nodejs_kernel_backend.ts

/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */

import * as tf from '@tensorflow/tfjs';
import {backend_util, BackendTimingInfo, DataId, DataType, fill, KernelBackend, ones, Rank, rsqrt, Scalar, scalar, ShapeMap, Tensor, Tensor1D, tensor1d, Tensor2D, tensor2d, Tensor3D, Tensor4D, Tensor5D, TensorInfo, tidy, util} from '@tensorflow/tfjs';
import {isArray, isNullOrUndefined} from 'util';

import {Int64Scalar} from './int64_tensors';
import {TensorMetadata, TFEOpAttr, TFJSBinding} from './tfjs_binding';

type TensorData = {
  shape: number[],
  dtype: number,
  values: backend_util.BackendValues,
  id: number
};

export class NodeJSKernelBackend extends KernelBackend {
  binding: TFJSBinding;
  isGPUPackage: boolean;
  isUsingGpuDevice: boolean;
  private tensorMap: tf.DataStorage<TensorData>;

  constructor(binding: TFJSBinding, packageName: string) {
    super();
    this.binding = binding;
    this.isGPUPackage = packageName === '@tensorflow/tfjs-node-gpu';
    this.isUsingGpuDevice = this.binding.isUsingGpuDevice();
    this.tensorMap = new tf.DataStorage<TensorData>(this, tf.engine());
  }

  private getDTypeInteger(dtype: DataType): number {
    switch (dtype) {
      case 'float32':
        return this.binding.TF_FLOAT;
      case 'int32':
        return this.binding.TF_INT32;
      case 'bool':
        return this.binding.TF_BOOL;
      case 'complex64':
        return this.binding.TF_COMPLEX64;
      case 'string':
        return this.binding.TF_STRING;
      default:
        throw new Error(`Unsupported DType: ${dtype}`);
    }
  }

  private typeAttributeFromTensor(value: Tensor): number {
    return this.getDTypeInteger(value.dtype);
  }

  // Creates a new Tensor and maps the dataId to the passed in ID.
  private createOutputTensor(metadata: TensorMetadata): Tensor {
    const newId = {};

    this.tensorMap.set(newId, {
      shape: metadata.shape,
      dtype: metadata.dtype,
      id: metadata.id,
      values: null
    });

    let dtype: DataType;
    switch (metadata.dtype) {
      case this.binding.TF_FLOAT:
        dtype = 'float32';
        break;
      case this.binding.TF_INT32:
        dtype = 'int32';
        break;
      case this.binding.TF_BOOL:
        dtype = 'bool';
        break;
      case this.binding.TF_COMPLEX64:
        dtype = 'complex64';
        break;
      case this.binding.TF_STRING:
        dtype = 'string';
        break;
      case this.binding.TF_RESOURCE:
        // NOTE(cais): We currently represent resource-type Tensors
        // as string of ubytes.
        dtype = 'string';
        break;
      case this.binding.TF_UINT8:
        // TensorFlow uses UINT8 as dtype for image tensor. UINT8 is not
        // supported in TFJS yet, cast it to int32.
        dtype = 'int32';
        break;
      default:
        throw new Error(`Unknown dtype enum ${metadata.dtype}`);
    }
    return tf.engine().makeTensorFromDataId(newId, metadata.shape, dtype);
  }

  // Prepares Tensor instances for Op execution.
  private getInputTensorIds(tensors: Array<TensorInfo|Int64Scalar>): number[] {
    const ids: number[] = [];
    for (let i = 0; i < tensors.length; i++) {
      if (tensors[i] instanceof Int64Scalar) {
        // Then `tensors[i]` is a Int64Scalar, which we currently represent
        // using an `Int32Array`.
        const value = (tensors[i] as Int64Scalar).valueArray;
        const id = this.binding.createTensor([], this.binding.TF_INT64, value);
        ids.push(id);
      } else {
        const info = this.tensorMap.get((tensors[i] as TensorInfo).dataId);
        // TODO - what about ID in this case? Handle in write()??
        if (info.values != null) {
          // Values were delayed to write into the TensorHandle. Do that before
          // Op execution and clear stored values.
          info.id =
              this.binding.createTensor(info.shape, info.dtype, info.values);
          info.values = null;
        }
        ids.push(info.id);
      }
    }
    return ids;
  }

