @license See the LICENSE file. */
  var version7 = "3.3.0";
  /**
   * @license
   * Copyright 2018 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var version8 = {
    "tfjs-core": version,
    "tfjs-backend-cpu": version5,
    "tfjs-backend-webgl": version6,
    "tfjs-data": version4,
    "tfjs-layers": version2,
    "tfjs-converter": version3,
    tfjs: version7
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var CppDType;
  (function(CppDType2) {
    CppDType2[CppDType2["float32"] = 0] = "float32";
    CppDType2[CppDType2["int32"] = 1] = "int32";
    CppDType2[CppDType2["bool"] = 2] = "bool";
    CppDType2[CppDType2["string"] = 3] = "string";
    CppDType2[CppDType2["complex64"] = 4] = "complex64";
  })(CppDType || (CppDType = {}));
  var FusableActivation;
  (function(FusableActivation2) {
    FusableActivation2[FusableActivation2["linear"] = 0] = "linear";
    FusableActivation2[FusableActivation2["relu"] = 1] = "relu";
    FusableActivation2[FusableActivation2["relu6"] = 2] = "relu6";
    FusableActivation2[FusableActivation2["prelu"] = 3] = "prelu";
    FusableActivation2[FusableActivation2["leakyrelu"] = 4] = "leakyrelu";
  })(FusableActivation || (FusableActivation = {}));
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmFusedMatMul;
  function setup(backend2) {
    wasmFusedMatMul = backend2.wasm.cwrap(_FusedMatMul, null, [
      "number",
      "array",
      "number",
      "number",
      "array",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function fusedBatchMatMul(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {a, b, bias, preluActivationWeights} = inputs;
    if (a.dtype !== "float32" || b.dtype !== "float32") {
      throw new Error(`_FusedMatMul for non non-float32 tensors not yet supported.`);
    }
    const {transposeA, transposeB, activation: activation2, leakyreluAlpha} = attrs;
    const aId = backend2.dataIdMap.get(a.dataId).id;
    const bId = backend2.dataIdMap.get(b.dataId).id;
    let biasId = 0;
    if (bias != null) {
      const biasData = backend2.dataIdMap.get(bias.dataId);
      if (biasData.shape.length !== 1) {
        throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);
      }
      biasId = biasData.id;
    }
    const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;
    const fusedActivation = FusableActivation[activation2];
    if (fusedActivation == null) {
      throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);
    }
    const leftDim = transposeA ? a.shape[2] : a.shape[1];
    const rightDim = transposeB ? b.shape[1] : b.shape[2];
    const batchDim = a.shape[0];
    const out = backend2.makeOutput([batchDim, leftDim, rightDim], a.dtype);
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer);
    const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);
    wasmFusedMatMul(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, transposeA, transposeB, fusedActivation, biasId, preluActivationWeightsId, leakyreluAlpha || 0, outId);
    return out;
  }
  var fusedMatMulConfig = {
    kernelName: _FusedMatMul,
    backendName: "wasm",
    setupFunc: setup,
    kernelFunc: fusedBatchMatMul
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function createUnaryKernelConfig(kernelName) {
    let wasmFunc9;
    function setupFunc3(backend2) {
      wasmFunc9 = backend2.wasm.cwrap(kernelName, null, ["number", "number"]);
    }
    function kernelFunc3(args) {
      const {backend: backend2, inputs: {x}} = args;
      const xId = backend2.dataIdMap.get(x.dataId).id;
      const out = backend2.makeOutput(x.shape, x.dtype);
      const outId = backend2.dataIdMap.get(out.dataId).id;
      if (util_exports.sizeFromShape(out.shape) === 0) {
        return out;
      }
      wasmFunc9(xId, outId);
      return out;
    }
    return {kernelName, backendName: "wasm", setupFunc: setupFunc3, kernelFunc: kernelFunc3};
  }
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var absConfig3 = createUnaryKernelConfig(Abs);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function createBinaryKernelConfig(kernelName, supportsFullBroadcast17, dtype) {
    let wasmFunc9;
    function setupFunc3(backend2) {
      wasmFunc9 = backend2.wasm.cwrap(kernelName, null, [
        "number",
        "array",
        "number",
        "number",
        "array",
        "number",
        "number",
        "number"
      ]);
    }
    function kernelFunc3(args) {
      const {backend: backend2, inputs} = args;
      const {a, b} = inputs;
      const aId = backend2.dataIdMap.get(a.dataId).id;
      const bId = backend2.dataIdMap.get(b.dataId).id;
      const outputType = dtype != null ? dtype : a.dtype;
      const newShape = backend_util_exports.assertAndGetBroadcastShape(a.shape, b.shape);
      const out = backend2.makeOutput(newShape, outputType);
      if (util_exports.sizeFromShape(newShape) === 0) {
        return out;
      }
      const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer);
      const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);
      const outId = backend2.dataIdMap.get(out.dataId).id;
      const kernelFunc4 = () => wasmFunc9(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, CppDType[a.dtype], outId);
      if (supportsFullBroadcast17 && a.dtype === "float32") {
        kernelFunc4();
        return out;
      }
      const aBroadcastDims = backend_util_exports.getBroadcastDims(a.shape, newShape);
      const bBroadcastDims = backend_util_exports.getBroadcastDims(b.shape, newShape);
      const loopsOverAllOfA = aBroadcastDims.every((v, i) => v === i);
      const loopsOverAllOfB = bBroadcastDims.every((v, i) => v === i);
      if (loopsOverAllOfA && loopsOverAllOfB) {
        kernelFunc4();
        return out;
      } else {
        throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`);
      }
    }
    return {kernelName, backendName: "wasm", setupFunc: setupFunc3, kernelFunc: kernelFunc3};
  }
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast = true;
  var addConfig3 = createBinaryKernelConfig(Add, supportsFullBroadcast);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmFunc;
  function setupFunc(backend2) {
    wasmFunc = backend2.wasm.cwrap(AddN, null, [
      "array",
      "number",
      "number",
      "number"
    ]);
  }
  function addn(args) {
    const {inputs, backend: backend2} = args;
    const out = backend2.makeOutput(inputs[0].shape, inputs[0].dtype);
    if (util_exports.sizeFromShape(out.shape) === 0) {
      return out;
    }
    const inputIds = inputs.map((x) => backend2.dataIdMap.get(x.dataId).id);
    const inputIdsBytes = new Uint8Array(new Int32Array(inputIds).buffer);
    const outId = backend2.dataIdMap.get(out.dataId).id;
    wasmFunc(inputIdsBytes, inputIds.length, CppDType[out.dtype], outId);
    return out;
  }
  var addNConfig3 = {
    kernelName: AddN,
    backendName: "wasm",
    setupFunc,
    kernelFunc: addn
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function identity4(args) {
    const {inputs: {x}, backend: backend2} = args;
    const out = backend2.makeOutput(x.shape, x.dtype);
    const inVals = backend2.typedArrayFromHeap(x);
    const outVals = backend2.typedArrayFromHeap(out);
    outVals.set(inVals);
    return out;
  }
  var identityConfig3 = {
    kernelName: Identity,
    backendName: "wasm",
    kernelFunc: identity4
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmTranspose;
  function setup2(backend2) {
    wasmTranspose = backend2.wasm.cwrap(Transpose, null, [
      "number",
      "array",
      "number",
      "number",
      "number",
      "array",
      "number"
    ]);
  }
  function transpose4(args) {
    const {inputs, backend: backend2, attrs} = args;
    const [reducedShape, perm] = removeOneSizeDims(inputs.x.shape, attrs.perm);
    let permIsNoOp = true;
    for (let i = 0; i < perm.length; i++) {
      if (perm[i] !== i) {
        permIsNoOp = false;
      }
    }
    const outShape = computeOutShape4(inputs.x.shape, attrs.perm);
    const x = {
      dataId: inputs.x.dataId,
      shape: reducedShape,
      dtype: inputs.x.dtype
    };
    if (permIsNoOp) {
      const cloned = identity4({inputs, backend: backend2});
      cloned.shape = outShape;
      return cloned;
    }
    const out = backend2.makeOutput(outShape, x.dtype);
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const permBytes = new Uint8Array(new Int32Array(perm).buffer);
    const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
    wasmTranspose(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], outId, permBytes, perm.length);
    return out;
  }
  function computeOutShape4(inShape, perm) {
    const outShape = new Array(inShape.length);
    for (let i = 0; i < outShape.length; i++) {
      outShape[i] = inShape[perm[i]];
    }
    return outShape;
  }
  function removeOneSizeDims(shape, perm) {
    const newShape = [];
    const newPerm = [];
    for (let i = 0; i < shape.length; ++i) {
      if (shape[i] !== 1) {
        newShape.push(shape[i]);
      }
      if (shape[perm[i]] !== 1) {
        newPerm.push(perm[i]);
      }
    }
    for (let i = 0; i < newPerm.length; ++i) {
      let minValIdx = -1;
      for (let j = 0; j < newPerm.length; ++j) {
        if (newPerm[j] >= i && (minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) {
          minValIdx = j;
        }
      }
      newPerm[minValIdx] = i;
    }
    return [newShape, newPerm];
  }
  var transposeConfig3 = {
    kernelName: Transpose,
    backendName: "wasm",
    kernelFunc: transpose4,
    setupFunc: setup2
  };
  /**
   * @license
   * Copyright 2020 Google Inc. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function permuteAxesAndTranspose(x, axis, backend2) {
    const xShape = x.shape;
    const xRank = x.shape.length;
    const originalAxes = util_exports.parseAxisParam(axis, xShape);
    let axes = originalAxes;
    const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
    let xTransposed = null;
    let inputWasTransposed = false;
    if (permutedAxes != null) {
      const newShape = new Array(xRank);
      for (let i = 0; i < newShape.length; i++) {
        newShape[i] = xShape[permutedAxes[i]];
      }
      axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
      xTransposed = transpose4({inputs: {x}, attrs: {perm: permutedAxes}, backend: backend2});
      const xId = backend2.dataIdMap.get(x.dataId).id;
      const transposedId = backend2.dataIdMap.get(xTransposed.dataId).id;
      if (transposedId !== xId) {
        inputWasTransposed = true;
      }
    }
    return {transposed: xTransposed, originalAxes, axes, inputWasTransposed};
  }
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmFunc2;
  function setup3(backend2) {
    wasmFunc2 = backend2.wasm.cwrap(ArgMax, null, [
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function argmax(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {axis} = attrs;
    const {x} = inputs;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    let inputId = xId;
    let input2 = x;
    const {transposed, axes, inputWasTransposed} = permuteAxesAndTranspose(x, axis, backend2);
    if (inputWasTransposed) {
      const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
      if (transposedId !== xId) {
        input2 = transposed;
        inputId = transposedId;
      }
    }
    const outShape = input2.shape.slice(0, -1);
    const out = backend2.makeOutput(outShape, "int32");
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const outerSize = util_exports.sizeFromShape(out.shape);
    const innerSize = input2.shape[axes[0]];
    wasmFunc2(inputId, CppDType[input2.dtype], outerSize, innerSize, outId);
    if (inputWasTransposed) {
      backend2.disposeData(transposed.dataId);
    }
    return out;
  }
  var argMaxConfig3 = {
    kernelName: ArgMax,
    backendName: "wasm",
    kernelFunc: argmax,
    setupFunc: setup3
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmAvgPool;
  function setup4(backend2) {
    wasmAvgPool = backend2.wasm.cwrap(AvgPool, null, [
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function avgPool4(args) {
    const {inputs, attrs, backend: backend2} = args;
    const x = inputs.x;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const {filterSize, strides, pad: pad3, dimRoundingMode} = attrs;
    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const padTop = convInfo.padInfo.top;
    const padRight = convInfo.padInfo.right;
    const padBottom = convInfo.padInfo.bottom;
    const padLeft = convInfo.padInfo.left;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const channels = convInfo.inChannels;
    if (convInfo.dataFormat !== "channelsLast") {
      throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
    }
    if (convInfo.dilationWidth !== 1 || convInfo.dilationHeight !== 1) {
      throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);
    }
    const out = backend2.makeOutput(convInfo.outShape, "float32");
    const outId = backend2.dataIdMap.get(out.dataId).id;
    wasmAvgPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, strideHeight, strideWidth, channels, outId);
    return out;
  }
  var avgPoolConfig3 = {
    kernelName: AvgPool,
    backendName: "wasm",
    setupFunc: setup4,
    kernelFunc: avgPool4
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function reshape5(args) {
    const {inputs, attrs} = args;
    const {x} = inputs;
    const {shape} = attrs;
    const xSize = util_exports.sizeFromShape(x.shape);
    const $shape = util_exports.inferFromImplicitShape(shape, xSize);
    util_exports.assert(xSize === util_exports.sizeFromShape($shape), () => `new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`);
    args.backend.incRef(x.dataId);
    return {dataId: x.dataId, shape: $shape, dtype: x.dtype};
  }
  var reshapeConfig3 = {
    kernelName: Reshape,
    backendName: "wasm",
    kernelFunc: reshape5
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmBatchMatMul;
  function setup5(backend2) {
    wasmBatchMatMul = backend2.wasm.cwrap(BatchMatMul, null, [
      "number",
      "array",
      "number",
      "number",
      "array",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function batchMatMul3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {a, b} = inputs;
    const {transposeA, transposeB} = attrs;
    if (a.dtype !== "float32" || b.dtype !== "float32") {
      throw new Error(`BatchMatMul for non non-float32 tensors not yet supported.`);
    }
    const aRank = a.shape.length;
    const bRank = b.shape.length;
    const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];
    const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
    const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];
    const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
    const outerDimsA = a.shape.slice(0, -2);
    const outerDimsB = b.shape.slice(0, -2);
    const batchDimA = util_exports.sizeFromShape(outerDimsA);
    const batchDimB = util_exports.sizeFromShape(outerDimsB);
    const batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;
    util_exports.assert(aRank >= 2 && bRank >= 2 && batchDimsCompatible, () => `Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);
    const outShapeOuterDims = batchDimA > batchDimB ? a.shape.slice(0, -2) : b.shape.slice(0, -2);
    const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
    util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
    const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];
    const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];
    const a3d = reshape5({inputs: {x: a}, backend: backend2, attrs: {shape: a3dShape}});
    const b3d = reshape5({inputs: {x: b}, backend: backend2, attrs: {shape: b3dShape}});
    const a3dId = backend2.dataIdMap.get(a3d.dataId).id;
    const b3dId = backend2.dataIdMap.get(b3d.dataId).id;
    const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];
    const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];
    const batchDim = Math.max(batchDimA, batchDimB);
    const out = backend2.makeOutput([batchDim, leftDim, rightDim], a3d.dtype);
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const aShapeBytes = new Uint8Array(new Int32Array(a3d.shape).buffer);
    const bShapeBytes = new Uint8Array(new Int32Array(b3d.shape).buffer);
    wasmBatchMatMul(a3dId, aShapeBytes, a3d.shape.length, b3dId, bShapeBytes, b3d.shape.length, transposeA, transposeB, outId);
    backend2.disposeData(a3d.dataId);
    backend2.disposeData(b3d.dataId);
    out.shape = outShape;
    return out;
  }
  var batchMatMulConfig3 = {
    kernelName: BatchMatMul,
    backendName: "wasm",
    setupFunc: setup5,
    kernelFunc: batchMatMul3
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function cast5(args) {
    const {inputs: {x}, attrs: {dtype}, backend: backend2} = args;
    const out = backend2.makeOutput(x.shape, dtype);
    const inVals = backend2.typedArrayFromHeap(x);
    const outVals = backend2.typedArrayFromHeap(out);
    outVals.set(inVals);
    return out;
  }
  var castConfig3 = {
    kernelName: Cast,
    backendName: "wasm",
    kernelFunc: cast5
  };
  /**
   * @license
   * Copyright 2021 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ceilConfig3 = createUnaryKernelConfig(Ceil);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmClip;
  function setup6(backend2) {
    wasmClip = backend2.wasm.cwrap(ClipByValue, null, [
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function clip2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {clipValueMin, clipValueMax} = attrs;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const out = backend2.makeOutput(x.shape, x.dtype);
    const outId = backend2.dataIdMap.get(out.dataId).id;
    wasmClip(xId, clipValueMin, clipValueMax, outId);
    return out;
  }
  var clipByValueConfig2 = {
    kernelName: ClipByValue,
    backendName: "wasm",
    setupFunc: setup6,
    kernelFunc: clip2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function concatImpl3(inputs, outShape, dtype, simplyConcat) {
    const outVals = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(outShape));
    if (simplyConcat && dtype !== "string") {
      let offset = 0;
      inputs.forEach((input2) => {
        const size = util_exports.sizeFromShape(input2.shape);
        outVals.set(input2.vals, offset);
        offset += size;
      });
    } else {
      let colOffset = 0;
      inputs.forEach((input2) => {
        const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(input2.vals) : input2.vals;
        let tIdx = 0;
        for (let row = 0; row < input2.shape[0]; ++row) {
          const resIdx = row * outShape[1] + colOffset;
          for (let col = 0; col < input2.shape[1]; ++col) {
            outVals[resIdx + col] = decodedData[tIdx++];
          }
        }
        colOffset += input2.shape[1];
      });
    }
    return outVals;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function rangeImpl2(start, stop, step5, dtype) {
    const sameStartStop = start === stop;
    const increasingRangeNegativeStep = start < stop && step5 < 0;
    const decreasingRangePositiveStep = stop < start && step5 > 1;
    if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) {
      return util_exports.makeZerosTypedArray(0, dtype);
    }
    const numElements = Math.abs(Math.ceil((stop - start) / step5));
    const values = util_exports.makeZerosTypedArray(numElements, dtype);
    if (stop < start && step5 === 1) {
      step5 = -1;
    }
    values[0] = start;
    for (let i = 1; i < values.length; i++) {
      values[i] = values[i - 1] + step5;
    }
    return values;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function sliceImpl2(vals, begin, size, shape, dtype) {
    const isContinous = slice_util_exports.isSliceContinous(shape, begin, size);
    const length = util_exports.sizeFromShape(size);
    const xStrides = util_exports.computeStrides(shape);
    if (isContinous) {
      const flatOffset = slice_util_exports.computeFlatOffset(begin, xStrides);
      if (dtype === "string") {
        return vals.slice(flatOffset, flatOffset + length);
      }
      return vals.subarray(flatOffset, flatOffset + length);
    }
    const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(vals) : vals;
    const inBuf = buffer(shape, dtype, decodedData);
    const outBuf = buffer(size, dtype);
    for (let i = 0; i < outBuf.size; ++i) {
      const outLoc = outBuf.indexToLoc(i);
      const inLoc = outLoc.map((idx, j) => idx + begin[j]);
      outBuf.set(inBuf.get(...inLoc), ...outLoc);
    }
    if (dtype === "string") {
      return backend_util_exports.fromStringArrayToUint8(outBuf.values);
    }
    return outBuf.values;
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function concat4(args) {
    const {inputs, backend: backend2} = args;
    const axis = util_exports.parseAxisParam(args.attrs.axis, inputs[0].shape)[0];
    let outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);
    const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);
    if ($inputs.length === 1) {
      return identity4({inputs: {x: $inputs[0]}, backend: backend2});
    }
    const out = backend2.makeOutput(outShape, inputs[0].dtype);
    if (util_exports.sizeFromShape(outShape) === 0) {
      return out;
    }
    const shapes = $inputs.map((t) => t.shape);
    backend_util_exports.assertParamsConsistent(shapes, axis);
    if ($inputs[0].dtype === "string") {
      const inputs2D = $inputs.map((t) => {
        const innerSize = util_exports.sizeFromShape(t.shape.slice(axis));
        const shape = [-1, innerSize];
        return reshape5({inputs: {x: t}, backend: backend2, attrs: {shape}});
      });
      const inputsValShapes = inputs2D.map((t) => {
        return {vals: backend2.readSync(t.dataId), shape: t.shape};
      });
      outShape = backend_util_exports.computeOutShape(inputs2D.map((t) => t.shape), 1);
      const simplyConcat = inputs2D[0].shape[0] === 1;
      const outVals2 = concatImpl3(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);
      const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t) => t.shape), axis);
      out.shape = finalOutShape;
      const outData = backend2.dataIdMap.get(out.dataId);
      outData.stringBytes = backend_util_exports.fromStringArrayToUint8(outVals2);
      inputs2D.forEach((t) => backend2.disposeData(t.dataId));
      return out;
    }
    const batchDim = util_exports.sizeFromShape($inputs[0].shape.slice(0, axis));
    let sumInnerDims = 0;
    const innerDims = $inputs.map((input2) => {
      const innerDim = util_exports.sizeFromShape(input2.shape.slice(axis));
      sumInnerDims += innerDim;
      return innerDim;
    });
    const inVals = $inputs.map((input2) => backend2.typedArrayFromHeap(input2));
    const outVals = backend2.typedArrayFromHeap(out);
    for (let b = 0; b < batchDim; b++) {
      let outOffset = b * sumInnerDims;
      for (let i = 0; i < inVals.length; i++) {
        const innerDim = innerDims[i];
        const inOffset = b * innerDim;
        const vals = inVals[i].subarray(inOffset, inOffset + innerDim);
        outVals.set(vals, outOffset);
        outOffset += innerDim;
      }
    }
    return out;
  }
  var concatConfig3 = {
    kernelName: Concat,
    backendName: "wasm",
    kernelFunc: concat4
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmConv2d;
  function setup7(backend2) {
    wasmConv2d = backend2.wasm.cwrap(Conv2D, null, [
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function conv2d5(args) {
    const {inputs, attrs, backend: backend2} = args;
    const {x, filter} = inputs;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const filterId = backend2.dataIdMap.get(filter.dataId).id;
    const {strides, dilations, pad: pad3, dimRoundingMode, dataFormat} = attrs;
    const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
    const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const padTop = convInfo.padInfo.top;
    const padRight = convInfo.padInfo.right;
    const padBottom = convInfo.padInfo.bottom;
    const padLeft = convInfo.padInfo.left;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const inputChannels = convInfo.inChannels;
    const outputChannels = convInfo.outChannels;
    const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
    if (convInfo.dataFormat !== "channelsLast") {
      throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
    }
    const out = backend2.makeOutput(convInfo.outShape, "float32");
    const outId = backend2.dataIdMap.get(out.dataId).id;
    wasmConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);
    return out;
  }
  var conv2DConfig3 = {
    kernelName: Conv2D,
    backendName: "wasm",
    setupFunc: setup7,
    kernelFunc: conv2d5
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmConv2DBackpropInput;
  function setup8(backend2) {
    wasmConv2DBackpropInput = backend2.wasm.cwrap(Conv2DBackpropInput, null, [
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function conv2DBackpropInput4(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {dy, filter} = inputs;
    const {strides, pad: pad3, dataFormat, dimRoundingMode, inputShape} = attrs;
    const dilations = 1;
    const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
    const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);
    const {batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth} = convInfo;
    const topPad = filterHeight - 1 - convInfo.padInfo.top;
    const leftPad = filterWidth - 1 - convInfo.padInfo.left;
    const isChannelsLast = convInfo.dataFormat === "channelsLast";
    const dxStrides = util_exports.computeStrides(convInfo.inShape);
    const dyStrides = util_exports.computeStrides(dy.shape);
    const [fltS0, fltS1, fltS2] = util_exports.computeStrides(filter.shape);
    const xBatchStride = dxStrides[0];
    const xRowStride = isChannelsLast ? dxStrides[1] : dxStrides[2];
    const xColStride = isChannelsLast ? dxStrides[2] : 1;
    const xChannelStride = isChannelsLast ? 1 : dxStrides[1];
    const yBatchStride = dyStrides[0];
    const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];
    const yColStride = isChannelsLast ? dyStrides[2] : 1;
    const yChannelStride = isChannelsLast ? 1 : dyStrides[1];
    const out = backend2.makeOutput(convInfo.inShape, "float32");
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const dyId = backend2.dataIdMap.get(dy.dataId).id;
    const filterId = backend2.dataIdMap.get(filter.dataId).id;
    wasmConv2DBackpropInput(dyId, filterId, batchSize, filterHeight, filterWidth, inHeight, inWidth, inChannels, outHeight, outWidth, outChannels, strideHeight, strideWidth, topPad, leftPad, fltS0, fltS1, fltS2, xBatchStride, xRowStride, xColStride, xChannelStride, yBatchStride, yRowStride, yColStride, yChannelStride, outId);
    return out;
  }
  var conv2DBackpropInputConfig3 = {
    kernelName: Conv2DBackpropInput,
    backendName: "wasm",
    setupFunc: setup8,
    kernelFunc: conv2DBackpropInput4
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var cosConfig3 = createUnaryKernelConfig(Cos);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var InterpolationMethod;
  (function(InterpolationMethod2) {
    InterpolationMethod2[InterpolationMethod2["bilinear"] = 0] = "bilinear";
    InterpolationMethod2[InterpolationMethod2["nearest"] = 1] = "nearest";
  })(InterpolationMethod || (InterpolationMethod = {}));
  var wasmCropAndResize;
  function setup9(backend2) {
    wasmCropAndResize = backend2.wasm.cwrap(CropAndResize, null, [
      "number",
      "number",
      "number",
      "number",
      "array",
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function cropAndResize4(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {method, extrapolationValue, cropSize} = attrs;
    const {image: image3, boxes, boxInd} = inputs;
    const numBoxes = boxes.shape[0];
    const [cropHeight, cropWidth] = cropSize;
    const outShape = [numBoxes, cropHeight, cropWidth, image3.shape[3]];
    let imagesData = backend2.dataIdMap.get(image3.dataId);
    let castedData;
    if (image3.dtype !== "float32") {
      castedData = cast5({backend: backend2, inputs: {x: image3}, attrs: {dtype: "float32"}});
      imagesData = backend2.dataIdMap.get(castedData.dataId);
    }
    const imagesId = imagesData.id;
    const boxesId = backend2.dataIdMap.get(boxes.dataId).id;
    const boxIndId = backend2.dataIdMap.get(boxInd.dataId).id;
    const out = backend2.makeOutput(outShape, "float32");
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const imagesShapeBytes = new Uint8Array(new Int32Array(image3.shape).buffer);
    wasmCropAndResize(imagesId, boxesId, boxIndId, numBoxes, imagesShapeBytes, cropHeight, cropWidth, InterpolationMethod[method], extrapolationValue, outId);
    if (castedData != null) {
      backend2.disposeData(castedData.dataId);
    }
    return out;
  }
  var cropAndResizeConfig3 = {
    kernelName: CropAndResize,
    backendName: "wasm",
    setupFunc: setup9,
    kernelFunc: cropAndResize4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmCumsum;
  function setup10(backend2) {
    wasmCumsum = backend2.wasm.cwrap(Cumsum, null, [
      "number",
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function cumsum4(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {axis, exclusive, reverse: reverse5} = attrs;
    const xRank = x.shape.length;
    util_exports.assert(x.dtype === "float32" || x.dtype === "int32", () => `cumsum does not support ${x.dtype} tensors in the WASM backend`);
    const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
    let permutedX = x;
    if (permutation !== null) {
      permutedX = transpose4({inputs: {x}, attrs: {perm: permutation}, backend: backend2});
    }
    const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
    backend_util_exports.assertAxesAreInnerMostDims("cumsum", [permutedAxis], xRank);
    const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype);
    const finalDim = permutedX.shape[permutedAxis];
    const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id;
    const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id;
    wasmCumsum(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]);
    let out = permutedOut;
    if (permutation !== null) {
      const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation);
      out = transpose4({inputs: {x: permutedOut}, attrs: {perm: undoPermutation}, backend: backend2});
      backend2.disposeData(permutedX.dataId);
      backend2.disposeData(permutedOut.dataId);
    }
    return out;
  }
  var cumsumConfig3 = {
    kernelName: Cumsum,
    backendName: "wasm",
    setupFunc: setup10,
    kernelFunc: cumsum4
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmDepthToSpace;
  function setup11(backend2) {
    wasmDepthToSpace = backend2.wasm.cwrap(DepthToSpace, null, [
      "number",
      "number",
      "number",
      "array",
      "number",
      "array",
      "array",
      "number",
      "number"
    ]);
  }
  function depthToSpace4(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {x} = inputs;
    const {blockSize, dataFormat} = attrs;
    util_exports.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);
    const batchSize = x.shape[0];
    const inputHeight = dataFormat === "NHWC" ? x.shape[1] : x.shape[2];
    const inputWidth = dataFormat === "NHWC" ? x.shape[2] : x.shape[3];
    const inputDepth = dataFormat === "NHWC" ? x.shape[3] : x.shape[1];
    const outputHeight = inputHeight * blockSize;
    const outputWidth = inputWidth * blockSize;
    const outputDepth = inputDepth / (blockSize * blockSize);
    const outputShape = dataFormat === "NHWC" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];
    const out = backend2.makeOutput(outputShape, "float32");
    const xData = backend2.dataIdMap.get(x.dataId);
    const xId = xData.id;
    const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer);
    const outputShapeBytes = new Uint8Array(new Int32Array(outputShape).buffer);
    const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer);
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const channelsLast = dataFormat === "NHWC" ? 1 : 0;
    wasmDepthToSpace(xId, blockSize, channelsLast, xStridesBytes, x.shape.length - 1, outputShapeBytes, outStridesBytes, outputShape.length, outId);
    return out;
  }
  var depthToSpaceConfig3 = {
    kernelName: DepthToSpace,
    backendName: "wasm",
    setupFunc: setup11,
    kernelFunc: depthToSpace4
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmDepthwiseConv2d;
  function setup12(backend2) {
    wasmDepthwiseConv2d = backend2.wasm.cwrap(DepthwiseConv2dNative, null, [
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function depthwiseConv2d5(args) {
    const {inputs, attrs, backend: backend2} = args;
    const {x, filter} = inputs;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const filterId = backend2.dataIdMap.get(filter.dataId).id;
    const {strides, dilations, pad: pad3, dimRoundingMode} = attrs;
    const $dilations = dilations == null ? [1, 1] : dilations;
    const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const padTop = convInfo.padInfo.top;
    const padRight = convInfo.padInfo.right;
    const padBottom = convInfo.padInfo.bottom;
    const padLeft = convInfo.padInfo.left;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const inputChannels = convInfo.inChannels;
    const outputChannels = convInfo.outChannels;
    const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
    if (convInfo.dataFormat !== "channelsLast") {
      throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
    }
    const out = backend2.makeOutput(convInfo.outShape, "float32");
    const outId = backend2.dataIdMap.get(out.dataId).id;
    wasmDepthwiseConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);
    return out;
  }
  var depthwiseConv2dNativeConfig3 = {
    kernelName: DepthwiseConv2dNative,
    backendName: "wasm",
    setupFunc: setup12,
    kernelFunc: depthwiseConv2d5
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast2 = false;
  var equalConfig3 = createBinaryKernelConfig(Equal, supportsFullBroadcast2, "bool");
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var expConfig3 = createUnaryKernelConfig(Exp);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function expandDims5(args) {
    const {inputs, attrs, backend: backend2} = args;
    const {input: input2} = inputs;
    const {dim} = attrs;
    const inputRank = input2.shape.length;
    const newShape = input2.shape.slice();
    let $dim = dim;
    if (dim < 0) {
      util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);
      $dim = inputRank + dim + 1;
    }
    newShape.splice($dim, 0, 1);
    return reshape5({inputs: {x: input2}, backend: backend2, attrs: {shape: newShape}});
  }
  var expandDimsConfig3 = {
    kernelName: ExpandDims,
    backendName: "wasm",
    kernelFunc: expandDims5
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function fill4(args) {
    const {attrs: {shape, value, dtype}, backend: backend2} = args;
    const out = backend2.makeOutput(shape, dtype);
    const outVals = backend2.typedArrayFromHeap(out);
    outVals.fill(value);
    return out;
  }
  var fillConfig3 = {
    kernelName: Fill,
    backendName: "wasm",
    kernelFunc: fill4
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmFlipLeftRight;
  function setup13(backend2) {
    wasmFlipLeftRight = backend2.wasm.cwrap(FlipLeftRight, null, [
      "number",
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function flipLeftRight2(args) {
    const {inputs, backend: backend2} = args;
    const {image: image3} = inputs;
    const out = backend2.makeOutput(image3.shape, image3.dtype);
    const imageId = backend2.dataIdMap.get(image3.dataId).id;
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const [batch, imageHeight, imageWidth, numChannels] = image3.shape;
    wasmFlipLeftRight(imageId, batch, imageHeight, imageWidth, numChannels, outId);
    return out;
  }
  var flipLeftRightConfig3 = {
    kernelName: FlipLeftRight,
    backendName: "wasm",
    kernelFunc: flipLeftRight2,
    setupFunc: setup13
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var floorConfig3 = createUnaryKernelConfig(Floor);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast3 = false;
  var floorDivConfig3 = createBinaryKernelConfig(FloorDiv, supportsFullBroadcast3);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmBatchNorm;
  function setup14(backend2) {
    wasmBatchNorm = backend2.wasm.cwrap(FusedBatchNorm, null, ["number", "number", "number", "number", "number", "number", "number"]);
  }
  function fusedBatchNorm(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {varianceEpsilon} = attrs;
    const {x, mean: mean4, variance, offset, scale: scale22} = inputs;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const meanId = backend2.dataIdMap.get(mean4.dataId).id;
    const varianceId = backend2.dataIdMap.get(variance.dataId).id;
    const offsetId = offset != null ? backend2.dataIdMap.get(offset.dataId).id : 0;
    const scaleId = scale22 != null ? backend2.dataIdMap.get(scale22.dataId).id : 0;
    const out = backend2.makeOutput(x.shape, x.dtype);
    if (util_exports.sizeFromShape(x.shape) === 0) {
      return out;
    }
    const outId = backend2.dataIdMap.get(out.dataId).id;
    wasmBatchNorm(xId, meanId, varianceId, offsetId, scaleId, varianceEpsilon, outId);
    return out;
  }
  var fusedBatchNormConfig = {
    kernelName: FusedBatchNorm,
    backendName: "wasm",
    setupFunc: setup14,
    kernelFunc: fusedBatchNorm
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmFusedConv2d;
  function setup15(backend2) {
    wasmFusedConv2d = backend2.wasm.cwrap(FusedConv2D, null, [
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function fusedConv2d2(args) {
    const {inputs, attrs, backend: backend2} = args;
    const {x, filter, bias, preluActivationWeights} = inputs;
    const {strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha} = attrs;
    const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode);
    const fusedActivation = FusableActivation[activation2];
    if (fusedActivation == null) {
      throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);
    }
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const filterId = backend2.dataIdMap.get(filter.dataId).id;
    const outputChannels = convInfo.outChannels;
    let biasId = 0;
    if (bias != null) {
      const biasData = backend2.dataIdMap.get(bias.dataId);
      if (biasData.shape.length !== 1) {
        throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);
      }
      if (biasData.shape[0] !== outputChannels) {
        throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);
      }
      biasId = biasData.id;
    }
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const padTop = convInfo.padInfo.top;
    const padRight = convInfo.padInfo.right;
    const padBottom = convInfo.padInfo.bottom;
    const padLeft = convInfo.padInfo.left;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const inputChannels = convInfo.inChannels;
    const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
    const batchSize = convInfo.batchSize;
    const inHeight = convInfo.inHeight;
    const inWidth = convInfo.inWidth;
    if (dataFormat !== "NHWC") {
      throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);
    }
    const out = backend2.makeOutput(convInfo.outShape, "float32");
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;
    wasmFusedConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);
    return out;
  }
  var fusedConv2DConfig3 = {
    kernelName: FusedConv2D,
    backendName: "wasm",
    setupFunc: setup15,
    kernelFunc: fusedConv2d2
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmFusedDepthwiseConv2d;
  function setup16(backend2) {
    wasmFusedDepthwiseConv2d = backend2.wasm.cwrap(FusedDepthwiseConv2D, null, [
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function fusedDepthwiseConv2d(args) {
    const {inputs, attrs, backend: backend2} = args;
    const {x, filter, bias, preluActivationWeights} = inputs;
    const {strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha} = attrs;
    const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, true);
    const fusedActivation = FusableActivation[activation2];
    if (fusedActivation == null) {
      throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);
    }
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const filterId = backend2.dataIdMap.get(filter.dataId).id;
    const outputChannels = convInfo.outChannels;
    let biasId = 0;
    if (bias != null) {
      const biasData = backend2.dataIdMap.get(bias.dataId);
      if (biasData.shape.length !== 1) {
        throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);
      }
      if (biasData.shape[0] !== outputChannels) {
        throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);
      }
      biasId = biasData.id;
    }
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const padTop = convInfo.padInfo.top;
    const padRight = convInfo.padInfo.right;
    const padBottom = convInfo.padInfo.bottom;
    const padLeft = convInfo.padInfo.left;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const inputChannels = convInfo.inChannels;
    const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
    const batchSize = convInfo.batchSize;
    const inHeight = convInfo.inHeight;
    const inWidth = convInfo.inWidth;
    if (dataFormat !== "NHWC") {
      throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);
    }
    const out = backend2.makeOutput(convInfo.outShape, "float32");
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;
    wasmFusedDepthwiseConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);
    return out;
  }
  var fusedDepthwiseConv2DConfig3 = {
    kernelName: FusedDepthwiseConv2D,
    backendName: "wasm",
    setupFunc: setup16,
    kernelFunc: fusedDepthwiseConv2d
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmGatherNd;
  function setup17(backend2) {
    wasmGatherNd = backend2.wasm.cwrap(GatherNd, null, [
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "array",
      "number"
    ]);
  }
  function gatherNd3(args) {
    const {backend: backend2, inputs} = args;
    const {params, indices} = inputs;
    const [resultShape, numSlices, sliceSize, strides] = gather_nd_util_exports.prepareAndValidate(params, indices);
    const out = backend2.makeOutput(resultShape, params.dtype);
    if (numSlices === 0) {
      return out;
    }
    const indicesShape = indices.shape;
    const sliceRank = indicesShape[indicesShape.length - 1];
    const xData = backend2.dataIdMap.get(params.dataId);
    const xId = xData.id;
    const indicesData = backend2.dataIdMap.get(indices.dataId);
    const indicesId = indicesData.id;
    const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);
    const outId = backend2.dataIdMap.get(out.dataId).id;
    wasmGatherNd(xId, CppDType[params.dtype], indicesId, numSlices, sliceRank, sliceSize, stridesBytes, outId);
    return out;
  }
  var gatherNdConfig3 = {
    kernelName: GatherNd,
    backendName: "wasm",
    setupFunc: setup17,
    kernelFunc: gatherNd3
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmGather;
  function setup18(backend2) {
    wasmGather = backend2.wasm.cwrap("Gather", null, [
      "number",
      "number",
      "array",
      "number",
      "number",
      "number",
      "array",
      "number"
    ]);
  }
  function gatherV23(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {x, indices} = inputs;
    const {axis, batchDims} = attrs;
    const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
    const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);
    const flattenX = reshape5({
      inputs: {x},
      attrs: {
        shape: [
          shapeInfo.batchSize,
          shapeInfo.outerSize,
          shapeInfo.dimSize,
          shapeInfo.sliceSize
        ]
      },
      backend: backend2
    });
    const indicesSize = util_exports.sizeFromShape(indices.shape);
    const flattenIndex = reshape5({
      inputs: {x: indices},
      attrs: {shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize]},
      backend: backend2
    });
    const flattenOutputShape = [
      shapeInfo.batchSize,
      shapeInfo.outerSize,
      indicesSize / shapeInfo.batchSize,
      shapeInfo.sliceSize
    ];
    const out = backend2.makeOutput(flattenOutputShape, x.dtype);
    if (util_exports.sizeFromShape(x.shape) === 0) {
      return out;
    }
    const stridesSize = flattenX.shape.length - 1;
    const xData = backend2.dataIdMap.get(flattenX.dataId);
    const xId = xData.id;
    const indicesData = backend2.dataIdMap.get(flattenIndex.dataId);
    const indicesId = indicesData.id;
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer);
    const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);
    wasmGather(xId, CppDType[x.dtype], xStridesBytes, stridesSize, indicesId, shapeInfo.batchSize, outStridesBytes, outId);
    backend2.disposeData(flattenX.dataId);
    backend2.disposeData(flattenIndex.dataId);
    out.shape = shapeInfo.outputShape;
    return out;
  }
  var gatherV2Config3 = {
    kernelName: GatherV2,
    backendName: "wasm",
    setupFunc: setup18,
    kernelFunc: gatherV23
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast4 = false;
  var greaterConfig3 = createBinaryKernelConfig(Greater, supportsFullBroadcast4, "bool");
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast5 = false;
  var greaterEqualConfig3 = createBinaryKernelConfig(GreaterEqual, supportsFullBroadcast5, "bool");
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmFunc3;
  function setupFunc2(backend2) {
    wasmFunc3 = backend2.wasm.cwrap(LeakyRelu, null, [
      "number",
      "number",
      "number"
    ]);
  }
  function leakyRelu4(args) {
    const {inputs: {x}, attrs: {alpha}, backend: backend2} = args;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const out = backend2.makeOutput(x.shape, x.dtype);
    if (util_exports.sizeFromShape(x.shape) !== 0) {
      const outId = backend2.dataIdMap.get(out.dataId).id;
      wasmFunc3(xId, alpha, outId);
    }
    return out;
  }
  var leakyReluConfig3 = {
    kernelName: LeakyRelu,
    backendName: "wasm",
    setupFunc: setupFunc2,
    kernelFunc: leakyRelu4
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast6 = false;
  var lessConfig3 = createBinaryKernelConfig(Less, supportsFullBroadcast6, "bool");
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast7 = false;
  var lessEqualConfig3 = createBinaryKernelConfig(LessEqual, supportsFullBroadcast7, "bool");
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var logConfig3 = createUnaryKernelConfig(Log);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast8 = false;
  var logicalAndConfig3 = createBinaryKernelConfig(LogicalAnd, supportsFullBroadcast8, "bool");
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmMax;
  function setup19(backend2) {
    wasmMax = backend2.wasm.cwrap(Max, null, ["number, number, number"]);
  }
  function max5(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {reductionIndices: axis, keepDims} = attrs;
    const {x} = inputs;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    let inputId = xId;
    let input2 = x;
    const {transposed, axes, originalAxes, inputWasTransposed} = permuteAxesAndTranspose(x, axis, backend2);
    if (inputWasTransposed) {
      const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
      input2 = transposed;
      inputId = transposedId;
    }
    const inputRank = input2.shape.length;
    backend_util_exports.assertAxesAreInnerMostDims("max", axes, inputRank);
    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);
    const reduceSize = util_exports.sizeFromShape(reduceShape);
    const out = backend2.makeOutput(outShape, x.dtype);
    if (util_exports.sizeFromShape(input2.shape) !== 0) {
      const outId = backend2.dataIdMap.get(out.dataId).id;
      wasmMax(inputId, reduceSize, outId);
    }
    if (inputWasTransposed) {
      backend2.disposeData(transposed.dataId);
    }
    if (keepDims) {
      const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
      out.shape = newShape;
    }
    return out;
  }
  var maxConfig3 = {
    kernelName: Max,
    backendName: "wasm",
    setupFunc: setup19,
    kernelFunc: max5
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast9 = false;
  var maximumConfig3 = createBinaryKernelConfig(Maximum, supportsFullBroadcast9);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmMaxPool;
  function setup20(backend2) {
    wasmMaxPool = backend2.wasm.cwrap(MaxPool, null, [
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function maxPool4(args) {
    const {inputs, attrs, backend: backend2} = args;
    const x = inputs.x;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const {filterSize, strides, pad: pad3, dimRoundingMode} = attrs;
    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const padTop = convInfo.padInfo.top;
    const padRight = convInfo.padInfo.right;
    const padBottom = convInfo.padInfo.bottom;
