human/assets/tf-backend-wasm.es2017.js

/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
(function (global, factory) {
  typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('@tensorflow/tfjs-core'), require('path'), require('fs'), require('worker_threads'), require('perf_hooks')) :
  typeof define === 'function' && define.amd ? define(['exports', '@tensorflow/tfjs-core', 'path', 'fs', 'worker_threads', 'perf_hooks'], factory) :
  (global = global || self, factory((global.tf = global.tf || {}, global.tf.wasm = global.tf.wasm || {}), global.tf, global.path, global.fs, global.worker_threads, global.perf_hooks));
}(this, (function (exports, tfjsCore, path, fs, worker_threads, perf_hooks) { 'use strict';

  path = path && path.hasOwnProperty('default') ? path['default'] : path;
  fs = fs && fs.hasOwnProperty('default') ? fs['default'] : fs;
  worker_threads = worker_threads && worker_threads.hasOwnProperty('default') ? worker_threads['default'] : worker_threads;
  perf_hooks = perf_hooks && perf_hooks.hasOwnProperty('default') ? perf_hooks['default'] : perf_hooks;

  /**
   * @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.
   * =============================================================================
   */
  // This enum must align with the enum defined in cc/backend.h.
  var CppDType;
  (function (CppDType) {
      CppDType[CppDType["float32"] = 0] = "float32";
      CppDType[CppDType["int32"] = 1] = "int32";
      CppDType[CppDType["bool"] = 2] = "bool";
      CppDType[CppDType["string"] = 3] = "string";
      CppDType[CppDType["complex64"] = 4] = "complex64";
  })(CppDType || (CppDType = {}));
  // Must match enum in cc/fusable_activations.h.
  var FusableActivation;
  (function (FusableActivation) {
      FusableActivation[FusableActivation["linear"] = 0] = "linear";
      FusableActivation[FusableActivation["relu"] = 1] = "relu";
      FusableActivation[FusableActivation["relu6"] = 2] = "relu6";
      FusableActivation[FusableActivation["prelu"] = 3] = "prelu";
  })(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.
   * =============================================================================
   */
  let wasmFusedMatMul;
  function setup(backend) {
      wasmFusedMatMul = backend.wasm.cwrap(tfjsCore._FusedMatMul, null /* void */, [
          'number',
          'array',
          'number',
          'number',
          'array',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number' // out_id
      ]);
  }
  function fusedBatchMatMul(args) {
      const { inputs, backend, 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 } = attrs;
      const aId = backend.dataIdMap.get(a.dataId).id;
      const bId = backend.dataIdMap.get(b.dataId).id;
      let biasId = 0;
      if (bias != null) {
          const biasData = backend.dataIdMap.get(bias.dataId);
          if (biasData.shape.length !== 1) {
              throw new Error(`_FusedMatMul only supports rank-1 bias but got ` +
                  `rank ${biasData.shape.length}.`);
          }
          biasId = biasData.id;
      }
      const preluActivationWeightsId = preluActivationWeights == null ?
          0 :
          backend.dataIdMap.get(preluActivationWeights.dataId).id;
      const fusedActivation = FusableActivation[activation];
      if (fusedActivation == null) {
          throw new Error(`${activation} 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 = backend.makeOutput([batchDim, leftDim, rightDim], a.dtype);
      const outId = backend.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, outId);
      return out;
  }
  const fusedMatMulConfig = {
      kernelName: tfjsCore._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 wasmFunc;
      function setupFunc(backend) {
          wasmFunc =
              backend.wasm.cwrap(kernelName, null /* void */, ['number', 'number']);
      }
      function kernelFunc(args) {
          const { backend, inputs: { x } } = args;
          const xId = backend.dataIdMap.get(x.dataId).id;
          const out = backend.makeOutput(x.shape, x.dtype);
          const outId = backend.dataIdMap.get(out.dataId).id;
          // Short-circuit zero-sized tensors.
          if (tfjsCore.util.sizeFromShape(out.shape) === 0) {
              return out;
          }
          wasmFunc(xId, outId);
          return out;
      }
      return { kernelName, backendName: 'wasm', setupFunc, kernelFunc };
  }

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const absConfig = createUnaryKernelConfig(tfjsCore.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, supportsFullBroadcast, dtype) {
      let wasmFunc;
      function setupFunc(backend) {
          wasmFunc = backend.wasm.cwrap(kernelName, null /* void */, [
              'number',
              'array',
              'number',
              'number',
              'array',
              'number',
              'number',
              'number' // out_id
          ]);
      }
      function kernelFunc(args) {
          const { backend, inputs } = args;
          const { a, b } = inputs;
          const aId = backend.dataIdMap.get(a.dataId).id;
          const bId = backend.dataIdMap.get(b.dataId).id;
          const outputType = dtype != null ? dtype : a.dtype;
          const newShape = tfjsCore.backend_util.assertAndGetBroadcastShape(a.shape, b.shape);
          const out = backend.makeOutput(newShape, outputType);
          // Short-circuit zero-sized tensors.
          if (tfjsCore.util.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 = backend.dataIdMap.get(out.dataId).id;
          const kernelFunc = () => wasmFunc(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, CppDType[a.dtype], outId);
          // Currently only some float operations support full broadcast.
          if (supportsFullBroadcast && a.dtype === 'float32') {
              kernelFunc();
              return out;
          }
          const aBroadcastDims = tfjsCore.backend_util.getBroadcastDims(a.shape, newShape);
          const bBroadcastDims = tfjsCore.backend_util.getBroadcastDims(b.shape, newShape);
          const loopsOverAllOfA = aBroadcastDims.every((v, i) => v === i);
          const loopsOverAllOfB = bBroadcastDims.every((v, i) => v === i);
          if (loopsOverAllOfA && loopsOverAllOfB) {
              kernelFunc();
              return out;
          }
          else {
              throw new Error(`Broadcasting along outer dims is not yet ` +
                  `supported for ${a.dtype} ${kernelName}.`);
          }
      }
      return { kernelName, backendName: 'wasm', setupFunc, kernelFunc };
  }

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const supportsFullBroadcast = true;
  const addConfig = createBinaryKernelConfig(tfjsCore.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.
   * =============================================================================
   */
  let wasmFunc;
  function setupFunc(backend) {
      wasmFunc = backend.wasm.cwrap(tfjsCore.AddN, null /* void */, [
          'array',
          'number',
          'number',
          'number',
      ]);
  }
  function addn(args) {
      const { inputs, backend } = args;
      const out = backend.makeOutput(inputs[0].shape, inputs[0].dtype);
      // Short-circuit zero-sized tensors.
      if (tfjsCore.util.sizeFromShape(out.shape) === 0) {
          return out;
      }
      const inputIds = inputs.map(x => backend.dataIdMap.get(x.dataId).id);
      const inputIdsBytes = new Uint8Array(new Int32Array(inputIds).buffer);
      const outId = backend.dataIdMap.get(out.dataId).id;
      wasmFunc(inputIdsBytes, inputIds.length, CppDType[out.dtype], outId);
      return out;
  }
  const addNConfig = {
      kernelName: tfjsCore.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 identity(args) {
      const { inputs: { x }, backend } = args;
      const out = backend.makeOutput(x.shape, x.dtype);
      const inVals = backend.typedArrayFromHeap(x);
      const outVals = backend.typedArrayFromHeap(out);
      outVals.set(inVals);
      return out;
  }
  const identityConfig = {
      kernelName: tfjsCore.Identity,
      backendName: 'wasm',
      kernelFunc: identity,
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmTranspose;
  function setup$1(backend) {
      wasmTranspose = backend.wasm.cwrap(tfjsCore.Transpose, null /* void */, [
          'number',
          'array',
          'number',
          'number',
          'number',
          'array',
          'number',
      ]);
  }
  function transpose(args) {
      const { inputs, backend, attrs } = args;
      // Reduce any dimensions with size one. Lower-rank transpose kernel performs
      // better due to simpler memory access pattern.
      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 = computeOutShape(inputs.x.shape, attrs.perm);
      const x = {
          dataId: inputs.x.dataId,
          shape: reducedShape,
          dtype: inputs.x.dtype
      };
      if (permIsNoOp) {
          const cloned = identity({ inputs, backend });
          cloned.shape = outShape;
          return cloned;
      }
      const out = backend.makeOutput(outShape, x.dtype);
      const xId = backend.dataIdMap.get(x.dataId).id;
      const outId = backend.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 computeOutShape(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];
  }
  const transposeConfig = {
      kernelName: tfjsCore.Transpose,
      backendName: 'wasm',
      kernelFunc: transpose,
      setupFunc: setup$1,
  };

  /**
   * @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.
   * =============================================================================
   */
  /**
   * Compute permutation axes and do a transpose if necessary.
   *
   * Used by reduction ops.
   * @param x input TensorInfo
   * @param axis reduction axes
   * @param backend wasm backend instance
   */
  function permuteAxesAndTranspose(x, axis, backend) {
      const xShape = x.shape;
      const xRank = x.shape.length;
      const originalAxes = tfjsCore.util.parseAxisParam(axis, xShape);
      let axes = originalAxes;
      const permutedAxes = tfjsCore.backend_util.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 = tfjsCore.backend_util.getInnerMostAxes(axes.length, xRank);
          xTransposed =
              transpose({ inputs: { x }, attrs: { perm: permutedAxes }, backend });
          const xId = backend.dataIdMap.get(x.dataId).id;
          const transposedId = backend.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.
   * =============================================================================
   */
  let wasmFunc$1;
  function setup$2(backend) {
      wasmFunc$1 = backend.wasm.cwrap(tfjsCore.ArgMax, null /* void */, [
          'number',
          'number',
          'number',
          'number',
          'number' // out_id
      ]);
  }
  function argmax(args) {
      const { backend, inputs, attrs } = args;
      const { axis } = attrs;
      const { x } = inputs;
      const xId = backend.dataIdMap.get(x.dataId).id;
      let inputId = xId;
      let input = x;
      const { transposed, axes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend);
      if (inputWasTransposed) {
          const transposedId = backend.dataIdMap.get(transposed.dataId).id;
          if (transposedId !== xId) {
              // transpose was not a no-op. We will need to dispose of this
              // once we are done.
              input = transposed;
              inputId = transposedId;
          }
      }
      const outShape = input.shape.slice(0, -1);
      const out = backend.makeOutput(outShape, 'int32');
      const outId = backend.dataIdMap.get(out.dataId).id;
      const outerSize = tfjsCore.util.sizeFromShape(out.shape);
      const innerSize = input.shape[axes[0]];
      wasmFunc$1(inputId, CppDType[input.dtype], outerSize, innerSize, outId);
      if (inputWasTransposed) {
          // dispose of the transposed tensor.
          backend.disposeData(transposed.dataId);
      }
      return out;
  }
  const argMaxConfig = {
      kernelName: tfjsCore.ArgMax,
      backendName: 'wasm',
      kernelFunc: argmax,
      setupFunc: setup$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.
   * =============================================================================
   */
  let wasmAvgPool;
  function setup$3(backend) {
      wasmAvgPool = backend.wasm.cwrap(tfjsCore.AvgPool, null /* void */, [
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
      ]);
  }
  function avgPool(args) {
      const { inputs, attrs, backend } = args;
      const x = inputs.x;
      const xId = backend.dataIdMap.get(x.dataId).id;
      const { filterSize, strides, pad, dimRoundingMode } = attrs;
      const convInfo = tfjsCore.backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);
      const filterHeight = convInfo.filterHeight;
      const filterWidth = convInfo.filterWidth;
      const padTop = convInfo.padInfo.top;
      const padRight = convInfo.padInfo.right;
      const padBottom = convInfo.padInfo.bottom;
      const padLeft = convInfo.padInfo.left;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const channels = convInfo.inChannels;
      if (convInfo.dataFormat !== 'channelsLast') {
          throw new Error(`wasm backend does not support dataFormat:'` +
              `${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 = backend.makeOutput(convInfo.outShape, 'float32');
      const outId = backend.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;
  }
  const avgPoolConfig = {
      kernelName: tfjsCore.AvgPool,
      backendName: 'wasm',
      setupFunc: setup$3,
      kernelFunc: avgPool
  };