  private createReductionOpAttrs(tensor: Tensor): TFEOpAttr[] {
    return [
      {name: 'keep_dims', type: this.binding.TF_ATTR_BOOL, value: false},
      createTensorsTypeOpAttr('T', tensor.dtype),
      createTensorsTypeOpAttr('Tidx', 'int32')
    ];
  }

  private executeSingleInput(name: string, input: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr('T', input.dtype)];
    return this.executeSingleOutput(name, opAttrs, [input]);
  }

  floatPrecision(): 16|32 {
    return 32;
  }

  epsilon(): number {
    return super.epsilon();
  }

  /**
   * Executes a TensorFlow Eager Op that provides one output Tensor.
   * @param name The name of the Op to execute.
   * @param opAttrs The list of Op attributes required to execute.
   * @param inputs The list of input Tensors for the Op.
   * @return A resulting Tensor from Op execution.
   */
  executeSingleOutput(name: string, opAttrs: TFEOpAttr[], inputs: TensorInfo[]):
      Tensor {
    const outputMetadata = this.binding.executeOp(
        name, opAttrs, this.getInputTensorIds(inputs), 1);
    return this.createOutputTensor(outputMetadata[0]);
  }

  /**
   * Executes a TensorFlow Eager Op that provides multiple output Tensors.
   * @param name The name of the Op to execute.
   * @param opAttrs The list of Op attributes required to execute.
   * @param inputs The list of input Tensors for the Op.
   * @param numOutputs The number of output Tensors for Op execution.
   * @return A resulting Tensor array from Op execution.
   */
  executeMultipleOutputs(
      name: string, opAttrs: TFEOpAttr[], inputs: Tensor[],
      numOutputs: number): Tensor[] {
    const outputMetadata = this.binding.executeOp(
        name, opAttrs, this.getInputTensorIds(inputs), numOutputs);
    return outputMetadata.map(m => this.createOutputTensor(m));
  }

  numDataIds(): number {
    return this.tensorMap.numDataIds();
  }

  dispose(): void {}

  async read(dataId: DataId): Promise<backend_util.BackendValues> {
    return this.readSync(dataId);
  }

  readSync(dataId: DataId): backend_util.BackendValues {
    if (!this.tensorMap.has(dataId)) {
      throw new Error(`Tensor ${dataId} was not registered!`);
    }
    const info = this.tensorMap.get(dataId);
    if (info.values != null) {
      return info.values;
    } else {
      return this.binding.tensorDataSync(info.id);
    }
  }

  disposeData(dataId: DataId): void {
    // No-op if already disposed.
    if (!this.tensorMap.has(dataId)) {
      return;
    }
    const id = this.tensorMap.get(dataId).id;
    if (id != null && id >= 0) {
      this.binding.deleteTensor(id);
    }
    this.tensorMap.delete(dataId);
  }

  move(
      dataId: DataId, values: backend_util.BackendValues, shape: number[],
      dtype: DataType): void {
    this.tensorMap.set(
        dataId, {shape, dtype: getTFDType(dtype), values, id: -1});
  }

  write(values: backend_util.BackendValues, shape: number[], dtype: DataType):
      DataId {
    const dataId = {};
    this.move(dataId, values, shape, dtype);
    return dataId;
  }

  fill<R extends Rank>(
      shape: ShapeMap[R], value: number|string, dtype?: DataType): Tensor<R> {
    // TODO(cais, nkreeger): Investigate whether this can be made into
    // a dtype helper method. The underlying op kernel doesn't accept undefined
    // or null dtype.
    if (dtype == null) {
      if (typeof value === 'number') {
        dtype = 'float32';
      } else {
        dtype = 'string';
      }
    }
    const shapeTensor = tensor1d(shape, 'int32');
    const valueTensor = scalar(value, dtype);
    const opAttrs = [
      {
        name: 'T',
        type: this.binding.TF_ATTR_TYPE,
        value: this.getDTypeInteger(dtype)
      },
      {
        name: 'index_type',
        type: this.binding.TF_ATTR_TYPE,
        value: this.binding.TF_INT32
      }
    ];
    return this.executeSingleOutput(
               'Fill', opAttrs, [shapeTensor, valueTensor]) as Tensor<R>;
  }