    const padLeft = convInfo.padInfo.left;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const inputChannels = convInfo.inChannels;
    const outputChannels = convInfo.outChannels;
    if (convInfo.dataFormat !== "channelsLast") {
      throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
    }
    const out = backend2.makeOutput(convInfo.outShape, "float32");
    const outId = backend2.dataIdMap.get(out.dataId).id;
    wasmMaxPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);
    return out;
  }
  var maxPoolConfig3 = {
    kernelName: MaxPool,
    backendName: "wasm",
    setupFunc: setup20,
    kernelFunc: maxPool4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmMean;
  function setup21(backend2) {
    wasmMean = backend2.wasm.cwrap(Mean, null, ["number, number, number"]);
  }
  function mean3(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {axis, keepDims} = attrs;
    const {x} = inputs;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    let inputId = xId;
    let input2 = x;
    const {transposed, axes, originalAxes, inputWasTransposed} = permuteAxesAndTranspose(x, axis, backend2);
    let reductionAxes = axes;
    if (inputWasTransposed) {
      const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
      if (transposedId !== xId) {
        input2 = transposed;
        inputId = transposedId;
        reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);
      }
    }
    backend_util_exports.assertAxesAreInnerMostDims("mean", reductionAxes, input2.shape.length);
    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);
    const reduceSize = util_exports.sizeFromShape(reduceShape);
    let castedInput = input2;
    if (input2.dtype !== "float32") {
      castedInput = cast5({backend: backend2, inputs: {x: input2}, attrs: {dtype: "float32"}});
      inputId = backend2.dataIdMap.get(castedInput.dataId).id;
    }
    const out = backend2.makeOutput(outShape, "float32");
    if (util_exports.sizeFromShape(input2.shape) !== 0) {
      const outId = backend2.dataIdMap.get(out.dataId).id;
      wasmMean(inputId, reduceSize, outId);
    }
    if (inputWasTransposed) {
      backend2.disposeData(transposed.dataId);
    }
    if (keepDims) {
      const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
      out.shape = newShape;
    }
    if (input2.dtype !== "float32") {
      backend2.disposeData(castedInput.dataId);
    }
    return out;
  }
  var meanConfig3 = {
    kernelName: Mean,
    backendName: "wasm",
    setupFunc: setup21,
    kernelFunc: mean3
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmMin;
  function setup22(backend2) {
    wasmMin = backend2.wasm.cwrap(Min, null, ["number, number, number"]);
  }
  function min5(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {axis, keepDims} = attrs;
    const {x} = inputs;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    let inputId = xId;
    let input2 = x;
    const {transposed, axes, originalAxes, inputWasTransposed} = permuteAxesAndTranspose(x, axis, backend2);
    if (inputWasTransposed) {
      const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
      if (transposedId !== xId) {
        input2 = transposed;
        inputId = transposedId;
      }
    }
    const inputRank = input2.shape.length;
    backend_util_exports.assertAxesAreInnerMostDims("min", axes, inputRank);
    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);
    const reduceSize = util_exports.sizeFromShape(reduceShape);
    const out = backend2.makeOutput(outShape, input2.dtype);
    if (util_exports.sizeFromShape(input2.shape) !== 0) {
      const outId = backend2.dataIdMap.get(out.dataId).id;
      wasmMin(inputId, reduceSize, outId);
    }
    if (inputWasTransposed) {
      backend2.disposeData(transposed.dataId);
    }
    if (keepDims) {
      const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
      out.shape = newShape;
    }
    return out;
  }
  var minConfig3 = {
    kernelName: Min,
    backendName: "wasm",
    setupFunc: setup22,
    kernelFunc: min5
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast10 = false;
  var minimumConfig3 = createBinaryKernelConfig(Minimum, supportsFullBroadcast10);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast11 = true;
  var multiplyConfig3 = createBinaryKernelConfig(Multiply, supportsFullBroadcast11);
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var negConfig3 = createUnaryKernelConfig(Neg);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function parseResultStruct(backend2, resOffset) {
    const result = new Int32Array(backend2.wasm.HEAPU8.buffer, resOffset, 4);
    const pSelectedIndices = result[0];
    const selectedSize = result[1];
    const pSelectedScores = result[2];
    const pValidOutputs = result[3];
    backend2.wasm._free(resOffset);
    return {pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs};
  }
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmFunc4;
  function setup23(backend2) {
    wasmFunc4 = backend2.wasm.cwrap(NonMaxSuppressionV3, "number", [
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function kernelFunc(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {iouThreshold, maxOutputSize, scoreThreshold} = attrs;
    const {boxes, scores} = inputs;
    const boxesId = backend2.dataIdMap.get(boxes.dataId).id;
    const scoresId = backend2.dataIdMap.get(scores.dataId).id;
    const resOffset = wasmFunc4(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold);
    const {pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs} = parseResultStruct(backend2, resOffset);
    backend2.wasm._free(pSelectedScores);
    backend2.wasm._free(pValidOutputs);
    const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices);
    return selectedIndicesTensor;
  }
  var nonMaxSuppressionV3Config3 = {
    kernelName: NonMaxSuppressionV3,
    backendName: "wasm",
    setupFunc: setup23,
    kernelFunc
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmFunc5;
  function setup24(backend2) {
    wasmFunc5 = backend2.wasm.cwrap(NonMaxSuppressionV4, "number", [
      "number",
      "number",
      "number",
      "number",
      "number",
      "bool"
    ]);
  }
  function nonMaxSuppressionV43(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {iouThreshold, maxOutputSize, scoreThreshold, padToMaxOutputSize} = attrs;
    const {boxes, scores} = inputs;
    const boxesId = backend2.dataIdMap.get(boxes.dataId).id;
    const scoresId = backend2.dataIdMap.get(scores.dataId).id;
    const resOffset = wasmFunc5(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
    const {pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs} = parseResultStruct(backend2, resOffset);
    backend2.wasm._free(pSelectedScores);
    const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices);
    const validOutputsTensor = backend2.makeOutput([], "int32", pValidOutputs);
    return [selectedIndicesTensor, validOutputsTensor];
  }
  var nonMaxSuppressionV4Config3 = {
    kernelName: NonMaxSuppressionV4,
    backendName: "wasm",
    setupFunc: setup24,
    kernelFunc: nonMaxSuppressionV43
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmFunc6;
  function setup25(backend2) {
    wasmFunc6 = backend2.wasm.cwrap(NonMaxSuppressionV5, "number", [
      "number",
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function kernelFunc2(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {iouThreshold, maxOutputSize, scoreThreshold, softNmsSigma} = attrs;
    const {boxes, scores} = inputs;
    const boxesId = backend2.dataIdMap.get(boxes.dataId).id;
    const scoresId = backend2.dataIdMap.get(scores.dataId).id;
    const resOffset = wasmFunc6(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
    const {pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs} = parseResultStruct(backend2, resOffset);
    backend2.wasm._free(pValidOutputs);
    const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices);
    const selectedScoresTensor = backend2.makeOutput([selectedSize], "float32", pSelectedScores);
    return [selectedIndicesTensor, selectedScoresTensor];
  }
  var nonMaxSuppressionV5Config3 = {
    kernelName: NonMaxSuppressionV5,
    backendName: "wasm",
    setupFunc: setup25,
    kernelFunc: kernelFunc2
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast12 = false;
  var notEqualConfig3 = createBinaryKernelConfig(NotEqual, supportsFullBroadcast12, "bool");
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmOneHot;
  function setup26(backend2) {
    wasmOneHot = backend2.wasm.cwrap(OneHot, null, [
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function oneHot4(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {indices} = inputs;
    const {depth, onValue, offValue} = attrs;
    const out = backend2.makeOutput([...indices.shape, depth], "int32");
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const indicesData = backend2.dataIdMap.get(indices.dataId);
    const indicesId = indicesData.id;
    wasmOneHot(indicesId, depth, onValue, offValue, outId);
    return out;
  }
  var oneHotConfig3 = {
    kernelName: OneHot,
    backendName: "wasm",
    setupFunc: setup26,
    kernelFunc: oneHot4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function onesLike4(args) {
    const {inputs: {x}, backend: backend2} = args;
    const out = backend2.makeOutput(x.shape, x.dtype);
    const outVals = backend2.typedArrayFromHeap(out);
    outVals.fill(1);
    return out;
  }
  var onesLikeConfig3 = {
    kernelName: OnesLike,
    backendName: "wasm",
    kernelFunc: onesLike4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function pack3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {axis} = attrs;
    if (inputs.length === 1) {
      return expandDims5({inputs: {input: inputs[0]}, backend: backend2, attrs: {dim: axis}});
    }
    const shape = inputs[0].shape;
    const dtype = inputs[0].dtype;
    inputs.forEach((t) => {
      util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
      util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes");
    });
    const intermediateTensorInfos = [];
    const expandedTensors = inputs.map((t) => {
      const expandedT = expandDims5({inputs: {input: t}, backend: backend2, attrs: {dim: axis}});
      intermediateTensorInfos.push(expandedT);
      return expandedT;
    });
    const result = concat4({inputs: expandedTensors, backend: backend2, attrs: {axis}});
    intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId));
    return result;
  }
  var packConfig3 = {
    kernelName: Pack,
    backendName: "wasm",
    kernelFunc: pack3
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmPadV2;
  function setup27(backend2) {
    wasmPadV2 = backend2.wasm.cwrap(PadV2, null, [
      "number",
      "array",
      "number",
      "number",
      "array",
      "array",
      "number",
      "number"
    ]);
  }
  function pad2(args) {
    const {inputs: {x}, backend: backend2, attrs: {paddings, constantValue}} = args;
    const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const out = backend2.makeOutput(outShape, x.dtype);
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
    const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]);
    const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]);
    const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer);
    const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer);
    wasmPadV2(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, constantValue, outId);
    return out;
  }
  var padV2Config3 = {
    kernelName: PadV2,
    backendName: "wasm",
    kernelFunc: pad2,
    setupFunc: setup27
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast13 = false;
  var powConfig3 = createBinaryKernelConfig(Pow, supportsFullBroadcast13);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmPrelu;
  function setup28(backend2) {
    wasmPrelu = backend2.wasm.cwrap(Prelu, null, [
      "number",
      "number",
      "number"
    ]);
  }
  function prelu5(args) {
    const {inputs, backend: backend2} = args;
    const {x, alpha} = inputs;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const weightsId = backend2.dataIdMap.get(alpha.dataId).id;
    const out = backend2.makeOutput(x.shape, "float32");
    const outId = backend2.dataIdMap.get(out.dataId).id;
    wasmPrelu(xId, weightsId, outId);
    return out;
  }
  var preluConfig3 = {
    kernelName: Prelu,
    backendName: "wasm",
    setupFunc: setup28,
    kernelFunc: prelu5
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmProd;
  function setup29(backend2) {
    wasmProd = backend2.wasm.cwrap(Prod, null, [
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function prod4(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {axis, keepDims} = attrs;
    const {x} = inputs;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    let inputId = xId;
    let input2 = x;
    const {transposed, axes, originalAxes, inputWasTransposed} = permuteAxesAndTranspose(x, axis, backend2);
    let reductionAxes = axes;
    if (inputWasTransposed) {
      const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
      if (transposedId !== xId) {
        input2 = transposed;
        inputId = transposedId;
        reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);
      }
    }
    backend_util_exports.assertAxesAreInnerMostDims("prod", reductionAxes, input2.shape.length);
    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);
    const reduceSize = util_exports.sizeFromShape(reduceShape);
    const out = backend2.makeOutput(outShape, input2.dtype);
    if (util_exports.sizeFromShape(input2.shape) !== 0) {
      const outId = backend2.dataIdMap.get(out.dataId).id;
      wasmProd(inputId, reduceSize, CppDType[out.dtype], outId);
    }
    if (inputWasTransposed) {
      backend2.disposeData(transposed.dataId);
    }
    if (keepDims) {
      const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
      out.shape = newShape;
    }
    return out;
  }
  var prodConfig3 = {
    kernelName: Prod,
    backendName: "wasm",
    setupFunc: setup29,
    kernelFunc: prod4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var range5 = (args) => {
    const {backend: backend2, attrs} = args;
    const {start, stop, step: step5, dtype} = attrs;
    const values = rangeImpl2(start, stop, step5, dtype);
    const out = backend2.makeOutput([values.length], dtype);
    const outVals = backend2.typedArrayFromHeap(out);
    outVals.set(values);
    return out;
  };
  var rangeConfig3 = {
    kernelName: Range,
    backendName: "wasm",
    kernelFunc: range5
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast14 = true;
  var realDivConfig3 = createBinaryKernelConfig(RealDiv, supportsFullBroadcast14);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var reluConfig3 = createUnaryKernelConfig(Relu);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var relu6Config3 = createUnaryKernelConfig(Relu6);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmResizeBilinear;
  function setup30(backend2) {
    wasmResizeBilinear = backend2.wasm.cwrap(ResizeBilinear, null, [
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function resizeBilinear4(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {images} = inputs;
    const {alignCorners, halfPixelCenters, size} = attrs;
    const [newHeight, newWidth] = size;
    const [batch, oldHeight, oldWidth, numChannels] = images.shape;
    const outShape = [batch, newHeight, newWidth, numChannels];
    let xData = backend2.dataIdMap.get(images.dataId);
    let castedData;
    if (xData.dtype !== "float32") {
      castedData = cast5({backend: backend2, inputs: {x: images}, attrs: {dtype: "float32"}});
      xData = backend2.dataIdMap.get(castedData.dataId);
    }
    const xId = xData.id;
    const out = backend2.makeOutput(outShape, "float32");
    if (util_exports.sizeFromShape(images.shape) === 0) {
      return out;
    }
    const outId = backend2.dataIdMap.get(out.dataId).id;
    wasmResizeBilinear(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, halfPixelCenters ? 1 : 0, outId);
    if (castedData != null) {
      backend2.disposeData(castedData.dataId);
    }
    return out;
  }
  var resizeBilinearConfig3 = {
    kernelName: ResizeBilinear,
    backendName: "wasm",
    setupFunc: setup30,
    kernelFunc: resizeBilinear4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmReverse;
  function setup31(backend2) {
    wasmReverse = backend2.wasm.cwrap(Reverse, null, [
      "number",
      "array",
      "number",
      "array",
      "number",
      "number"
    ]);
  }
  function reverse4(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const {dims} = attrs;
    const axes = util_exports.parseAxisParam(dims, x.shape);
    if (x.shape.length === 0) {
      return identity4({inputs: {x}, backend: backend2});
    }
    const out = backend2.makeOutput(x.shape, x.dtype);
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const axesBytes = new Uint8Array(new Int32Array(axes).buffer);
    const outShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
    wasmReverse(xId, axesBytes, axes.length, outShapeBytes, x.shape.length, outId);
    const reshaped = reshape5({inputs: {x: out}, attrs: {shape: x.shape}, backend: backend2});
    backend2.disposeData(out.dataId);
    return reshaped;
  }
  var reverseConfig3 = {
    kernelName: Reverse,
    backendName: "wasm",
    kernelFunc: reverse4,
    setupFunc: setup31
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmRotate;
  function setup32(backend2) {
    wasmRotate = backend2.wasm.cwrap(RotateWithOffset, null, [
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "array",
      "number",
      "number"
    ]);
  }
  function rotateWithOffset2(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {image: image3} = inputs;
    const {radians, fillValue, center} = attrs;
    const out = backend2.makeOutput(image3.shape, image3.dtype);
    const imageId = backend2.dataIdMap.get(image3.dataId).id;
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const [batch, imageHeight, imageWidth, numChannels] = image3.shape;
    const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);
    const fillIsBlack = fillValue === 0;
    const fullOpacityValue = 255;
    const fillValues2 = typeof fillValue === "number" ? [fillValue, fillValue, fillValue, fillIsBlack ? 0 : fullOpacityValue] : [...fillValue, fullOpacityValue];
    const fillBytes = new Uint8Array(new Int32Array(fillValues2).buffer);
    wasmRotate(imageId, batch, imageHeight, imageWidth, numChannels, radians, centerX, centerY, fillBytes, fillValues2.length, outId);
    return out;
  }
  var rotateWithOffsetConfig3 = {
    kernelName: RotateWithOffset,
    backendName: "wasm",
    kernelFunc: rotateWithOffset2,
    setupFunc: setup32
  };
  /**
   * @license
   * Copyright 2021 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var roundConfig3 = createUnaryKernelConfig(Round);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var rsqrtConfig3 = createUnaryKernelConfig(Rsqrt);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmScatterNd;
  function setup33(backend2) {
    wasmScatterNd = backend2.wasm.cwrap(ScatterNd, null, [
      "number",
      "number",
      "number",
      "number",
      "number",
      "number",
      "array",
      "number",
      "number"
    ]);
  }
  function scatterNd3(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {indices, updates} = inputs;
    const {shape} = attrs;
    const out = backend2.makeOutput(shape, updates.dtype);
    if (util_exports.sizeFromShape(shape) === 0) {
      return out;
    }
    const {sliceRank, numUpdates, sliceSize, strides, outputSize} = scatter_nd_util_exports.calculateShapes(updates, indices, shape);
    const indicesData = backend2.dataIdMap.get(indices.dataId);
    const indicesId = indicesData.id;
    const updatesData = backend2.dataIdMap.get(updates.dataId);
    const updatesId = updatesData.id;
    const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);
    const outId = backend2.dataIdMap.get(out.dataId).id;
    wasmScatterNd(indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId);
    return out;
  }
  var scatterNdConfig3 = {
    kernelName: ScatterNd,
    backendName: "wasm",
    setupFunc: setup33,
    kernelFunc: scatterNd3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmSelect;
  function setup34(backend2) {
    wasmSelect = backend2.wasm.cwrap("SelectV2", null, [
      "number",
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function select3(args) {
    const {inputs, backend: backend2} = args;
    const {condition, t, e} = inputs;
    const conditionId = backend2.dataIdMap.get(condition.dataId).id;
    const tId = backend2.dataIdMap.get(t.dataId).id;
    const eId = backend2.dataIdMap.get(e.dataId).id;
    const out = backend2.makeOutput(t.shape, t.dtype);
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const cRank = condition.shape.length;
    const tRank = t.shape.length;
    const offset = cRank === 0 || cRank > 1 || tRank === 1 ? 1 : util_exports.sizeFromShape(t.shape.slice(1));
    wasmSelect(conditionId, tId, eId, offset, outId);
    return out;
  }
  var selectConfig3 = {
    kernelName: Select,
    backendName: "wasm",
    kernelFunc: select3,
    setupFunc: setup34
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmFunc7;
  function setup35(backend2) {
    wasmFunc7 = backend2.wasm.cwrap(Sigmoid, null, ["number", "number"]);
  }
  function sigmoid4(args) {
    const {backend: backend2, inputs: {x}} = args;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const out = backend2.makeOutput(x.shape, x.dtype);
    const outId = backend2.dataIdMap.get(out.dataId).id;
    if (util_exports.sizeFromShape(out.shape) === 0) {
      return out;
    }
    wasmFunc7(xId, outId);
    return out;
  }
  var sigmoidConfig3 = {
    kernelName: "Sigmoid",
    backendName: "wasm",
    setupFunc: setup35,
    kernelFunc: sigmoid4
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var sinConfig3 = createUnaryKernelConfig(Sin);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function slice4(args) {
    const {inputs: {x}, attrs: {begin, size}, backend: backend2} = args;
    const [begin_, size_] = slice_util_exports.parseSliceParams(x, begin, size);
    const isContinous = slice_util_exports.isSliceContinous(x.shape, begin_, size_);
    const xVals = backend2.readSync(x.dataId);
    const out = backend2.makeOutput(size_, x.dtype);
    const xStrides = util_exports.computeStrides(x.shape);
    const outData = backend2.dataIdMap.get(out.dataId);
    if (isContinous) {
      const flatOffset = slice_util_exports.computeFlatOffset(begin_, xStrides);
      if (x.dtype === "string") {
        outData.stringBytes = xVals.slice(flatOffset, flatOffset + util_exports.sizeFromShape(size_));
      } else {
        const outVals2 = backend2.typedArrayFromHeap(out);
        outVals2.set(xVals.subarray(flatOffset, flatOffset + util_exports.sizeFromShape(size_)));
      }
      return out;
    }
    if (x.dtype === "string") {
      const res = sliceImpl2(xVals, begin_, size_, x.shape, x.dtype);
      outData.stringBytes = res;
      return out;
    }
    const outVals = backend2.typedArrayFromHeap(out);
    const rank = x.shape.length;
    if (rank === 2) {
      slice2d2(xVals, xStrides[0], outVals, begin_, size_);
    } else if (rank === 3) {
      slice3d2(xVals, xStrides[0], xStrides[1], outVals, begin_, size_);
    } else if (rank === 4) {
      slice4d2(xVals, xStrides[0], xStrides[1], xStrides[2], outVals, begin_, size_);
    } else {
      const res = sliceImpl2(xVals, begin_, size_, x.shape, x.dtype);
      outVals.set(res);
    }
    return out;
  }
  function slice2d2(xVals, xStride, outVals, begin, size) {
    let outOffset = 0;
    const beginI = begin[0];
    const beginJ = begin[1];
    const endI = beginI + size[0];
    for (let i = beginI; i < endI; i++) {
      const xOffset = i * xStride + beginJ;
      outVals.set(xVals.subarray(xOffset, xOffset + size[1]), outOffset);
      outOffset += size[1];
    }
  }
  function slice3d2(xVals, xStride1, xStride2, outVals, begin, size) {
    let outOffset = 0;
    const beginI = begin[0];
    const beginJ = begin[1];
    const beginK = begin[2];
    const endI = beginI + size[0];
    const endJ = beginJ + size[1];
    for (let i = beginI; i < endI; i++) {
      for (let j = beginJ; j < endJ; j++) {
        const xOffset = i * xStride1 + j * xStride2 + beginK;
        outVals.set(xVals.subarray(xOffset, xOffset + size[2]), outOffset);
        outOffset += size[2];
      }
    }
  }
  function slice4d2(xVals, xStride1, xStride2, xStride3, outVals, begin, size) {
    let outOffset = 0;
    const beginI = begin[0];
    const beginJ = begin[1];
    const beginK = begin[2];
    const endI = beginI + size[0];
    const endJ = beginJ + size[1];
    const endK = beginK + size[2];
    const beginL = begin[3];
    for (let i = beginI; i < endI; i++) {
      for (let j = beginJ; j < endJ; j++) {
        for (let k = beginK; k < endK; k++) {
          const xOffset = i * xStride1 + j * xStride2 + k * xStride3 + beginL;
          outVals.set(xVals.subarray(xOffset, xOffset + size[3]), outOffset);
          outOffset += size[3];
        }
      }
    }
  }
  var sliceConfig3 = {
    kernelName: Slice,
    backendName: "wasm",
    kernelFunc: slice4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmFunc8;
  function setup36(backend2) {
    wasmFunc8 = backend2.wasm.cwrap(Softmax, null, [
      "number",
      "number",
      "number",
      "number"
    ]);
  }
  function softmax5(args) {
    const {backend: backend2, inputs: {logits}, attrs: {dim}} = args;
    const xId = backend2.dataIdMap.get(logits.dataId).id;
    const out = backend2.makeOutput(logits.shape, logits.dtype);
    const outId = backend2.dataIdMap.get(out.dataId).id;
    const channels = logits.shape[dim];
    const batch = util_exports.sizeFromShape(logits.shape) / channels;
    if (util_exports.sizeFromShape(out.shape) === 0) {
      return out;
    }
    wasmFunc8(xId, outId, channels, batch);
    return out;
  }
  var softmaxConfig3 = {
    kernelName: Softmax,
    backendName: "wasm",
    setupFunc: setup36,
    kernelFunc: softmax5
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function splitV3(args) {
    const {inputs, attrs, backend: backend2} = args;
    const {x} = inputs;
    const {numOrSizeSplits, axis} = attrs;
    const $axis = util_exports.parseAxisParam(axis, x.shape)[0];
    const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);
    const begin = new Array(x.shape.length).fill(0);
    const size = x.shape.slice();
    return splitSizes.map((s) => {
      const xSliceSize = [...size];
      xSliceSize[$axis] = s;
      const xSlice = slice4({inputs: {x}, attrs: {begin, size: xSliceSize}, backend: backend2});
      begin[$axis] += s;
      return xSlice;
    });
  }
  var splitVConfig3 = {
    kernelName: SplitV,
    backendName: "wasm",
    kernelFunc: splitV3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var sqrtConfig3 = createUnaryKernelConfig(Sqrt);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var squareConfig3 = createUnaryKernelConfig(Square);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast15 = true;
  var squaredDifferenceConfig3 = createBinaryKernelConfig(SquaredDifference, supportsFullBroadcast15);
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmStep;
  function setup37(backend2) {
    wasmStep = backend2.wasm.cwrap(Step, null, [
      "number",
      "number",
      "number"
    ]);
  }
  function step4(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {alpha} = attrs;
    const {x} = inputs;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const out = backend2.makeOutput(x.shape, x.dtype);
    const outId = backend2.dataIdMap.get(out.dataId).id;
    wasmStep(xId, alpha, outId);
    return out;
  }
  var stepConfig3 = {
    kernelName: Step,
    backendName: "wasm",
    setupFunc: setup37,
    kernelFunc: step4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmStridedSlice;
  function setup38(backend2) {
    wasmStridedSlice = backend2.wasm.cwrap(StridedSlice, null, [
      "number",
      "array",
      "number",
      "array",
      "array",
      "array",
      "array",
      "array",
      "number",
      "number"
    ]);
  }
  function stridedSlice4(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {x} = inputs;
    let {begin, end, strides} = attrs;
    if (strides == null) {
      strides = new Array(begin.length);
    }
    const {beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask} = attrs;
    const ellipsisAxes = backend_util_exports.slice_util.maskToAxes(ellipsisMask);
    if (ellipsisAxes.length > 1) {
      throw new Error("Multiple ellipses in slice is not allowed.");
    }
    if (ellipsisMask !== 0 && newAxisMask !== 0) {
      throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");
    }
    if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {
      throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");
    }
    const numInterpolatedAxes = x.shape.length - begin.length;
    const expandAxes = backend_util_exports.slice_util.maskToAxes(newAxisMask);
    const newShape = x.shape.slice();
    expandAxes.forEach((axis) => {
      begin[axis] = 0;
      end[axis] = 1;
      newShape.splice(axis, 0, 1);
    });
    const xReshaped = reshape5({inputs: {x}, attrs: {shape: newShape}, backend: backend2});
    const {begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides} = backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);
    begin = normalizedBegin;
    end = normalizedEnd;
    strides = normalizedStrides;
    const shrinkAxes = backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);
    shrinkAxes.forEach((axis) => {
      end[axis] = begin[axis] + 1;
      strides[axis] = 1;
    });
    const size = backend_util_exports.slice_util.computeOutShape(begin, end, strides);
    const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);
    const nonStrided = strides.every((v) => v === 1);
    if (nonStrided) {
      const xSliced = slice4({inputs: {x: xReshaped}, attrs: {begin, size}, backend: backend2});
      backend2.disposeData(xReshaped.dataId);
      const reshaped2 = reshape5({inputs: {x: xSliced}, attrs: {shape: outShape}, backend: backend2});
      backend2.disposeData(xSliced.dataId);
      return reshaped2;
    }
    const out = backend2.makeOutput(outShape, "float32");
    if (!outShape.some((axis) => axis === 0)) {
      const xId = backend2.dataIdMap.get(xReshaped.dataId).id;
      const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer);
      const beginBytes = new Uint8Array(new Int32Array(begin).buffer);
      const endBytes = new Uint8Array(new Int32Array(end).buffer);
      const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);
      const outputShapeBytes = new Uint8Array(new Int32Array(outShape).buffer);
      const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer);
      const outId = backend2.dataIdMap.get(out.dataId).id;
      wasmStridedSlice(xId, xStridesBytes, xReshaped.shape.length, beginBytes, endBytes, stridesBytes, outputShapeBytes, outStridesBytes, outShape.length, outId);
    }
    backend2.disposeData(xReshaped.dataId);
    const reshaped = reshape5({inputs: {x: out}, attrs: {shape: outShape}, backend: backend2});
    backend2.disposeData(out.dataId);
    return reshaped;
  }
  var stridedSliceConfig3 = {
    kernelName: StridedSlice,
    backendName: "wasm",
    setupFunc: setup38,
    kernelFunc: stridedSlice4
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var supportsFullBroadcast16 = true;
  var subConfig3 = createBinaryKernelConfig(Sub, supportsFullBroadcast16);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmSum;
  function setup39(backend2) {
    wasmSum = backend2.wasm.cwrap(Sum, null, ["number, number, number"]);
  }
  function sum5(args) {
    const {backend: backend2, inputs, attrs} = args;
    const {axis, keepDims} = attrs;
    const {x} = inputs;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    let inputId = xId;
    let input2 = x;
    const {transposed, axes, originalAxes, inputWasTransposed} = permuteAxesAndTranspose(x, axis, backend2);
    let reductionAxes = axes;
    if (inputWasTransposed) {
      const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
      if (transposedId !== xId) {
        input2 = transposed;
        inputId = transposedId;
        reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);
      }
    }
    backend_util_exports.assertAxesAreInnerMostDims("sum", reductionAxes, input2.shape.length);
    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);
    const reduceSize = util_exports.sizeFromShape(reduceShape);
    const out = backend2.makeOutput(outShape, input2.dtype);
    if (util_exports.sizeFromShape(input2.shape) !== 0) {
      const outId = backend2.dataIdMap.get(out.dataId).id;
      wasmSum(inputId, reduceSize, outId);
    }
    if (inputWasTransposed) {
      backend2.disposeData(transposed.dataId);
    }
    if (keepDims) {
      const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
      out.shape = newShape;
    }
    return out;
  }
  var sumConfig3 = {
    kernelName: Sum,
    backendName: "wasm",
    setupFunc: setup39,
    kernelFunc: sum5
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var tanhConfig3 = createUnaryKernelConfig(Tanh);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmTile;
  function setup40(backend2) {
    wasmTile = backend2.wasm.cwrap(Tile, null, [
      "number",
      "array",
      "number",
      "array",
      "number",
      "number"
    ]);
  }
  function tile5(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {x} = inputs;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const {reps} = attrs;
    const newShape = new Array(x.shape.length);
    for (let i = 0; i < newShape.length; i++) {
      newShape[i] = x.shape[i] * reps[i];
    }
    const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
    const newShapeBytes = new Uint8Array(new Int32Array(newShape).buffer);
    const out = backend2.makeOutput(newShape, x.dtype);
    const outId = backend2.dataIdMap.get(out.dataId).id;
    wasmTile(xId, xShapeBytes, x.shape.length, newShapeBytes, newShape.length, CppDType[out.dtype], outId);
    return out;
  }
  var tileConfig3 = {
    kernelName: Tile,
    backendName: "wasm",
    setupFunc: setup40,
    kernelFunc: tile5
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var wasmTopK;
  function setup41(backend2) {
    wasmTopK = backend2.wasm.cwrap(TopK, null, [
      "number",
      "array",
      "number",
      "number",
      "number",
      "bool",
      "number",
      "number"
    ]);
  }
  var topk2 = ({inputs, backend: backend2, attrs}) => {
    const {x} = inputs;
    const {k, sorted} = attrs;
    const xId = backend2.dataIdMap.get(x.dataId).id;
    const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
    const outputShape = x.shape.slice();
    outputShape[outputShape.length - 1] = k;
    const outValues = backend2.makeOutput(outputShape, x.dtype);
    const outValuesId = backend2.dataIdMap.get(outValues.dataId).id;
    const outIndices = backend2.makeOutput(outputShape, "int32");
    const outIndicesId = backend2.dataIdMap.get(outIndices.dataId).id;
    wasmTopK(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], k, sorted, outValuesId, outIndicesId);
    return [outValues, outIndices];
  };
  var topKConfig3 = {
    kernelName: TopK,
    backendName: "wasm",
    setupFunc: setup41,
    kernelFunc: topk2
  };
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function unpack3(args) {
    const {inputs, backend: backend2, attrs} = args;
    const {value} = inputs;
    let {axis} = attrs;
    if (axis < 0) {
      axis += value.shape.length;
    }
    const numOutputs = value.shape[axis];
    const rank = value.shape.length;
    const outShape = new Array(rank - 1);
    let outIndex = 0;
    for (let i = 0; i < rank; i++) {
      if (i !== axis) {
        outShape[outIndex++] = value.shape[i];
      }
    }
    const outs = new Array(numOutputs);
    const begin = new Array(rank).fill(0);
    const size = value.shape.slice();
    size[axis] = 1;
    for (let i = 0; i < outs.length; i++) {
      begin[axis] = i;
      outs[i] = slice4({inputs: {x: value}, attrs: {begin, size}, backend: backend2});
    }
    return outs.map(({dataId, dtype}) => ({dataId, dtype, shape: outShape}));
  }
  var unpackConfig3 = {
    kernelName: Unpack,
    backendName: "wasm",
    kernelFunc: unpack3
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  function zerosLike4(args) {
    const {inputs: {x}, backend: backend2} = args;
    const out = backend2.makeOutput(x.shape, x.dtype);
    const outVals = backend2.typedArrayFromHeap(out);
    outVals.fill(0);
    return out;
  }
  var zerosLikeConfig3 = {
    kernelName: ZerosLike,
    backendName: "wasm",
    kernelFunc: zerosLike4
  };
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var kernelConfigs3 = [
    absConfig3,
    addConfig3,
    addNConfig3,
    argMaxConfig3,
    avgPoolConfig3,
    batchMatMulConfig3,
    castConfig3,
    ceilConfig3,
    clipByValueConfig2,
    concatConfig3,
    conv2DConfig3,
    conv2DBackpropInputConfig3,
    cosConfig3,
    cropAndResizeConfig3,
    cumsumConfig3,
    depthToSpaceConfig3,
    depthwiseConv2dNativeConfig3,
    equalConfig3,
    expConfig3,
    expandDimsConfig3,
    fillConfig3,
    flipLeftRightConfig3,
    floorConfig3,
    floorDivConfig3,
    fusedMatMulConfig,
    fusedBatchNormConfig,
    fusedConv2DConfig3,
    fusedDepthwiseConv2DConfig3,
    gatherNdConfig3,
    gatherV2Config3,
    greaterConfig3,
    greaterEqualConfig3,
    identityConfig3,
    leakyReluConfig3,
    lessConfig3,
    lessEqualConfig3,
    logConfig3,
    logicalAndConfig3,
    maxConfig3,
    maximumConfig3,
    maxPoolConfig3,
    meanConfig3,
    minConfig3,
    minimumConfig3,
    multiplyConfig3,
    negConfig3,
    nonMaxSuppressionV3Config3,
    nonMaxSuppressionV4Config3,
    nonMaxSuppressionV5Config3,
    notEqualConfig3,
    oneHotConfig3,
    onesLikeConfig3,
    packConfig3,
    padV2Config3,
    powConfig3,
    preluConfig3,
    prodConfig3,
    rangeConfig3,
    realDivConfig3,
    reluConfig3,
    relu6Config3,
    reshapeConfig3,
    resizeBilinearConfig3,
    reverseConfig3,
    rotateWithOffsetConfig3,
    rsqrtConfig3,
    roundConfig3,
    scatterNdConfig3,
    selectConfig3,
    sigmoidConfig3,
    sinConfig3,
    sliceConfig3,
    softmaxConfig3,
    splitVConfig3,
    sqrtConfig3,
    squareConfig3,
    squaredDifferenceConfig3,
    stepConfig3,
    stridedSliceConfig3,
    subConfig3,
    sumConfig3,
    tanhConfig3,
    tileConfig3,
    topKConfig3,
    transposeConfig3,
    unpackConfig3,
    zerosLikeConfig3
  ];
  for (const kernelConfig of kernelConfigs3) {
    registerKernel(kernelConfig);
  }
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var ENV4 = env();
  ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => WebAssembly.validate(new Uint8Array([
    0,
    97,
    115,
    109,
    1,
    0,
    0,
    0,
    1,
    4,
    1,
    96,
    0,
    0,
    3,
    2,
    1,
    0,
    10,
    9,
    1,
    7,
    0,
    65,
    0,
    253,
    15,
    26,
    11
  ])));
  ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
    if (ENV4.get("IS_NODE")) {
      return false;
    }
    try {
      new MessageChannel().port1.postMessage(new SharedArrayBuffer(1));
      return WebAssembly.validate(new Uint8Array([
        0,
        97,
        115,
        109,
        1,
        0,
        0,
        0,
        1,
        4,
        1,
        96,
        0,
        0,
        3,
        2,
        1,
        0,
        5,
        4,
        1,
        3,
        1,
        1,
        10,
        11,
        1,
        9,
        0,
        65,
        0,
        254,
        16,
        2,
        0,
        26,
        11
      ]));
    } catch (e) {
      return false;
    }
  });
  var import_tfjs_backend_wasm_threaded_simd = __toModule(require_tfjs_backend_wasm_threaded_simd());
  var wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["getNoExitRuntime"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}';
  var import_tfjs_backend_wasm = __toModule(require_tfjs_backend_wasm());
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var BackendWasm = class extends KernelBackend {
    constructor(wasm) {
      super();
      this.wasm = wasm;
      this.dataIdNextNumber = 1;
      this.wasm.tfjs.init();
      this.dataIdMap = new DataStorage(this, engine());
    }
    write(values, shape, dtype) {
      const dataId = {id: this.dataIdNextNumber++};
      this.move(dataId, values, shape, dtype, 1);
      return dataId;
    }
    numDataIds() {
      return this.dataIdMap.numDataIds();
    }
    async time(f) {
      const start = util_exports.now();
      f();
      const kernelMs = util_exports.now() - start;
      return {kernelMs};
    }
    move(dataId, values, shape, dtype, refCount) {
      const id = this.dataIdNextNumber++;
      if (dtype === "string") {
        const stringBytes = values;
        this.dataIdMap.set(dataId, {id, stringBytes, shape, dtype, memoryOffset: null, refCount});
        return;
      }
      const size = util_exports.sizeFromShape(shape);
      const numBytes = size * util_exports.bytesPerElement(dtype);
      const memoryOffset = this.wasm._malloc(numBytes);
      this.dataIdMap.set(dataId, {id, memoryOffset, shape, dtype, refCount});
      this.wasm.tfjs.registerTensor(id, size, memoryOffset);
      if (values != null) {
        this.wasm.HEAPU8.set(new Uint8Array(values.buffer, values.byteOffset, numBytes), memoryOffset);
      }
    }
    async read(dataId) {
      return this.readSync(dataId);
    }
    readSync(dataId) {
      const {memoryOffset, dtype, shape, stringBytes} = this.dataIdMap.get(dataId);
      if (dtype === "string") {
        return stringBytes;
      }
      const bytes = this.wasm.HEAPU8.slice(memoryOffset, memoryOffset + util_exports.sizeFromShape(shape) * util_exports.bytesPerElement(dtype));
      return typedArrayFromBuffer(bytes.buffer, dtype);
    }
    disposeData(dataId, force = false) {
      if (this.dataIdMap.has(dataId)) {
        const data = this.dataIdMap.get(dataId);
        data.refCount--;
        if (!force && data.refCount > 0) {
          return false;
        }
        this.wasm._free(data.memoryOffset);
        this.wasm.tfjs.disposeData(data.id);
        this.dataIdMap.delete(dataId);
      }
      return true;
    }
    refCount(dataId) {
      if (this.dataIdMap.has(dataId)) {
        const tensorData = this.dataIdMap.get(dataId);
        return tensorData.refCount;
      }
      return 0;
    }
    incRef(dataId) {
      const data = this.dataIdMap.get(dataId);
      if (data != null) {
        data.refCount++;
      }
    }
    floatPrecision() {
      return 32;
    }
    getMemoryOffset(dataId) {
      return this.dataIdMap.get(dataId).memoryOffset;
    }
    dispose() {
      this.wasm.tfjs.dispose();
      this.wasm = null;
    }
    memory() {
      return {unreliable: false};
    }
    makeOutput(shape, dtype, memoryOffset) {
      let dataId;
      if (memoryOffset == null) {
        dataId = this.write(null, shape, dtype);
      } else {
        const id = this.dataIdNextNumber++;
        dataId = {id};
        this.dataIdMap.set(dataId, {id, memoryOffset, shape, dtype, refCount: 1});
        const size = util_exports.sizeFromShape(shape);
        this.wasm.tfjs.registerTensor(id, size, memoryOffset);
      }
      return {dataId, shape, dtype};
    }
    typedArrayFromHeap({shape, dtype, dataId}) {
      const buffer2 = this.wasm.HEAPU8.buffer;
      const {memoryOffset} = this.dataIdMap.get(dataId);
      const size = util_exports.sizeFromShape(shape);
      switch (dtype) {
        case "float32":
          return new Float32Array(buffer2, memoryOffset, size);
        case "int32":
          return new Int32Array(buffer2, memoryOffset, size);
        case "bool":
          return new Uint8Array(buffer2, memoryOffset, size);
        default:
          throw new Error(`Unknown dtype ${dtype}`);
      }
    }
  };
  function createInstantiateWasmFunc(path) {
    return (imports, callback) => {
      util_exports.fetch(path, {credentials: "same-origin"}).then((response) => {
        if (!response["ok"]) {
          imports.env.a(`failed to load wasm binary file at '${path}'`);
        }
        response.arrayBuffer().then((binary) => {
          WebAssembly.instantiate(binary, imports).then((output) => {
            callback(output.instance);
          });
        });
      });
      return {};
    };
  }
  function getPathToWasmBinary(simdSupported, threadsSupported, wasmModuleFolder) {
    if (wasmPath != null) {
      return wasmPath;
    }
    let path = "tfjs-backend-wasm.wasm";
    if (simdSupported && threadsSupported) {
      path = "tfjs-backend-wasm-threaded-simd.wasm";
    } else if (simdSupported) {
      path = "tfjs-backend-wasm-simd.wasm";
    }
    if (wasmFileMap != null) {
      if (wasmFileMap[path] != null) {
        return wasmFileMap[path];
      }
    }
    return wasmModuleFolder + path;
  }
  async function init() {
    const [simdSupported, threadsSupported] = await Promise.all([
      env().getAsync("WASM_HAS_SIMD_SUPPORT"),
      env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")
    ]);
    return new Promise((resolve, reject) => {
      const factoryConfig = {};
      factoryConfig.locateFile = (path, prefix) => {
        if (path.endsWith(".worker.js")) {
          const response = wasmWorkerContents;
          const blob = new Blob([response], {type: "application/javascript"});
          return URL.createObjectURL(blob);
        }
        if (path.endsWith(".wasm")) {
          return getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : prefix);
        }
        return prefix + path;
      };
      if (customFetch) {
        factoryConfig.instantiateWasm = createInstantiateWasmFunc(getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : ""));
      }
      let initialized = false;
      factoryConfig.onAbort = () => {
        if (initialized) {
          return;
        }
        if (initAborted) {
          return;
        }
        initAborted = true;
        const rejectMsg = "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers";
        reject({message: rejectMsg});
      };
      let wasm;
      if (threadsSupported && simdSupported && wasmPath == null) {
        factoryConfig.mainScriptUrlOrBlob = new Blob([`var WasmBackendModuleThreadedSimd = ` + import_tfjs_backend_wasm_threaded_simd.default.toString()], {type: "text/javascript"});
        wasm = (0, import_tfjs_backend_wasm_threaded_simd.default)(factoryConfig);
      } else {
        wasm = (0, import_tfjs_backend_wasm.default)(factoryConfig);
      }
      wasm.then((module2) => {
        initialized = true;
        initAborted = false;
        const voidReturnType = null;
        module2.tfjs = {
          init: module2.cwrap("init", null, []),
          registerTensor: module2.cwrap("register_tensor", null, [
            "number",
            "number",
            "number"
          ]),
          disposeData: module2.cwrap("dispose_data", voidReturnType, ["number"]),
          dispose: module2.cwrap("dispose", voidReturnType, [])
        };
        resolve({wasm: module2});
      });
    });
  }
  function typedArrayFromBuffer(buffer2, dtype) {
    switch (dtype) {
      case "float32":
        return new Float32Array(buffer2);
      case "int32":
        return new Int32Array(buffer2);
      case "bool":
        return new Uint8Array(buffer2);
      default:
        throw new Error(`Unknown dtype ${dtype}`);
    }
  }
  var wasmBinaryNames = [
    "tfjs-backend-wasm.wasm",
    "tfjs-backend-wasm-simd.wasm",
    "tfjs-backend-wasm-threaded-simd.wasm"
  ];
  var wasmPath = null;
  var wasmPathPrefix = null;
  var wasmFileMap = {};
  var initAborted = false;
  var customFetch = false;
  function setWasmPath(path, usePlatformFetch = false) {
    deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release.");
    if (initAborted) {
      throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");
    }
    wasmPath = path;
    customFetch = usePlatformFetch;
  }
  function setWasmPaths(prefixOrFileMap, usePlatformFetch = false) {
    if (initAborted) {
      throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");
    }
    if (typeof prefixOrFileMap === "string") {
      wasmPathPrefix = prefixOrFileMap;
    } else {
      wasmFileMap = prefixOrFileMap;
      const missingPaths = wasmBinaryNames.filter((name) => wasmFileMap[name] == null);
      if (missingPaths.length > 0) {
        throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`);
      }
    }
    customFetch = usePlatformFetch;
  }
  /** @license See the LICENSE file. */
  var version9 = "3.3.0";
  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  var WASM_PRIORITY = 2;
  registerBackend("wasm", async () => {
    const {wasm} = await init();
    return new BackendWasm(wasm);
  }, WASM_PRIORITY);
  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */

  // src/draw/index.ts
  var draw_exports = {};
  __export(draw_exports, {
    AnchorPosition: () => AnchorPosition,
    DrawBox: () => DrawBox,
    DrawBoxOptions: () => DrawBoxOptions,
    DrawFaceLandmarks: () => DrawFaceLandmarks,
    DrawFaceLandmarksOptions: () => DrawFaceLandmarksOptions,
    DrawTextField: () => DrawTextField,
    DrawTextFieldOptions: () => DrawTextFieldOptions,
    drawContour: () => drawContour,
    drawDetections: () => drawDetections,
    drawFaceExpressions: () => drawFaceExpressions,
    drawFaceLandmarks: () => drawFaceLandmarks
  });

  // src/draw/drawContour.ts
  function drawContour(ctx, points, isClosed = false) {
    ctx.beginPath();
    points.slice(1).forEach(({x, y}, prevIdx) => {
      const from = points[prevIdx];
      ctx.moveTo(from.x, from.y);
      ctx.lineTo(x, y);
    });
    if (isClosed) {
      const from = points[points.length - 1];
      const to = points[0];
      if (!from || !to) {
        return;
      }
      ctx.moveTo(from.x, from.y);
      ctx.lineTo(to.x, to.y);
    }
    ctx.stroke();
  }