  /**
   * @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 reshape(args) {
      const { inputs, attrs } = args;
      const { x } = inputs;
      const { shape } = attrs;
      const xSize = tfjsCore.util.sizeFromShape(x.shape);
      const $shape = tfjsCore.util.inferFromImplicitShape(shape, xSize);
      tfjsCore.util.assert(xSize === tfjsCore.util.sizeFromShape($shape), () => `new shape: ${$shape}, old shape: ${x.shape}. New shape and old ` +
          `shape must have the same number of elements.`);
      return { dataId: x.dataId, shape: $shape, dtype: x.dtype };
  }
  const reshapeConfig = {
      kernelName: tfjsCore.Reshape,
      backendName: 'wasm',
      kernelFunc: reshape,
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmBatchMatMul;
  function setup$4(backend) {
      wasmBatchMatMul = backend.wasm.cwrap(tfjsCore.BatchMatMul, null /* void */, [
          'number',
          'array',
          'number',
          'number',
          'array',
          'number',
          'number',
          'number',
          'number' // out_id
      ]);
  }
  function batchMatMul(args) {
      const { inputs, backend, attrs } = args;
      const { a, b } = inputs;
      const { transposeA, transposeB } = attrs;
      if (a.dtype !== 'float32' || b.dtype !== 'float32') {
          throw new Error(`BatchMatMul for non non-float32 tensors not yet supported.`);
      }
      const aRank = a.shape.length;
      const bRank = b.shape.length;
      const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];
      const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
      const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];
      const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
      const outerDimsA = a.shape.slice(0, -2);
      const outerDimsB = b.shape.slice(0, -2);
      const batchDimA = tfjsCore.util.sizeFromShape(outerDimsA);
      const batchDimB = tfjsCore.util.sizeFromShape(outerDimsB);
      const batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;
      tfjsCore.util.assert(aRank >= 2 && bRank >= 2 && batchDimsCompatible, () => `Error in matMul: the input batch dimensions must either be the ` +
          `same or at least one input batch dimension must be 1. Got input ` +
          `batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);
      const outShapeOuterDims = batchDimA > batchDimB ? a.shape.slice(0, -2) : b.shape.slice(0, -2);
      const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
      tfjsCore.util.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${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];
      // The rest of the implementation is designed to operate on rank-3 tensors
      const a3d = reshape({ inputs: { x: a }, backend, attrs: { shape: a3dShape } });
      const b3d = reshape({ inputs: { x: b }, backend, attrs: { shape: b3dShape } });
      const a3dId = backend.dataIdMap.get(a3d.dataId).id;
      const b3dId = backend.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 = backend.makeOutput([batchDim, leftDim, rightDim], a3d.dtype);
      const outId = backend.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);
      out.shape = outShape;
      return out;
  }
  const batchMatMulConfig = {
      kernelName: tfjsCore.BatchMatMul,
      backendName: 'wasm',
      setupFunc: setup$4,
      kernelFunc: batchMatMul
  };

  /**
   * @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 cast(args) {
      const { inputs: { x }, attrs: { dtype }, backend } = args;
      const out = backend.makeOutput(x.shape, dtype);
      const inVals = backend.typedArrayFromHeap(x);
      const outVals = backend.typedArrayFromHeap(out);
      outVals.set(inVals);
      return out;
  }
  const castConfig = {
      kernelName: tfjsCore.Cast,
      backendName: 'wasm',
      kernelFunc: cast,
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmClip;
  function setup$5(backend) {
      wasmClip = backend.wasm.cwrap(tfjsCore.ClipByValue, null /* void */, [
          'number',
          'number',
          'number',
          'number' // out_id
      ]);
  }
  function clip(args) {
      const { inputs, backend, attrs } = args;
      const { x } = inputs;
      const { clipValueMin, clipValueMax } = attrs;
      const xId = backend.dataIdMap.get(x.dataId).id;
      const out = backend.makeOutput(x.shape, x.dtype);
      const outId = backend.dataIdMap.get(out.dataId).id;
      wasmClip(xId, clipValueMin, clipValueMax, outId);
      return out;
  }
  const clipByValueConfig = {
      kernelName: tfjsCore.ClipByValue,
      backendName: 'wasm',
      setupFunc: setup$5,
      kernelFunc: clip
  };