  onesLike<R extends Rank>(x: Tensor<R>): Tensor<R> {
    const opAttrs = [{
      name: 'T',
      type: this.binding.TF_ATTR_TYPE,
      value: this.getDTypeInteger(x.dtype)
    }];
    return this.executeSingleOutput('OnesLike', opAttrs, [x]) as Tensor<R>;
  }

  zerosLike<R extends Rank>(x: Tensor<R>): Tensor<R> {
    const opAttrs = [{
      name: 'T',
      type: this.binding.TF_ATTR_TYPE,
      value: this.getDTypeInteger(x.dtype)
    }];
    return this.executeSingleOutput('ZerosLike', opAttrs, [x]) as Tensor<R>;
  }

  stridedSlice<T extends Tensor>(
      x: T, begin: number[], end: number[], strides: number[]): T {
    const beginTensor = tensor1d(begin, 'int32');
    for (let axis = 0; axis < end.length; axis++) {
      // Unlike Numpy, when the strides are negative, TF C uses -n-1 instead of
      // -1 as the "end" in order to include the first element.
      if (strides[axis] < 0 && end[axis] === -1) {
        end[axis] -= x.shape[axis];
      }
    }
    const endTensor = tensor1d(end, 'int32');
    const stridesTensor = tensor1d(strides, 'int32');
    // All of the masks have already been accounted for in the high level op,
    // so the backend does NOT need to deal with masks.
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      createTensorsTypeOpAttr('Index', 'int32'),
      {name: 'begin_mask', type: this.binding.TF_ATTR_INT, value: 0},
      {name: 'end_mask', type: this.binding.TF_ATTR_INT, value: 0},
      {name: 'ellipsis_mask', type: this.binding.TF_ATTR_INT, value: 0},
      {name: 'new_axis_mask', type: this.binding.TF_ATTR_INT, value: 0},
      {name: 'shrink_axis_mask', type: this.binding.TF_ATTR_INT, value: 0}
    ];
    return this.executeSingleOutput(
               'StridedSlice', opAttrs,
               [x, beginTensor, endTensor, stridesTensor]) as T;
  }

  unstack(x: Tensor, axis: number): Tensor[] {
    if (axis >= x.shape.length) {
      throw new Error(
          `Invalid axis supplied: ${axis} shape length: ${x.shape.length}`);
    }
    const num = x.shape[axis];
    const opAttrs = [
      {name: 'num', type: this.binding.TF_ATTR_INT, value: num},
      createTensorsTypeOpAttr('T', x.dtype),
      {name: 'axis', type: this.binding.TF_ATTR_INT, value: axis}
    ];
    return this.executeMultipleOutputs('Unpack', opAttrs, [x], num);
  }

  batchMatMul(
      a: Tensor<Rank.R3>, b: Tensor<Rank.R3>, transposeA: boolean,
      transposeB: boolean): Tensor<Rank.R3> {
    const opAttrs = [
      createTensorsTypeOpAttr('T', a.dtype),
      {name: 'adj_x', type: this.binding.TF_ATTR_BOOL, value: transposeA},
      {name: 'adj_y', type: this.binding.TF_ATTR_BOOL, value: transposeB}
    ];
    return this.executeSingleOutput('BatchMatMul', opAttrs, [a, b]) as
        Tensor<Rank.R3>;
  }

  private applyActivation<T extends Tensor>(
      input: T, activation: string, preluActivationWeights?: Tensor): T {
    let result = input;
    if (activation != null) {
      if (activation === 'linear') {
        // No-op
      } else if (activation === 'relu') {
        result = this.relu(result);
      } else if (activation === 'prelu') {
        result = this.prelu(result, preluActivationWeights) as T;
      } else if (activation === 'elu') {
        result = this.elu(result);
      } else if (activation === 'relu6') {
        result = this.relu6(result);
      } else {
        throw new Error(`Activation: ${
            activation} has not been implemented for the Node.js backend`);
      }
    }
    return result;
  }

  fusedConv2d(
      {input, filter, convInfo, bias, activation, preluActivationWeights}:
          backend_util.FusedConv2DConfig): Tensor4D {
    let result = this.conv2d(input, filter, convInfo);

    if (bias != null) {
      result = this.add(result, bias) as Tensor4D;
    }

    result = this.applyActivation(result, activation, preluActivationWeights);