  // src/utils/index.ts
  var utils_exports = {};
  __export(utils_exports, {
    computeReshapedDimensions: () => computeReshapedDimensions,
    getCenterPoint: () => getCenterPoint,
    isDimensions: () => isDimensions,
    isEven: () => isEven2,
    isFloat: () => isFloat,
    isTensor: () => isTensor,
    isTensor1D: () => isTensor1D,
    isTensor2D: () => isTensor2D,
    isTensor3D: () => isTensor3D,
    isTensor4D: () => isTensor4D,
    isValidNumber: () => isValidNumber,
    isValidProbablitiy: () => isValidProbablitiy,
    range: () => range6,
    round: () => round5
  });

  // src/classes/Dimensions.ts
  var Dimensions = class {
    constructor(width, height) {
      if (!isValidNumber(width) || !isValidNumber(height)) {
        throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width, height})}`);
      }
      this._width = width;
      this._height = height;
    }
    get width() {
      return this._width;
    }
    get height() {
      return this._height;
    }
    reverse() {
      return new Dimensions(1 / this.width, 1 / this.height);
    }
  };

  // src/utils/index.ts
  function isTensor(tensor2, dim) {
    return tensor2 instanceof Tensor && tensor2.shape.length === dim;
  }
  function isTensor1D(tensor2) {
    return isTensor(tensor2, 1);
  }
  function isTensor2D(tensor2) {
    return isTensor(tensor2, 2);
  }
  function isTensor3D(tensor2) {
    return isTensor(tensor2, 3);
  }
  function isTensor4D(tensor2) {
    return isTensor(tensor2, 4);
  }
  function isFloat(num) {
    return num % 1 !== 0;
  }
  function isEven2(num) {
    return num % 2 === 0;
  }
  function round5(num, prec = 2) {
    const f = 10 ** prec;
    return Math.floor(num * f) / f;
  }
  function isDimensions(obj) {
    return obj && obj.width && obj.height;
  }
  function computeReshapedDimensions({width, height}, inputSize) {
    const scale3 = inputSize / Math.max(height, width);
    return new Dimensions(Math.round(width * scale3), Math.round(height * scale3));
  }
  function getCenterPoint(pts) {
    return pts.reduce((sum6, pt) => sum6.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length));
  }
  function range6(num, start, step5) {
    return Array(num).fill(0).map((_, i) => start + i * step5);
  }
  function isValidNumber(num) {
    return !!num && num !== Infinity && num !== -Infinity && !Number.isNaN(num) || num === 0;
  }
  function isValidProbablitiy(num) {
    return isValidNumber(num) && num >= 0 && num <= 1;
  }

  // src/classes/Point.ts
  var Point = class {
    constructor(x, y) {
      this._x = x;
      this._y = y;
    }
    get x() {
      return this._x;
    }
    get y() {
      return this._y;
    }
    add(pt) {
      return new Point(this.x + pt.x, this.y + pt.y);
    }
    sub(pt) {
      return new Point(this.x - pt.x, this.y - pt.y);
    }
    mul(pt) {
      return new Point(this.x * pt.x, this.y * pt.y);
    }
    div(pt) {
      return new Point(this.x / pt.x, this.y / pt.y);
    }
    abs() {
      return new Point(Math.abs(this.x), Math.abs(this.y));
    }
    magnitude() {
      return Math.sqrt(this.x ** 2 + this.y ** 2);
    }
    floor() {
      return new Point(Math.floor(this.x), Math.floor(this.y));
    }
  };

  // src/classes/Box.ts
  var Box = class {
    static isRect(rect) {
      return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);
    }
    static assertIsValidBox(box, callee, allowNegativeDimensions = false) {
      if (!Box.isRect(box)) {
        throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);
      }
      if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {
        throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);
      }
    }
    constructor(_box, allowNegativeDimensions = true) {
      const box = _box || {};
      const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);
      const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);
      if (!isRect && !isBbox) {
        throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);
      }
      const [x, y, width, height] = isRect ? [box.x, box.y, box.width, box.height] : [box.left, box.top, box.right - box.left, box.bottom - box.top];
      Box.assertIsValidBox({
        x,
        y,
        width,
        height
      }, "Box.constructor", allowNegativeDimensions);
      this._x = x;
      this._y = y;
      this._width = width;
      this._height = height;
    }
    get x() {
      return this._x;
    }
    get y() {
      return this._y;
    }
    get width() {
      return this._width;
    }
    get height() {
      return this._height;
    }
    get left() {
      return this.x;
    }
    get top() {
      return this.y;
    }
    get right() {
      return this.x + this.width;
    }
    get bottom() {
      return this.y + this.height;
    }
    get area() {
      return this.width * this.height;
    }
    get topLeft() {
      return new Point(this.left, this.top);
    }
    get topRight() {
      return new Point(this.right, this.top);
    }
    get bottomLeft() {
      return new Point(this.left, this.bottom);
    }
    get bottomRight() {
      return new Point(this.right, this.bottom);
    }
    round() {
      const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.round(val));
      return new Box({
        x,
        y,
        width,
        height
      });
    }
    floor() {
      const [x, y, width, height] = [this.x, this.y, this.width, this.height].map((val) => Math.floor(val));
      return new Box({
        x,
        y,
        width,
        height
      });
    }
    toSquare() {
      let {
        x,
        y,
        width,
        height
      } = this;
      const diff = Math.abs(width - height);
      if (width < height) {
        x -= diff / 2;
        width += diff;
      }
      if (height < width) {
        y -= diff / 2;
        height += diff;
      }
      return new Box({x, y, width, height});
    }
    rescale(s) {
      const scaleX = isDimensions(s) ? s.width : s;
      const scaleY = isDimensions(s) ? s.height : s;
      return new Box({
        x: this.x * scaleX,
        y: this.y * scaleY,
        width: this.width * scaleX,
        height: this.height * scaleY
      });
    }
    pad(padX, padY) {
      const [x, y, width, height] = [
        this.x - padX / 2,
        this.y - padY / 2,
        this.width + padX,
        this.height + padY
      ];
      return new Box({
        x,
        y,
        width,
        height
      });
    }
    clipAtImageBorders(imgWidth, imgHeight) {
      const {x, y, right, bottom} = this;
      const clippedX = Math.max(x, 0);
      const clippedY = Math.max(y, 0);
      const newWidth = right - clippedX;
      const newHeight = bottom - clippedY;
      const clippedWidth = Math.min(newWidth, imgWidth - clippedX);
      const clippedHeight = Math.min(newHeight, imgHeight - clippedY);
      return new Box({
        x: clippedX,
        y: clippedY,
        width: clippedWidth,
        height: clippedHeight
      }).floor();
    }
    shift(sx, sy) {
      const {width, height} = this;
      const x = this.x + sx;
      const y = this.y + sy;
      return new Box({
        x,
        y,
        width,
        height
      });
    }
    padAtBorders(imageHeight, imageWidth) {
      const w = this.width + 1;
      const h = this.height + 1;
      const dx = 1;
      const dy = 1;
      let edx = w;
      let edy = h;
      let x = this.left;
      let y = this.top;
      let ex = this.right;
      let ey = this.bottom;
      if (ex > imageWidth) {
        edx = -ex + imageWidth + w;
        ex = imageWidth;
      }
      if (ey > imageHeight) {
        edy = -ey + imageHeight + h;
        ey = imageHeight;
      }
      if (x < 1) {
        edy = 2 - x;
        x = 1;
      }
      if (y < 1) {
        edy = 2 - y;
        y = 1;
      }
      return {
        dy,
        edy,
        dx,
        edx,
        y,
        ey,
        x,
        ex,
        w,
        h
      };
    }
    calibrate(region) {
      return new Box({
        left: this.left + region.left * this.width,
        top: this.top + region.top * this.height,
        right: this.right + region.right * this.width,
        bottom: this.bottom + region.bottom * this.height
      }).toSquare().round();
    }
  };

  // src/classes/BoundingBox.ts
  var BoundingBox = class extends Box {
    constructor(left, top, right, bottom, allowNegativeDimensions = false) {
      super({
        left,
        top,
        right,
        bottom
      }, allowNegativeDimensions);
    }
  };

  // src/classes/ObjectDetection.ts
  var ObjectDetection = class {
    constructor(score, classScore, className, relativeBox, imageDims) {
      this._imageDims = new Dimensions(imageDims.width, imageDims.height);
      this._score = score;
      this._classScore = classScore;
      this._className = className;
      this._box = new Box(relativeBox).rescale(this._imageDims);
    }
    get score() {
      return this._score;
    }
    get classScore() {
      return this._classScore;
    }
    get className() {
      return this._className;
    }
    get box() {
      return this._box;
    }
    get imageDims() {
      return this._imageDims;
    }
    get imageWidth() {
      return this.imageDims.width;
    }
    get imageHeight() {
      return this.imageDims.height;
    }
    get relativeBox() {
      return new Box(this._box).rescale(this.imageDims.reverse());
    }
    forSize(width, height) {
      return new ObjectDetection(this.score, this.classScore, this.className, this.relativeBox, {width, height});
    }
  };

  // src/classes/FaceDetection.ts
  var FaceDetection = class extends ObjectDetection {
    constructor(score, relativeBox, imageDims) {
      super(score, score, "", relativeBox, imageDims);
    }
    forSize(width, height) {
      const {score, relativeBox, imageDims} = super.forSize(width, height);
      return new FaceDetection(score, relativeBox, imageDims);
    }
  };

  // src/ops/iou.ts
  function iou(box1, box2, isIOU = true) {
    const width = Math.max(0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));
    const height = Math.max(0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));
    const interSection = width * height;
    return isIOU ? interSection / (box1.area + box2.area - interSection) : interSection / Math.min(box1.area, box2.area);
  }

  // src/ops/minBbox.ts
  function minBbox(pts) {
    const xs = pts.map((pt) => pt.x);
    const ys = pts.map((pt) => pt.y);
    const minX = xs.reduce((min6, x) => x < min6 ? x : min6, Infinity);
    const minY = ys.reduce((min6, y) => y < min6 ? y : min6, Infinity);
    const maxX = xs.reduce((max6, x) => max6 < x ? x : max6, 0);
    const maxY = ys.reduce((max6, y) => max6 < y ? y : max6, 0);
    return new BoundingBox(minX, minY, maxX, maxY);
  }

  // src/ops/nonMaxSuppression.ts
  function nonMaxSuppression2(boxes, scores, iouThreshold, isIOU = true) {
    let indicesSortedByScore = scores.map((score, boxIndex) => ({score, boxIndex})).sort((c1, c2) => c1.score - c2.score).map((c) => c.boxIndex);
    const pick = [];
    while (indicesSortedByScore.length > 0) {
      const curr = indicesSortedByScore.pop();
      pick.push(curr);
      const indices = indicesSortedByScore;
      const outputs = [];
      for (let i = 0; i < indices.length; i++) {
        const idx = indices[i];
        const currBox = boxes[curr];
        const idxBox = boxes[idx];
        outputs.push(iou(currBox, idxBox, isIOU));
      }
      indicesSortedByScore = indicesSortedByScore.filter((_, j) => outputs[j] <= iouThreshold);
    }
    return pick;
  }

  // src/ops/normalize.ts
  function normalize(x, meanRgb) {
    return tidy(() => {
      const [r, g, b] = meanRgb;
      const avg_r = fill([...x.shape.slice(0, 3), 1], r, "float32");
      const avg_g = fill([...x.shape.slice(0, 3), 1], g, "float32");
      const avg_b = fill([...x.shape.slice(0, 3), 1], b, "float32");
      const avg_rgb = concat([avg_r, avg_g, avg_b], 3);
      return sub(x, avg_rgb);
    });
  }

  // src/ops/padToSquare.ts
  function padToSquare(imgTensor, isCenterImage = false) {
    return tidy(() => {
      const [height, width] = imgTensor.shape.slice(1);
      if (height === width) {
        return imgTensor;
      }
      const dimDiff = Math.abs(height - width);
      const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));
      const paddingAxis = height > width ? 2 : 1;
      const createPaddingTensor = (paddingAmountLocal) => {
        const paddingTensorShape = imgTensor.shape.slice();
        paddingTensorShape[paddingAxis] = paddingAmountLocal;
        return fill(paddingTensorShape, 0, "float32");
      };
      const paddingTensorAppend = createPaddingTensor(paddingAmount);
      const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
      const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null;
      const tensorsToStack = [
        paddingTensorPrepend,
        imgTensor,
        paddingTensorAppend
      ].filter((t) => !!t).map((t) => cast(t, "float32"));
      return concat(tensorsToStack, paddingAxis);
    });
  }

  // src/ops/shuffleArray.ts
  function shuffleArray(inputArray) {
    const array2 = inputArray.slice();
    for (let i = array2.length - 1; i > 0; i--) {
      const j = Math.floor(Math.random() * (i + 1));
      const x = array2[i];
      array2[i] = array2[j];
      array2[j] = x;
    }
    return array2;
  }

  // src/ops/index.ts
  function sigmoid5(x) {
    return 1 / (1 + Math.exp(-x));
  }
  function inverseSigmoid(x) {
    return Math.log(x / (1 - x));
  }

  // src/classes/Rect.ts
  var Rect = class extends Box {
    constructor(x, y, width, height, allowNegativeDimensions = false) {
      super({
        x,
        y,
        width,
        height
      }, allowNegativeDimensions);
    }
  };

  // src/classes/FaceLandmarks.ts
  var relX = 0.5;
  var relY = 0.43;
  var relScale = 0.45;
  var FaceLandmarks = class {
    constructor(relativeFaceLandmarkPositions, imgDims, shift = new Point(0, 0)) {
      const {width, height} = imgDims;
      this._imgDims = new Dimensions(width, height);
      this._shift = shift;
      this._positions = relativeFaceLandmarkPositions.map((pt) => pt.mul(new Point(width, height)).add(shift));
    }
    get shift() {
      return new Point(this._shift.x, this._shift.y);
    }
    get imageWidth() {
      return this._imgDims.width;
    }
    get imageHeight() {
      return this._imgDims.height;
    }
    get positions() {
      return this._positions;
    }
    get relativePositions() {
      return this._positions.map((pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)));
    }
    forSize(width, height) {
      return new this.constructor(this.relativePositions, {width, height});
    }
    shiftBy(x, y) {
      return new this.constructor(this.relativePositions, this._imgDims, new Point(x, y));
    }
    shiftByPoint(pt) {
      return this.shiftBy(pt.x, pt.y);
    }
    align(detection, options = {}) {
      if (detection) {
        const box = detection instanceof FaceDetection ? detection.box.floor() : new Box(detection);
        return this.shiftBy(box.x, box.y).align(null, options);
      }
      const {useDlibAlignment, minBoxPadding} = {useDlibAlignment: false, minBoxPadding: 0.2, ...options};
      if (useDlibAlignment) {
        return this.alignDlib();
      }
      return this.alignMinBbox(minBoxPadding);
    }
    alignDlib() {
      const centers = this.getRefPointsForAlignment();
      const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;
      const distToMouth = (pt) => mouthCenter.sub(pt).magnitude();
      const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
      const size = Math.floor(eyeToMouthDist / relScale);
      const refPoint = getCenterPoint(centers);
      const x = Math.floor(Math.max(0, refPoint.x - relX * size));
      const y = Math.floor(Math.max(0, refPoint.y - relY * size));
      return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));
    }
    alignMinBbox(padding) {
      const box = minBbox(this.positions);
      return box.pad(box.width * padding, box.height * padding);
    }
    getRefPointsForAlignment() {
      throw new Error("getRefPointsForAlignment not implemented by base class");
    }
  };

  // src/classes/FaceLandmarks5.ts
  var FaceLandmarks5 = class extends FaceLandmarks {
    getRefPointsForAlignment() {
      const pts = this.positions;
      return [
        pts[0],
        pts[1],
        getCenterPoint([pts[3], pts[4]])
      ];
    }
  };

  // src/classes/FaceLandmarks68.ts
  var FaceLandmarks68 = class extends FaceLandmarks {
    getJawOutline() {
      return this.positions.slice(0, 17);
    }
    getLeftEyeBrow() {
      return this.positions.slice(17, 22);
    }
    getRightEyeBrow() {
      return this.positions.slice(22, 27);
    }
    getNose() {
      return this.positions.slice(27, 36);
    }
    getLeftEye() {
      return this.positions.slice(36, 42);
    }
    getRightEye() {
      return this.positions.slice(42, 48);
    }
    getMouth() {
      return this.positions.slice(48, 68);
    }
    getRefPointsForAlignment() {
      return [
        this.getLeftEye(),
        this.getRightEye(),
        this.getMouth()
      ].map(getCenterPoint);
    }
  };

  // src/classes/FaceMatch.ts
  var FaceMatch = class {
    constructor(label, distance) {
      this._label = label;
      this._distance = distance;
    }
    get label() {
      return this._label;
    }
    get distance() {
      return this._distance;
    }
    toString(withDistance = true) {
      return `${this.label}${withDistance ? ` (${round5(this.distance)})` : ""}`;
    }
  };

  // src/classes/LabeledBox.ts
  var LabeledBox = class extends Box {
    static assertIsValidLabeledBox(box, callee) {
      Box.assertIsValidBox(box, callee);
      if (!isValidNumber(box.label)) {
        throw new Error(`${callee} - expected property label (${box.label}) to be a number`);
      }
    }
    constructor(box, label) {
      super(box);
      this._label = label;
    }
    get label() {
      return this._label;
    }
  };

  // src/classes/LabeledFaceDescriptors.ts
  var LabeledFaceDescriptors = class {
    constructor(label, descriptors) {
      if (!(typeof label === "string")) {
        throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");
      }
      if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {
        throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");
      }
      this._label = label;
      this._descriptors = descriptors;
    }
    get label() {
      return this._label;
    }
    get descriptors() {
      return this._descriptors;
    }
    toJSON() {
      return {
        label: this.label,
        descriptors: this.descriptors.map((d) => Array.from(d))
      };
    }
    static fromJSON(json18) {
      const descriptors = json18.descriptors.map((d) => new Float32Array(d));
      return new LabeledFaceDescriptors(json18.label, descriptors);
    }
  };

  // src/classes/PredictedBox.ts
  var PredictedBox = class extends LabeledBox {
    static assertIsValidPredictedBox(box, callee) {
      LabeledBox.assertIsValidLabeledBox(box, callee);
      if (!isValidProbablitiy(box.score) || !isValidProbablitiy(box.classScore)) {
        throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);
      }
    }
    constructor(box, label, score, classScore) {
      super(box, label);
      this._score = score;
      this._classScore = classScore;
    }
    get score() {
      return this._score;
    }
    get classScore() {
      return this._classScore;
    }
  };

  // src/factories/WithFaceDetection.ts
  function isWithFaceDetection(obj) {
    return obj.detection instanceof FaceDetection;
  }
  function extendWithFaceDetection(sourceObj, detection) {
    const extension = {detection};
    return {...sourceObj, ...extension};
  }

  // src/env/createBrowserEnv.ts
  function createBrowserEnv() {
    const fetch3 = window.fetch;
    if (!fetch3)
      throw new Error("fetch - missing fetch implementation for browser environment");
    const readFile = () => {
      throw new Error("readFile - filesystem not available for browser environment");
    };
    return {
      Canvas: HTMLCanvasElement,
      CanvasRenderingContext2D,
      Image: HTMLImageElement,
      ImageData,
      Video: HTMLVideoElement,
      createCanvasElement: () => document.createElement("canvas"),
      createImageElement: () => document.createElement("img"),
      fetch: fetch3,
      readFile
    };
  }

  // src/env/createFileSystem.ts
  function createFileSystem(fs) {
    let requireFsError = "";
    if (!fs) {
      try {
        fs = require("fs");
      } catch (err) {
        requireFsError = err.toString();
      }
    }
    const readFile = fs ? (filePath) => new Promise((resolve, reject) => {
      fs.readFile(filePath, (err, buffer2) => err ? reject(err) : resolve(buffer2));
    }) : () => {
      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);
    };
    return {
      readFile
    };
  }

  // src/env/createNodejsEnv.ts
  function createNodejsEnv() {
    const Canvas = global["Canvas"] || global.HTMLCanvasElement;
    const Image = global.Image || global.HTMLImageElement;
    const createCanvasElement = () => {
      if (Canvas)
        return new Canvas();
      throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment");
    };
    const createImageElement = () => {
      if (Image)
        return new Image();
      throw new Error("createImageElement - missing Image implementation for nodejs environment");
    };
    const fetch3 = global.fetch;
    const fileSystem = createFileSystem();
    return {
      Canvas: Canvas || class {
      },
      CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {
      },
      Image: Image || class {
      },
      ImageData: global.ImageData || class {
      },
      Video: global.HTMLVideoElement || class {
      },
      createCanvasElement,
      createImageElement,
      fetch: fetch3,
      ...fileSystem
    };
  }

  // src/env/isBrowser.ts
  function isBrowser2() {
    return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined";
  }

  // src/env/isNodejs.ts
  function isNodejs() {
    return typeof global === "object" && true && typeof module !== "undefined" && typeof process !== "undefined" && !!process.version;
  }

  // src/env/index.ts
  var environment;
  function getEnv() {
    if (!environment) {
      throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");
    }
    return environment;
  }
  function setEnv(env3) {
    environment = env3;
  }
  function initialize() {
    if (isBrowser2())
      return setEnv(createBrowserEnv());
    if (isNodejs())
      return setEnv(createNodejsEnv());
    return null;
  }
  function monkeyPatch(env3) {
    if (!environment) {
      initialize();
    }
    if (!environment) {
      throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");
    }
    const {Canvas = environment.Canvas, Image = environment.Image} = env3;
    environment.Canvas = Canvas;
    environment.Image = Image;
    environment.createCanvasElement = env3.createCanvasElement || (() => new Canvas());
    environment.createImageElement = env3.createImageElement || (() => new Image());
    environment.ImageData = env3.ImageData || environment.ImageData;
    environment.Video = env3.Video || environment.Video;
    environment.fetch = env3.fetch || environment.fetch;
    environment.readFile = env3.readFile || environment.readFile;
  }
  var env2 = {
    getEnv,
    setEnv,
    initialize,
    createBrowserEnv,
    createFileSystem,
    createNodejsEnv,
    monkeyPatch,
    isBrowser: isBrowser2,
    isNodejs
  };
  initialize();

  // src/dom/resolveInput.ts
  function resolveInput(arg) {
    if (!env2.isNodejs() && typeof arg === "string") {
      return document.getElementById(arg);
    }
    return arg;
  }

  // src/dom/getContext2dOrThrow.ts
  function getContext2dOrThrow(canvasArg) {
    const {Canvas, CanvasRenderingContext2D: CanvasRenderingContext2D2} = env2.getEnv();
    if (canvasArg instanceof CanvasRenderingContext2D2) {
      return canvasArg;
    }
    const canvas = resolveInput(canvasArg);
    if (!(canvas instanceof Canvas)) {
      throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");
    }
    const ctx = canvas.getContext("2d");
    if (!ctx) {
      throw new Error("resolveContext2d - canvas 2d context is null");
    }
    return ctx;
  }

  // src/draw/DrawTextField.ts
  var AnchorPosition;
  (function(AnchorPosition2) {
    AnchorPosition2["TOP_LEFT"] = "TOP_LEFT";
    AnchorPosition2["TOP_RIGHT"] = "TOP_RIGHT";
    AnchorPosition2["BOTTOM_LEFT"] = "BOTTOM_LEFT";
    AnchorPosition2["BOTTOM_RIGHT"] = "BOTTOM_RIGHT";
  })(AnchorPosition || (AnchorPosition = {}));
  var DrawTextFieldOptions = class {
    constructor(options = {}) {
      const {
        anchorPosition,
        backgroundColor,
        fontColor,
        fontSize,
        fontStyle,
        padding
      } = options;
      this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;
      this.backgroundColor = backgroundColor || "rgba(0, 0, 0, 0.5)";
      this.fontColor = fontColor || "rgba(255, 255, 255, 1)";
      this.fontSize = fontSize || 14;
      this.fontStyle = fontStyle || "Georgia";
      this.padding = padding || 4;
    }
  };
  var DrawTextField = class {
    constructor(text, anchor, options = {}) {
      this.text = typeof text === "string" ? [text] : text instanceof DrawTextField ? text.text : text;
      this.anchor = anchor;
      this.options = new DrawTextFieldOptions(options);
    }
    measureWidth(ctx) {
      const {padding} = this.options;
      return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + 2 * padding;
    }
    measureHeight() {
      const {fontSize, padding} = this.options;
      return this.text.length * fontSize + 2 * padding;
    }
    getUpperLeft(ctx, canvasDims) {
      const {anchorPosition} = this.options;
      const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;
      const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;
      const textFieldWidth = this.measureWidth(ctx);
      const textFieldHeight = this.measureHeight();
      const x = isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x;
      const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;
      if (canvasDims) {
        const {width, height} = canvasDims;
        const newX = Math.max(Math.min(x, width - textFieldWidth), 0);
        const newY = Math.max(Math.min(y, height - textFieldHeight), 0);
        return {x: newX, y: newY};
      }
      return {x, y};
    }
    draw(canvasArg) {
      const canvas = resolveInput(canvasArg);
      const ctx = getContext2dOrThrow(canvas);
      const {
        backgroundColor,
        fontColor,
        fontSize,
        fontStyle,
        padding
      } = this.options;
      ctx.font = `${fontSize}px ${fontStyle}`;
      const maxTextWidth = this.measureWidth(ctx);
      const textHeight = this.measureHeight();
      ctx.fillStyle = backgroundColor;
      const upperLeft = this.getUpperLeft(ctx, canvas);
      ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);
      ctx.fillStyle = fontColor;
      this.text.forEach((textLine, i) => {
        const x = padding + upperLeft.x;
        const y = padding + upperLeft.y + (i + 1) * fontSize;
        ctx.fillText(textLine, x, y);
      });
    }
  };

  // src/draw/DrawBox.ts
  var DrawBoxOptions = class {
    constructor(options = {}) {
      const {
        boxColor,
        lineWidth,
        label,
        drawLabelOptions
      } = options;
      this.boxColor = boxColor || "rgba(0, 0, 255, 1)";
      this.lineWidth = lineWidth || 2;
      this.label = label;
      const defaultDrawLabelOptions = {
        anchorPosition: AnchorPosition.BOTTOM_LEFT,
        backgroundColor: this.boxColor
      };
      this.drawLabelOptions = new DrawTextFieldOptions({...defaultDrawLabelOptions, ...drawLabelOptions});
    }
  };
  var DrawBox = class {
    constructor(box, options = {}) {
      this.box = new Box(box);
      this.options = new DrawBoxOptions(options);
    }
    draw(canvasArg) {
      const ctx = getContext2dOrThrow(canvasArg);
      const {boxColor, lineWidth} = this.options;
      const {
        x,
        y,
        width,
        height
      } = this.box;
      ctx.strokeStyle = boxColor;
      ctx.lineWidth = lineWidth;
      ctx.strokeRect(x, y, width, height);
      const {label} = this.options;
      if (label) {
        new DrawTextField([label], {x: x - lineWidth / 2, y}, this.options.drawLabelOptions).draw(canvasArg);
      }
    }
  };