  /**
   * @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 concat(args) {
      const { inputs, backend } = args;
      const axis = tfjsCore.util.parseAxisParam(args.attrs.axis, inputs[0].shape)[0];
      const outShape = tfjsCore.backend_util.computeOutShape(inputs.map(t => t.shape), axis);
      const out = backend.makeOutput(outShape, inputs[0].dtype);
      if (tfjsCore.util.sizeFromShape(outShape) === 0) {
          return out;
      }
      // Keep only non-empty tensors (ignore tensors with 0 in their shape).
      const $inputs = inputs.filter(t => tfjsCore.util.sizeFromShape(t.shape) > 0);
      if ($inputs.length === 1) {
          return $inputs[0];
      }
      const shapes = $inputs.map(t => t.shape);
      tfjsCore.backend_util.assertParamsConsistent(shapes, axis);
      const batchDim = tfjsCore.util.sizeFromShape($inputs[0].shape.slice(0, axis));
      let sumInnerDims = 0;
      const innerDims = $inputs.map(input => {
          const innerDim = tfjsCore.util.sizeFromShape(input.shape.slice(axis));
          sumInnerDims += innerDim;
          return innerDim;
      });
      const inVals = $inputs.map(input => backend.typedArrayFromHeap(input));
      const outVals = backend.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;
  }
  const concatConfig = {
      kernelName: tfjsCore.Concat,
      backendName: 'wasm',
      kernelFunc: concat,
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmConv2d;
  function setup$6(backend) {
      wasmConv2d = backend.wasm.cwrap(tfjsCore.Conv2D, null /* void */, [
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
      ]);
  }
  function conv2d(args) {
      const { inputs, attrs, backend } = args;
      const { x, filter } = inputs;
      const xId = backend.dataIdMap.get(x.dataId).id;
      const filterId = backend.dataIdMap.get(filter.dataId).id;
      const { strides, dilations, pad, dimRoundingMode, dataFormat } = attrs;
      const $dataFormat = tfjsCore.backend_util.convertConv2DDataFormat(dataFormat);
      const convInfo = tfjsCore.backend_util.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad, 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 = backend.makeOutput(convInfo.outShape, 'float32');
      const outId = backend.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;
  }
  const conv2DConfig = {
      kernelName: tfjsCore.Conv2D,
      backendName: 'wasm',
      setupFunc: setup$6,
      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.
   * =============================================================================
   */
  let wasmConv2DBackpropInput;
  function setup$7(backend) {
      wasmConv2DBackpropInput = backend.wasm.cwrap(tfjsCore.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 conv2DBackpropInput(args) {
      const { backend, inputs, attrs } = args;
      const { dy, filter } = inputs;
      const { strides, pad, dataFormat, dimRoundingMode, inputShape } = attrs;
      const dilations = 1;
      const $dataFormat = tfjsCore.backend_util.convertConv2DDataFormat(dataFormat);
      const convInfo = tfjsCore.backend_util.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad, dimRoundingMode, false /* depthwise */, $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 = tfjsCore.util.computeStrides(convInfo.inShape);
      const dyStrides = tfjsCore.util.computeStrides(dy.shape);
      const [fltS0, fltS1, fltS2] = tfjsCore.util.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 = backend.makeOutput(convInfo.inShape, 'float32');
      const outId = backend.dataIdMap.get(out.dataId).id;
      const dyId = backend.dataIdMap.get(dy.dataId).id;
      const filterId = backend.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;
  }
  const conv2DBackpropInputConfig = {
      kernelName: tfjsCore.Conv2DBackpropInput,
      backendName: 'wasm',
      setupFunc: setup$7,
      kernelFunc: conv2DBackpropInput
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const cosConfig = createUnaryKernelConfig(tfjsCore.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.
   * =============================================================================
   */
  // Must match enum in CropAndResize.cc
  var InterpolationMethod;
  (function (InterpolationMethod) {
      InterpolationMethod[InterpolationMethod["bilinear"] = 0] = "bilinear";
      InterpolationMethod[InterpolationMethod["nearest"] = 1] = "nearest";
  })(InterpolationMethod || (InterpolationMethod = {}));
  let wasmCropAndResize;
  function setup$8(backend) {
      wasmCropAndResize = backend.wasm.cwrap(tfjsCore.CropAndResize, null /*void*/, [
          'number',
          'number',
          'number',
          'number',
          'array',
          'number',
          'number',
          'number',
          'number',
          'number' // out id
      ]);
  }
  function cropAndResize(args) {
      const { backend, inputs, attrs } = args;
      const { method, extrapolationValue, cropSize } = attrs;
      const { image, boxes, boxInd } = inputs;
      const numBoxes = boxes.shape[0];
      const [cropHeight, cropWidth] = cropSize;
      const outShape = [numBoxes, cropHeight, cropWidth, image.shape[3]];
      let imagesData = backend.dataIdMap.get(image.dataId);
      let castedData;
      if (image.dtype !== 'float32') {
          castedData = cast({ backend, inputs: { x: image }, attrs: { dtype: 'float32' } });
          imagesData = backend.dataIdMap.get(castedData.dataId);
      }
      const imagesId = imagesData.id;
      const boxesId = backend.dataIdMap.get(boxes.dataId).id;
      const boxIndId = backend.dataIdMap.get(boxInd.dataId).id;
      const out = backend.makeOutput(outShape, 'float32');
      const outId = backend.dataIdMap.get(out.dataId).id;
      const imagesShapeBytes = new Uint8Array(new Int32Array(image.shape).buffer);
      wasmCropAndResize(imagesId, boxesId, boxIndId, numBoxes, imagesShapeBytes, cropHeight, cropWidth, InterpolationMethod[method], extrapolationValue, outId);
      if (castedData != null) {
          backend.disposeData(castedData.dataId);
      }
      return out;
  }
  const cropAndResizeConfig = {
      kernelName: tfjsCore.CropAndResize,
      backendName: 'wasm',
      setupFunc: setup$8,
      kernelFunc: 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.
   * =============================================================================
   */
  let wasmCumsum;
  function setup$9(backend) {
      wasmCumsum = backend.wasm.cwrap(tfjsCore.Cumsum, null /* void */, [
          'number',
          'number',
          'number',
          'number',
          'number',
          'number' // dtype
      ]);
  }
  function cumsum(args) {
      const { inputs, backend, attrs } = args;
      const { x } = inputs;
      const { axis, exclusive, reverse } = attrs;
      const xRank = x.shape.length;
      tfjsCore.util.assert(x.dtype === 'float32' || x.dtype === 'int32', () => `cumsum does not support ${x.dtype} tensors in the WASM backend`);
      // permute required axis to inner most axis
      const permutation = tfjsCore.backend_util.getAxesPermutation([axis], xRank);
      let permutedX = x;
      if (permutation !== null) {
          permutedX = transpose({ inputs: { x }, attrs: { perm: permutation }, backend });
      }
      const permutedAxis = tfjsCore.backend_util.getInnerMostAxes(1, xRank)[0];
      tfjsCore.backend_util.assertAxesAreInnerMostDims('cumsum', [permutedAxis], xRank);
      const permutedOut = backend.makeOutput(permutedX.shape, permutedX.dtype);
      const finalDim = permutedX.shape[permutedAxis];
      const permutedXId = backend.dataIdMap.get(permutedX.dataId).id;
      const permutedOutId = backend.dataIdMap.get(permutedOut.dataId).id;
      wasmCumsum(permutedXId, exclusive ? 1 : 0, reverse ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]);
      // transpose data back if permuted
      let out = permutedOut;
      if (permutation !== null) {
          const undoPermutation = tfjsCore.backend_util.getUndoAxesPermutation(permutation);
          out = transpose({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend });
          backend.disposeData(permutedX.dataId);
          backend.disposeData(permutedOut.dataId);
      }
      return out;
  }
  const cumsumConfig = {
      kernelName: tfjsCore.Cumsum,
      backendName: 'wasm',
      setupFunc: setup$9,
      kernelFunc: cumsum
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmDepthToSpace;
  function setup$a(backend) {
      wasmDepthToSpace = backend.wasm.cwrap(tfjsCore.DepthToSpace, null /*void*/, [
          'number',
          'number',
          'number',
          'array',
          'number',
          'array',
          'array',
          'number',
          'number',
      ]);
  }
  function depthToSpace(args) {
      const { backend, inputs, attrs } = args;
      const { x } = inputs;
      const { blockSize, dataFormat } = attrs;
      tfjsCore.util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);
      const batchSize = x.shape[0];
      const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];
      const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];
      const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];
      const outputHeight = inputHeight * blockSize;
      const outputWidth = inputWidth * blockSize;
      const outputDepth = inputDepth / (blockSize * blockSize);
      const outputShape = (dataFormat === 'NHWC') ?
          [batchSize, outputHeight, outputWidth, outputDepth] :
          [batchSize, outputDepth, outputHeight, outputWidth];
      const out = backend.makeOutput(outputShape, 'float32');
      const xData = backend.dataIdMap.get(x.dataId);
      const xId = xData.id;
      const xStridesBytes = new Uint8Array(new Int32Array(tfjsCore.util.computeStrides(x.shape)).buffer);
      const outputShapeBytes = new Uint8Array(new Int32Array(outputShape).buffer);
      const outStridesBytes = new Uint8Array(new Int32Array(tfjsCore.util.computeStrides(outputShape)).buffer);
      const outId = backend.dataIdMap.get(out.dataId).id;
      const channelsLast = dataFormat === 'NHWC' ? 1 : 0;
      wasmDepthToSpace(xId, blockSize, channelsLast, xStridesBytes, x.shape.length - 1, outputShapeBytes, outStridesBytes, outputShape.length, outId);
      return out;
  }
  const depthToSpaceConfig = {
      kernelName: tfjsCore.DepthToSpace,
      backendName: 'wasm',
      setupFunc: setup$a,
      kernelFunc: depthToSpace
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmDepthwiseConv2d;
  function setup$b(backend) {
      wasmDepthwiseConv2d =
          backend.wasm.cwrap(tfjsCore.DepthwiseConv2dNative, null /* void */, [
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
          ]);
  }
  function depthwiseConv2d(args) {
      const { inputs, attrs, backend } = args;
      const { x, filter } = inputs;
      const xId = backend.dataIdMap.get(x.dataId).id;
      const filterId = backend.dataIdMap.get(filter.dataId).id;
      const { strides, dilations, pad, dimRoundingMode } = attrs;
      const $dilations = dilations == null ? [1, 1] : dilations;
      const convInfo = tfjsCore.backend_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */);
      const filterHeight = convInfo.filterHeight;
      const filterWidth = convInfo.filterWidth;
      const padTop = convInfo.padInfo.top;
      const padRight = convInfo.padInfo.right;
      const padBottom = convInfo.padInfo.bottom;
      const padLeft = convInfo.padInfo.left;
      const dilationHeight = convInfo.dilationHeight;
      const dilationWidth = convInfo.dilationWidth;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const inputChannels = convInfo.inChannels;
      const outputChannels = convInfo.outChannels;
      const isSamePad = convInfo.padInfo.type === 'SAME' ? 1 : 0;
      if (convInfo.dataFormat !== 'channelsLast') {
          throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'` +
              `${convInfo.dataFormat}'. Please use 'channelsLast'.`);
      }
      const out = backend.makeOutput(convInfo.outShape, 'float32');
      const outId = backend.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;
  }
  const depthwiseConv2dNativeConfig = {
      kernelName: tfjsCore.DepthwiseConv2dNative,
      backendName: 'wasm',
      setupFunc: setup$b,
      kernelFunc: depthwiseConv2d
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const supportsFullBroadcast$1 = true;
  const divConfig = createBinaryKernelConfig(tfjsCore.Div, supportsFullBroadcast$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.
   * =============================================================================
   */
  const supportsFullBroadcast$2 = false;
  const equalConfig = createBinaryKernelConfig(tfjsCore.Equal, supportsFullBroadcast$2, '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.
   * =============================================================================
   */
  const expConfig = createUnaryKernelConfig(tfjsCore.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 fill(args) {
      const { attrs: { shape, value, dtype }, backend } = args;
      const out = backend.makeOutput(shape, dtype);
      const outVals = backend.typedArrayFromHeap(out);
      outVals.fill(value);
      return out;
  }
  const fillConfig = {
      kernelName: tfjsCore.Fill,
      backendName: 'wasm',
      kernelFunc: fill,
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmFlipLeftRight;
  function setup$c(backend) {
      wasmFlipLeftRight = backend.wasm.cwrap(tfjsCore.FlipLeftRight, null /* void */, [
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
      ]);
  }
  function flipLeftRight(args) {
      const { inputs, backend } = args;
      const { image } = inputs;
      const out = backend.makeOutput(image.shape, image.dtype);
      const imageId = backend.dataIdMap.get(image.dataId).id;
      const outId = backend.dataIdMap.get(out.dataId).id;
      const [batch, imageHeight, imageWidth, numChannels] = image.shape;
      wasmFlipLeftRight(imageId, batch, imageHeight, imageWidth, numChannels, outId);
      return out;
  }
  const flipLeftRightConfig = {
      kernelName: tfjsCore.FlipLeftRight,
      backendName: 'wasm',
      kernelFunc: flipLeftRight,
      setupFunc: setup$c
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const supportsFullBroadcast$3 = false;
  const floorDivConfig = createBinaryKernelConfig(tfjsCore.FloorDiv, supportsFullBroadcast$3);