    return result;
  }

  fusedBatchMatMul(
      {a, b, transposeA, transposeB, bias, activation, preluActivationWeights}:
          backend_util.FusedBatchMatMulConfig): Tensor3D {
    // Core TensorFlow does not have a fused BatchMatMul op. Combine calls to
    // achieve the same results:
    let result = this.batchMatMul(a, b, transposeA, transposeB);
    if (bias != null) {
      result = this.add(result, bias) as Tensor3D;
    }

    result = this.applyActivation(result, activation, preluActivationWeights);

    return result;
  }

  slice<T extends Tensor>(x: T, begin: number[], size: number[]): T {
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      createTensorsTypeOpAttr('Index', 'int32')
    ];

    // Bind tensor values
    const beginTensor = tensor1d(begin, 'int32');
    const sizeTensor = tensor1d(size, 'int32');

    return this.executeSingleOutput(
               'Slice', opAttrs, [x, beginTensor, sizeTensor]) as T;
  }

  reverse<T extends Tensor>(a: T, axis: number[]): T {
    const opAttrs = [
      createTensorsTypeOpAttr('Tidx', 'int32'),
      createTensorsTypeOpAttr('T', a.dtype)
    ];
    const axisTensor = tensor1d(axis, 'int32');
    return this.executeSingleOutput('ReverseV2', opAttrs, [a, axisTensor]) as T;
  }

  concat(tensors: Tensor[], axis: number): Tensor {
    const opAttrs = [
      {name: 'N', type: this.binding.TF_ATTR_INT, value: tensors.length}, {
        name: 'Tidx',
        type: this.binding.TF_ATTR_TYPE,
        value: this.binding.TF_INT32
      },
      createTensorsTypeOpAttr('T', tensors)
    ];

    const inputs = Array.from(tensors);
    inputs.push(scalar(axis, 'int32'));
    return this.executeSingleOutput('ConcatV2', opAttrs, inputs);
  }

  neg<T extends Tensor>(a: T): T {
    return this.executeSingleInput('Neg', a) as T;
  }

  diag(x: Tensor): Tensor {
    return this.executeSingleInput('Diag', x);
  }

  add(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('Add', opAttrs, [a, b]);
  }

  select(condition: Tensor, a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('Select', opAttrs, [condition, a, b]);
  }

  addN<T extends Tensor>(tensors: T[]): T {
    const opAttrs = [
      createTensorsTypeOpAttr('T', tensors[0].dtype),
      {name: 'N', type: this.binding.TF_ATTR_INT, value: tensors.length}
    ];
    return this.executeSingleOutput('AddN', opAttrs, tensors) as T;
  }

  subtract(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('Sub', opAttrs, [a, b]);
  }

  multiply(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('Mul', opAttrs, [a, b]);
  }

  realDivide(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('RealDiv', opAttrs, [a, b]);
  }

  floorDiv(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('FloorDiv', opAttrs, [a, b]);
  }

  divide(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('Div', opAttrs, [a, b]);
  }

  divNoNan(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('DivNoNan', opAttrs, [a, b]);
  }

  unsortedSegmentSum<T extends Tensor>(
      x: T, segmentIds: Tensor1D, numSegments: number): Tensor {
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      createTensorsTypeOpAttr('Tindices', 'int32'),
      createTensorsTypeOpAttr('Tnumsegments', 'int32')
    ];
    return this.executeSingleOutput(
        'UnsortedSegmentSum', opAttrs,
        [x, segmentIds, scalar(numSegments, 'int32')]);
  }

  sum(x: Tensor, axes: number[]): Tensor {
    const axisTensor = tensor1d(axes, 'int32');
    return this.executeSingleOutput(
        'Sum', this.createReductionOpAttrs(x), [x, axisTensor]);
  }

  prod(x: Tensor, axes: number[]): Tensor {
    const axesTensor = tensor1d(axes, 'int32');
    const opAttrs = [
      {name: 'keep_dims', type: this.binding.TF_ATTR_BOOL, value: false},
      createTensorsTypeOpAttr('T', x.dtype),
      createTensorsTypeOpAttr('Tidx', 'int32')
    ];
    return this.executeSingleOutput('Prod', opAttrs, [x, axesTensor]);
  }