  // src/draw/drawDetections.ts
  function drawDetections(canvasArg, detections) {
    const detectionsArray = Array.isArray(detections) ? detections : [detections];
    detectionsArray.forEach((det) => {
      const score = det instanceof FaceDetection ? det.score : isWithFaceDetection(det) ? det.detection.score : void 0;
      const box = det instanceof FaceDetection ? det.box : isWithFaceDetection(det) ? det.detection.box : new Box(det);
      const label = score ? `${round5(score)}` : void 0;
      new DrawBox(box, {label}).draw(canvasArg);
    });
  }

  // src/dom/isMediaLoaded.ts
  function isMediaLoaded(media) {
    const {Image, Video} = env2.getEnv();
    return media instanceof Image && media.complete || media instanceof Video && media.readyState >= 3;
  }

  // src/dom/awaitMediaLoaded.ts
  function awaitMediaLoaded(media) {
    return new Promise((resolve, reject) => {
      if (media instanceof env2.getEnv().Canvas || isMediaLoaded(media))
        return resolve(null);
      function onError(e) {
        if (!e.currentTarget)
          return;
        e.currentTarget.removeEventListener("load", onLoad);
        e.currentTarget.removeEventListener("error", onError);
        reject(e);
      }
      function onLoad(e) {
        if (!e.currentTarget)
          return;
        e.currentTarget.removeEventListener("load", onLoad);
        e.currentTarget.removeEventListener("error", onError);
        resolve(e);
      }
      media.addEventListener("load", onLoad);
      media.addEventListener("error", onError);
    });
  }

  // src/dom/bufferToImage.ts
  function bufferToImage(buf) {
    return new Promise((resolve, reject) => {
      if (!(buf instanceof Blob))
        reject(new Error("bufferToImage - expected buf to be of type: Blob"));
      const reader = new FileReader();
      reader.onload = () => {
        if (typeof reader.result !== "string")
          reject(new Error("bufferToImage - expected reader.result to be a string, in onload"));
        const img = env2.getEnv().createImageElement();
        img.onload = () => resolve(img);
        img.onerror = reject;
        img.src = reader.result;
      };
      reader.onerror = reject;
      reader.readAsDataURL(buf);
    });
  }

  // src/dom/getMediaDimensions.ts
  function getMediaDimensions(input2) {
    const {Image, Video} = env2.getEnv();
    if (input2 instanceof Image) {
      return new Dimensions(input2.naturalWidth, input2.naturalHeight);
    }
    if (input2 instanceof Video) {
      return new Dimensions(input2.videoWidth, input2.videoHeight);
    }
    return new Dimensions(input2.width, input2.height);
  }

  // src/dom/createCanvas.ts
  function createCanvas2({width, height}) {
    const {createCanvasElement} = env2.getEnv();
    const canvas = createCanvasElement();
    canvas.width = width;
    canvas.height = height;
    return canvas;
  }
  function createCanvasFromMedia(media, dims) {
    const {ImageData: ImageData2} = env2.getEnv();
    if (!(media instanceof ImageData2) && !isMediaLoaded(media)) {
      throw new Error("createCanvasFromMedia - media has not finished loading yet");
    }
    const {width, height} = dims || getMediaDimensions(media);
    const canvas = createCanvas2({width, height});
    if (media instanceof ImageData2) {
      getContext2dOrThrow(canvas).putImageData(media, 0, 0);
    } else {
      getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
    }
    return canvas;
  }

  // src/dom/imageTensorToCanvas.ts
  async function imageTensorToCanvas(imgTensor, canvas) {
    const targetCanvas = canvas || env2.getEnv().createCanvasElement();
    const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);
    const imgTensor3D = tidy(() => imgTensor.as3D(height, width, numChannels).toInt());
    await browser_exports.toPixels(imgTensor3D, targetCanvas);
    imgTensor3D.dispose();
    return targetCanvas;
  }

  // src/dom/isMediaElement.ts
  function isMediaElement(input2) {
    const {Image, Canvas, Video} = env2.getEnv();
    return input2 instanceof Image || input2 instanceof Canvas || input2 instanceof Video;
  }

  // src/dom/imageToSquare.ts
  function imageToSquare(input2, inputSize, centerImage = false) {
    const {Image, Canvas} = env2.getEnv();
    if (!(input2 instanceof Image || input2 instanceof Canvas)) {
      throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");
    }
    if (inputSize <= 0)
      return createCanvas2({width: 1, height: 1});
    const dims = getMediaDimensions(input2);
    const scale3 = inputSize / Math.max(dims.height, dims.width);
    const width = scale3 * dims.width;
    const height = scale3 * dims.height;
    const targetCanvas = createCanvas2({width: inputSize, height: inputSize});
    const inputCanvas = input2 instanceof Canvas ? input2 : createCanvasFromMedia(input2);
    const offset = Math.abs(width - height) / 2;
    const dx = centerImage && width < height ? offset : 0;
    const dy = centerImage && height < width ? offset : 0;
    if (inputCanvas.width > 0 && inputCanvas.height > 0)
      getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);
    return targetCanvas;
  }

  // src/dom/NetInput.ts
  var NetInput = class {
    constructor(inputs, treatAsBatchInput = false) {
      this._imageTensors = [];
      this._canvases = [];
      this._treatAsBatchInput = false;
      this._inputDimensions = [];
      if (!Array.isArray(inputs)) {
        throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);
      }
      this._treatAsBatchInput = treatAsBatchInput;
      this._batchSize = inputs.length;
      inputs.forEach((input2, idx) => {
        if (isTensor3D(input2)) {
          this._imageTensors[idx] = input2;
          this._inputDimensions[idx] = input2.shape;
          return;
        }
        if (isTensor4D(input2)) {
          const batchSize = input2.shape[0];
          if (batchSize !== 1) {
            throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);
          }
          this._imageTensors[idx] = input2;
          this._inputDimensions[idx] = input2.shape.slice(1);
          return;
        }
        const canvas = input2 instanceof env2.getEnv().Canvas ? input2 : createCanvasFromMedia(input2);
        this._canvases[idx] = canvas;
        this._inputDimensions[idx] = [canvas.height, canvas.width, 3];
      });
    }
    get imageTensors() {
      return this._imageTensors;
    }
    get canvases() {
      return this._canvases;
    }
    get isBatchInput() {
      return this.batchSize > 1 || this._treatAsBatchInput;
    }
    get batchSize() {
      return this._batchSize;
    }
    get inputDimensions() {
      return this._inputDimensions;
    }
    get inputSize() {
      return this._inputSize;
    }
    get reshapedInputDimensions() {
      return range6(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx));
    }
    getInput(batchIdx) {
      return this.canvases[batchIdx] || this.imageTensors[batchIdx];
    }
    getInputDimensions(batchIdx) {
      return this._inputDimensions[batchIdx];
    }
    getInputHeight(batchIdx) {
      return this._inputDimensions[batchIdx][0];
    }
    getInputWidth(batchIdx) {
      return this._inputDimensions[batchIdx][1];
    }
    getReshapedInputDimensions(batchIdx) {
      if (typeof this.inputSize !== "number") {
        throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");
      }
      const width = this.getInputWidth(batchIdx);
      const height = this.getInputHeight(batchIdx);
      return computeReshapedDimensions({width, height}, this.inputSize);
    }
    toBatchTensor(inputSize, isCenterInputs = true) {
      this._inputSize = inputSize;
      return tidy(() => {
        const inputTensors = range6(this.batchSize, 0, 1).map((batchIdx) => {
          const input2 = this.getInput(batchIdx);
          if (input2 instanceof Tensor) {
            let imgTensor = isTensor4D(input2) ? input2 : expandDims(input2);
            imgTensor = padToSquare(imgTensor, isCenterInputs);
            if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
              imgTensor = image.resizeBilinear(imgTensor, [inputSize, inputSize], false, false);
            }
            return imgTensor.as3D(inputSize, inputSize, 3);
          }
          if (input2 instanceof env2.getEnv().Canvas) {
            return browser_exports.fromPixels(imageToSquare(input2, inputSize, isCenterInputs));
          }
          throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input2}`);
        });
        const batchTensor = stack(inputTensors.map((t) => cast(t, "float32"))).as4D(this.batchSize, inputSize, inputSize, 3);
        return batchTensor;
      });
    }
  };

  // src/dom/toNetInput.ts
  async function toNetInput(inputs) {
    if (inputs instanceof NetInput)
      return inputs;
    const inputArgArray = Array.isArray(inputs) ? inputs : [inputs];
    if (!inputArgArray.length)
      throw new Error("toNetInput - empty array passed as input");
    const getIdxHint = (idx) => Array.isArray(inputs) ? ` at input index ${idx}:` : "";
    const inputArray = inputArgArray.map(resolveInput);
    inputArray.forEach((input2, i) => {
      if (!isMediaElement(input2) && !isTensor3D(input2) && !isTensor4D(input2)) {
        if (typeof inputArgArray[i] === "string")
          throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);
        throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);
      }
      if (isTensor4D(input2)) {
        const batchSize = input2.shape[0];
        if (batchSize !== 1)
          throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);
      }
    });
    await Promise.all(inputArray.map((input2) => isMediaElement(input2) && awaitMediaLoaded(input2)));
    return new NetInput(inputArray, Array.isArray(inputs));
  }

  // src/dom/extractFaces.ts
  async function extractFaces(input2, detections) {
    const {Canvas} = env2.getEnv();
    let canvas = input2;
    if (!(input2 instanceof Canvas)) {
      const netInput = await toNetInput(input2);
      if (netInput.batchSize > 1)
        throw new Error("extractFaces - batchSize > 1 not supported");
      const tensorOrCanvas = netInput.getInput(0);
      canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);
    }
    const ctx = getContext2dOrThrow(canvas);
    const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det).map((box) => box.clipAtImageBorders(canvas.width, canvas.height));
    return boxes.map(({x, y, width, height}) => {
      const faceImg = createCanvas2({width, height});
      if (width > 0 && height > 0)
        getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);
      return faceImg;
    });
  }

  // src/dom/extractFaceTensors.ts
  async function extractFaceTensors(imageTensor, detections) {
    if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {
      throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");
    }
    if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
      throw new Error("extractFaceTensors - batchSize > 1 not supported");
    }
    return tidy(() => {
      const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);
      const boxes = detections.map((det) => det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det).map((box) => box.clipAtImageBorders(imgWidth, imgHeight));
      const faceTensors = boxes.map(({
        x,
        y,
        width,
        height
      }) => slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));
      return faceTensors;
    });
  }

  // src/dom/fetchOrThrow.ts
  async function fetchOrThrow(url, init2) {
    const {fetch: fetch3} = env2.getEnv();
    const res = await fetch3(url, init2);
    if (!(res.status < 400)) {
      throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);
    }
    return res;
  }

  // src/dom/fetchImage.ts
  async function fetchImage(uri) {
    const res = await fetchOrThrow(uri);
    const blob = await res.blob();
    if (!blob.type.startsWith("image/")) {
      throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);
    }
    return bufferToImage(blob);
  }

  // src/dom/fetchJson.ts
  async function fetchJson(uri) {
    return (await fetchOrThrow(uri)).json();
  }

  // src/dom/fetchNetWeights.ts
  async function fetchNetWeights(uri) {
    return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());
  }

  // src/common/getModelUris.ts
  function getModelUris(uri, defaultModelName) {
    const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;
    if (!uri) {
      return {
        modelBaseUri: "",
        manifestUri: defaultManifestFilename
      };
    }
    if (uri === "/") {
      return {
        modelBaseUri: "/",
        manifestUri: `/${defaultManifestFilename}`
      };
    }
    const protocol = uri.startsWith("http://") ? "http://" : uri.startsWith("https://") ? "https://" : "";
    uri = uri.replace(protocol, "");
    const parts = uri.split("/").filter((s) => s);
    const manifestFile = uri.endsWith(".json") ? parts[parts.length - 1] : defaultManifestFilename;
    let modelBaseUri = protocol + (uri.endsWith(".json") ? parts.slice(0, parts.length - 1) : parts).join("/");
    modelBaseUri = uri.startsWith("/") ? `/${modelBaseUri}` : modelBaseUri;
    return {
      modelBaseUri,
      manifestUri: modelBaseUri === "/" ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`
    };
  }

  // src/dom/loadWeightMap.ts
  async function loadWeightMap(uri, defaultModelName) {
    const {manifestUri, modelBaseUri} = getModelUris(uri, defaultModelName);
    const manifest = await fetchJson(manifestUri);
    return io_exports.loadWeights(manifest, modelBaseUri);
  }

  // src/dom/matchDimensions.ts
  function matchDimensions(input2, reference, useMediaDimensions = false) {
    const {width, height} = useMediaDimensions ? getMediaDimensions(reference) : reference;
    input2.width = width;
    input2.height = height;
    return {width, height};
  }

  // src/NeuralNetwork.ts
  var NeuralNetwork = class {
    constructor(name) {
      this._params = void 0;
      this._paramMappings = [];
      this._name = name;
    }
    get params() {
      return this._params;
    }
    get paramMappings() {
      return this._paramMappings;
    }
    get isLoaded() {
      return !!this.params;
    }
    getParamFromPath(paramPath) {
      const {obj, objProp} = this.traversePropertyPath(paramPath);
      return obj[objProp];
    }
    reassignParamFromPath(paramPath, tensor2) {
      const {obj, objProp} = this.traversePropertyPath(paramPath);
      obj[objProp].dispose();
      obj[objProp] = tensor2;
    }
    getParamList() {
      return this._paramMappings.map(({paramPath}) => ({
        path: paramPath,
        tensor: this.getParamFromPath(paramPath)
      }));
    }
    getTrainableParams() {
      return this.getParamList().filter((param) => param.tensor instanceof Variable);
    }
    getFrozenParams() {
      return this.getParamList().filter((param) => !(param.tensor instanceof Variable));
    }
    variable() {
      this.getFrozenParams().forEach(({path, tensor: tensor2}) => {
        this.reassignParamFromPath(path, tensor2.variable());
      });
    }
    freeze() {
      this.getTrainableParams().forEach(({path, tensor: variable2}) => {
        const tensor2 = tensor(variable2.dataSync());
        variable2.dispose();
        this.reassignParamFromPath(path, tensor2);
      });
    }
    dispose(throwOnRedispose = true) {
      this.getParamList().forEach((param) => {
        if (throwOnRedispose && param.tensor.isDisposed) {
          throw new Error(`param tensor has already been disposed for path ${param.path}`);
        }
        param.tensor.dispose();
      });
      this._params = void 0;
    }
    serializeParams() {
      return new Float32Array(this.getParamList().map(({tensor: tensor2}) => Array.from(tensor2.dataSync())).reduce((flat, arr) => flat.concat(arr)));
    }
    async load(weightsOrUrl) {
      if (weightsOrUrl instanceof Float32Array) {
        this.extractWeights(weightsOrUrl);
        return;
      }
      await this.loadFromUri(weightsOrUrl);
    }
    async loadFromUri(uri) {
      if (uri && typeof uri !== "string") {
        throw new Error(`${this._name}.loadFromUri - expected model uri`);
      }
      const weightMap = await loadWeightMap(uri, this.getDefaultModelName());
      this.loadFromWeightMap(weightMap);
    }
    async loadFromDisk(filePath) {
      if (filePath && typeof filePath !== "string") {
        throw new Error(`${this._name}.loadFromDisk - expected model file path`);
      }
      const {readFile} = env2.getEnv();
      const {manifestUri, modelBaseUri} = getModelUris(filePath, this.getDefaultModelName());
      const fetchWeightsFromDisk = (filePaths) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)));
      const loadWeights2 = io_exports.weightsLoaderFactory(fetchWeightsFromDisk);
      const manifest = JSON.parse((await readFile(manifestUri)).toString());
      const weightMap = await loadWeights2(manifest, modelBaseUri);
      this.loadFromWeightMap(weightMap);
    }
    loadFromWeightMap(weightMap) {
      const {paramMappings, params} = this.extractParamsFromWeightMap(weightMap);
      this._paramMappings = paramMappings;
      this._params = params;
    }
    extractWeights(weights) {
      const {paramMappings, params} = this.extractParams(weights);
      this._paramMappings = paramMappings;
      this._params = params;
    }
    traversePropertyPath(paramPath) {
      if (!this.params) {
        throw new Error("traversePropertyPath - model has no loaded params");
      }
      const result = paramPath.split("/").reduce((res, objProp2) => {
        if (!res.nextObj.hasOwnProperty(objProp2)) {
          throw new Error(`traversePropertyPath - object does not have property ${objProp2}, for path ${paramPath}`);
        }
        return {obj: res.nextObj, objProp: objProp2, nextObj: res.nextObj[objProp2]};
      }, {nextObj: this.params});
      const {obj, objProp} = result;
      if (!obj || !objProp || !(obj[objProp] instanceof Tensor)) {
        throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);
      }
      return {obj, objProp};
    }
  };

  // src/common/depthwiseSeparableConv.ts
  function depthwiseSeparableConv(x, params, stride) {
    return tidy(() => {
      let out = separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, "same");
      out = add2(out, params.bias);
      return out;
    });
  }

  // src/faceFeatureExtractor/denseBlock.ts
  function denseBlock3(x, denseBlockParams, isFirstLayer = false) {
    return tidy(() => {
      const out1 = relu(isFirstLayer ? add2(conv2d(x, denseBlockParams.conv0.filters, [2, 2], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
      const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
      const in3 = relu(add2(out1, out2));
      const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
      return relu(add2(out1, add2(out2, out3)));
    });
  }
  function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = true) {
    return tidy(() => {
      const out1 = relu(isFirstLayer ? add2(conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], "same"), denseBlockParams.conv0.bias) : depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]));
      const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
      const in3 = relu(add2(out1, out2));
      const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
      const in4 = relu(add2(out1, add2(out2, out3)));
      const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);
      return relu(add2(out1, add2(out2, add2(out3, out4))));
    });
  }

  // src/common/convLayer.ts
  function convLayer(x, params, padding = "same", withRelu = false) {
    return tidy(() => {
      const out = add2(conv2d(x, params.filters, [1, 1], padding), params.bias);
      return withRelu ? relu(out) : out;
    });
  }

  // src/common/disposeUnusedWeightTensors.ts
  function disposeUnusedWeightTensors(weightMap, paramMappings) {
    Object.keys(weightMap).forEach((path) => {
      if (!paramMappings.some((pm) => pm.originalPath === path)) {
        weightMap[path].dispose();
      }
    });
  }

  // src/common/extractConvParamsFactory.ts
  function extractConvParamsFactory(extractWeights, paramMappings) {
    return (channelsIn, channelsOut, filterSize, mappedPrefix) => {
      const filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
      const bias = tensor1d(extractWeights(channelsOut));
      paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/bias`});
      return {filters, bias};
    };
  }

  // src/common/extractFCParamsFactory.ts
  function extractFCParamsFactory(extractWeights, paramMappings) {
    return (channelsIn, channelsOut, mappedPrefix) => {
      const fc_weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
      const fc_bias = tensor1d(extractWeights(channelsOut));
      paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/bias`});
      return {
        weights: fc_weights,
        bias: fc_bias
      };
    };
  }

  // src/common/types.ts
  var SeparableConvParams = class {
    constructor(depthwise_filter, pointwise_filter, bias) {
      this.depthwise_filter = depthwise_filter;
      this.pointwise_filter = pointwise_filter;
      this.bias = bias;
    }
  };

  // src/common/extractSeparableConvParamsFactory.ts
  function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
    return (channelsIn, channelsOut, mappedPrefix) => {
      const depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
      const pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
      const bias = tensor1d(extractWeights(channelsOut));
      paramMappings.push({paramPath: `${mappedPrefix}/depthwise_filter`}, {paramPath: `${mappedPrefix}/pointwise_filter`}, {paramPath: `${mappedPrefix}/bias`});
      return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
    };
  }
  function loadSeparableConvParamsFactory(extractWeightEntry) {
    return (prefix) => {
      const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4);
      const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4);
      const bias = extractWeightEntry(`${prefix}/bias`, 1);
      return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
    };
  }

  // src/common/extractWeightEntryFactory.ts
  function extractWeightEntryFactory(weightMap, paramMappings) {
    return (originalPath, paramRank, mappedPath) => {
      const tensor2 = weightMap[originalPath];
      if (!isTensor(tensor2, paramRank)) {
        throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);
      }
      paramMappings.push({originalPath, paramPath: mappedPath || originalPath});
      return tensor2;
    };
  }

  // src/common/extractWeightsFactory.ts
  function extractWeightsFactory(weights) {
    let remainingWeights = weights;
    function extractWeights(numWeights) {
      const ret = remainingWeights.slice(0, numWeights);
      remainingWeights = remainingWeights.slice(numWeights);
      return ret;
    }
    function getRemainingWeights() {
      return remainingWeights;
    }
    return {
      extractWeights,
      getRemainingWeights
    };
  }

  // src/faceFeatureExtractor/extractorsFactory.ts
  function extractorsFactory(extractWeights, paramMappings) {
    const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
    const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
    function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) {
      const conv0 = isFirstLayer ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`) : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);
      const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);
      const conv22 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);
      return {conv0, conv1, conv2: conv22};
    }
    function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) {
      const {conv0, conv1, conv2: conv22} = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);
      const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);
      return {
        conv0,
        conv1,
        conv2: conv22,
        conv3
      };
    }
    return {
      extractDenseBlock3Params,
      extractDenseBlock4Params
    };
  }

  // src/faceFeatureExtractor/extractParams.ts
  function extractParams(weights) {
    const paramMappings = [];
    const {
      extractWeights,
      getRemainingWeights
    } = extractWeightsFactory(weights);
    const {
      extractDenseBlock4Params
    } = extractorsFactory(extractWeights, paramMappings);
    const dense0 = extractDenseBlock4Params(3, 32, "dense0", true);
    const dense1 = extractDenseBlock4Params(32, 64, "dense1");
    const dense2 = extractDenseBlock4Params(64, 128, "dense2");
    const dense3 = extractDenseBlock4Params(128, 256, "dense3");
    if (getRemainingWeights().length !== 0) {
      throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
    }
    return {
      paramMappings,
      params: {
        dense0,
        dense1,
        dense2,
        dense3
      }
    };
  }

  // src/common/loadConvParamsFactory.ts
  function loadConvParamsFactory(extractWeightEntry) {
    return (prefix) => {
      const filters = extractWeightEntry(`${prefix}/filters`, 4);
      const bias = extractWeightEntry(`${prefix}/bias`, 1);
      return {filters, bias};
    };
  }

  // src/faceFeatureExtractor/loadParamsFactory.ts
  function loadParamsFactory(weightMap, paramMappings) {
    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
    const extractConvParams = loadConvParamsFactory(extractWeightEntry);
    const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
    function extractDenseBlock3Params(prefix, isFirstLayer = false) {
      const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`);
      const conv1 = extractSeparableConvParams(`${prefix}/conv1`);
      const conv22 = extractSeparableConvParams(`${prefix}/conv2`);
      return {conv0, conv1, conv2: conv22};
    }
    function extractDenseBlock4Params(prefix, isFirstLayer = false) {
      const conv0 = isFirstLayer ? extractConvParams(`${prefix}/conv0`) : extractSeparableConvParams(`${prefix}/conv0`);
      const conv1 = extractSeparableConvParams(`${prefix}/conv1`);
      const conv22 = extractSeparableConvParams(`${prefix}/conv2`);
      const conv3 = extractSeparableConvParams(`${prefix}/conv3`);
      return {
        conv0,
        conv1,
        conv2: conv22,
        conv3
      };
    }
    return {
      extractDenseBlock3Params,
      extractDenseBlock4Params
    };
  }

  // src/faceFeatureExtractor/extractParamsFromWeightMap.ts
  function extractParamsFromWeightMap(weightMap) {
    const paramMappings = [];
    const {
      extractDenseBlock4Params
    } = loadParamsFactory(weightMap, paramMappings);
    const params = {
      dense0: extractDenseBlock4Params("dense0", true),
      dense1: extractDenseBlock4Params("dense1"),
      dense2: extractDenseBlock4Params("dense2"),
      dense3: extractDenseBlock4Params("dense3")
    };
    disposeUnusedWeightTensors(weightMap, paramMappings);
    return {params, paramMappings};
  }

  // src/faceFeatureExtractor/FaceFeatureExtractor.ts
  var FaceFeatureExtractor = class extends NeuralNetwork {
    constructor() {
      super("FaceFeatureExtractor");
    }
    forwardInput(input2) {
      const {params} = this;
      if (!params) {
        throw new Error("FaceFeatureExtractor - load model before inference");
      }
      return tidy(() => {
        const batchTensor = cast(input2.toBatchTensor(112, true), "float32");
        const meanRgb = [122.782, 117.001, 104.298];
        const normalized = normalize(batchTensor, meanRgb).div(255);
        let out = denseBlock4(normalized, params.dense0, true);
        out = denseBlock4(out, params.dense1);
        out = denseBlock4(out, params.dense2);
        out = denseBlock4(out, params.dense3);
        out = avgPool(out, [7, 7], [2, 2], "valid");
        return out;
      });
    }
    async forward(input2) {
      return this.forwardInput(await toNetInput(input2));
    }
    getDefaultModelName() {
      return "face_feature_extractor_model";
    }
    extractParamsFromWeightMap(weightMap) {
      return extractParamsFromWeightMap(weightMap);
    }
    extractParams(weights) {
      return extractParams(weights);
    }
  };

  // src/common/fullyConnectedLayer.ts
  function fullyConnectedLayer(x, params) {
    return tidy(() => add2(matMul(x, params.weights), params.bias));
  }

  // src/faceProcessor/extractParams.ts
  function extractParams2(weights, channelsIn, channelsOut) {
    const paramMappings = [];
    const {
      extractWeights,
      getRemainingWeights
    } = extractWeightsFactory(weights);
    const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
    const fc = extractFCParams(channelsIn, channelsOut, "fc");
    if (getRemainingWeights().length !== 0) {
      throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
    }
    return {
      paramMappings,
      params: {fc}
    };
  }

  // src/faceProcessor/extractParamsFromWeightMap.ts
  function extractParamsFromWeightMap2(weightMap) {
    const paramMappings = [];
    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
    function extractFcParams(prefix) {
      const weights = extractWeightEntry(`${prefix}/weights`, 2);
      const bias = extractWeightEntry(`${prefix}/bias`, 1);
      return {weights, bias};
    }
    const params = {
      fc: extractFcParams("fc")
    };
    disposeUnusedWeightTensors(weightMap, paramMappings);
    return {params, paramMappings};
  }

  // src/faceProcessor/util.ts
  function seperateWeightMaps(weightMap) {
    const featureExtractorMap = {};
    const classifierMap = {};
    Object.keys(weightMap).forEach((key) => {
      const map = key.startsWith("fc") ? classifierMap : featureExtractorMap;
      map[key] = weightMap[key];
    });
    return {featureExtractorMap, classifierMap};
  }

  // src/faceProcessor/FaceProcessor.ts
  var FaceProcessor = class extends NeuralNetwork {
    constructor(_name, faceFeatureExtractor) {
      super(_name);
      this._faceFeatureExtractor = faceFeatureExtractor;
    }
    get faceFeatureExtractor() {
      return this._faceFeatureExtractor;
    }
    runNet(input2) {
      const {params} = this;
      if (!params) {
        throw new Error(`${this._name} - load model before inference`);
      }
      return tidy(() => {
        const bottleneckFeatures = input2 instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input2) : input2;
        return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);
      });
    }
    dispose(throwOnRedispose = true) {
      this.faceFeatureExtractor.dispose(throwOnRedispose);
      super.dispose(throwOnRedispose);
    }
    loadClassifierParams(weights) {
      const {params, paramMappings} = this.extractClassifierParams(weights);
      this._params = params;
      this._paramMappings = paramMappings;
    }
    extractClassifierParams(weights) {
      return extractParams2(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());
    }
    extractParamsFromWeightMap(weightMap) {
      const {featureExtractorMap, classifierMap} = seperateWeightMaps(weightMap);
      this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
      return extractParamsFromWeightMap2(classifierMap);
    }
    extractParams(weights) {
      const cIn = this.getClassifierChannelsIn();
      const cOut = this.getClassifierChannelsOut();
      const classifierWeightSize = cOut * cIn + cOut;
      const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);
      const classifierWeights = weights.slice(weights.length - classifierWeightSize);
      this.faceFeatureExtractor.extractWeights(featureExtractorWeights);
      return this.extractClassifierParams(classifierWeights);
    }
  };

  // src/faceExpressionNet/FaceExpressions.ts
  var FACE_EXPRESSION_LABELS = ["neutral", "happy", "sad", "angry", "fearful", "disgusted", "surprised"];
  var FaceExpressions = class {
    constructor(probabilities) {
      if (probabilities.length !== 7) {
        throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);
      }
      FACE_EXPRESSION_LABELS.forEach((expression, idx) => {
        this[expression] = probabilities[idx];
      });
    }
    asSortedArray() {
      return FACE_EXPRESSION_LABELS.map((expression) => ({expression, probability: this[expression]})).sort((e0, e1) => e1.probability - e0.probability);
    }
  };