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmBatchNorm;
  function setup$d(backend) {
      wasmBatchNorm = backend.wasm.cwrap(tfjsCore.FusedBatchNorm, null /* void */, ['number', 'number', 'number', 'number', 'number', 'number', 'number']);
  }
  function fusedBatchNorm(args) {
      const { backend, inputs, attrs } = args;
      const { varianceEpsilon } = attrs;
      const { x, mean, variance, offset, scale } = inputs;
      const xId = backend.dataIdMap.get(x.dataId).id;
      const meanId = backend.dataIdMap.get(mean.dataId).id;
      const varianceId = backend.dataIdMap.get(variance.dataId).id;
      const offsetId = offset != null ? backend.dataIdMap.get(offset.dataId).id : 0;
      const scaleId = scale != null ? backend.dataIdMap.get(scale.dataId).id : 0;
      const out = backend.makeOutput(x.shape, x.dtype);
      // Short-circuit zero-sized tensors.
      if (tfjsCore.util.sizeFromShape(x.shape) === 0) {
          return out;
      }
      const outId = backend.dataIdMap.get(out.dataId).id;
      wasmBatchNorm(xId, meanId, varianceId, offsetId, scaleId, varianceEpsilon, outId);
      return out;
  }
  const fusedBatchNormConfig = {
      kernelName: tfjsCore.FusedBatchNorm,
      backendName: 'wasm',
      setupFunc: setup$d,
      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.
   * =============================================================================
   */
  let wasmFusedConv2d;
  function setup$e(backend) {
      wasmFusedConv2d = backend.wasm.cwrap(tfjsCore.FusedConv2D, null /* void */, [
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
      ]);
  }
  function fusedConv2d(args) {
      const { inputs, attrs, backend } = args;
      const { x, filter, bias, preluActivationWeights } = inputs;
      const { strides, pad, dilations, dataFormat, dimRoundingMode, activation } = attrs;
      const convInfo = tfjsCore.backend_util.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad, dimRoundingMode);
      const fusedActivation = FusableActivation[activation];
      if (fusedActivation == null) {
          throw new Error(`${activation} activation not yet supported for FusedConv2D ` +
              `in the wasm backend.`);
      }
      const xId = backend.dataIdMap.get(x.dataId).id;
      const filterId = backend.dataIdMap.get(filter.dataId).id;
      const outputChannels = convInfo.outChannels;
      let biasId = 0;
      if (bias != null) {
          const biasData = backend.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 = backend.makeOutput(convInfo.outShape, 'float32');
      const outId = backend.dataIdMap.get(out.dataId).id;
      const preluActivationWeightsId = preluActivationWeights == null ?
          0 :
          backend.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, outId);
      return out;
  }
  const fusedConv2DConfig = {
      kernelName: tfjsCore.FusedConv2D,
      backendName: 'wasm',
      setupFunc: setup$e,
      kernelFunc: fusedConv2d
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmFusedDepthwiseConv2d;
  function setup$f(backend) {
      wasmFusedDepthwiseConv2d =
          backend.wasm.cwrap(tfjsCore.FusedDepthwiseConv2D, null /* void */, [
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
              'number',
          ]);
  }
  function fusedDepthwiseConv2d(args) {
      const { inputs, attrs, backend } = args;
      const { x, filter, bias, preluActivationWeights } = inputs;
      const { strides, pad, dilations, dataFormat, dimRoundingMode, activation } = attrs;
      const convInfo = tfjsCore.backend_util.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);
      const fusedActivation = FusableActivation[activation];
      if (fusedActivation == null) {
          throw new Error(`${activation} activation not yet supported for FusedDepthwiseConv2D ` +
              `in the wasm backend.`);
      }
      const xId = backend.dataIdMap.get(x.dataId).id;
      const filterId = backend.dataIdMap.get(filter.dataId).id;
      const outputChannels = convInfo.outChannels;
      let biasId = 0;
      if (bias != null) {
          const biasData = backend.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 = backend.makeOutput(convInfo.outShape, 'float32');
      const outId = backend.dataIdMap.get(out.dataId).id;
      const preluActivationWeightsId = preluActivationWeights == null ?
          0 :
          backend.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, outId);
      return out;
  }
  const fusedDepthwiseConv2DConfig = {
      kernelName: tfjsCore.FusedDepthwiseConv2D,
      backendName: 'wasm',
      setupFunc: setup$f,
      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.
   * =============================================================================
   */
  let wasmGatherNd;
  function setup$g(backend) {
      wasmGatherNd = backend.wasm.cwrap(tfjsCore.GatherNd, null /*void*/, [
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'array',
          'number' // outId
      ]);
  }
  function gatherNd(args) {
      const { backend, inputs } = args;
      const { params, indices } = inputs;
      const [resultShape, numSlices, sliceSize, strides] = tfjsCore.gather_util.prepareAndValidate(params, indices);
      const out = backend.makeOutput(resultShape, params.dtype);
      if (numSlices === 0) {
          return out;
      }
      const indicesShape = indices.shape;
      const sliceRank = indicesShape[indicesShape.length - 1];
      const xData = backend.dataIdMap.get(params.dataId);
      const xId = xData.id;
      const indicesData = backend.dataIdMap.get(indices.dataId);
      const indicesId = indicesData.id;
      const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);
      const outId = backend.dataIdMap.get(out.dataId).id;
      wasmGatherNd(xId, CppDType[params.dtype], indicesId, numSlices, sliceRank, sliceSize, stridesBytes, outId);
      return out;
  }
  const gatherNdConfig = {
      kernelName: tfjsCore.GatherNd,
      backendName: 'wasm',
      setupFunc: setup$g,
      kernelFunc: 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.
   * =============================================================================
   */
  let wasmGather;
  function setup$h(backend) {
      wasmGather = backend.wasm.cwrap('Gather', null /*void*/, [
          'number',
          'number',
          'array',
          'number',
          'number',
          'number',
          'array',
          'number' // outId
      ]);
  }
  function gatherV2(args) {
      const { backend, inputs, attrs } = args;
      const { x, indices } = inputs;
      const { axis } = attrs;
      const newShape = x.shape.slice();
      newShape[axis] = tfjsCore.util.sizeFromShape(indices.shape);
      const stridesSize = x.shape.length - 1;
      const out = backend.makeOutput(newShape, x.dtype);
      if (tfjsCore.util.sizeFromShape(x.shape) === 0) {
          return out;
      }
      const xData = backend.dataIdMap.get(x.dataId);
      const xId = xData.id;
      const indicesData = backend.dataIdMap.get(indices.dataId);
      const indicesId = indicesData.id;
      const outId = backend.dataIdMap.get(out.dataId).id;
      const xStridesBytes = new Uint8Array(new Int32Array(tfjsCore.util.computeStrides(x.shape)).buffer);
      const outStridesBytes = new Uint8Array(new Int32Array(tfjsCore.util.computeStrides(newShape)).buffer);
      wasmGather(xId, CppDType[x.dtype], xStridesBytes, stridesSize, indicesId, axis, outStridesBytes, outId);
      // reshape
      const parsedAxis = tfjsCore.util.parseAxisParam(axis, x.shape)[0];
      const shapeInfo = tfjsCore.backend_util.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis);
      out.shape = shapeInfo.outputShape;
      return out;
  }
  const gatherV2Config = {
      kernelName: tfjsCore.GatherV2,
      backendName: 'wasm',
      setupFunc: setup$h,
      kernelFunc: gatherV2
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const supportsFullBroadcast$4 = false;
  const greaterConfig = createBinaryKernelConfig(tfjsCore.Greater, supportsFullBroadcast$4, '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.
   * =============================================================================
   */
  const supportsFullBroadcast$5 = false;
  const greaterEqualConfig = createBinaryKernelConfig(tfjsCore.GreaterEqual, supportsFullBroadcast$5, '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.
   * =============================================================================
   */
  const supportsFullBroadcast$6 = false;
  const lessConfig = createBinaryKernelConfig(tfjsCore.Less, supportsFullBroadcast$6, '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.
   * =============================================================================
   */
  const supportsFullBroadcast$7 = false;
  const lessEqualConfig = createBinaryKernelConfig(tfjsCore.LessEqual, supportsFullBroadcast$7, '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.
   * =============================================================================
   */
  const logConfig = createUnaryKernelConfig(tfjsCore.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.
   * =============================================================================
   */
  const supportsFullBroadcast$8 = false;
  const logicalAndConfig = createBinaryKernelConfig(tfjsCore.LogicalAnd, supportsFullBroadcast$8, '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.
   * =============================================================================
   */
  let wasmMax;
  function setup$i(backend) {
      wasmMax = backend.wasm.cwrap(tfjsCore.Max, null /*void*/, ['number, number, number']);
  }
  function max(args) {
      const { backend, inputs, attrs } = args;
      const { reductionIndices: axis, keepDims } = attrs;
      const { x } = inputs;
      const xId = backend.dataIdMap.get(x.dataId).id;
      let inputId = xId;
      let input = x;
      const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend);
      if (inputWasTransposed) {
          const transposedId = backend.dataIdMap.get(transposed.dataId).id;
          input = transposed;
          inputId = transposedId;
      }
      const inputRank = input.shape.length;
      tfjsCore.backend_util.assertAxesAreInnerMostDims('max', axes, inputRank);
      const [outShape, reduceShape] = tfjsCore.backend_util.computeOutAndReduceShapes(input.shape, axes);
      const reduceSize = tfjsCore.util.sizeFromShape(reduceShape);
      const out = backend.makeOutput(outShape, x.dtype);
      if (tfjsCore.util.sizeFromShape(input.shape) !== 0) {
          const outId = backend.dataIdMap.get(out.dataId).id;
          wasmMax(inputId, reduceSize, outId);
      }
      if (inputWasTransposed) {
          // dispose of the transposed tensor.
          backend.disposeData(transposed.dataId);
      }
      if (keepDims) {
          // reshape
          const newShape = tfjsCore.backend_util.expandShapeToKeepDim(out.shape, originalAxes);
          out.shape = newShape;
      }
      return out;
  }
  const maxConfig = {
      kernelName: tfjsCore.Max,
      backendName: 'wasm',
      setupFunc: setup$i,
      kernelFunc: max
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const supportsFullBroadcast$9 = false;
  const maximumConfig = createBinaryKernelConfig(tfjsCore.Maximum, supportsFullBroadcast$9);

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmMaxPool;
  function setup$j(backend) {
      wasmMaxPool = backend.wasm.cwrap(tfjsCore.MaxPool, null /* void */, [
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
      ]);
  }
  function maxPool(args) {
      const { inputs, attrs, backend } = args;
      const x = inputs.x;
      const xId = backend.dataIdMap.get(x.dataId).id;
      const { filterSize, strides, pad, dimRoundingMode } = attrs;
      const convInfo = tfjsCore.backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);
      const filterHeight = convInfo.filterHeight;
      const filterWidth = convInfo.filterWidth;
      const padTop = convInfo.padInfo.top;
      const padRight = convInfo.padInfo.right;
      const padBottom = convInfo.padInfo.bottom;
      const padLeft = convInfo.padInfo.left;
      const dilationHeight = convInfo.dilationHeight;
      const dilationWidth = convInfo.dilationWidth;
      const strideHeight = convInfo.strideHeight;
      const strideWidth = convInfo.strideWidth;
      const inputChannels = convInfo.inChannels;
      const outputChannels = convInfo.outChannels;
      if (convInfo.dataFormat !== 'channelsLast') {
          throw new Error(`wasm backend does not support dataFormat:'` +
              `${convInfo.dataFormat}'. Please use 'channelsLast'.`);
      }
      const out = backend.makeOutput(convInfo.outShape, 'float32');
      const outId = backend.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;
  }
  const maxPoolConfig = {
      kernelName: tfjsCore.MaxPool,
      backendName: 'wasm',
      setupFunc: setup$j,
      kernelFunc: maxPool
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmMin;
  function setup$k(backend) {
      wasmMin = backend.wasm.cwrap(tfjsCore.Min, null /*void*/, ['number, number, number']);
  }
  function min(args) {
      const { backend, inputs, attrs } = args;
      const { axis, keepDims } = attrs;
      const { x } = inputs;
      const xId = backend.dataIdMap.get(x.dataId).id;
      let inputId = xId;
      let input = x;
      const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend);
      if (inputWasTransposed) {
          const transposedId = backend.dataIdMap.get(transposed.dataId).id;
          if (transposedId !== xId) {
              // transpose was not a no-op. We will need to dispose of this
              // once we are done.
              input = transposed;
              inputId = transposedId;
          }
      }
      const inputRank = input.shape.length;
      tfjsCore.backend_util.assertAxesAreInnerMostDims('min', axes, inputRank);
      const [outShape, reduceShape] = tfjsCore.backend_util.computeOutAndReduceShapes(input.shape, axes);
      const reduceSize = tfjsCore.util.sizeFromShape(reduceShape);
      const out = backend.makeOutput(outShape, input.dtype);
      if (tfjsCore.util.sizeFromShape(input.shape) !== 0) {
          const outId = backend.dataIdMap.get(out.dataId).id;
          wasmMin(inputId, reduceSize, outId);
      }
      if (inputWasTransposed) {
          // dispose of the transposed tensor.
          backend.disposeData(transposed.dataId);
      }
      if (keepDims) {
          // reshape
          const newShape = tfjsCore.backend_util.expandShapeToKeepDim(out.shape, originalAxes);
          out.shape = newShape;
      }
      return out;
  }
  const minConfig = {
      kernelName: tfjsCore.Min,
      backendName: 'wasm',
      setupFunc: setup$k,
      kernelFunc: min
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const supportsFullBroadcast$a = false;
  const minimumConfig = createBinaryKernelConfig(tfjsCore.Minimum, supportsFullBroadcast$a);

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const supportsFullBroadcast$b = true;
  const multiplyConfig = createBinaryKernelConfig(tfjsCore.Multiply, supportsFullBroadcast$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.
   * =============================================================================
   */
  const negateConfig = createUnaryKernelConfig(tfjsCore.Negate);