  argMin(x: Tensor, axis: number): Tensor {
    const xInput = x.dtype === 'bool' ? x.toInt() : x;
    const axisScalar = scalar(axis, 'int32');
    const opAttrs = [
      createTensorsTypeOpAttr('T', xInput.dtype),
      createTensorsTypeOpAttr('Tidx', 'int32'),
      createTensorsTypeOpAttr('output_type', 'int32')
    ];
    return this.executeSingleOutput('ArgMin', opAttrs, [xInput, axisScalar]);
  }

  argMax(x: Tensor, axis: number): Tensor {
    const xInput = x.dtype === 'bool' ? x.toInt() : x;
    const axisScalar = scalar(axis, 'int32');
    const opAttrs = [
      createTensorsTypeOpAttr('T', xInput.dtype),
      createTensorsTypeOpAttr('Tidx', 'int32'),
      createTensorsTypeOpAttr('output_type', 'int32')
    ];
    return this.executeSingleOutput('ArgMax', opAttrs, [xInput, axisScalar]);
  }

  equal(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('Equal', opAttrs, [a, b]);
  }

  notEqual(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('NotEqual', opAttrs, [a, b]);
  }

  less(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('Less', opAttrs, [a, b]);
  }

  lessEqual(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('LessEqual', opAttrs, [a, b]);
  }

  greater(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('Greater', opAttrs, [a, b]);
  }

  greaterEqual(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('GreaterEqual', opAttrs, [a, b]);
  }

  logicalNot<T extends Tensor>(a: T): T {
    return this.executeSingleOutput('LogicalNot', [], [a]) as T;
  }

  logicalAnd(a: Tensor, b: Tensor): Tensor {
    return this.executeSingleOutput('LogicalAnd', [], [a, b]);
  }

  logicalOr(a: Tensor, b: Tensor): Tensor {
    return this.executeSingleOutput('LogicalOr', [], [a, b]);
  }

  where(condition: Tensor): Tensor2D {
    return this.executeSingleOutput('Where', [], [condition]) as Tensor2D;
  }

  topKValues<T extends Tensor>(x: T, k: number): Tensor1D {
    throw new Error('Method not implemented.');
  }

  topKIndices(x: Tensor, k: number): Tensor1D {
    throw new Error('Method not implemented.');
  }

  topk<T extends Tensor>(x: T, k?: number, sorted?: boolean): [T, T] {
    const kCount = isNullOrUndefined(k) ? 1 : k;
    const isSorted = isNullOrUndefined(sorted) ? true : sorted;
    const opAttrs = [
      {name: 'sorted', type: this.binding.TF_ATTR_BOOL, value: isSorted},
      createTensorsTypeOpAttr('T', x.dtype),
    ];
    const kTensor = scalar(kCount, 'int32');

    // 'TopKV2' has two-hard coded output attributes:
    return this.executeMultipleOutputs(
               'TopKV2', opAttrs, [x, kTensor], 2) as [T, T];
  }

  min(x: Tensor, axes: number[]): Tensor {
    const axesTensor = tensor1d(axes, 'int32');
    return this.executeSingleOutput(
        'Min', this.createReductionOpAttrs(x), [x, axesTensor]);
  }

  minimum(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('Minimum', opAttrs, [a, b]);
  }

  max(x: Tensor, axes: number[]): Tensor {
    const axesTensor = tensor1d(axes, 'int32');
    return this.executeSingleOutput(
        'Max', this.createReductionOpAttrs(x), [x, axesTensor]);
  }

  maximum(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr(
        'T', backend_util.upcastType(a.dtype, b.dtype))];
    return this.executeSingleOutput('Maximum', opAttrs, [a, b]);
  }

  all(x: Tensor, axes: number[]): Tensor {
    const opAttrs = [
      {name: 'keep_dims', type: this.binding.TF_ATTR_BOOL, value: false},
      createTensorsTypeOpAttr('Tidx', 'int32')
    ];
    const axesTensor = tensor1d(axes, 'int32');
    return this.executeSingleOutput('All', opAttrs, [x, axesTensor]);
  }

  any(x: Tensor, axes: number[]): Tensor {
    const opAttrs = [
      {name: 'keep_dims', type: this.binding.TF_ATTR_BOOL, value: false},
      createTensorsTypeOpAttr('Tidx', 'int32')
    ];
    const axesTensor = tensor1d(axes, 'int32');
    return this.executeSingleOutput('Any', opAttrs, [x, axesTensor]);
  }