  // src/faceExpressionNet/FaceExpressionNet.ts
  var FaceExpressionNet = class extends FaceProcessor {
    constructor(faceFeatureExtractor = new FaceFeatureExtractor()) {
      super("FaceExpressionNet", faceFeatureExtractor);
    }
    forwardInput(input2) {
      return tidy(() => softmax(this.runNet(input2)));
    }
    async forward(input2) {
      return this.forwardInput(await toNetInput(input2));
    }
    async predictExpressions(input2) {
      const netInput = await toNetInput(input2);
      const out = await this.forwardInput(netInput);
      const probabilitesByBatch = await Promise.all(unstack(out).map(async (t) => {
        const data = t.dataSync();
        t.dispose();
        return data;
      }));
      out.dispose();
      const predictionsByBatch = probabilitesByBatch.map((probabilites) => new FaceExpressions(probabilites));
      return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0];
    }
    getDefaultModelName() {
      return "face_expression_model";
    }
    getClassifierChannelsIn() {
      return 256;
    }
    getClassifierChannelsOut() {
      return 7;
    }
  };

  // src/factories/WithFaceExpressions.ts
  function isWithFaceExpressions(obj) {
    return obj.expressions instanceof FaceExpressions;
  }
  function extendWithFaceExpressions(sourceObj, expressions) {
    const extension = {expressions};
    return {...sourceObj, ...extension};
  }

  // src/draw/drawFaceExpressions.ts
  function drawFaceExpressions(canvasArg, faceExpressions, minConfidence = 0.1, textFieldAnchor) {
    const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];
    faceExpressionsArray.forEach((e) => {
      const expr = e instanceof FaceExpressions ? e : isWithFaceExpressions(e) ? e.expressions : void 0;
      if (!expr) {
        throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");
      }
      const sorted = expr.asSortedArray();
      const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);
      const anchor = isWithFaceDetection(e) ? e.detection.box.bottomLeft : textFieldAnchor || new Point(0, 0);
      const drawTextField = new DrawTextField(resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round5(exprLocal.probability)})`), anchor);
      drawTextField.draw(canvasArg);
    });
  }

  // src/factories/WithFaceLandmarks.ts
  function isWithFaceLandmarks(obj) {
    return isWithFaceDetection(obj) && obj["landmarks"] instanceof FaceLandmarks && obj["unshiftedLandmarks"] instanceof FaceLandmarks && obj["alignedRect"] instanceof FaceDetection;
  }
  function calculateFaceAngle(mesh) {
    const radians = (a12, a22, b1, b2) => Math.atan2(b2 - a22, b1 - a12) % Math.PI;
    const degrees = (theta) => theta * 180 / Math.PI;
    const angle = {roll: void 0, pitch: void 0, yaw: void 0};
    if (!mesh || !mesh._positions || mesh._positions.length !== 68)
      return angle;
    const pt = mesh._positions;
    angle.roll = -radians(pt[36]._x, pt[36]._y, pt[45]._x, pt[45]._y);
    angle.pitch = radians(0, Math.abs(pt[0]._x - pt[30]._x) / pt[30]._x, Math.PI, Math.abs(pt[16]._x - pt[30]._x) / pt[30]._x);
    const bottom = pt.reduce((prev, cur) => prev < cur._y ? prev : cur._y, Infinity);
    const top = pt.reduce((prev, cur) => prev > cur._y ? prev : cur._y, -Infinity);
    angle.yaw = Math.PI * (mesh._imgDims._height / (top - bottom) / 1.4 - 1);
    return angle;
  }
  function extendWithFaceLandmarks(sourceObj, unshiftedLandmarks) {
    const {box: shift} = sourceObj.detection;
    const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);
    const rect = landmarks.align();
    const {imageDims} = sourceObj.detection;
    const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);
    const angle = calculateFaceAngle(unshiftedLandmarks);
    const extension = {
      landmarks,
      unshiftedLandmarks,
      alignedRect,
      angle
    };
    return {...sourceObj, ...extension};
  }

  // src/draw/DrawFaceLandmarks.ts
  var DrawFaceLandmarksOptions = class {
    constructor(options = {}) {
      const {
        drawLines = true,
        drawPoints = true,
        lineWidth,
        lineColor,
        pointSize,
        pointColor
      } = options;
      this.drawLines = drawLines;
      this.drawPoints = drawPoints;
      this.lineWidth = lineWidth || 1;
      this.pointSize = pointSize || 2;
      this.lineColor = lineColor || "rgba(0, 255, 255, 1)";
      this.pointColor = pointColor || "rgba(255, 0, 255, 1)";
    }
  };
  var DrawFaceLandmarks = class {
    constructor(faceLandmarks, options = {}) {
      this.faceLandmarks = faceLandmarks;
      this.options = new DrawFaceLandmarksOptions(options);
    }
    draw(canvasArg) {
      const ctx = getContext2dOrThrow(canvasArg);
      const {
        drawLines,
        drawPoints,
        lineWidth,
        lineColor,
        pointSize,
        pointColor
      } = this.options;
      if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {
        ctx.strokeStyle = lineColor;
        ctx.lineWidth = lineWidth;
        drawContour(ctx, this.faceLandmarks.getJawOutline());
        drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());
        drawContour(ctx, this.faceLandmarks.getRightEyeBrow());
        drawContour(ctx, this.faceLandmarks.getNose());
        drawContour(ctx, this.faceLandmarks.getLeftEye(), true);
        drawContour(ctx, this.faceLandmarks.getRightEye(), true);
        drawContour(ctx, this.faceLandmarks.getMouth(), true);
      }
      if (drawPoints) {
        ctx.strokeStyle = pointColor;
        ctx.fillStyle = pointColor;
        const drawPoint = (pt) => {
          ctx.beginPath();
          ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);
          ctx.fill();
        };
        this.faceLandmarks.positions.forEach(drawPoint);
      }
    }
  };
  function drawFaceLandmarks(canvasArg, faceLandmarks) {
    const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
    faceLandmarksArray.forEach((f) => {
      const landmarks = f instanceof FaceLandmarks ? f : isWithFaceLandmarks(f) ? f.landmarks : void 0;
      if (!landmarks) {
        throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");
      }
      new DrawFaceLandmarks(landmarks).draw(canvasArg);
    });
  }

  // package.json
  var version10 = "1.1.11";

  // src/xception/extractParams.ts
  function extractorsFactory2(extractWeights, paramMappings) {
    const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
    const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
    function extractReductionBlockParams(channelsIn, channelsOut, mappedPrefix) {
      const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);
      const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);
      const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);
      return {separable_conv0, separable_conv1, expansion_conv};
    }
    function extractMainBlockParams(channels, mappedPrefix) {
      const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);
      const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);
      const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);
      return {separable_conv0, separable_conv1, separable_conv2};
    }
    return {
      extractConvParams,
      extractSeparableConvParams,
      extractReductionBlockParams,
      extractMainBlockParams
    };
  }
  function extractParams3(weights, numMainBlocks) {
    const paramMappings = [];
    const {
      extractWeights,
      getRemainingWeights
    } = extractWeightsFactory(weights);
    const {
      extractConvParams,
      extractSeparableConvParams,
      extractReductionBlockParams,
      extractMainBlockParams
    } = extractorsFactory2(extractWeights, paramMappings);
    const entry_flow_conv_in = extractConvParams(3, 32, 3, "entry_flow/conv_in");
    const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, "entry_flow/reduction_block_0");
    const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, "entry_flow/reduction_block_1");
    const entry_flow = {
      conv_in: entry_flow_conv_in,
      reduction_block_0: entry_flow_reduction_block_0,
      reduction_block_1: entry_flow_reduction_block_1
    };
    const middle_flow = {};
    range6(numMainBlocks, 0, 1).forEach((idx) => {
      middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);
    });
    const exit_flow_reduction_block = extractReductionBlockParams(128, 256, "exit_flow/reduction_block");
    const exit_flow_separable_conv = extractSeparableConvParams(256, 512, "exit_flow/separable_conv");
    const exit_flow = {
      reduction_block: exit_flow_reduction_block,
      separable_conv: exit_flow_separable_conv
    };
    if (getRemainingWeights().length !== 0) {
      throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
    }
    return {
      paramMappings,
      params: {entry_flow, middle_flow, exit_flow}
    };
  }

  // src/xception/extractParamsFromWeightMap.ts
  function loadParamsFactory2(weightMap, paramMappings) {
    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
    const extractConvParams = loadConvParamsFactory(extractWeightEntry);
    const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
    function extractReductionBlockParams(mappedPrefix) {
      const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);
      const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);
      const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);
      return {separable_conv0, separable_conv1, expansion_conv};
    }
    function extractMainBlockParams(mappedPrefix) {
      const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);
      const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);
      const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);
      return {separable_conv0, separable_conv1, separable_conv2};
    }
    return {
      extractConvParams,
      extractSeparableConvParams,
      extractReductionBlockParams,
      extractMainBlockParams
    };
  }
  function extractParamsFromWeightMap3(weightMap, numMainBlocks) {
    const paramMappings = [];
    const {
      extractConvParams,
      extractSeparableConvParams,
      extractReductionBlockParams,
      extractMainBlockParams
    } = loadParamsFactory2(weightMap, paramMappings);
    const entry_flow_conv_in = extractConvParams("entry_flow/conv_in");
    const entry_flow_reduction_block_0 = extractReductionBlockParams("entry_flow/reduction_block_0");
    const entry_flow_reduction_block_1 = extractReductionBlockParams("entry_flow/reduction_block_1");
    const entry_flow = {
      conv_in: entry_flow_conv_in,
      reduction_block_0: entry_flow_reduction_block_0,
      reduction_block_1: entry_flow_reduction_block_1
    };
    const middle_flow = {};
    range6(numMainBlocks, 0, 1).forEach((idx) => {
      middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);
    });
    const exit_flow_reduction_block = extractReductionBlockParams("exit_flow/reduction_block");
    const exit_flow_separable_conv = extractSeparableConvParams("exit_flow/separable_conv");
    const exit_flow = {
      reduction_block: exit_flow_reduction_block,
      separable_conv: exit_flow_separable_conv
    };
    disposeUnusedWeightTensors(weightMap, paramMappings);
    return {params: {entry_flow, middle_flow, exit_flow}, paramMappings};
  }

  // src/xception/TinyXception.ts
  function conv(x, params, stride) {
    return add2(conv2d(x, params.filters, stride, "same"), params.bias);
  }
  function reductionBlock(x, params, isActivateInput = true) {
    let out = isActivateInput ? relu(x) : x;
    out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);
    out = depthwiseSeparableConv(relu(out), params.separable_conv1, [1, 1]);
    out = maxPool(out, [3, 3], [2, 2], "same");
    out = add2(out, conv(x, params.expansion_conv, [2, 2]));
    return out;
  }
  function mainBlock(x, params) {
    let out = depthwiseSeparableConv(relu(x), params.separable_conv0, [1, 1]);
    out = depthwiseSeparableConv(relu(out), params.separable_conv1, [1, 1]);
    out = depthwiseSeparableConv(relu(out), params.separable_conv2, [1, 1]);
    out = add2(out, x);
    return out;
  }
  var TinyXception = class extends NeuralNetwork {
    constructor(numMainBlocks) {
      super("TinyXception");
      this._numMainBlocks = numMainBlocks;
    }
    forwardInput(input2) {
      const {params} = this;
      if (!params) {
        throw new Error("TinyXception - load model before inference");
      }
      return tidy(() => {
        const batchTensor = cast(input2.toBatchTensor(112, true), "float32");
        const meanRgb = [122.782, 117.001, 104.298];
        const normalized = normalize(batchTensor, meanRgb).div(255);
        let out = relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
        out = reductionBlock(out, params.entry_flow.reduction_block_0, false);
        out = reductionBlock(out, params.entry_flow.reduction_block_1);
        range6(this._numMainBlocks, 0, 1).forEach((idx) => {
          out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);
        });
        out = reductionBlock(out, params.exit_flow.reduction_block);
        out = relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));
        return out;
      });
    }
    async forward(input2) {
      return this.forwardInput(await toNetInput(input2));
    }
    getDefaultModelName() {
      return "tiny_xception_model";
    }
    extractParamsFromWeightMap(weightMap) {
      return extractParamsFromWeightMap3(weightMap, this._numMainBlocks);
    }
    extractParams(weights) {
      return extractParams3(weights, this._numMainBlocks);
    }
  };

  // src/ageGenderNet/extractParams.ts
  function extractParams4(weights) {
    const paramMappings = [];
    const {
      extractWeights,
      getRemainingWeights
    } = extractWeightsFactory(weights);
    const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
    const age = extractFCParams(512, 1, "fc/age");
    const gender = extractFCParams(512, 2, "fc/gender");
    if (getRemainingWeights().length !== 0) {
      throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
    }
    return {
      paramMappings,
      params: {fc: {age, gender}}
    };
  }

  // src/ageGenderNet/extractParamsFromWeightMap.ts
  function extractParamsFromWeightMap4(weightMap) {
    const paramMappings = [];
    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
    function extractFcParams(prefix) {
      const weights = extractWeightEntry(`${prefix}/weights`, 2);
      const bias = extractWeightEntry(`${prefix}/bias`, 1);
      return {weights, bias};
    }
    const params = {
      fc: {
        age: extractFcParams("fc/age"),
        gender: extractFcParams("fc/gender")
      }
    };
    disposeUnusedWeightTensors(weightMap, paramMappings);
    return {params, paramMappings};
  }

  // src/ageGenderNet/types.ts
  var Gender;
  (function(Gender2) {
    Gender2["FEMALE"] = "female";
    Gender2["MALE"] = "male";
  })(Gender || (Gender = {}));

  // src/ageGenderNet/AgeGenderNet.ts
  var AgeGenderNet = class extends NeuralNetwork {
    constructor(faceFeatureExtractor = new TinyXception(2)) {
      super("AgeGenderNet");
      this._faceFeatureExtractor = faceFeatureExtractor;
    }
    get faceFeatureExtractor() {
      return this._faceFeatureExtractor;
    }
    runNet(input2) {
      const {params} = this;
      if (!params) {
        throw new Error(`${this._name} - load model before inference`);
      }
      return tidy(() => {
        const bottleneckFeatures = input2 instanceof NetInput ? this.faceFeatureExtractor.forwardInput(input2) : input2;
        const pooled = avgPool(bottleneckFeatures, [7, 7], [2, 2], "valid").as2D(bottleneckFeatures.shape[0], -1);
        const age = fullyConnectedLayer(pooled, params.fc.age).as1D();
        const gender = fullyConnectedLayer(pooled, params.fc.gender);
        return {age, gender};
      });
    }
    forwardInput(input2) {
      return tidy(() => {
        const {age, gender} = this.runNet(input2);
        return {age, gender: softmax(gender)};
      });
    }
    async forward(input2) {
      return this.forwardInput(await toNetInput(input2));
    }
    async predictAgeAndGender(input2) {
      const netInput = await toNetInput(input2);
      const out = await this.forwardInput(netInput);
      const ages = unstack(out.age);
      const genders = unstack(out.gender);
      const ageAndGenderTensors = ages.map((ageTensor, i) => ({
        ageTensor,
        genderTensor: genders[i]
      }));
      const predictionsByBatch = await Promise.all(ageAndGenderTensors.map(async ({ageTensor, genderTensor}) => {
        const age = ageTensor.dataSync()[0];
        const probMale = genderTensor.dataSync()[0];
        const isMale = probMale > 0.5;
        const gender = isMale ? Gender.MALE : Gender.FEMALE;
        const genderProbability = isMale ? probMale : 1 - probMale;
        ageTensor.dispose();
        genderTensor.dispose();
        return {age, gender, genderProbability};
      }));
      out.age.dispose();
      out.gender.dispose();
      return netInput.isBatchInput ? predictionsByBatch : predictionsByBatch[0];
    }
    getDefaultModelName() {
      return "age_gender_model";
    }
    dispose(throwOnRedispose = true) {
      this.faceFeatureExtractor.dispose(throwOnRedispose);
      super.dispose(throwOnRedispose);
    }
    loadClassifierParams(weights) {
      const {params, paramMappings} = this.extractClassifierParams(weights);
      this._params = params;
      this._paramMappings = paramMappings;
    }
    extractClassifierParams(weights) {
      return extractParams4(weights);
    }
    extractParamsFromWeightMap(weightMap) {
      const {featureExtractorMap, classifierMap} = seperateWeightMaps(weightMap);
      this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
      return extractParamsFromWeightMap4(classifierMap);
    }
    extractParams(weights) {
      const classifierWeightSize = 512 * 1 + 1 + (512 * 2 + 2);
      const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);
      const classifierWeights = weights.slice(weights.length - classifierWeightSize);
      this.faceFeatureExtractor.extractWeights(featureExtractorWeights);
      return this.extractClassifierParams(classifierWeights);
    }
  };

  // src/faceLandmarkNet/FaceLandmark68NetBase.ts
  var FaceLandmark68NetBase = class extends FaceProcessor {
    postProcess(output, inputSize, originalDimensions) {
      const inputDimensions = originalDimensions.map(({width, height}) => {
        const scale3 = inputSize / Math.max(height, width);
        return {
          width: width * scale3,
          height: height * scale3
        };
      });
      const batchSize = inputDimensions.length;
      return tidy(() => {
        const createInterleavedTensor = (fillX, fillY) => stack([fill([68], fillX, "float32"), fill([68], fillY, "float32")], 1).as2D(1, 136).as1D();
        const getPadding2 = (batchIdx, cond) => {
          const {width, height} = inputDimensions[batchIdx];
          return cond(width, height) ? Math.abs(width - height) / 2 : 0;
        };
        const getPaddingX = (batchIdx) => getPadding2(batchIdx, (w, h) => w < h);
        const getPaddingY = (batchIdx) => getPadding2(batchIdx, (w, h) => h < w);
        const landmarkTensors = output.mul(fill([batchSize, 136], inputSize, "float32")).sub(stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
        return landmarkTensors;
      });
    }
    forwardInput(input2) {
      return tidy(() => {
        const out = this.runNet(input2);
        return this.postProcess(out, input2.inputSize, input2.inputDimensions.map(([height, width]) => ({height, width})));
      });
    }
    async forward(input2) {
      return this.forwardInput(await toNetInput(input2));
    }
    async detectLandmarks(input2) {
      const netInput = await toNetInput(input2);
      const landmarkTensors = tidy(() => unstack(this.forwardInput(netInput)));
      const landmarksForBatch = await Promise.all(landmarkTensors.map(async (landmarkTensor, batchIdx) => {
        const landmarksArray = Array.from(landmarkTensor.dataSync());
        const xCoords = landmarksArray.filter((_, i) => isEven2(i));
        const yCoords = landmarksArray.filter((_, i) => !isEven2(i));
        return new FaceLandmarks68(Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])), {
          height: netInput.getInputHeight(batchIdx),
          width: netInput.getInputWidth(batchIdx)
        });
      }));
      landmarkTensors.forEach((t) => t.dispose());
      return netInput.isBatchInput ? landmarksForBatch : landmarksForBatch[0];
    }
    getClassifierChannelsOut() {
      return 136;
    }
  };

  // src/faceLandmarkNet/FaceLandmark68Net.ts
  var FaceLandmark68Net = class extends FaceLandmark68NetBase {
    constructor(faceFeatureExtractor = new FaceFeatureExtractor()) {
      super("FaceLandmark68Net", faceFeatureExtractor);
    }
    getDefaultModelName() {
      return "face_landmark_68_model";
    }
    getClassifierChannelsIn() {
      return 256;
    }
  };

  // src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts
  function extractParamsFromWeightMapTiny(weightMap) {
    const paramMappings = [];
    const {
      extractDenseBlock3Params
    } = loadParamsFactory(weightMap, paramMappings);
    const params = {
      dense0: extractDenseBlock3Params("dense0", true),
      dense1: extractDenseBlock3Params("dense1"),
      dense2: extractDenseBlock3Params("dense2")
    };
    disposeUnusedWeightTensors(weightMap, paramMappings);
    return {params, paramMappings};
  }

  // src/faceFeatureExtractor/extractParamsTiny.ts
  function extractParamsTiny(weights) {
    const paramMappings = [];
    const {
      extractWeights,
      getRemainingWeights
    } = extractWeightsFactory(weights);
    const {
      extractDenseBlock3Params
    } = extractorsFactory(extractWeights, paramMappings);
    const dense0 = extractDenseBlock3Params(3, 32, "dense0", true);
    const dense1 = extractDenseBlock3Params(32, 64, "dense1");
    const dense2 = extractDenseBlock3Params(64, 128, "dense2");
    if (getRemainingWeights().length !== 0) {
      throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
    }
    return {
      paramMappings,
      params: {dense0, dense1, dense2}
    };
  }

  // src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
  var TinyFaceFeatureExtractor = class extends NeuralNetwork {
    constructor() {
      super("TinyFaceFeatureExtractor");
    }
    forwardInput(input2) {
      const {params} = this;
      if (!params) {
        throw new Error("TinyFaceFeatureExtractor - load model before inference");
      }
      return tidy(() => {
        const batchTensor = cast(input2.toBatchTensor(112, true), "float32");
        const meanRgb = [122.782, 117.001, 104.298];
        const normalized = normalize(batchTensor, meanRgb).div(255);
        let out = denseBlock3(normalized, params.dense0, true);
        out = denseBlock3(out, params.dense1);
        out = denseBlock3(out, params.dense2);
        out = avgPool(out, [14, 14], [2, 2], "valid");
        return out;
      });
    }
    async forward(input2) {
      return this.forwardInput(await toNetInput(input2));
    }
    getDefaultModelName() {
      return "face_feature_extractor_tiny_model";
    }
    extractParamsFromWeightMap(weightMap) {
      return extractParamsFromWeightMapTiny(weightMap);
    }
    extractParams(weights) {
      return extractParamsTiny(weights);
    }
  };

  // src/faceLandmarkNet/FaceLandmark68TinyNet.ts
  var FaceLandmark68TinyNet = class extends FaceLandmark68NetBase {
    constructor(faceFeatureExtractor = new TinyFaceFeatureExtractor()) {
      super("FaceLandmark68TinyNet", faceFeatureExtractor);
    }
    getDefaultModelName() {
      return "face_landmark_68_tiny_model";
    }
    getClassifierChannelsIn() {
      return 128;
    }
  };

  // src/faceLandmarkNet/index.ts
  var FaceLandmarkNet = class extends FaceLandmark68Net {
  };

  // src/faceRecognitionNet/scaleLayer.ts
  function scale2(x, params) {
    return add2(mul(x, params.weights), params.biases);
  }

  // src/faceRecognitionNet/convLayer.ts
  function convLayer2(x, params, strides, withRelu, padding = "same") {
    const {filters, bias} = params.conv;
    let out = conv2d(x, filters, strides, padding);
    out = add2(out, bias);
    out = scale2(out, params.scale);
    return withRelu ? relu(out) : out;
  }
  function conv2(x, params) {
    return convLayer2(x, params, [1, 1], true);
  }
  function convNoRelu(x, params) {
    return convLayer2(x, params, [1, 1], false);
  }
  function convDown(x, params) {
    return convLayer2(x, params, [2, 2], true, "valid");
  }

  // src/faceRecognitionNet/extractParams.ts
  function extractorsFactory3(extractWeights, paramMappings) {
    function extractFilterValues(numFilterValues, numFilters, filterSize) {
      const weights = extractWeights(numFilterValues);
      const depth = weights.length / (numFilters * filterSize * filterSize);
      if (isFloat(depth)) {
        throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);
      }
      return tidy(() => transpose(tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]));
    }
    function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
      const filters = extractFilterValues(numFilterValues, numFilters, filterSize);
      const bias = tensor1d(extractWeights(numFilters));
      paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/bias`});
      return {filters, bias};
    }
    function extractScaleLayerParams(numWeights, mappedPrefix) {
      const weights = tensor1d(extractWeights(numWeights));
      const biases = tensor1d(extractWeights(numWeights));
      paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/biases`});
      return {
        weights,
        biases
      };
    }
    function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
      const conv3 = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);
      const scale3 = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);
      return {conv: conv3, scale: scale3};
    }
    function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown = false) {
      const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);
      const conv22 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);
      return {conv1, conv2: conv22};
    }
    return {
      extractConvLayerParams,
      extractResidualLayerParams
    };
  }
  function extractParams5(weights) {
    const {
      extractWeights,
      getRemainingWeights
    } = extractWeightsFactory(weights);
    const paramMappings = [];
    const {
      extractConvLayerParams,
      extractResidualLayerParams
    } = extractorsFactory3(extractWeights, paramMappings);
    const conv32_down = extractConvLayerParams(4704, 32, 7, "conv32_down");
    const conv32_1 = extractResidualLayerParams(9216, 32, 3, "conv32_1");
    const conv32_2 = extractResidualLayerParams(9216, 32, 3, "conv32_2");
    const conv32_3 = extractResidualLayerParams(9216, 32, 3, "conv32_3");
    const conv64_down = extractResidualLayerParams(36864, 64, 3, "conv64_down", true);
    const conv64_1 = extractResidualLayerParams(36864, 64, 3, "conv64_1");
    const conv64_2 = extractResidualLayerParams(36864, 64, 3, "conv64_2");
    const conv64_3 = extractResidualLayerParams(36864, 64, 3, "conv64_3");
    const conv128_down = extractResidualLayerParams(147456, 128, 3, "conv128_down", true);
    const conv128_1 = extractResidualLayerParams(147456, 128, 3, "conv128_1");
    const conv128_2 = extractResidualLayerParams(147456, 128, 3, "conv128_2");
    const conv256_down = extractResidualLayerParams(589824, 256, 3, "conv256_down", true);
    const conv256_1 = extractResidualLayerParams(589824, 256, 3, "conv256_1");
    const conv256_2 = extractResidualLayerParams(589824, 256, 3, "conv256_2");
    const conv256_down_out = extractResidualLayerParams(589824, 256, 3, "conv256_down_out");
    const fc = tidy(() => transpose(tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]));
    paramMappings.push({paramPath: "fc"});
    if (getRemainingWeights().length !== 0) {
      throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
    }
    const params = {
      conv32_down,
      conv32_1,
      conv32_2,
      conv32_3,
      conv64_down,
      conv64_1,
      conv64_2,
      conv64_3,
      conv128_down,
      conv128_1,
      conv128_2,
      conv256_down,
      conv256_1,
      conv256_2,
      conv256_down_out,
      fc
    };
    return {params, paramMappings};
  }

  // src/faceRecognitionNet/extractParamsFromWeightMap.ts
  function extractorsFactory4(weightMap, paramMappings) {
    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
    function extractScaleLayerParams(prefix) {
      const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);
      const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);
      return {weights, biases};
    }
    function extractConvLayerParams(prefix) {
      const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);
      const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);
      const scale3 = extractScaleLayerParams(prefix);
      return {conv: {filters, bias}, scale: scale3};
    }
    function extractResidualLayerParams(prefix) {
      return {
        conv1: extractConvLayerParams(`${prefix}/conv1`),
        conv2: extractConvLayerParams(`${prefix}/conv2`)
      };
    }
    return {
      extractConvLayerParams,
      extractResidualLayerParams
    };
  }
  function extractParamsFromWeightMap5(weightMap) {
    const paramMappings = [];
    const {
      extractConvLayerParams,
      extractResidualLayerParams
    } = extractorsFactory4(weightMap, paramMappings);
    const conv32_down = extractConvLayerParams("conv32_down");
    const conv32_1 = extractResidualLayerParams("conv32_1");
    const conv32_2 = extractResidualLayerParams("conv32_2");
    const conv32_3 = extractResidualLayerParams("conv32_3");
    const conv64_down = extractResidualLayerParams("conv64_down");
    const conv64_1 = extractResidualLayerParams("conv64_1");
    const conv64_2 = extractResidualLayerParams("conv64_2");
    const conv64_3 = extractResidualLayerParams("conv64_3");
    const conv128_down = extractResidualLayerParams("conv128_down");
    const conv128_1 = extractResidualLayerParams("conv128_1");
    const conv128_2 = extractResidualLayerParams("conv128_2");
    const conv256_down = extractResidualLayerParams("conv256_down");
    const conv256_1 = extractResidualLayerParams("conv256_1");
    const conv256_2 = extractResidualLayerParams("conv256_2");
    const conv256_down_out = extractResidualLayerParams("conv256_down_out");
    const {fc} = weightMap;
    paramMappings.push({originalPath: "fc", paramPath: "fc"});
    if (!isTensor2D(fc)) {
      throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);
    }
    const params = {
      conv32_down,
      conv32_1,
      conv32_2,
      conv32_3,
      conv64_down,
      conv64_1,
      conv64_2,
      conv64_3,
      conv128_down,
      conv128_1,
      conv128_2,
      conv256_down,
      conv256_1,
      conv256_2,
      conv256_down_out,
      fc
    };
    disposeUnusedWeightTensors(weightMap, paramMappings);
    return {params, paramMappings};
  }

  // src/faceRecognitionNet/residualLayer.ts
  function residual(x, params) {
    let out = conv2(x, params.conv1);
    out = convNoRelu(out, params.conv2);
    out = add2(out, x);
    out = relu(out);
    return out;
  }
  function residualDown(x, params) {
    let out = convDown(x, params.conv1);
    out = convNoRelu(out, params.conv2);
    let pooled = avgPool(x, 2, 2, "valid");
    const zeros4 = zeros(pooled.shape);
    const isPad = pooled.shape[3] !== out.shape[3];
    const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];
    if (isAdjustShape) {
      const padShapeX = [...out.shape];
      padShapeX[1] = 1;
      const zerosW = zeros(padShapeX);
      out = concat([out, zerosW], 1);
      const padShapeY = [...out.shape];
      padShapeY[2] = 1;
      const zerosH = zeros(padShapeY);
      out = concat([out, zerosH], 2);
    }
    pooled = isPad ? concat([pooled, zeros4], 3) : pooled;
    out = add2(pooled, out);
    out = relu(out);
    return out;
  }