  /**
   * @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.
   * =============================================================================
   */
  /**
   * Parse the result of the c++ method, which has the shape equivalent to
   * `Result`.
   */
  function parseResultStruct(backend, resOffset) {
      const result = new Int32Array(backend.wasm.HEAPU8.buffer, resOffset, 4);
      const pSelectedIndices = result[0];
      const selectedSize = result[1];
      const pSelectedScores = result[2];
      const pValidOutputs = result[3];
      // Since the result was allocated on the heap, we have to delete it.
      backend.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.
   * =============================================================================
   */
  let wasmFunc$2;
  function setup$l(backend) {
      wasmFunc$2 = backend.wasm.cwrap(tfjsCore.NonMaxSuppressionV3, 'number', // Result*
      [
          'number',
          'number',
          'number',
          'number',
          'number',
      ]);
  }
  function kernelFunc(args) {
      const { backend, inputs, attrs } = args;
      const { iouThreshold, maxOutputSize, scoreThreshold } = attrs;
      const { boxes, scores } = inputs;
      const boxesId = backend.dataIdMap.get(boxes.dataId).id;
      const scoresId = backend.dataIdMap.get(scores.dataId).id;
      const resOffset = wasmFunc$2(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold);
      const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend, resOffset);
      // Since we are not using scores for V3, we have to delete it from the heap.
      backend.wasm._free(pSelectedScores);
      backend.wasm._free(pValidOutputs);
      const selectedIndicesTensor = backend.makeOutput([selectedSize], 'int32', pSelectedIndices);
      return selectedIndicesTensor;
  }
  const nonMaxSuppressionV3Config = {
      kernelName: tfjsCore.NonMaxSuppressionV3,
      backendName: 'wasm',
      setupFunc: setup$l,
      kernelFunc: 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.
   * =============================================================================
   */
  let wasmFunc$3;
  function setup$m(backend) {
      wasmFunc$3 = backend.wasm.cwrap(tfjsCore.NonMaxSuppressionV4, 'number', // Result*
      [
          'number',
          'number',
          'number',
          'number',
          'number',
          'bool',
      ]);
  }
  function nonMaxSuppressionV4(args) {
      const { backend, inputs, attrs } = args;
      const { iouThreshold, maxOutputSize, scoreThreshold, padToMaxOutputSize } = attrs;
      const { boxes, scores } = inputs;
      const boxesId = backend.dataIdMap.get(boxes.dataId).id;
      const scoresId = backend.dataIdMap.get(scores.dataId).id;
      const resOffset = wasmFunc$3(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
      const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend, resOffset);
      // Since we are not using scores for V4, we have to delete it from the heap.
      backend.wasm._free(pSelectedScores);
      const selectedIndicesTensor = backend.makeOutput([selectedSize], 'int32', pSelectedIndices);
      const validOutputsTensor = backend.makeOutput([], 'int32', pValidOutputs);
      return [selectedIndicesTensor, validOutputsTensor];
  }
  const nonMaxSuppressionV4Config = {
      kernelName: tfjsCore.NonMaxSuppressionV4,
      backendName: 'wasm',
      setupFunc: setup$m,
      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.
   * =============================================================================
   */
  let wasmFunc$4;
  function setup$n(backend) {
      wasmFunc$4 = backend.wasm.cwrap(tfjsCore.NonMaxSuppressionV5, 'number', // Result*
      [
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
      ]);
  }
  function kernelFunc$1(args) {
      const { backend, inputs, attrs } = args;
      const { iouThreshold, maxOutputSize, scoreThreshold, softNmsSigma } = attrs;
      const { boxes, scores } = inputs;
      const boxesId = backend.dataIdMap.get(boxes.dataId).id;
      const scoresId = backend.dataIdMap.get(scores.dataId).id;
      const resOffset = wasmFunc$4(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
      const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend, resOffset);
      // Since we are not using validOutputs for V5, we have to delete it from the
      // heap.
      backend.wasm._free(pValidOutputs);
      const selectedIndicesTensor = backend.makeOutput([selectedSize], 'int32', pSelectedIndices);
      const selectedScoresTensor = backend.makeOutput([selectedSize], 'float32', pSelectedScores);
      return [selectedIndicesTensor, selectedScoresTensor];
  }
  const nonMaxSuppressionV5Config = {
      kernelName: tfjsCore.NonMaxSuppressionV5,
      backendName: 'wasm',
      setupFunc: setup$n,
      kernelFunc: kernelFunc$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.
   * =============================================================================
   */
  const supportsFullBroadcast$c = false;
  const notEqualConfig = createBinaryKernelConfig(tfjsCore.NotEqual, supportsFullBroadcast$c, '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.
   * =============================================================================
   */
  let wasmOneHot;
  function setup$o(backend) {
      wasmOneHot = backend.wasm.cwrap(tfjsCore.OneHot, null /* void */, [
          'number',
          'number',
          'number',
          'number',
          'number' // out_id
      ]);
  }
  function oneHot(args) {
      const { inputs, backend, attrs } = args;
      const { indices } = inputs;
      const { depth, onValue, offValue } = attrs;
      const out = backend.makeOutput([...indices.shape, depth], 'int32');
      const outId = backend.dataIdMap.get(out.dataId).id;
      const indicesData = backend.dataIdMap.get(indices.dataId);
      const indicesId = indicesData.id;
      wasmOneHot(indicesId, depth, onValue, offValue, outId);
      return out;
  }
  const oneHotConfig = {
      kernelName: tfjsCore.OneHot,
      backendName: 'wasm',
      setupFunc: setup$o,
      kernelFunc: oneHot,
  };

  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES 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(args) {
      const { inputs: { x }, backend } = args;
      const out = backend.makeOutput(x.shape, x.dtype);
      const outVals = backend.typedArrayFromHeap(out);
      outVals.fill(1);
      return out;
  }
  const onesLikeConfig = {
      kernelName: tfjsCore.OnesLike,
      backendName: 'wasm',
      kernelFunc: onesLike,
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmPadV2;
  function setup$p(backend) {
      wasmPadV2 = backend.wasm.cwrap(tfjsCore.PadV2, null /* void */, [
          'number',
          'array',
          'number',
          'number',
          'array',
          'array',
          'number',
          'number',
      ]);
  }
  function pad(args) {
      const { inputs: { x }, backend, attrs: { paddings, constantValue } } = args;
      const outShape = paddings.map((p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);
      const xId = backend.dataIdMap.get(x.dataId).id;
      const out = backend.makeOutput(outShape, x.dtype);
      const outId = backend.dataIdMap.get(out.dataId).id;
      const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
      const prePaddingsFlat = paddings.map(padTuple => padTuple[0]);
      const postPaddingsFlat = paddings.map(padTuple => padTuple[1]);
      const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer);
      const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer);
      wasmPadV2(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, constantValue, outId);
      return out;
  }
  const padV2Config = {
      kernelName: tfjsCore.PadV2,
      backendName: 'wasm',
      kernelFunc: pad,
      setupFunc: setup$p
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const supportsFullBroadcast$d = false;
  const powConfig = createBinaryKernelConfig(tfjsCore.Pow, supportsFullBroadcast$d);