  ceil<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Ceil', x) as T;
  }

  floor<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Floor', x) as T;
  }

  pow<T extends Tensor>(a: T, b: Tensor): T {
    const dtype = backend_util.upcastType(a.dtype, b.dtype);
    const opAttrs = [createTensorsTypeOpAttr('T', dtype)];
    return this.executeSingleOutput(
               'Pow', opAttrs, [a.cast(dtype), b.cast(dtype)]) as T;
  }

  exp<T extends Tensor>(x: T): T {
    const xTensor = x.dtype === 'int32' ? x.toFloat() : x;
    return this.executeSingleInput('Exp', xTensor) as T;
  }

  log<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Log', x) as T;
  }

  log1p<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Log1p', x) as T;
  }

  sqrt<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Sqrt', x) as T;
  }

  square<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Square', x) as T;
  }

  relu<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Relu', x) as T;
  }

  relu6<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Relu6', x) as T;
  }

  prelu<T extends Tensor>(x: T, a: T): T {
    const pos = this.relu(x);
    const neg = a.mul(x.sub(this.abs(x))).mul(0.5);
    return pos.add(neg);
  }

  elu<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Elu', x) as T;
  }

  eluDer<T extends Tensor>(dy: T, y: T): T {
    const opAttrs = [createTensorsTypeOpAttr('T', y.dtype)];
    return this.executeSingleOutput('EluGrad', opAttrs, [dy, y]) as T;
  }

  selu<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Selu', x) as T;
  }

  int<T extends Tensor>(x: T): T {
    throw new Error('Method not implemented.');
  }

  clip<T extends Tensor>(x: T, min: number, max: number): T {
    const xMin = this.minimum(x, scalar(max));
    return this.maximum(xMin, scalar(min)) as T;
  }

  abs<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Abs', x) as T;
  }

  complexAbs<T extends Tensor>(x: T): T {
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      createTensorsTypeOpAttr('Tout', 'float32')
    ];
    return this.executeSingleOutput('ComplexAbs', opAttrs, [x]) as T;
  }

  sigmoid<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Sigmoid', x) as T;
  }

  sin<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Sin', x) as T;
  }

  cos<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Cos', x) as T;
  }

  tan<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Tan', x) as T;
  }

  asin<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Asin', x) as T;
  }

  acos<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Acos', x) as T;
  }

  atan<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Atan', x) as T;
  }

  sinh<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Sinh', x) as T;
  }

  cosh<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Cosh', x) as T;
  }

  tanh<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Tanh', x) as T;
  }

  mod(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr('T', a.dtype)];
    return this.executeSingleOutput('FloorMod', opAttrs, [a, b]);
  }
  round<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Round', x) as T;
  }
  sign<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Sign', x) as T;
  }
  isNaN<T extends Tensor>(x: T): T {
    return this.executeSingleInput('IsNan', x) as T;
  }
  isInf<T extends Tensor>(x: T): T {
    return this.executeSingleInput('IsInf', x) as T;
  }
  isFinite<T extends Tensor>(x: T): T {
    return this.executeSingleInput('IsFinite', x) as T;
  }
  rsqrt<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Rsqrt', x) as T;
  }
  reciprocal<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Reciprocal', x) as T;
  }
  asinh<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Asinh', x) as T;
  }
  acosh<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Acosh', x) as T;
  }
  atanh<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Atanh', x) as T;
  }

  erf<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Erf', x) as T;
  }

  squaredDifference(a: Tensor, b: Tensor): Tensor {
    const opAttrs = [createTensorsTypeOpAttr('T', a.dtype)];
    return this.executeSingleOutput('SquaredDifference', opAttrs, [a, b]);
  }

  expm1<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Expm1', x) as T;
  }

  softplus<T extends Tensor>(x: T): T {
    return this.executeSingleInput('Softplus', x) as T;
  }

  atan2<T extends Tensor>(a: T, b: T): T {
    const opAttrs = [createTensorsTypeOpAttr('T', a.dtype)];
    return this.executeSingleOutput('Atan2', opAttrs, [a, b]) as T;
  }

  step<T extends Tensor>(x: T, alpha: number): T {
    const dtype = x.dtype;
    const nans = this.isNaN(x);
    const stepNoNans = this.select(
        this.greater(x, scalar(0, dtype)), ones(x.shape),
        fill(x.shape, alpha, dtype));
    return this.select(nans, x, stepNoNans) as T;
  }