  // src/faceRecognitionNet/FaceRecognitionNet.ts
  var FaceRecognitionNet = class extends NeuralNetwork {
    constructor() {
      super("FaceRecognitionNet");
    }
    forwardInput(input2) {
      const {params} = this;
      if (!params) {
        throw new Error("FaceRecognitionNet - load model before inference");
      }
      return tidy(() => {
        const batchTensor = cast(input2.toBatchTensor(150, true), "float32");
        const meanRgb = [122.782, 117.001, 104.298];
        const normalized = normalize(batchTensor, meanRgb).div(255);
        let out = convDown(normalized, params.conv32_down);
        out = maxPool(out, 3, 2, "valid");
        out = residual(out, params.conv32_1);
        out = residual(out, params.conv32_2);
        out = residual(out, params.conv32_3);
        out = residualDown(out, params.conv64_down);
        out = residual(out, params.conv64_1);
        out = residual(out, params.conv64_2);
        out = residual(out, params.conv64_3);
        out = residualDown(out, params.conv128_down);
        out = residual(out, params.conv128_1);
        out = residual(out, params.conv128_2);
        out = residualDown(out, params.conv256_down);
        out = residual(out, params.conv256_1);
        out = residual(out, params.conv256_2);
        out = residualDown(out, params.conv256_down_out);
        const globalAvg = out.mean([1, 2]);
        const fullyConnected = matMul(globalAvg, params.fc);
        return fullyConnected;
      });
    }
    async forward(input2) {
      return this.forwardInput(await toNetInput(input2));
    }
    async computeFaceDescriptor(input2) {
      var _a;
      if ((_a = input2 == null ? void 0 : input2.shape) == null ? void 0 : _a.some((dim) => dim <= 0))
        return new Float32Array(128);
      const netInput = await toNetInput(input2);
      const faceDescriptorTensors = tidy(() => unstack(this.forwardInput(netInput)));
      const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data()));
      faceDescriptorTensors.forEach((t) => t.dispose());
      return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];
    }
    getDefaultModelName() {
      return "face_recognition_model";
    }
    extractParamsFromWeightMap(weightMap) {
      return extractParamsFromWeightMap5(weightMap);
    }
    extractParams(weights) {
      return extractParams5(weights);
    }
  };

  // src/faceRecognitionNet/index.ts
  function createFaceRecognitionNet(weights) {
    const net = new FaceRecognitionNet();
    net.extractWeights(weights);
    return net;
  }

  // src/factories/WithFaceDescriptor.ts
  function extendWithFaceDescriptor(sourceObj, descriptor) {
    const extension = {descriptor};
    return {...sourceObj, ...extension};
  }

  // src/factories/WithAge.ts
  function isWithAge(obj) {
    return typeof obj.age === "number";
  }
  function extendWithAge(sourceObj, age) {
    const extension = {age};
    return {...sourceObj, ...extension};
  }

  // src/factories/WithGender.ts
  function isWithGender(obj) {
    return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE) && isValidProbablitiy(obj.genderProbability);
  }
  function extendWithGender(sourceObj, gender, genderProbability) {
    const extension = {gender, genderProbability};
    return {...sourceObj, ...extension};
  }

  // src/ssdMobilenetv1/extractParams.ts
  function extractorsFactory5(extractWeights, paramMappings) {
    function extractDepthwiseConvParams(numChannels, mappedPrefix) {
      const filters = tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
      const batch_norm_scale = tensor1d(extractWeights(numChannels));
      const batch_norm_offset = tensor1d(extractWeights(numChannels));
      const batch_norm_mean = tensor1d(extractWeights(numChannels));
      const batch_norm_variance = tensor1d(extractWeights(numChannels));
      paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/batch_norm_scale`}, {paramPath: `${mappedPrefix}/batch_norm_offset`}, {paramPath: `${mappedPrefix}/batch_norm_mean`}, {paramPath: `${mappedPrefix}/batch_norm_variance`});
      return {
        filters,
        batch_norm_scale,
        batch_norm_offset,
        batch_norm_mean,
        batch_norm_variance
      };
    }
    function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
      const filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
      const bias = tensor1d(extractWeights(channelsOut));
      paramMappings.push({paramPath: `${mappedPrefix}/filters`}, {paramPath: `${mappedPrefix}/${isPointwiseConv ? "batch_norm_offset" : "bias"}`});
      return {filters, bias};
    }
    function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
      const {
        filters,
        bias
      } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);
      return {
        filters,
        batch_norm_offset: bias
      };
    }
    function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
      const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);
      const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);
      return {depthwise_conv, pointwise_conv};
    }
    function extractMobilenetV1Params() {
      const conv_0 = extractPointwiseConvParams(3, 32, 3, "mobilenetv1/conv_0");
      const conv_1 = extractConvPairParams(32, 64, "mobilenetv1/conv_1");
      const conv_2 = extractConvPairParams(64, 128, "mobilenetv1/conv_2");
      const conv_3 = extractConvPairParams(128, 128, "mobilenetv1/conv_3");
      const conv_4 = extractConvPairParams(128, 256, "mobilenetv1/conv_4");
      const conv_5 = extractConvPairParams(256, 256, "mobilenetv1/conv_5");
      const conv_6 = extractConvPairParams(256, 512, "mobilenetv1/conv_6");
      const conv_7 = extractConvPairParams(512, 512, "mobilenetv1/conv_7");
      const conv_8 = extractConvPairParams(512, 512, "mobilenetv1/conv_8");
      const conv_9 = extractConvPairParams(512, 512, "mobilenetv1/conv_9");
      const conv_10 = extractConvPairParams(512, 512, "mobilenetv1/conv_10");
      const conv_11 = extractConvPairParams(512, 512, "mobilenetv1/conv_11");
      const conv_12 = extractConvPairParams(512, 1024, "mobilenetv1/conv_12");
      const conv_13 = extractConvPairParams(1024, 1024, "mobilenetv1/conv_13");
      return {
        conv_0,
        conv_1,
        conv_2,
        conv_3,
        conv_4,
        conv_5,
        conv_6,
        conv_7,
        conv_8,
        conv_9,
        conv_10,
        conv_11,
        conv_12,
        conv_13
      };
    }
    function extractPredictionLayerParams() {
      const conv_0 = extractPointwiseConvParams(1024, 256, 1, "prediction_layer/conv_0");
      const conv_1 = extractPointwiseConvParams(256, 512, 3, "prediction_layer/conv_1");
      const conv_2 = extractPointwiseConvParams(512, 128, 1, "prediction_layer/conv_2");
      const conv_3 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_3");
      const conv_4 = extractPointwiseConvParams(256, 128, 1, "prediction_layer/conv_4");
      const conv_5 = extractPointwiseConvParams(128, 256, 3, "prediction_layer/conv_5");
      const conv_6 = extractPointwiseConvParams(256, 64, 1, "prediction_layer/conv_6");
      const conv_7 = extractPointwiseConvParams(64, 128, 3, "prediction_layer/conv_7");
      const box_encoding_0_predictor = extractConvParams(512, 12, 1, "prediction_layer/box_predictor_0/box_encoding_predictor");
      const class_predictor_0 = extractConvParams(512, 9, 1, "prediction_layer/box_predictor_0/class_predictor");
      const box_encoding_1_predictor = extractConvParams(1024, 24, 1, "prediction_layer/box_predictor_1/box_encoding_predictor");
      const class_predictor_1 = extractConvParams(1024, 18, 1, "prediction_layer/box_predictor_1/class_predictor");
      const box_encoding_2_predictor = extractConvParams(512, 24, 1, "prediction_layer/box_predictor_2/box_encoding_predictor");
      const class_predictor_2 = extractConvParams(512, 18, 1, "prediction_layer/box_predictor_2/class_predictor");
      const box_encoding_3_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_3/box_encoding_predictor");
      const class_predictor_3 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_3/class_predictor");
      const box_encoding_4_predictor = extractConvParams(256, 24, 1, "prediction_layer/box_predictor_4/box_encoding_predictor");
      const class_predictor_4 = extractConvParams(256, 18, 1, "prediction_layer/box_predictor_4/class_predictor");
      const box_encoding_5_predictor = extractConvParams(128, 24, 1, "prediction_layer/box_predictor_5/box_encoding_predictor");
      const class_predictor_5 = extractConvParams(128, 18, 1, "prediction_layer/box_predictor_5/class_predictor");
      const box_predictor_0 = {
        box_encoding_predictor: box_encoding_0_predictor,
        class_predictor: class_predictor_0
      };
      const box_predictor_1 = {
        box_encoding_predictor: box_encoding_1_predictor,
        class_predictor: class_predictor_1
      };
      const box_predictor_2 = {
        box_encoding_predictor: box_encoding_2_predictor,
        class_predictor: class_predictor_2
      };
      const box_predictor_3 = {
        box_encoding_predictor: box_encoding_3_predictor,
        class_predictor: class_predictor_3
      };
      const box_predictor_4 = {
        box_encoding_predictor: box_encoding_4_predictor,
        class_predictor: class_predictor_4
      };
      const box_predictor_5 = {
        box_encoding_predictor: box_encoding_5_predictor,
        class_predictor: class_predictor_5
      };
      return {
        conv_0,
        conv_1,
        conv_2,
        conv_3,
        conv_4,
        conv_5,
        conv_6,
        conv_7,
        box_predictor_0,
        box_predictor_1,
        box_predictor_2,
        box_predictor_3,
        box_predictor_4,
        box_predictor_5
      };
    }
    return {
      extractMobilenetV1Params,
      extractPredictionLayerParams
    };
  }
  function extractParams6(weights) {
    const paramMappings = [];
    const {
      extractWeights,
      getRemainingWeights
    } = extractWeightsFactory(weights);
    const {
      extractMobilenetV1Params,
      extractPredictionLayerParams
    } = extractorsFactory5(extractWeights, paramMappings);
    const mobilenetv1 = extractMobilenetV1Params();
    const prediction_layer = extractPredictionLayerParams();
    const extra_dim = tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
    const output_layer = {
      extra_dim
    };
    paramMappings.push({paramPath: "output_layer/extra_dim"});
    if (getRemainingWeights().length !== 0) {
      throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
    }
    return {
      params: {
        mobilenetv1,
        prediction_layer,
        output_layer
      },
      paramMappings
    };
  }

  // src/ssdMobilenetv1/extractParamsFromWeightMap.ts
  function extractorsFactory6(weightMap, paramMappings) {
    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
    function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
      const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);
      const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);
      return {filters, batch_norm_offset};
    }
    function extractConvPairParams(idx) {
      const mappedPrefix = `mobilenetv1/conv_${idx}`;
      const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;
      const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;
      const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;
      const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);
      const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);
      const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);
      const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);
      const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);
      return {
        depthwise_conv: {
          filters,
          batch_norm_scale,
          batch_norm_offset,
          batch_norm_mean,
          batch_norm_variance
        },
        pointwise_conv: extractPointwiseConvParams("MobilenetV1", idx, mappedPrefixPointwiseConv)
      };
    }
    function extractMobilenetV1Params() {
      return {
        conv_0: extractPointwiseConvParams("MobilenetV1", 0, "mobilenetv1/conv_0"),
        conv_1: extractConvPairParams(1),
        conv_2: extractConvPairParams(2),
        conv_3: extractConvPairParams(3),
        conv_4: extractConvPairParams(4),
        conv_5: extractConvPairParams(5),
        conv_6: extractConvPairParams(6),
        conv_7: extractConvPairParams(7),
        conv_8: extractConvPairParams(8),
        conv_9: extractConvPairParams(9),
        conv_10: extractConvPairParams(10),
        conv_11: extractConvPairParams(11),
        conv_12: extractConvPairParams(12),
        conv_13: extractConvPairParams(13)
      };
    }
    function extractConvParams(prefix, mappedPrefix) {
      const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);
      const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);
      return {filters, bias};
    }
    function extractBoxPredictorParams(idx) {
      const box_encoding_predictor = extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`, `prediction_layer/box_predictor_${idx}/box_encoding_predictor`);
      const class_predictor = extractConvParams(`Prediction/BoxPredictor_${idx}/ClassPredictor`, `prediction_layer/box_predictor_${idx}/class_predictor`);
      return {box_encoding_predictor, class_predictor};
    }
    function extractPredictionLayerParams() {
      return {
        conv_0: extractPointwiseConvParams("Prediction", 0, "prediction_layer/conv_0"),
        conv_1: extractPointwiseConvParams("Prediction", 1, "prediction_layer/conv_1"),
        conv_2: extractPointwiseConvParams("Prediction", 2, "prediction_layer/conv_2"),
        conv_3: extractPointwiseConvParams("Prediction", 3, "prediction_layer/conv_3"),
        conv_4: extractPointwiseConvParams("Prediction", 4, "prediction_layer/conv_4"),
        conv_5: extractPointwiseConvParams("Prediction", 5, "prediction_layer/conv_5"),
        conv_6: extractPointwiseConvParams("Prediction", 6, "prediction_layer/conv_6"),
        conv_7: extractPointwiseConvParams("Prediction", 7, "prediction_layer/conv_7"),
        box_predictor_0: extractBoxPredictorParams(0),
        box_predictor_1: extractBoxPredictorParams(1),
        box_predictor_2: extractBoxPredictorParams(2),
        box_predictor_3: extractBoxPredictorParams(3),
        box_predictor_4: extractBoxPredictorParams(4),
        box_predictor_5: extractBoxPredictorParams(5)
      };
    }
    return {
      extractMobilenetV1Params,
      extractPredictionLayerParams
    };
  }
  function extractParamsFromWeightMap6(weightMap) {
    const paramMappings = [];
    const {
      extractMobilenetV1Params,
      extractPredictionLayerParams
    } = extractorsFactory6(weightMap, paramMappings);
    const extra_dim = weightMap["Output/extra_dim"];
    paramMappings.push({originalPath: "Output/extra_dim", paramPath: "output_layer/extra_dim"});
    if (!isTensor3D(extra_dim)) {
      throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);
    }
    const params = {
      mobilenetv1: extractMobilenetV1Params(),
      prediction_layer: extractPredictionLayerParams(),
      output_layer: {
        extra_dim
      }
    };
    disposeUnusedWeightTensors(weightMap, paramMappings);
    return {params, paramMappings};
  }

  // src/ssdMobilenetv1/pointwiseConvLayer.ts
  function pointwiseConvLayer(x, params, strides) {
    return tidy(() => {
      let out = conv2d(x, params.filters, strides, "same");
      out = add2(out, params.batch_norm_offset);
      return clipByValue(out, 0, 6);
    });
  }

  // src/ssdMobilenetv1/mobileNetV1.ts
  var epsilon3 = 0.0010000000474974513;
  function depthwiseConvLayer(x, params, strides) {
    return tidy(() => {
      let out = depthwiseConv2d(x, params.filters, strides, "same");
      out = batchNorm(out, params.batch_norm_mean, params.batch_norm_variance, params.batch_norm_offset, params.batch_norm_scale, epsilon3);
      return clipByValue(out, 0, 6);
    });
  }
  function getStridesForLayerIdx(layerIdx) {
    return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];
  }
  function mobileNetV1(x, params) {
    return tidy(() => {
      let conv11;
      let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
      const convPairParams = [
        params.conv_1,
        params.conv_2,
        params.conv_3,
        params.conv_4,
        params.conv_5,
        params.conv_6,
        params.conv_7,
        params.conv_8,
        params.conv_9,
        params.conv_10,
        params.conv_11,
        params.conv_12,
        params.conv_13
      ];
      convPairParams.forEach((param, i) => {
        const layerIdx = i + 1;
        const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
        out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
        out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
        if (layerIdx === 11)
          conv11 = out;
      });
      if (conv11 === null) {
        throw new Error("mobileNetV1 - output of conv layer 11 is null");
      }
      return {
        out,
        conv11
      };
    });
  }

  // src/ssdMobilenetv1/nonMaxSuppression.ts
  function IOU(boxes, i, j) {
    const boxesData = boxes.arraySync();
    const yminI = Math.min(boxesData[i][0], boxesData[i][2]);
    const xminI = Math.min(boxesData[i][1], boxesData[i][3]);
    const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);
    const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);
    const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);
    const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);
    const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);
    const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);
    const areaI = (ymaxI - yminI) * (xmaxI - xminI);
    const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
    if (areaI <= 0 || areaJ <= 0)
      return 0;
    const intersectionYmin = Math.max(yminI, yminJ);
    const intersectionXmin = Math.max(xminI, xminJ);
    const intersectionYmax = Math.min(ymaxI, ymaxJ);
    const intersectionXmax = Math.min(xmaxI, xmaxJ);
    const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0);
    return intersectionArea / (areaI + areaJ - intersectionArea);
  }
  function nonMaxSuppression3(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
    const numBoxes = boxes.shape[0];
    const outputSize = Math.min(maxOutputSize, numBoxes);
    const candidates = scores.map((score, boxIndex) => ({score, boxIndex})).filter((c) => c.score > scoreThreshold).sort((c1, c2) => c2.score - c1.score);
    const suppressFunc = (x) => x <= iouThreshold ? 1 : 0;
    const selected = [];
    candidates.forEach((c) => {
      if (selected.length >= outputSize)
        return;
      const originalScore = c.score;
      for (let j = selected.length - 1; j >= 0; --j) {
        const iou2 = IOU(boxes, c.boxIndex, selected[j]);
        if (iou2 === 0)
          continue;
        c.score *= suppressFunc(iou2);
        if (c.score <= scoreThreshold)
          break;
      }
      if (originalScore === c.score) {
        selected.push(c.boxIndex);
      }
    });
    return selected;
  }

  // src/ssdMobilenetv1/outputLayer.ts
  function getCenterCoordinatesAndSizesLayer(x) {
    const vec = unstack(transpose(x, [1, 0]));
    const sizes = [
      sub(vec[2], vec[0]),
      sub(vec[3], vec[1])
    ];
    const centers = [
      add2(vec[0], div(sizes[0], 2)),
      add2(vec[1], div(sizes[1], 2))
    ];
    return {sizes, centers};
  }
  function decodeBoxesLayer(x0, x1) {
    const {sizes, centers} = getCenterCoordinatesAndSizesLayer(x0);
    const vec = unstack(transpose(x1, [1, 0]));
    const div0_out = div(mul(exp(div(vec[2], 5)), sizes[0]), 2);
    const add0_out = add2(mul(div(vec[0], 10), sizes[0]), centers[0]);
    const div1_out = div(mul(exp(div(vec[3], 5)), sizes[1]), 2);
    const add1_out = add2(mul(div(vec[1], 10), sizes[1]), centers[1]);
    return transpose(stack([
      sub(add0_out, div0_out),
      sub(add1_out, div1_out),
      add2(add0_out, div0_out),
      add2(add1_out, div1_out)
    ]), [1, 0]);
  }
  function outputLayer(boxPredictions, classPredictions, params) {
    return tidy(() => {
      const batchSize = boxPredictions.shape[0];
      let boxes = decodeBoxesLayer(reshape(tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), reshape(boxPredictions, [-1, 4]));
      boxes = reshape(boxes, [batchSize, boxes.shape[0] / batchSize, 4]);
      const scoresAndClasses = sigmoid(slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
      let scores = slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
      scores = reshape(scores, [batchSize, scores.shape[1]]);
      const boxesByBatch = unstack(boxes);
      const scoresByBatch = unstack(scores);
      return {boxes: boxesByBatch, scores: scoresByBatch};
    });
  }

  // src/ssdMobilenetv1/boxPredictionLayer.ts
  function boxPredictionLayer(x, params) {
    return tidy(() => {
      const batchSize = x.shape[0];
      const boxPredictionEncoding = reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
      const classPrediction = reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
      return {boxPredictionEncoding, classPrediction};
    });
  }

  // src/ssdMobilenetv1/predictionLayer.ts
  function predictionLayer(x, conv11, params) {
    return tidy(() => {
      const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);
      const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
      const conv22 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
      const conv3 = pointwiseConvLayer(conv22, params.conv_3, [2, 2]);
      const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
      const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
      const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
      const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
      const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);
      const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);
      const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);
      const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);
      const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);
      const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);
      const boxPredictions = concat([
        boxPrediction0.boxPredictionEncoding,
        boxPrediction1.boxPredictionEncoding,
        boxPrediction2.boxPredictionEncoding,
        boxPrediction3.boxPredictionEncoding,
        boxPrediction4.boxPredictionEncoding,
        boxPrediction5.boxPredictionEncoding
      ], 1);
      const classPredictions = concat([
        boxPrediction0.classPrediction,
        boxPrediction1.classPrediction,
        boxPrediction2.classPrediction,
        boxPrediction3.classPrediction,
        boxPrediction4.classPrediction,
        boxPrediction5.classPrediction
      ], 1);
      return {
        boxPredictions,
        classPredictions
      };
    });
  }

  // src/ssdMobilenetv1/SsdMobilenetv1Options.ts
  var SsdMobilenetv1Options = class {
    constructor({minConfidence, maxResults} = {}) {
      this._name = "SsdMobilenetv1Options";
      this._minConfidence = minConfidence || 0.5;
      this._maxResults = maxResults || 100;
      if (typeof this._minConfidence !== "number" || this._minConfidence <= 0 || this._minConfidence >= 1) {
        throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);
      }
      if (typeof this._maxResults !== "number") {
        throw new Error(`${this._name} - expected maxResults to be a number`);
      }
    }
    get minConfidence() {
      return this._minConfidence;
    }
    get maxResults() {
      return this._maxResults;
    }
  };

  // src/ssdMobilenetv1/SsdMobilenetv1.ts
  var SsdMobilenetv1 = class extends NeuralNetwork {
    constructor() {
      super("SsdMobilenetv1");
    }
    forwardInput(input2) {
      const {params} = this;
      if (!params)
        throw new Error("SsdMobilenetv1 - load model before inference");
      return tidy(() => {
        const batchTensor = cast(input2.toBatchTensor(512, false), "float32");
        const x = sub(div(batchTensor, 127.5), 1);
        const features = mobileNetV1(x, params.mobilenetv1);
        const {boxPredictions, classPredictions} = predictionLayer(features.out, features.conv11, params.prediction_layer);
        return outputLayer(boxPredictions, classPredictions, params.output_layer);
      });
    }
    async forward(input2) {
      return this.forwardInput(await toNetInput(input2));
    }
    async locateFaces(input2, options = {}) {
      const {maxResults, minConfidence} = new SsdMobilenetv1Options(options);
      const netInput = await toNetInput(input2);
      const {boxes: _boxes, scores: _scores} = this.forwardInput(netInput);
      const boxes = _boxes[0];
      const scores = _scores[0];
      for (let i = 1; i < _boxes.length; i++) {
        _boxes[i].dispose();
        _scores[i].dispose();
      }
      const scoresData = Array.from(scores.dataSync());
      const iouThreshold = 0.5;
      const indices = nonMaxSuppression3(boxes, scoresData, maxResults, iouThreshold, minConfidence);
      const reshapedDims = netInput.getReshapedInputDimensions(0);
      const inputSize = netInput.inputSize;
      const padX = inputSize / reshapedDims.width;
      const padY = inputSize / reshapedDims.height;
      const boxesData = boxes.arraySync();
      const results = indices.map((idx) => {
        const [top, bottom] = [
          Math.max(0, boxesData[idx][0]),
          Math.min(1, boxesData[idx][2])
        ].map((val) => val * padY);
        const [left, right] = [
          Math.max(0, boxesData[idx][1]),
          Math.min(1, boxesData[idx][3])
        ].map((val) => val * padX);
        return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {height: netInput.getInputHeight(0), width: netInput.getInputWidth(0)});
      });
      boxes.dispose();
      scores.dispose();
      return results;
    }
    getDefaultModelName() {
      return "ssd_mobilenetv1_model";
    }
    extractParamsFromWeightMap(weightMap) {
      return extractParamsFromWeightMap6(weightMap);
    }
    extractParams(weights) {
      return extractParams6(weights);
    }
  };

  // src/ssdMobilenetv1/index.ts
  function createSsdMobilenetv1(weights) {
    const net = new SsdMobilenetv1();
    net.extractWeights(weights);
    return net;
  }
  function createFaceDetectionNet(weights) {
    return createSsdMobilenetv1(weights);
  }
  var FaceDetectionNet = class extends SsdMobilenetv1 {
  };

  // src/tinyYolov2/const.ts
  var IOU_THRESHOLD = 0.4;
  var BOX_ANCHORS = [
    new Point(0.738768, 0.874946),
    new Point(2.42204, 2.65704),
    new Point(4.30971, 7.04493),
    new Point(10.246, 4.59428),
    new Point(12.6868, 11.8741)
  ];
  var BOX_ANCHORS_SEPARABLE = [
    new Point(1.603231, 2.094468),
    new Point(6.041143, 7.080126),
    new Point(2.882459, 3.518061),
    new Point(4.266906, 5.178857),
    new Point(9.041765, 10.66308)
  ];
  var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404];
  var DEFAULT_MODEL_NAME2 = "tiny_yolov2_model";
  var DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model";

  // src/tinyYolov2/config.ts
  var isNumber2 = (arg) => typeof arg === "number";
  function validateConfig(config) {
    if (!config) {
      throw new Error(`invalid config: ${config}`);
    }
    if (typeof config.withSeparableConvs !== "boolean") {
      throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);
    }
    if (!isNumber2(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1) {
      throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);
    }
    if (!Array.isArray(config.classes) || !config.classes.length || !config.classes.every((c) => typeof c === "string")) {
      throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);
    }
    if (!Array.isArray(config.anchors) || !config.anchors.length || !config.anchors.map((a) => a || {}).every((a) => isNumber2(a.x) && isNumber2(a.y))) {
      throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);
    }
    if (config.meanRgb && (!Array.isArray(config.meanRgb) || config.meanRgb.length !== 3 || !config.meanRgb.every(isNumber2))) {
      throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);
    }
  }

  // src/tinyYolov2/leaky.ts
  function leaky(x) {
    return tidy(() => {
      const min6 = mul(x, scalar(0.10000000149011612));
      return add2(relu(sub(x, min6)), min6);
    });
  }

  // src/tinyYolov2/convWithBatchNorm.ts
  function convWithBatchNorm(x, params) {
    return tidy(() => {
      let out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
      out = conv2d(out, params.conv.filters, [1, 1], "valid");
      out = sub(out, params.bn.sub);
      out = mul(out, params.bn.truediv);
      out = add2(out, params.conv.bias);
      return leaky(out);
    });
  }

  // src/tinyYolov2/depthwiseSeparableConv.ts
  function depthwiseSeparableConv2(x, params) {
    return tidy(() => {
      let out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
      out = separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid");
      out = add2(out, params.bias);
      return leaky(out);
    });
  }

  // src/tinyYolov2/extractParams.ts
  function extractorsFactory7(extractWeights, paramMappings) {
    const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
    function extractBatchNormParams(size, mappedPrefix) {
      const sub4 = tensor1d(extractWeights(size));
      const truediv = tensor1d(extractWeights(size));
      paramMappings.push({paramPath: `${mappedPrefix}/sub`}, {paramPath: `${mappedPrefix}/truediv`});
      return {sub: sub4, truediv};
    }
    function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
      const conv3 = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);
      const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);
      return {conv: conv3, bn};
    }
    const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
    return {
      extractConvParams,
      extractConvWithBatchNormParams,
      extractSeparableConvParams
    };
  }
  function extractParams7(weights, config, boxEncodingSize, filterSizes) {
    const {
      extractWeights,
      getRemainingWeights
    } = extractWeightsFactory(weights);
    const paramMappings = [];
    const {
      extractConvParams,
      extractConvWithBatchNormParams,
      extractSeparableConvParams
    } = extractorsFactory7(extractWeights, paramMappings);
    let params;
    if (config.withSeparableConvs) {
      const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;
      const conv0 = config.isFirstLayerConv2d ? extractConvParams(s0, s1, 3, "conv0") : extractSeparableConvParams(s0, s1, "conv0");
      const conv1 = extractSeparableConvParams(s1, s2, "conv1");
      const conv22 = extractSeparableConvParams(s2, s3, "conv2");
      const conv3 = extractSeparableConvParams(s3, s4, "conv3");
      const conv4 = extractSeparableConvParams(s4, s5, "conv4");
      const conv5 = extractSeparableConvParams(s5, s6, "conv5");
      const conv6 = s7 ? extractSeparableConvParams(s6, s7, "conv6") : void 0;
      const conv7 = s8 ? extractSeparableConvParams(s7, s8, "conv7") : void 0;
      const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, "conv8");
      params = {
        conv0,
        conv1,
        conv2: conv22,
        conv3,
        conv4,
        conv5,
        conv6,
        conv7,
        conv8
      };
    } else {
      const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;
      const conv0 = extractConvWithBatchNormParams(s0, s1, "conv0");
      const conv1 = extractConvWithBatchNormParams(s1, s2, "conv1");
      const conv22 = extractConvWithBatchNormParams(s2, s3, "conv2");
      const conv3 = extractConvWithBatchNormParams(s3, s4, "conv3");
      const conv4 = extractConvWithBatchNormParams(s4, s5, "conv4");
      const conv5 = extractConvWithBatchNormParams(s5, s6, "conv5");
      const conv6 = extractConvWithBatchNormParams(s6, s7, "conv6");
      const conv7 = extractConvWithBatchNormParams(s7, s8, "conv7");
      const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, "conv8");
      params = {
        conv0,
        conv1,
        conv2: conv22,
        conv3,
        conv4,
        conv5,
        conv6,
        conv7,
        conv8
      };
    }
    if (getRemainingWeights().length !== 0) {
      throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
    }
    return {params, paramMappings};
  }