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmPrelu;
  function setup$q(backend) {
      wasmPrelu = backend.wasm.cwrap(tfjsCore.Prelu, null /* void */, [
          'number',
          'number',
          'number' // out_id
      ]);
  }
  function prelu(args) {
      const { inputs, backend } = args;
      const { x, alpha } = inputs;
      const xId = backend.dataIdMap.get(x.dataId).id;
      const weightsId = backend.dataIdMap.get(alpha.dataId).id;
      const out = backend.makeOutput(x.shape, 'float32');
      const outId = backend.dataIdMap.get(out.dataId).id;
      wasmPrelu(xId, weightsId, outId);
      return out;
  }
  const preluConfig = {
      kernelName: tfjsCore.Prelu,
      backendName: 'wasm',
      setupFunc: setup$q,
      kernelFunc: prelu
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const reluConfig = createUnaryKernelConfig(tfjsCore.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.
   * =============================================================================
   */
  const relu6Config = createUnaryKernelConfig(tfjsCore.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.
   * =============================================================================
   */
  let wasmResizeBilinear;
  function setup$r(backend) {
      wasmResizeBilinear = backend.wasm.cwrap(tfjsCore.ResizeBilinear, null /*void*/, [
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number' // outId
      ]);
  }
  function resizeBilinear(args) {
      const { backend, inputs, attrs } = args;
      const { images } = inputs;
      const { alignCorners, size } = attrs;
      const [newHeight, newWidth] = size;
      const [batch, oldHeight, oldWidth, numChannels] = images.shape;
      const outShape = [batch, newHeight, newWidth, numChannels];
      let xData = backend.dataIdMap.get(images.dataId);
      let castedData;
      if (xData.dtype !== 'float32') {
          castedData =
              cast({ backend, inputs: { x: images }, attrs: { dtype: 'float32' } });
          xData = backend.dataIdMap.get(castedData.dataId);
      }
      const xId = xData.id;
      const out = backend.makeOutput(outShape, 'float32');
      if (tfjsCore.util.sizeFromShape(images.shape) === 0) {
          return out;
      }
      const outId = backend.dataIdMap.get(out.dataId).id;
      wasmResizeBilinear(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, outId);
      if (castedData != null) {
          backend.disposeData(castedData.dataId);
      }
      return out;
  }
  const resizeBilinearConfig = {
      kernelName: tfjsCore.ResizeBilinear,
      backendName: 'wasm',
      setupFunc: setup$r,
      kernelFunc: 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.
   * =============================================================================
   */
  let wasmReverse;
  function setup$s(backend) {
      wasmReverse = backend.wasm.cwrap(tfjsCore.Reverse, null, [
          'number',
          'array',
          'number',
          'array',
          'number',
          'number' // out_id
      ]);
  }
  function reverse(args) {
      const { inputs, backend, attrs } = args;
      const { x } = inputs;
      const { dims } = attrs;
      const axes = tfjsCore.util.parseAxisParam(dims, x.shape);
      if (x.shape.length === 0) {
          return identity({ inputs: { x }, backend });
      }
      const out = backend.makeOutput(x.shape, x.dtype);
      const xId = backend.dataIdMap.get(x.dataId).id;
      const outId = backend.dataIdMap.get(out.dataId).id;
      const axesBytes = new Uint8Array(new Int32Array(axes).buffer);
      const outShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
      wasmReverse(xId, axesBytes, axes.length, outShapeBytes, x.shape.length, outId);
      return reshape({ inputs: { x: out }, attrs: { shape: x.shape }, backend });
  }
  const reverseConfig = {
      kernelName: tfjsCore.Reverse,
      backendName: 'wasm',
      kernelFunc: reverse,
      setupFunc: setup$s
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmRotate;
  function setup$t(backend) {
      wasmRotate = backend.wasm.cwrap(tfjsCore.RotateWithOffset, null /* void */, [
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'array',
          'number',
          'number',
      ]);
  }
  function rotateWithOffset(args) {
      const { inputs, backend, attrs } = args;
      const { image } = inputs;
      const { radians, fillValue, center } = attrs;
      const out = backend.makeOutput(image.shape, image.dtype);
      const imageId = backend.dataIdMap.get(image.dataId).id;
      const outId = backend.dataIdMap.get(out.dataId).id;
      const [batch, imageHeight, imageWidth, numChannels] = image.shape;
      const [centerX, centerY] = tfjsCore.backend_util.getImageCenter(center, imageHeight, imageWidth);
      const fillIsBlack = fillValue === 0;
      const fullOpacityValue = 255;
      const fillValues = typeof fillValue === 'number' ?
          [fillValue, fillValue, fillValue, fillIsBlack ? 0 : fullOpacityValue] :
          [...fillValue, fullOpacityValue];
      const fillBytes = new Uint8Array(new Int32Array(fillValues).buffer);
      wasmRotate(imageId, batch, imageHeight, imageWidth, numChannels, radians, centerX, centerY, fillBytes, fillValues.length, outId);
      return out;
  }
  const rotateWithOffsetConfig = {
      kernelName: tfjsCore.RotateWithOffset,
      backendName: 'wasm',
      kernelFunc: rotateWithOffset,
      setupFunc: setup$t
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const rsqrtConfig = createUnaryKernelConfig(tfjsCore.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.
   * =============================================================================
   */
  let wasmScatterNd;
  function setup$u(backend) {
      wasmScatterNd = backend.wasm.cwrap(tfjsCore.ScatterNd, null /*void*/, [
          'number',
          'number',
          'number',
          'number',
          'number',
          'number',
          'array',
          'number',
          'number' // outId
      ]);
  }
  function scatterNd(args) {
      const { backend, inputs, attrs } = args;
      const { indices, updates } = inputs;
      const { shape } = attrs;
      const out = backend.makeOutput(shape, updates.dtype);
      if (tfjsCore.util.sizeFromShape(shape) === 0) {
          return out;
      }
      const { sliceRank, numUpdates, sliceSize, strides, outputSize } = tfjsCore.scatter_util.calculateShapes(updates, indices, shape);
      const indicesData = backend.dataIdMap.get(indices.dataId);
      const indicesId = indicesData.id;
      const updatesData = backend.dataIdMap.get(updates.dataId);
      const updatesId = updatesData.id;
      const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);
      const outId = backend.dataIdMap.get(out.dataId).id;
      wasmScatterNd(indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId);
      return out;
  }
  const scatterNdConfig = {
      kernelName: tfjsCore.ScatterNd,
      backendName: 'wasm',
      setupFunc: setup$u,
      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.
   * =============================================================================
   */
  let wasmSelect;
  function setup$v(backend) {
      wasmSelect = backend.wasm.cwrap(tfjsCore.SelectV2, null, [
          'number',
          'number',
          'number',
          'number',
          'number',
      ]);
  }
  function select(args) {
      const { inputs, backend } = args;
      const { condition, t, e } = inputs;
      const conditionId = backend.dataIdMap.get(condition.dataId).id;
      const tId = backend.dataIdMap.get(t.dataId).id;
      const eId = backend.dataIdMap.get(e.dataId).id;
      const out = backend.makeOutput(t.shape, t.dtype);
      const outId = backend.dataIdMap.get(out.dataId).id;
      const cRank = condition.shape.length;
      const tRank = t.shape.length;
      const offset = cRank === 0 || cRank > 1 || tRank === 1 ?
          1 :
          tfjsCore.util.sizeFromShape(t.shape.slice(1));
      wasmSelect(conditionId, tId, eId, offset, outId);
      return out;
  }
  const selectV2Config = {
      kernelName: tfjsCore.SelectV2,
      backendName: 'wasm',
      kernelFunc: select,
      setupFunc: setup$v
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmFunc$5;
  function setup$w(backend) {
      wasmFunc$5 = backend.wasm.cwrap(tfjsCore.Sigmoid, null /* void */, ['number', 'number']);
  }
  function sigmoid(args) {
      const { backend, inputs: { x } } = args;
      const xId = backend.dataIdMap.get(x.dataId).id;
      const out = backend.makeOutput(x.shape, x.dtype);
      const outId = backend.dataIdMap.get(out.dataId).id;
      // Short-circuit zero-sized tensors.
      if (tfjsCore.util.sizeFromShape(out.shape) === 0) {
          return out;
      }
      wasmFunc$5(xId, outId);
      return out;
  }
  const sigmoidConfig = {
      kernelName: 'Sigmoid',
      backendName: 'wasm',
      setupFunc: setup$w,
      kernelFunc: sigmoid
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const sinConfig = createUnaryKernelConfig(tfjsCore.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 slice(args) {
      const { inputs: { x }, attrs: { begin, size }, backend } = args;
      const [begin_, size_] = tfjsCore.slice_util.parseSliceParams(x, begin, size);
      const isContinous = tfjsCore.slice_util.isSliceContinous(x.shape, begin_, size_);
      const xVals = backend.typedArrayFromHeap(x);
      const out = backend.makeOutput(size_, x.dtype);
      const outVals = backend.typedArrayFromHeap(out);
      const xStrides = tfjsCore.util.computeStrides(x.shape);
      if (isContinous) {
          const flatOffset = tfjsCore.slice_util.computeFlatOffset(begin_, xStrides);
          outVals.set(xVals.subarray(flatOffset, flatOffset + tfjsCore.util.sizeFromShape(size_)));
          return out;
      }
      const rank = x.shape.length;
      if (rank === 2) {
          slice2d(xVals, xStrides[0], outVals, begin_, size_);
      }
      else if (rank === 3) {
          slice3d(xVals, xStrides[0], xStrides[1], outVals, begin_, size_);
      }
      else if (rank === 4) {
          slice4d(xVals, xStrides[0], xStrides[1], xStrides[2], outVals, begin_, size_);
      }
      else {
          genericSliceSlow(xVals, x, outVals, begin_, size_);
      }
      return out;
  }
  function slice2d(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 slice3d(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 slice4d(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];
              }
          }
      }
  }
  function genericSliceSlow(xVals, xInfo, outVals, begin, size) {
      const outBuf = tfjsCore.buffer(size, xInfo.dtype, outVals);
      const xBuf = tfjsCore.buffer(xInfo.shape, xInfo.dtype, xVals);
      for (let i = 0; i < outBuf.size; ++i) {
          const loc = outBuf.indexToLoc(i);
          const xLoc = loc.map((idx, j) => idx + begin[j]);
          outVals[i] = xBuf.get(...xLoc);
      }
  }
  const sliceConfig = {
      kernelName: tfjsCore.Slice,
      backendName: 'wasm',
      kernelFunc: slice,
  };

  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmFunc$6;
  function setup$x(backend) {
      wasmFunc$6 = backend.wasm.cwrap(tfjsCore.Softmax, null /* void */, [
          'number',
          'number',
          'number',
          'number' // batch
      ]);
  }
  function softmax(args) {
      const { backend, inputs: { logits }, attrs: { dim } } = args;
      const xId = backend.dataIdMap.get(logits.dataId).id;
      const out = backend.makeOutput(logits.shape, logits.dtype);
      const outId = backend.dataIdMap.get(out.dataId).id;
      const channels = logits.shape[dim];
      const batch = tfjsCore.util.sizeFromShape(logits.shape) / channels;
      // Short-circuit zero-sized tensors.
      if (tfjsCore.util.sizeFromShape(out.shape) === 0) {
          return out;
      }
      wasmFunc$6(xId, outId, channels, batch);
      return out;
  }
  const softmaxConfig = {
      kernelName: tfjsCore.Softmax,
      backendName: 'wasm',
      setupFunc: setup$x,
      kernelFunc: 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 split(args) {
      const { inputs, attrs, backend } = args;
      const { x } = inputs;
      const { numOrSizeSplits, axis } = attrs;
      const $axis = tfjsCore.util.parseAxisParam(axis, x.shape)[0];
      const splitSizes = tfjsCore.backend_util.prepareSplitSize(x, numOrSizeSplits, axis);
      const begin = new Array(x.shape.length).fill(0);
      const size = x.shape.slice();
      return splitSizes.map(s => {
          const xSliceSize = [...size];
          xSliceSize[$axis] = s;
          const xSlice = slice({ inputs: { x }, attrs: { begin, size: xSliceSize }, backend });
          begin[$axis] += s;
          return xSlice;
      });
  }
  const splitVConfig = {
      kernelName: tfjsCore.SplitV,
      backendName: 'wasm',
      kernelFunc: split
  };

  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const sqrtConfig = createUnaryKernelConfig(tfjsCore.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.
   * =============================================================================
   */
  const squareConfig = createUnaryKernelConfig(tfjsCore.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.
   * =============================================================================
   */
  const supportsFullBroadcast$e = true;
  const squaredDifferenceConfig = createBinaryKernelConfig(tfjsCore.SquaredDifference, supportsFullBroadcast$e);

  /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmStridedSlice;
  function setup$y(backend) {
      wasmStridedSlice = backend.wasm.cwrap(tfjsCore.StridedSlice, null /*void*/, [
          'number',
          'array',
          'number',
          'array',
          'array',
          'array',
          'array',
          'array',
          'number',
          'number',
      ]);
  }
  function stridedSlice(args) {
      const { backend, 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 = tfjsCore.backend_util.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;
      // Expand the dims of x based on the newAxisMask.
      const expandAxes = tfjsCore.backend_util.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 = reshape({ inputs: { x }, attrs: { shape: newShape }, backend });
      const { begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides } = tfjsCore.backend_util.slice_util.getNormalizedAxes(xReshaped.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);
      begin = normalizedBegin;
      end = normalizedEnd;
      strides = normalizedStrides;
      const shrinkAxes = tfjsCore.backend_util.slice_util.maskToAxes(shrinkAxisMask);
      // Adjust the ends based on the shrink mask.
      shrinkAxes.forEach(axis => {
          end[axis] = begin[axis] + 1;
          strides[axis] = 1;
      });
      // Figure out the output shape.
      const size = tfjsCore.backend_util.slice_util.computeOutShape(begin, end, strides);
      // Remove the axes based on shrinkMask.
      const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);
      const nonStrided = strides.every(v => v === 1);
      if (nonStrided) {
          const xSliced = slice({ inputs: { x }, attrs: { begin, size }, backend });
          return reshape({ inputs: { x: xSliced }, attrs: { shape: outShape }, backend });
      }
      const out = backend.makeOutput(outShape, 'float32');
      if (!outShape.some(axis => axis === 0)) {
          const xId = backend.dataIdMap.get(xReshaped.dataId).id;
          const xStridesBytes = new Uint8Array(new Int32Array(tfjsCore.util.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(tfjsCore.util.computeStrides(outShape)).buffer);
          const outId = backend.dataIdMap.get(out.dataId).id;
          wasmStridedSlice(xId, xStridesBytes, xReshaped.shape.length, beginBytes, endBytes, stridesBytes, outputShapeBytes, outStridesBytes, outShape.length, outId);
      }
      return reshape({ inputs: { x: out }, attrs: { shape: outShape }, backend });
  }
  const stridedSliceConfig = {
      kernelName: tfjsCore.StridedSlice,
      backendName: 'wasm',
      setupFunc: setup$y,
      kernelFunc: stridedSlice
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const supportsFullBroadcast$f = true;
  const subConfig = createBinaryKernelConfig(tfjsCore.Sub, supportsFullBroadcast$f);