  conv2d(x: Tensor4D, filter: Tensor4D, convInfo: backend_util.Conv2DInfo):
      Tensor4D {
    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME' &&
        convInfo.padInfo.type !== 'EXPLICIT') {
      throw new Error(
          `TF Backend supports only 'valid' and 'same' padding ` +
          `while padding was ${convInfo.padInfo.type}`);
    }
    const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
    const padding = convInfo.padInfo.type;
    const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
    const dilations = [1, convInfo.dilationHeight, convInfo.dilationWidth, 1];
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding},
      {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
      {name: 'use_cudnn_on_gpu', type: this.binding.TF_ATTR_BOOL, value: true},
      {name: 'dilations', type: this.binding.TF_ATTR_INT, value: dilations},
    ];
    if (padding === 'EXPLICIT') {
      const padValue = [
        convInfo.padInfo.top, convInfo.padInfo.bottom, convInfo.padInfo.left,
        convInfo.padInfo.right
      ];
      opAttrs.push({
        name: 'explicit_paddings',
        type: this.binding.TF_ATTR_INT,
        value: dataFormat === 'NHWC' ? [0, 0, ...padValue, 0, 0] :
                                       [0, 0, 0, 0, ...padValue]
      });
    }
    return this.executeSingleOutput('Conv2D', opAttrs, [x, filter]) as Tensor4D;
  }

  conv2dDerInput(
      dy: Tensor4D, filter: Tensor4D,
      convInfo: backend_util.Conv2DInfo): Tensor4D {
    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
      throw new Error(
          `TF Backend supports only 'valid' and 'same' padding ` +
          `while padding was ${convInfo.padInfo.type}`);
    }
    const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
    const padding = convInfo.padInfo.type;
    const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
    const dilations = [1, convInfo.dilationHeight, convInfo.dilationWidth, 1];
    const opAttrs = [
      createTensorsTypeOpAttr('T', 'float32'),
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding}, {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
      {name: 'use_cudnn_on_gpu', type: this.binding.TF_ATTR_BOOL, value: true},
      {name: 'dilations', type: this.binding.TF_ATTR_INT, value: dilations}
    ];
    const inputSizes = tensor1d(convInfo.inShape, 'int32');
    return this.executeSingleOutput(
               'Conv2DBackpropInput', opAttrs, [inputSizes, filter, dy]) as
        Tensor4D;
  }

  conv2dDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: backend_util.Conv2DInfo):
      Tensor4D {
    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
      throw new Error(
          `TF Backend supports only 'valid' and 'same' padding ` +
          `while padding was ${convInfo.padInfo.type}`);
    }
    const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
    const padding = convInfo.padInfo.type;
    const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
    const dilations = [1, convInfo.dilationHeight, convInfo.dilationWidth, 1];
    const opAttrs = [
      createTensorsTypeOpAttr('T', 'float32'),
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding}, {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
      {name: 'use_cudnn_on_gpu', type: this.binding.TF_ATTR_BOOL, value: true},
      {name: 'dilations', type: this.binding.TF_ATTR_INT, value: dilations}
    ];
    const filterSizes = tensor1d(convInfo.filterShape, 'int32');
    return this.executeSingleOutput(
               'Conv2DBackpropFilter', opAttrs, [x, filterSizes, dy]) as
        Tensor4D;
  }

  depthwiseConv2DDerInput(
      dy: Tensor4D, filter: Tensor4D,
      convInfo: backend_util.Conv2DInfo): Tensor4D {
    const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
    const padding = convInfo.padInfo.type;
    const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
    const dilations = [1, convInfo.dilationHeight, convInfo.dilationWidth, 1];
    const opAttrs = [
      createTensorsTypeOpAttr('T', 'float32'),
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding}, {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
      {name: 'dilations', type: this.binding.TF_ATTR_INT, value: dilations}
    ];

    const inputSizes = tensor1d(convInfo.inShape, 'int32');
    return this.executeSingleOutput(
               'DepthwiseConv2dNativeBackpropInput', opAttrs,
               [inputSizes, filter, dy]) as Tensor4D;
  }