  // src/tinyYolov2/extractParamsFromWeightMap.ts
  function extractorsFactory8(weightMap, paramMappings) {
    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
    function extractBatchNormParams(prefix) {
      const sub4 = extractWeightEntry(`${prefix}/sub`, 1);
      const truediv = extractWeightEntry(`${prefix}/truediv`, 1);
      return {sub: sub4, truediv};
    }
    function extractConvParams(prefix) {
      const filters = extractWeightEntry(`${prefix}/filters`, 4);
      const bias = extractWeightEntry(`${prefix}/bias`, 1);
      return {filters, bias};
    }
    function extractConvWithBatchNormParams(prefix) {
      const conv3 = extractConvParams(`${prefix}/conv`);
      const bn = extractBatchNormParams(`${prefix}/bn`);
      return {conv: conv3, bn};
    }
    const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
    return {
      extractConvParams,
      extractConvWithBatchNormParams,
      extractSeparableConvParams
    };
  }
  function extractParamsFromWeightMap7(weightMap, config) {
    const paramMappings = [];
    const {
      extractConvParams,
      extractConvWithBatchNormParams,
      extractSeparableConvParams
    } = extractorsFactory8(weightMap, paramMappings);
    let params;
    if (config.withSeparableConvs) {
      const numFilters = config.filterSizes && config.filterSizes.length || 9;
      params = {
        conv0: config.isFirstLayerConv2d ? extractConvParams("conv0") : extractSeparableConvParams("conv0"),
        conv1: extractSeparableConvParams("conv1"),
        conv2: extractSeparableConvParams("conv2"),
        conv3: extractSeparableConvParams("conv3"),
        conv4: extractSeparableConvParams("conv4"),
        conv5: extractSeparableConvParams("conv5"),
        conv6: numFilters > 7 ? extractSeparableConvParams("conv6") : void 0,
        conv7: numFilters > 8 ? extractSeparableConvParams("conv7") : void 0,
        conv8: extractConvParams("conv8")
      };
    } else {
      params = {
        conv0: extractConvWithBatchNormParams("conv0"),
        conv1: extractConvWithBatchNormParams("conv1"),
        conv2: extractConvWithBatchNormParams("conv2"),
        conv3: extractConvWithBatchNormParams("conv3"),
        conv4: extractConvWithBatchNormParams("conv4"),
        conv5: extractConvWithBatchNormParams("conv5"),
        conv6: extractConvWithBatchNormParams("conv6"),
        conv7: extractConvWithBatchNormParams("conv7"),
        conv8: extractConvParams("conv8")
      };
    }
    disposeUnusedWeightTensors(weightMap, paramMappings);
    return {params, paramMappings};
  }

  // src/tinyYolov2/TinyYolov2Options.ts
  var TinyYolov2Options = class {
    constructor({inputSize, scoreThreshold} = {}) {
      this._name = "TinyYolov2Options";
      this._inputSize = inputSize || 416;
      this._scoreThreshold = scoreThreshold || 0.5;
      if (typeof this._inputSize !== "number" || this._inputSize % 32 !== 0) {
        throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);
      }
      if (typeof this._scoreThreshold !== "number" || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {
        throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);
      }
    }
    get inputSize() {
      return this._inputSize;
    }
    get scoreThreshold() {
      return this._scoreThreshold;
    }
  };

  // src/tinyYolov2/TinyYolov2Base.ts
  var _TinyYolov2Base = class extends NeuralNetwork {
    constructor(config) {
      super("TinyYolov2");
      validateConfig(config);
      this._config = config;
    }
    get config() {
      return this._config;
    }
    get withClassScores() {
      return this.config.withClassScores || this.config.classes.length > 1;
    }
    get boxEncodingSize() {
      return 5 + (this.withClassScores ? this.config.classes.length : 0);
    }
    runTinyYolov2(x, params) {
      let out = convWithBatchNorm(x, params.conv0);
      out = maxPool(out, [2, 2], [2, 2], "same");
      out = convWithBatchNorm(out, params.conv1);
      out = maxPool(out, [2, 2], [2, 2], "same");
      out = convWithBatchNorm(out, params.conv2);
      out = maxPool(out, [2, 2], [2, 2], "same");
      out = convWithBatchNorm(out, params.conv3);
      out = maxPool(out, [2, 2], [2, 2], "same");
      out = convWithBatchNorm(out, params.conv4);
      out = maxPool(out, [2, 2], [2, 2], "same");
      out = convWithBatchNorm(out, params.conv5);
      out = maxPool(out, [2, 2], [1, 1], "same");
      out = convWithBatchNorm(out, params.conv6);
      out = convWithBatchNorm(out, params.conv7);
      return convLayer(out, params.conv8, "valid", false);
    }
    runMobilenet(x, params) {
      let out = this.config.isFirstLayerConv2d ? leaky(convLayer(x, params.conv0, "valid", false)) : depthwiseSeparableConv2(x, params.conv0);
      out = maxPool(out, [2, 2], [2, 2], "same");
      out = depthwiseSeparableConv2(out, params.conv1);
      out = maxPool(out, [2, 2], [2, 2], "same");
      out = depthwiseSeparableConv2(out, params.conv2);
      out = maxPool(out, [2, 2], [2, 2], "same");
      out = depthwiseSeparableConv2(out, params.conv3);
      out = maxPool(out, [2, 2], [2, 2], "same");
      out = depthwiseSeparableConv2(out, params.conv4);
      out = maxPool(out, [2, 2], [2, 2], "same");
      out = depthwiseSeparableConv2(out, params.conv5);
      out = maxPool(out, [2, 2], [1, 1], "same");
      out = params.conv6 ? depthwiseSeparableConv2(out, params.conv6) : out;
      out = params.conv7 ? depthwiseSeparableConv2(out, params.conv7) : out;
      return convLayer(out, params.conv8, "valid", false);
    }
    forwardInput(input2, inputSize) {
      const {params} = this;
      if (!params) {
        throw new Error("TinyYolov2 - load model before inference");
      }
      return tidy(() => {
        let batchTensor = cast(input2.toBatchTensor(inputSize, false), "float32");
        batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor;
        batchTensor = batchTensor.div(255);
        return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params);
      });
    }
    async forward(input2, inputSize) {
      return this.forwardInput(await toNetInput(input2), inputSize);
    }
    async detect(input2, forwardParams = {}) {
      const {inputSize, scoreThreshold} = new TinyYolov2Options(forwardParams);
      const netInput = await toNetInput(input2);
      const out = await this.forwardInput(netInput, inputSize);
      const out0 = tidy(() => unstack(out)[0].expandDims());
      const inputDimensions = {
        width: netInput.getInputWidth(0),
        height: netInput.getInputHeight(0)
      };
      const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);
      out.dispose();
      out0.dispose();
      const boxes = results.map((res) => res.box);
      const scores = results.map((res) => res.score);
      const classScores = results.map((res) => res.classScore);
      const classNames = results.map((res) => this.config.classes[res.label]);
      const indices = nonMaxSuppression2(boxes.map((box) => box.rescale(inputSize)), scores, this.config.iouThreshold, true);
      const detections = indices.map((idx) => new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx], inputDimensions));
      return detections;
    }
    getDefaultModelName() {
      return "";
    }
    extractParamsFromWeightMap(weightMap) {
      return extractParamsFromWeightMap7(weightMap, this.config);
    }
    extractParams(weights) {
      const filterSizes = this.config.filterSizes || _TinyYolov2Base.DEFAULT_FILTER_SIZES;
      const numFilters = filterSizes ? filterSizes.length : void 0;
      if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {
        throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);
      }
      return extractParams7(weights, this.config, this.boxEncodingSize, filterSizes);
    }
    async extractBoxes(outputTensor, inputBlobDimensions, scoreThreshold) {
      const {width, height} = inputBlobDimensions;
      const inputSize = Math.max(width, height);
      const correctionFactorX = inputSize / width;
      const correctionFactorY = inputSize / height;
      const numCells = outputTensor.shape[1];
      const numBoxes = this.config.anchors.length;
      const [boxesTensor, scoresTensor, classScoresTensor] = tidy(() => {
        const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);
        const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);
        const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);
        const classScores = this.withClassScores ? softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3) : scalar(0);
        return [boxes, scores, classScores];
      });
      const results = [];
      const scoresData = await scoresTensor.array();
      const boxesData = await boxesTensor.array();
      for (let row = 0; row < numCells; row++) {
        for (let col = 0; col < numCells; col++) {
          for (let anchor = 0; anchor < numBoxes; anchor++) {
            const score = sigmoid5(scoresData[row][col][anchor][0]);
            if (!scoreThreshold || score > scoreThreshold) {
              const ctX = (col + sigmoid5(boxesData[row][col][anchor][0])) / numCells * correctionFactorX;
              const ctY = (row + sigmoid5(boxesData[row][col][anchor][1])) / numCells * correctionFactorY;
              const widthLocal = Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x / numCells * correctionFactorX;
              const heightLocal = Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y / numCells * correctionFactorY;
              const x = ctX - widthLocal / 2;
              const y = ctY - heightLocal / 2;
              const pos = {row, col, anchor};
              const {classScore, label} = this.withClassScores ? await this.extractPredictedClass(classScoresTensor, pos) : {classScore: 1, label: 0};
              results.push({
                box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),
                score,
                classScore: score * classScore,
                label,
                ...pos
              });
            }
          }
        }
      }
      boxesTensor.dispose();
      scoresTensor.dispose();
      classScoresTensor.dispose();
      return results;
    }
    async extractPredictedClass(classesTensor, pos) {
      const {row, col, anchor} = pos;
      const classesData = await classesTensor.array();
      return Array(this.config.classes.length).fill(0).map((_, i) => classesData[row][col][anchor][i]).map((classScore, label) => ({
        classScore,
        label
      })).reduce((max6, curr) => max6.classScore > curr.classScore ? max6 : curr);
    }
  };
  var TinyYolov2Base = _TinyYolov2Base;
  TinyYolov2Base.DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];

  // src/tinyYolov2/TinyYolov2.ts
  var TinyYolov2 = class extends TinyYolov2Base {
    constructor(withSeparableConvs = true) {
      const config = {
        withSeparableConvs,
        iouThreshold: IOU_THRESHOLD,
        classes: ["face"],
        ...withSeparableConvs ? {
          anchors: BOX_ANCHORS_SEPARABLE,
          meanRgb: MEAN_RGB_SEPARABLE
        } : {
          anchors: BOX_ANCHORS,
          withClassScores: true
        }
      };
      super(config);
    }
    get withSeparableConvs() {
      return this.config.withSeparableConvs;
    }
    get anchors() {
      return this.config.anchors;
    }
    async locateFaces(input2, forwardParams) {
      const objectDetections = await this.detect(input2, forwardParams);
      return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, {width: det.imageWidth, height: det.imageHeight}));
    }
    getDefaultModelName() {
      return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME2;
    }
    extractParamsFromWeightMap(weightMap) {
      return super.extractParamsFromWeightMap(weightMap);
    }
  };

  // src/tinyYolov2/index.ts
  function createTinyYolov2(weights, withSeparableConvs = true) {
    const net = new TinyYolov2(withSeparableConvs);
    net.extractWeights(weights);
    return net;
  }

  // src/tinyFaceDetector/TinyFaceDetectorOptions.ts
  var TinyFaceDetectorOptions = class extends TinyYolov2Options {
    constructor() {
      super(...arguments);
      this._name = "TinyFaceDetectorOptions";
    }
  };

  // src/globalApi/ComposableTask.ts
  var ComposableTask = class {
    async then(onfulfilled) {
      return onfulfilled(await this.run());
    }
    async run() {
      throw new Error("ComposableTask - run is not implemented");
    }
  };

  // src/globalApi/extractFacesAndComputeResults.ts
  async function extractAllFacesAndComputeResults(parentResults, input2, computeResults, extractedFaces, getRectForAlignment = ({alignedRect}) => alignedRect) {
    const faceBoxes = parentResults.map((parentResult) => isWithFaceLandmarks(parentResult) ? getRectForAlignment(parentResult) : parentResult.detection);
    const faces = extractedFaces || (input2 instanceof Tensor ? await extractFaceTensors(input2, faceBoxes) : await extractFaces(input2, faceBoxes));
    const results = await computeResults(faces);
    faces.forEach((f) => f instanceof Tensor && f.dispose());
    return results;
  }
  async function extractSingleFaceAndComputeResult(parentResult, input2, computeResult, extractedFaces, getRectForAlignment) {
    return extractAllFacesAndComputeResults([parentResult], input2, async (faces) => computeResult(faces[0]), extractedFaces, getRectForAlignment);
  }

  // src/tinyFaceDetector/const.ts
  var IOU_THRESHOLD2 = 0.4;
  var BOX_ANCHORS2 = [
    new Point(1.603231, 2.094468),
    new Point(6.041143, 7.080126),
    new Point(2.882459, 3.518061),
    new Point(4.266906, 5.178857),
    new Point(9.041765, 10.66308)
  ];
  var MEAN_RGB = [117.001, 114.697, 97.404];

  // src/tinyFaceDetector/TinyFaceDetector.ts
  var TinyFaceDetector = class extends TinyYolov2Base {
    constructor() {
      const config = {
        withSeparableConvs: true,
        iouThreshold: IOU_THRESHOLD2,
        classes: ["face"],
        anchors: BOX_ANCHORS2,
        meanRgb: MEAN_RGB,
        isFirstLayerConv2d: true,
        filterSizes: [3, 16, 32, 64, 128, 256, 512]
      };
      super(config);
    }
    get anchors() {
      return this.config.anchors;
    }
    async locateFaces(input2, forwardParams) {
      const objectDetections = await this.detect(input2, forwardParams);
      return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, {width: det.imageWidth, height: det.imageHeight}));
    }
    getDefaultModelName() {
      return "tiny_face_detector_model";
    }
    extractParamsFromWeightMap(weightMap) {
      return super.extractParamsFromWeightMap(weightMap);
    }
  };

  // src/globalApi/nets.ts
  var nets = {
    ssdMobilenetv1: new SsdMobilenetv1(),
    tinyFaceDetector: new TinyFaceDetector(),
    tinyYolov2: new TinyYolov2(),
    faceLandmark68Net: new FaceLandmark68Net(),
    faceLandmark68TinyNet: new FaceLandmark68TinyNet(),
    faceRecognitionNet: new FaceRecognitionNet(),
    faceExpressionNet: new FaceExpressionNet(),
    ageGenderNet: new AgeGenderNet()
  };
  var ssdMobilenetv1 = (input2, options) => nets.ssdMobilenetv1.locateFaces(input2, options);
  var tinyFaceDetector = (input2, options) => nets.tinyFaceDetector.locateFaces(input2, options);
  var tinyYolov2 = (input2, options) => nets.tinyYolov2.locateFaces(input2, options);
  var detectFaceLandmarks = (input2) => nets.faceLandmark68Net.detectLandmarks(input2);
  var detectFaceLandmarksTiny = (input2) => nets.faceLandmark68TinyNet.detectLandmarks(input2);
  var computeFaceDescriptor = (input2) => nets.faceRecognitionNet.computeFaceDescriptor(input2);
  var recognizeFaceExpressions = (input2) => nets.faceExpressionNet.predictExpressions(input2);
  var predictAgeAndGender = (input2) => nets.ageGenderNet.predictAgeAndGender(input2);
  var loadSsdMobilenetv1Model = (url) => nets.ssdMobilenetv1.load(url);
  var loadTinyFaceDetectorModel = (url) => nets.tinyFaceDetector.load(url);
  var loadTinyYolov2Model = (url) => nets.tinyYolov2.load(url);
  var loadFaceLandmarkModel = (url) => nets.faceLandmark68Net.load(url);
  var loadFaceLandmarkTinyModel = (url) => nets.faceLandmark68TinyNet.load(url);
  var loadFaceRecognitionModel = (url) => nets.faceRecognitionNet.load(url);
  var loadFaceExpressionModel = (url) => nets.faceExpressionNet.load(url);
  var loadAgeGenderModel = (url) => nets.ageGenderNet.load(url);
  var loadFaceDetectionModel = loadSsdMobilenetv1Model;
  var locateFaces = ssdMobilenetv1;
  var detectLandmarks = detectFaceLandmarks;

  // src/globalApi/PredictFaceExpressionsTask.ts
  var PredictFaceExpressionsTaskBase = class extends ComposableTask {
    constructor(parentTask, input2, extractedFaces) {
      super();
      this.parentTask = parentTask;
      this.input = input2;
      this.extractedFaces = extractedFaces;
    }
  };
  var PredictAllFaceExpressionsTask = class extends PredictFaceExpressionsTaskBase {
    async run() {
      const parentResults = await this.parentTask;
      const faceExpressionsByFace = await extractAllFacesAndComputeResults(parentResults, this.input, async (faces) => Promise.all(faces.map((face) => nets.faceExpressionNet.predictExpressions(face))), this.extractedFaces);
      return parentResults.map((parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]));
    }
    withAgeAndGender() {
      return new PredictAllAgeAndGenderTask(this, this.input);
    }
  };
  var PredictSingleFaceExpressionsTask = class extends PredictFaceExpressionsTaskBase {
    async run() {
      const parentResult = await this.parentTask;
      if (!parentResult) {
        return void 0;
      }
      const faceExpressions = await extractSingleFaceAndComputeResult(parentResult, this.input, (face) => nets.faceExpressionNet.predictExpressions(face), this.extractedFaces);
      return extendWithFaceExpressions(parentResult, faceExpressions);
    }
    withAgeAndGender() {
      return new PredictSingleAgeAndGenderTask(this, this.input);
    }
  };
  var PredictAllFaceExpressionsWithFaceAlignmentTask = class extends PredictAllFaceExpressionsTask {
    withAgeAndGender() {
      return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
    }
    withFaceDescriptors() {
      return new ComputeAllFaceDescriptorsTask(this, this.input);
    }
  };
  var PredictSingleFaceExpressionsWithFaceAlignmentTask = class extends PredictSingleFaceExpressionsTask {
    withAgeAndGender() {
      return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
    }
    withFaceDescriptor() {
      return new ComputeSingleFaceDescriptorTask(this, this.input);
    }
  };

  // src/globalApi/PredictAgeAndGenderTask.ts
  var PredictAgeAndGenderTaskBase = class extends ComposableTask {
    constructor(parentTask, input2, extractedFaces) {
      super();
      this.parentTask = parentTask;
      this.input = input2;
      this.extractedFaces = extractedFaces;
    }
  };
  var PredictAllAgeAndGenderTask = class extends PredictAgeAndGenderTaskBase {
    async run() {
      const parentResults = await this.parentTask;
      const ageAndGenderByFace = await extractAllFacesAndComputeResults(parentResults, this.input, async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face))), this.extractedFaces);
      return parentResults.map((parentResult, i) => {
        const {age, gender, genderProbability} = ageAndGenderByFace[i];
        return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);
      });
    }
    withFaceExpressions() {
      return new PredictAllFaceExpressionsTask(this, this.input);
    }
  };
  var PredictSingleAgeAndGenderTask = class extends PredictAgeAndGenderTaskBase {
    async run() {
      const parentResult = await this.parentTask;
      if (!parentResult)
        return void 0;
      const {age, gender, genderProbability} = await extractSingleFaceAndComputeResult(parentResult, this.input, (face) => nets.ageGenderNet.predictAgeAndGender(face), this.extractedFaces);
      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);
    }
    withFaceExpressions() {
      return new PredictSingleFaceExpressionsTask(this, this.input);
    }
  };
  var PredictAllAgeAndGenderWithFaceAlignmentTask = class extends PredictAllAgeAndGenderTask {
    withFaceExpressions() {
      return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
    }
    withFaceDescriptors() {
      return new ComputeAllFaceDescriptorsTask(this, this.input);
    }
  };
  var PredictSingleAgeAndGenderWithFaceAlignmentTask = class extends PredictSingleAgeAndGenderTask {
    withFaceExpressions() {
      return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
    }
    withFaceDescriptor() {
      return new ComputeSingleFaceDescriptorTask(this, this.input);
    }
  };

  // src/globalApi/ComputeFaceDescriptorsTasks.ts
  var ComputeFaceDescriptorsTaskBase = class extends ComposableTask {
    constructor(parentTask, input2) {
      super();
      this.parentTask = parentTask;
      this.input = input2;
    }
  };
  var ComputeAllFaceDescriptorsTask = class extends ComputeFaceDescriptorsTaskBase {
    async run() {
      const parentResults = await this.parentTask;
      const descriptors = await extractAllFacesAndComputeResults(parentResults, this.input, (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face))), null, (parentResult) => parentResult.landmarks.align(null, {useDlibAlignment: true}));
      return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor));
    }
    withFaceExpressions() {
      return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
    }
    withAgeAndGender() {
      return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
    }
  };
  var ComputeSingleFaceDescriptorTask = class extends ComputeFaceDescriptorsTaskBase {
    async run() {
      const parentResult = await this.parentTask;
      if (!parentResult) {
        return void 0;
      }
      const descriptor = await extractSingleFaceAndComputeResult(parentResult, this.input, (face) => nets.faceRecognitionNet.computeFaceDescriptor(face), null, (parentResult2) => parentResult2.landmarks.align(null, {useDlibAlignment: true}));
      return extendWithFaceDescriptor(parentResult, descriptor);
    }
    withFaceExpressions() {
      return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
    }
    withAgeAndGender() {
      return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
    }
  };

  // src/globalApi/DetectFaceLandmarksTasks.ts
  var DetectFaceLandmarksTaskBase = class extends ComposableTask {
    constructor(parentTask, input2, useTinyLandmarkNet) {
      super();
      this.parentTask = parentTask;
      this.input = input2;
      this.useTinyLandmarkNet = useTinyLandmarkNet;
    }
    get landmarkNet() {
      return this.useTinyLandmarkNet ? nets.faceLandmark68TinyNet : nets.faceLandmark68Net;
    }
  };
  var DetectAllFaceLandmarksTask = class extends DetectFaceLandmarksTaskBase {
    async run() {
      const parentResults = await this.parentTask;
      const detections = parentResults.map((res) => res.detection);
      const faces = this.input instanceof Tensor ? await extractFaceTensors(this.input, detections) : await extractFaces(this.input, detections);
      const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face)));
      faces.forEach((f) => f instanceof Tensor && f.dispose());
      return parentResults.map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));
    }
    withFaceExpressions() {
      return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
    }
    withAgeAndGender() {
      return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
    }
    withFaceDescriptors() {
      return new ComputeAllFaceDescriptorsTask(this, this.input);
    }
  };
  var DetectSingleFaceLandmarksTask = class extends DetectFaceLandmarksTaskBase {
    async run() {
      const parentResult = await this.parentTask;
      if (!parentResult) {
        return void 0;
      }
      const {detection} = parentResult;
      const faces = this.input instanceof Tensor ? await extractFaceTensors(this.input, [detection]) : await extractFaces(this.input, [detection]);
      const landmarks = await this.landmarkNet.detectLandmarks(faces[0]);
      faces.forEach((f) => f instanceof Tensor && f.dispose());
      return extendWithFaceLandmarks(parentResult, landmarks);
    }
    withFaceExpressions() {
      return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
    }
    withAgeAndGender() {
      return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
    }
    withFaceDescriptor() {
      return new ComputeSingleFaceDescriptorTask(this, this.input);
    }
  };

  // src/globalApi/DetectFacesTasks.ts
  var DetectFacesTaskBase = class extends ComposableTask {
    constructor(input2, options = new SsdMobilenetv1Options()) {
      super();
      this.input = input2;
      this.options = options;
    }
  };
  var DetectAllFacesTask = class extends DetectFacesTaskBase {
    async run() {
      const {input: input2, options} = this;
      let result;
      if (options instanceof TinyFaceDetectorOptions)
        result = nets.tinyFaceDetector.locateFaces(input2, options);
      else if (options instanceof SsdMobilenetv1Options)
        result = nets.ssdMobilenetv1.locateFaces(input2, options);
      else if (options instanceof TinyYolov2Options)
        result = nets.tinyYolov2.locateFaces(input2, options);
      else
        throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");
      return result;
    }
    runAndExtendWithFaceDetections() {
      return new Promise((resolve, reject) => {
        this.run().then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection)))).catch((err) => reject(err));
      });
    }
    withFaceLandmarks(useTinyLandmarkNet = false) {
      return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(), this.input, useTinyLandmarkNet);
    }
    withFaceExpressions() {
      return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(), this.input);
    }
    withAgeAndGender() {
      return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(), this.input);
    }
  };
  var DetectSingleFaceTask = class extends DetectFacesTaskBase {
    async run() {
      const faceDetections = await new DetectAllFacesTask(this.input, this.options);
      let faceDetectionWithHighestScore = faceDetections[0];
      faceDetections.forEach((faceDetection) => {
        if (faceDetection.score > faceDetectionWithHighestScore.score)
          faceDetectionWithHighestScore = faceDetection;
      });
      return faceDetectionWithHighestScore;
    }
    runAndExtendWithFaceDetection() {
      return new Promise(async (resolve) => {
        const detection = await this.run();
        resolve(detection ? extendWithFaceDetection({}, detection) : void 0);
      });
    }
    withFaceLandmarks(useTinyLandmarkNet = false) {
      return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(), this.input, useTinyLandmarkNet);
    }
    withFaceExpressions() {
      return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(), this.input);
    }
    withAgeAndGender() {
      return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(), this.input);
    }
  };

  // src/globalApi/detectFaces.ts
  function detectSingleFace(input2, options = new SsdMobilenetv1Options()) {
    return new DetectSingleFaceTask(input2, options);
  }
  function detectAllFaces(input2, options = new SsdMobilenetv1Options()) {
    return new DetectAllFacesTask(input2, options);
  }

  // src/globalApi/allFaces.ts
  async function allFacesSsdMobilenetv1(input2, minConfidence) {
    return detectAllFaces(input2, new SsdMobilenetv1Options(minConfidence ? {minConfidence} : {})).withFaceLandmarks().withFaceDescriptors();
  }
  async function allFacesTinyYolov2(input2, forwardParams = {}) {
    return detectAllFaces(input2, new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors();
  }
  var allFaces = allFacesSsdMobilenetv1;

  // src/euclideanDistance.ts
  function euclideanDistance(arr1, arr2) {
    if (arr1.length !== arr2.length)
      throw new Error("euclideanDistance: arr1.length !== arr2.length");
    const desc1 = Array.from(arr1);
    const desc2 = Array.from(arr2);
    return Math.sqrt(desc1.map((val, i) => val - desc2[i]).reduce((res, diff) => res + diff ** 2, 0));
  }

  // src/globalApi/FaceMatcher.ts
  var FaceMatcher = class {
    constructor(inputs, distanceThreshold = 0.6) {
      this._distanceThreshold = distanceThreshold;
      const inputArray = Array.isArray(inputs) ? inputs : [inputs];
      if (!inputArray.length) {
        throw new Error("FaceRecognizer.constructor - expected atleast one input");
      }
      let count2 = 1;
      const createUniqueLabel = () => `person ${count2++}`;
      this._labeledDescriptors = inputArray.map((desc) => {
        if (desc instanceof LabeledFaceDescriptors) {
          return desc;
        }
        if (desc instanceof Float32Array) {
          return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);
        }
        if (desc.descriptor && desc.descriptor instanceof Float32Array) {
          return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);
        }
        throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>");
      });
    }
    get labeledDescriptors() {
      return this._labeledDescriptors;
    }
    get distanceThreshold() {
      return this._distanceThreshold;
    }
    computeMeanDistance(queryDescriptor, descriptors) {
      return descriptors.map((d) => euclideanDistance(d, queryDescriptor)).reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1);
    }
    matchDescriptor(queryDescriptor) {
      return this.labeledDescriptors.map(({descriptors, label}) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors))).reduce((best, curr) => best.distance < curr.distance ? best : curr);
    }
    findBestMatch(queryDescriptor) {
      const bestMatch = this.matchDescriptor(queryDescriptor);
      return bestMatch.distance < this.distanceThreshold ? bestMatch : new FaceMatch("unknown", bestMatch.distance);
    }
    toJSON() {
      return {
        distanceThreshold: this.distanceThreshold,
        labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())
      };
    }
    static fromJSON(json18) {
      const labeledDescriptors = json18.labeledDescriptors.map((ld) => LabeledFaceDescriptors.fromJSON(ld));
      return new FaceMatcher(labeledDescriptors, json18.distanceThreshold);
    }
  };

  // src/tinyFaceDetector/index.ts
  function createTinyFaceDetector(weights) {
    const net = new TinyFaceDetector();
    net.extractWeights(weights);
    return net;
  }

  // src/resizeResults.ts
  function resizeResults(results, dimensions) {
    const {width, height} = new Dimensions(dimensions.width, dimensions.height);
    if (width <= 0 || height <= 0) {
      throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width, height})}`);
    }
    if (Array.isArray(results)) {
      return results.map((obj) => resizeResults(obj, {width, height}));
    }
    if (isWithFaceLandmarks(results)) {
      const resizedDetection = results.detection.forSize(width, height);
      const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);
      return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);
    }
    if (isWithFaceDetection(results)) {
      return extendWithFaceDetection(results, results.detection.forSize(width, height));
    }
    if (results instanceof FaceLandmarks || results instanceof FaceDetection) {
      return results.forSize(width, height);
    }
    return results;
  }

  // src/index.ts
  var node = typeof process !== "undefined";
  var browser = typeof navigator !== "undefined" && typeof navigator.userAgent !== "undefined";
  var version11 = {faceapi: version10, node, browser};
  return src_exports;
})();
//# sourceMappingURL=face-api.js.map