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  let wasmSum;
  function setup$z(backend) {
      wasmSum = backend.wasm.cwrap(tfjsCore.Sum, null /*void*/, ['number, number, number']);
  }
  function sum(args) {
      const { backend, inputs, attrs } = args;
      const { axis, keepDims } = attrs;
      const { x } = inputs;
      const xId = backend.dataIdMap.get(x.dataId).id;
      let inputId = xId;
      let input = x;
      const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend);
      let reductionAxes = axes;
      if (inputWasTransposed) {
          const transposedId = backend.dataIdMap.get(transposed.dataId).id;
          if (transposedId !== xId) {
              // transpose was not a no-op. We will need to dispose of this
              // once we are done.
              input = transposed;
              inputId = transposedId;
              reductionAxes = tfjsCore.backend_util.getInnerMostAxes(reductionAxes.length, input.shape.length);
          }
      }
      tfjsCore.backend_util.assertAxesAreInnerMostDims('sum', reductionAxes, input.shape.length);
      const [outShape, reduceShape] = tfjsCore.backend_util.computeOutAndReduceShapes(input.shape, reductionAxes);
      const reduceSize = tfjsCore.util.sizeFromShape(reduceShape);
      const out = backend.makeOutput(outShape, input.dtype);
      if (tfjsCore.util.sizeFromShape(input.shape) !== 0) {
          const outId = backend.dataIdMap.get(out.dataId).id;
          wasmSum(inputId, reduceSize, outId);
      }
      if (inputWasTransposed) {
          // dispose of the transposed tensor.
          backend.disposeData(transposed.dataId);
      }
      if (keepDims) {
          // reshape
          const newShape = tfjsCore.backend_util.expandShapeToKeepDim(out.shape, originalAxes);
          out.shape = newShape;
      }
      return out;
  }
  const sumConfig = {
      kernelName: tfjsCore.Sum,
      backendName: 'wasm',
      setupFunc: setup$z,
      kernelFunc: sum
  };

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const tanhConfig = createUnaryKernelConfig(tfjsCore.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.
   * =============================================================================
   */
  let wasmTile;
  function setup$A(backend) {
      wasmTile = backend.wasm.cwrap(tfjsCore.Tile, null /* void */, [
          'number',
          'array',
          'number',
          'array',
          'number',
          'number' // out_id
      ]);
  }
  function tile(args) {
      const { inputs, backend, attrs } = args;
      const { x } = inputs;
      const xId = backend.dataIdMap.get(x.dataId).id;
      const { reps } = attrs;
      const newShape = new Array(x.shape.length);
      for (let i = 0; i < newShape.length; i++) {
          newShape[i] = x.shape[i] * reps[i];
      }
      const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
      const newShapeBytes = new Uint8Array(new Int32Array(newShape).buffer);
      const out = backend.makeOutput(newShape, x.dtype);
      const outId = backend.dataIdMap.get(out.dataId).id;
      wasmTile(xId, xShapeBytes, x.shape.length, newShapeBytes, newShape.length, CppDType[out.dtype], outId);
      return out;
  }
  const tileConfig = {
      kernelName: tfjsCore.Tile,
      backendName: 'wasm',
      setupFunc: setup$A,
      kernelFunc: tile
  };

  /**
   * @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 unpack(args) {
      const { inputs, backend, attrs } = args;
      const { value } = inputs;
      const { axis } = attrs;
      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] = slice({ inputs: { x: value }, attrs: { begin, size }, backend });
      }
      return outs.map(({ dataId, dtype }) => ({ dataId, dtype, shape: outShape }));
  }
  const unpackConfig = {
      kernelName: tfjsCore.Unpack,
      backendName: 'wasm',
      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 zerosLike(args) {
      const { inputs: { x }, backend } = args;
      const out = backend.makeOutput(x.shape, x.dtype);
      const outVals = backend.typedArrayFromHeap(out);
      outVals.fill(0);
      return out;
  }
  const zerosLikeConfig = {
      kernelName: tfjsCore.ZerosLike,
      backendName: 'wasm',
      kernelFunc: 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.
   * =============================================================================
   */
  // List all kernel configs here
  const kernelConfigs = [
      absConfig,
      addConfig,
      addNConfig,
      argMaxConfig,
      avgPoolConfig,
      batchMatMulConfig,
      castConfig,
      clipByValueConfig,
      concatConfig,
      conv2DConfig,
      conv2DBackpropInputConfig,
      cosConfig,
      cropAndResizeConfig,
      cumsumConfig,
      depthToSpaceConfig,
      depthwiseConv2dNativeConfig,
      divConfig,
      equalConfig,
      expConfig,
      fillConfig,
      flipLeftRightConfig,
      floorDivConfig,
      fusedMatMulConfig,
      fusedBatchNormConfig,
      fusedConv2DConfig,
      fusedDepthwiseConv2DConfig,
      gatherNdConfig,
      gatherV2Config,
      greaterConfig,
      greaterEqualConfig,
      identityConfig,
      lessConfig,
      lessEqualConfig,
      logConfig,
      logicalAndConfig,
      maxConfig,
      maximumConfig,
      maxPoolConfig,
      minConfig,
      minimumConfig,
      multiplyConfig,
      negateConfig,
      nonMaxSuppressionV3Config,
      nonMaxSuppressionV4Config,
      nonMaxSuppressionV5Config,
      notEqualConfig,
      oneHotConfig,
      onesLikeConfig,
      padV2Config,
      powConfig,
      preluConfig,
      reluConfig,
      relu6Config,
      reshapeConfig,
      resizeBilinearConfig,
      reverseConfig,
      rotateWithOffsetConfig,
      rsqrtConfig,
      scatterNdConfig,
      selectV2Config,
      sigmoidConfig,
      sinConfig,
      sliceConfig,
      softmaxConfig,
      splitVConfig,
      sqrtConfig,
      squareConfig,
      squaredDifferenceConfig,
      stridedSliceConfig,
      subConfig,
      sumConfig,
      tanhConfig,
      tileConfig,
      transposeConfig,
      unpackConfig,
      zerosLikeConfig
  ];
  for (const kernelConfig of kernelConfigs) {
      tfjsCore.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.
   * =============================================================================
   */
  const ENV = tfjsCore.env();
  /**
   * True if SIMD is supported.
   */
  // From: https://github.com/GoogleChromeLabs/wasm-feature-detect
  ENV.registerFlag(
  // This typed array passed in to WebAssembly.validate is WebAssembly binary
  // code. In this case it is a small program that contains SIMD
  // instructions.
  '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
  ])));
  /**
   * True if threads are supported.
   */
  // From: https://github.com/GoogleChromeLabs/wasm-feature-detect
  ENV.registerFlag('WASM_HAS_MULTITHREAD_SUPPORT', async () => {
      // TODO(annxingyuan): Enable node support once this is resolved:
      // https://github.com/tensorflow/tfjs/issues/3830
      if (ENV.get('IS_NODE')) {
          return false;
      }
      try {
          // Test for transferability of SABs (needed for Firefox)
          // https://groups.google.com/forum/#!msg/mozilla.dev.platform/IHkBZlHETpA/dwsMNchWEQAJ
          new MessageChannel().port1.postMessage(new SharedArrayBuffer(1));
          // This typed array is a WebAssembly program containing threaded
          // instructions.
          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;
      }
  });

  function createCommonjsModule(fn, module) {
  	return module = { exports: {} }, fn(module, module.exports), module.exports;
  }

  var tfjsBackendWasmThreadedSimd = createCommonjsModule(function (module, exports) {
  var WasmBackendModuleThreadedSimd = (function() {
    var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;
    if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;
    return (
  function(WasmBackendModuleThreadedSimd) {
    WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};

  function GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer);}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer);}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer);}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer);}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer);}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!=="undefined"?WasmBackendModuleThreadedSimd:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key];}}var arguments_=[];var thisProgram="./this.program";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window==="object";ENVIRONMENT_IS_WORKER=typeof importScripts==="function";ENVIRONMENT_IS_NODE=typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module["ENVIRONMENT_IS_PTHREAD"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module["buffer"];DYNAMIC_BASE=Module["DYNAMIC_BASE"];DYNAMICTOP_PTR=Module["DYNAMICTOP_PTR"];}var scriptDirectory="";function locateFile(path){if(Module["locateFile"]){return Module["locateFile"](path,scriptDirectory)}return scriptDirectory+path}var read_,readBinary;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=path.dirname(scriptDirectory)+"/";}else{scriptDirectory=__dirname+"/";}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=fs;if(!nodePath)nodePath=path;filename=nodePath["normalize"](filename);return nodeFS["readFileSync"](filename,binary?null:"utf8")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret);}assert(ret.buffer);return ret};if(process["argv"].length>1){thisProgram=process["argv"][1].replace(/\\/g,"/");}arguments_=process["argv"].slice(2);process["on"]("uncaughtException",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process["on"]("unhandledRejection",abort);quit_=function(status){process["exit"](status);};Module["inspect"]=function(){return "[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=worker_threads;}catch(e){console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker;}else if(ENVIRONMENT_IS_SHELL){if(typeof read!="undefined"){read_=function shell_read(f){return read(f)};}readBinary=function readBinary(f){var data;if(typeof readbuffer==="function"){return new Uint8Array(readbuffer(f))}data=read(f,"binary");assert(typeof data==="object");return data};if(typeof scriptArgs!="undefined"){arguments_=scriptArgs;}else if(typeof arguments!="undefined"){arguments_=arguments;}if(typeof quit==="function"){quit_=function(status){quit(status);};}if(typeof print!=="undefined"){if(typeof console==="undefined")console={};console.log=print;console.warn=console.error=typeof printErr!=="undefined"?printErr:print;}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href;}else if(document.currentScript){scriptDirectory=document.currentScript.src;}if(_scriptDir){scriptDirectory=_scriptDir;}if(scriptDirectory.indexOf("blob:")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1);}else{scriptDirectory="";}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=fs;if(!nodePath)nodePath=path;filename=nodePath["normalize"](filename);return nodeFS["readFileSync"](filename,binary?null:"utf8")};readBinary=function readBinary(filename){var ret=read_(filena


    return WasmBackendModuleThreadedSimd
  }
  );
  })();
  module.exports = WasmBackendModuleThreadedSimd;
  });

  const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}';

  var tfjsBackendWasm = createCommonjsModule(function (module, exports) {
  var WasmBackendModule = (function() {
    var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;
    if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;
    return (
  function(WasmBackendModule) {
    WasmBackendModule = WasmBackendModule || {};

  var Module=typeof WasmBackendModule!=="undefined"?WasmBackendModule:{};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_,readBinary;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=path.dirname(scriptDirectory)+"/";}else{scriptDirectory=__dirname+"/";}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=fs;if(!nodePath)nodePath=path;filename=nodePath["normalize"](filename);return nodeFS["readFileSync"](filename,binary?null:"utf8")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret);}assert(ret.buffer);return ret};if(process["argv"].length>1){thisProgram=process["argv"][1].replace(/\\/g,"/");}arguments_=process["argv"].slice(2);process["on"]("uncaughtException",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process["on"]("unhandledRejection",abort);quit_=function(status){process["exit"](status);};Module["inspect"]=function(){return "[Emscripten Module object]"};}else if(ENVIRONMENT_IS_SHELL){if(typeof read!="undefined"){read_=function shell_read(f){return read(f)};}readBinary=function readBinary(f){var data;if(typeof readbuffer==="function"){return new Uint8Array(readbuffer(f))}data=read(f,"binary");assert(typeof data==="object");return data};if(typeof scriptArgs!="undefined"){arguments_=scriptArgs;}else if(typeof arguments!="undefined"){arguments_=arguments;}if(typeof quit==="function"){quit_=function(status){quit(status);};}if(typeof print!=="undefined"){if(typeof console==="undefined")console={};console.log=print;console.warn=console.error=typeof printErr!=="undefined"?printErr:print;}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href;}else if(document.currentScript){scriptDirectory=document.currentScript.src;}if(_scriptDir){scriptDirectory=_scriptDir;}if(scriptDirectory.indexOf("blob:")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1);}else{scriptDirectory="";}{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open("GET",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open("GET",url,false);xhr.responseType="arraybuffer";xhr.send(null);return new Uint8Array(xhr.response)};}}}var out=Module["print"]||console.log.bind(console);var err=Module["printErr"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key];}}moduleOverrides=null;if(Module["arguments"])arguments_=Module["arguments"];if(Module["thisProgram"])thisProgram=Module["thisProgram"];if(Module["quit"])quit_=Module["quit"];var wasmBinary;if(Module["wasmBinary"])wasmBinary=Module["wasmBinary"];var noExitRuntime;if(Module["noExitRuntime"])noExitRuntime=Module["noExitRuntime"];if(typeof WebAssembly!=="object"){err("no native wasm support detected");}var wasmMemory;var wasmTable=new WebAssembly.Table({"initial":147,"maximum":147+0,"element":"anyfunc"});var ABORT=false;function assert(condition,text){if(!condition){abort("Assertion failed: "+text);}}function getCFunc(ident){var func=Module["_"+ident];assert(func,"Cannot call unknown function "+ident+", make sure it is exported");return func}function ccall(ident,returnType,argTypes,ar


    return WasmBackendModule
  }
  );
  })();
  module.exports = WasmBackendModule;
  });

  /**
   * @license
   * Copyright 2019 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   * =============================================================================
   */
  const WASM_PRIORITY = 2;
  class BackendWasm extends tfjsCore.KernelBackend {
      constructor(wasm) {
          super();
          this.wasm = wasm;
          // 0 is reserved for null data ids.
          this.dataIdNextNumber = 1;
          this.wasm.tfjs.init();
          this.dataIdMap = new tfjsCore.DataStorage(this, tfjsCore.engine());
      }
      write(values, shape, dtype) {
          const dataId = {};
          this.move(dataId, values, shape, dtype);
          return dataId;
      }
      numDataIds() {
          return this.dataIdMap.numDataIds();
      }
      async time(f) {
          const start = tfjsCore.util.now();
          f();
          const kernelMs = tfjsCore.util.now() - start;
          return { kernelMs };
      }
      move(dataId, values, shape, dtype) {
          const id = this.dataIdNextNumber++;
          if (dtype === 'string') {
              const stringBytes = values;
              this.dataIdMap.set(dataId, { id, stringBytes, shape, dtype, memoryOffset: null });
              return;
          }
          const size = tfjsCore.util.sizeFromShape(shape);
          const numBytes = size * tfjsCore.util.bytesPerElement(dtype);
          const memoryOffset = this.wasm._malloc(numBytes);
          this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype });
          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 + tfjsCore.util.sizeFromShape(shape) * tfjsCore.util.bytesPerElement(dtype));
          return typedArrayFromBuffer(bytes.buffer, dtype);
      }
      disposeData(dataId) {
          const data = this.dataIdMap.get(dataId);
          this.wasm._free(data.memoryOffset);
          this.wasm.tfjs.disposeData(data.id);
          this.dataIdMap.delete(dataId);
      }
      floatPrecision() {
          return 32;
      }
      // Returns the memory offset of a tensor. Useful for debugging and unit
      // testing.
      getMemoryOffset(dataId) {
          return this.dataIdMap.get(dataId).memoryOffset;
      }
      dispose() {
          this.wasm.tfjs.dispose();
          this.wasm = null;
      }
      memory() {
          return { unreliable: false };
      }
      /**
       * Make a tensor info for the output of an op. If `memoryOffset` is not
       * present, this method allocates memory on the WASM heap. If `memoryOffset`
       * is present, the memory was allocated elsewhere (in c++) and we just record
       * the pointer where that memory lives.
       */
      makeOutput(shape, dtype, memoryOffset) {
          let dataId;
          if (memoryOffset == null) {
              dataId = this.write(null /* values */, shape, dtype);
          }
          else {
              dataId = {};
              const id = this.dataIdNextNumber++;
              this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype });
              const size = tfjsCore.util.sizeFromShape(shape);
              this.wasm.tfjs.registerTensor(id, size, memoryOffset);
          }
          return { dataId, shape, dtype };
      }
      typedArrayFromHeap({ shape, dtype, dataId }) {
          const buffer = this.wasm.HEAPU8.buffer;
          const { memoryOffset } = this.dataIdMap.get(dataId);
          const size = tfjsCore.util.sizeFromShape(shape);
          switch (dtype) {
              case 'float32':
                  return new Float32Array(buffer, memoryOffset, size);
              case 'int32':
                  return new Int32Array(buffer, memoryOffset, size);
              case 'bool':
                  return new Uint8Array(buffer, memoryOffset, size);
              default:
                  throw new Error(`Unknown dtype ${dtype}`);
          }
      }
  }
  tfjsCore.registerBackend('wasm', async () => {
      const { wasm } = await init();
      return new BackendWasm(wasm);
  }, WASM_PRIORITY);
  function createInstantiateWasmFunc(path) {
      // tslint:disable-next-line:no-any
      return (imports, callback) => {
          tfjsCore.util.fetch(path, { credentials: 'same-origin' }).then((response) => {
              if (!response['ok']) {
                  imports.env.a(`failed to load wasm binary file at '${path}'`);
              }
              response.arrayBuffer().then(binary => {
                  WebAssembly.instantiate(binary, imports).then(output => {
                      callback(output.instance);
                  });
              });
          });
          return {};
      };
  }
  /**
   * Returns the path of the WASM binary.
   * @param simdSupported whether SIMD is supported
   * @param threadsSupported whether multithreading is supported
   * @param wasmModuleFolder the directory containing the WASM binaries.
   */
  function getPathToWasmBinary(simdSupported, threadsSupported, wasmModuleFolder) {
      if (wasmPath != null) {
          // If wasmPath is defined, the user has supplied a full path to
          // the vanilla .wasm binary.
          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;
  }
  /**
   * Initializes the wasm module and creates the js <--> wasm bridge.
   *
   * NOTE: We wrap the wasm module in a object with property 'wasm' instead of
   * returning Promise<BackendWasmModule> to avoid freezing Chrome (last tested
   * in Chrome 76).
   */
  async function init() {
      const [simdSupported, threadsSupported] = await Promise.all([
          tfjsCore.env().getAsync('WASM_HAS_SIMD_SUPPORT'),
          tfjsCore.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT')
      ]);
      return new Promise((resolve, reject) => {
          const factoryConfig = {};
          /**
           * This function overrides the Emscripten module locateFile utility.
           * @param path The relative path to the file that needs to be loaded.
           * @param prefix The path to the main JavaScript file's directory.
           */
          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;
          };
          // Use the instantiateWasm override when system fetch is not available.
          // Reference:
          // https://github.com/emscripten-core/emscripten/blob/2bca083cbbd5a4133db61fbd74d04f7feecfa907/tests/manual_wasm_instantiate.html#L170
          if (customFetch) {
              factoryConfig.instantiateWasm =
                  createInstantiateWasmFunc(getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : ''));
          }
          let wasm;
          // If `wasmPath` has been defined we must initialize the vanilla module.
          if (threadsSupported && simdSupported && wasmPath == null) {
              wasm = tfjsBackendWasmThreadedSimd(factoryConfig);
              wasm.mainScriptUrlOrBlob = new Blob([`var _scriptDir = undefined; var WasmBackendModuleThreadedSimd = ` +
                      tfjsBackendWasmThreadedSimd.toString()], { type: 'text/javascript' });
          }
          else {
              // The wasmFactory works for both vanilla and SIMD binaries.
              wasm = tfjsBackendWasm(factoryConfig);
          }
          const voidReturnType = null;
          // Using the tfjs namespace to avoid conflict with emscripten's API.
          wasm.tfjs = {
              init: wasm.cwrap('init', null, []),
              registerTensor: wasm.cwrap('register_tensor', null, [
                  'number',
                  'number',
                  'number',
              ]),
              disposeData: wasm.cwrap('dispose_data', voidReturnType, ['number']),
              dispose: wasm.cwrap('dispose', voidReturnType, []),
          };
          let initialized = false;
          wasm.onRuntimeInitialized = () => {
              initialized = true;
              initAborted = false;
              resolve({ wasm });
          };
          wasm.onAbort = () => {
              if (initialized) {
                  // Emscripten already called console.warn so no need to double log.
                  return;
              }
              if (initAborted) {
                  // Emscripten calls `onAbort` twice, resulting in double error
                  // messages.
                  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 });
          };
      });
  }
  function typedArrayFromBuffer(buffer, dtype) {
      switch (dtype) {
          case 'float32':
              return new Float32Array(buffer);
          case 'int32':
              return new Int32Array(buffer);
          case 'bool':
              return new Uint8Array(buffer);
          default:
              throw new Error(`Unknown dtype ${dtype}`);
      }
  }
  const wasmBinaryNames = [
      'tfjs-backend-wasm.wasm', 'tfjs-backend-wasm-simd.wasm',
      'tfjs-backend-wasm-threaded-simd.wasm'
  ];
  let wasmPath = null;
  let wasmPathPrefix = null;
  let wasmFileMap = {};
  let initAborted = false;
  let customFetch = false;
  /**
   * @deprecated Use `setWasmPaths` instead.
   * Sets the path to the `.wasm` file which will be fetched when the wasm
   * backend is initialized. See
   * https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers
   * for more details.
   * @param path wasm file path or url
   * @param usePlatformFetch optional boolean to use platform fetch to download
   *     the wasm file, default to false.
   *
   * @doc {heading: 'Environment', namespace: 'wasm'}
   */
  function setWasmPath(path, usePlatformFetch = false) {
      tfjsCore.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;
  }
  /**
   * Configures the locations of the WASM binaries.
   *
   * ```js
   * setWasmPaths({
   *  'tfjs-backend-wasm.wasm': 'renamed.wasm',
   *  'tfjs-backend-wasm-simd.wasm': 'renamed-simd.wasm',
   *  'tfjs-backend-wasm-threaded-simd.wasm': 'renamed-threaded-simd.wasm'
   * });
   * tf.setBackend('wasm');
   * ```
   *
   * @param prefixOrFileMap This can be either a string or object:
   *  - (string) The path to the directory where the WASM binaries are located.
   *     Note that this prefix will be used to load each binary (vanilla,
   *     SIMD-enabled, threading-enabled, etc.).
   *  - (object) Mapping from names of WASM binaries to custom
   *     full paths specifying the locations of those binaries. This is useful if
   *     your WASM binaries are not all located in the same directory, or if your
   *     WASM binaries have been renamed.
   * @param usePlatformFetch optional boolean to use platform fetch to download
   *     the wasm file, default to false.
   *
   * @doc {heading: 'Environment', namespace: 'wasm'}
   */
  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. */
  // This code is auto-generated, do not modify this file!
  const version = '2.7.0';

  exports.BackendWasm = BackendWasm;
  exports.setWasmPath = setWasmPath;
  exports.setWasmPaths = setWasmPaths;
  exports.version_wasm = version;

  Object.defineProperty(exports, '__esModule', { value: true });

})));
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