  depthwiseConv2DDerFilter(
      x: Tensor4D, dY: Tensor4D, convInfo: backend_util.Conv2DInfo): Tensor4D {
    const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
    const padding = convInfo.padInfo.type;
    const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
    const dilations = [1, convInfo.dilationHeight, convInfo.dilationWidth, 1];
    const opAttrs = [
      createTensorsTypeOpAttr('T', 'float32'),
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding}, {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
      {name: 'dilations', type: this.binding.TF_ATTR_INT, value: dilations}
    ];
    const filterSizes = tensor1d(convInfo.filterShape, 'int32');
    return this.executeSingleOutput(
               'DepthwiseConv2dNativeBackpropFilter', opAttrs,
               [x, filterSizes, dY]) as Tensor4D;
  }

  fusedDepthwiseConv2D(
      {input, filter, convInfo, bias, activation, preluActivationWeights}:
          backend_util.FusedConv2DConfig): Tensor4D {
    let result = this.depthwiseConv2D(input, filter, convInfo);

    if (bias != null) {
      result = this.add(result, bias) as Tensor4D;
    }

    result = this.applyActivation(result, activation, preluActivationWeights);

    return result;
  }

  depthwiseConv2D(
      input: Tensor4D, filter: Tensor4D,
      convInfo: backend_util.Conv2DInfo): Tensor4D {
    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
      throw new Error(
          `TF Backend supports only 'valid' and 'same' padding ` +
          `while padding was ${convInfo.padInfo.type}`);
    }
    const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
    const padding = convInfo.padInfo.type;
    const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
    const dilations = [1, convInfo.dilationHeight, convInfo.dilationWidth, 1];
    const opAttrs = [
      createTensorsTypeOpAttr('T', input.dtype),
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding}, {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
      {name: 'dilations', type: this.binding.TF_ATTR_INT, value: dilations}
    ];
    return this.executeSingleOutput(
               'DepthwiseConv2dNative', opAttrs, [input, filter]) as Tensor4D;
  }

  conv3d(
      x: Tensor<Rank.R5>, filter: Tensor<Rank.R5>,
      convInfo: backend_util.Conv3DInfo): Tensor<Rank.R5> {
    const strides = [
      1, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, 1
    ];
    const padding = convInfo.padInfo.type;
    const dataFormat =
        convInfo.dataFormat === 'channelsLast' ? 'NDHWC' : 'NCDHW';

    if (!this.isGPUPackage && convInfo.dilationDepth > 1) {
      throw new Error('CPU Dilation depth must be 1');
    }
    const dilations = [
      1, convInfo.dilationDepth, convInfo.dilationHeight,
      convInfo.dilationWidth, 1
    ];
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding}, {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
      {name: 'dilations', type: this.binding.TF_ATTR_INT, value: dilations}
    ];
    return this.executeSingleOutput('Conv3D', opAttrs, [x, filter]) as Tensor5D;
  }

  conv3dDerInput(
      dy: Tensor<Rank.R5>, filter: Tensor<Rank.R5>,
      convInfo: backend_util.Conv3DInfo): Tensor<Rank.R5> {
    const strides = [
      1, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, 1
    ];
    const padding = convInfo.padInfo.type;
    const dataFormat =
        convInfo.dataFormat === 'channelsLast' ? 'NDHWC' : 'NCDHW';
    if (!this.isGPUPackage && convInfo.dilationDepth > 1) {
      throw new Error('CPU Dilation depth must be 1');
    }
    const dilations = [
      1, convInfo.dilationDepth, convInfo.dilationHeight,
      convInfo.dilationWidth, 1
    ];
    const opAttrs = [
      createTensorsTypeOpAttr('T', dy.dtype),
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding}, {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
      {name: 'dilations', type: this.binding.TF_ATTR_INT, value: dilations},
      createTensorsTypeOpAttr('Tshape', 'int32')
    ];
    const inputSizes = tensor1d(convInfo.inShape, 'int32');
    return this.executeSingleOutput(
               'Conv3DBackpropInputV2', opAttrs, [inputSizes, filter, dy]) as
        Tensor5D;
  }

  conv3dDerFilter(
      x: Tensor<Rank.R5>, dY: Tensor<Rank.R5>,
      convInfo: backend_util.Conv3DInfo): Tensor<Rank.R5> {
    const strides = [
      1, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, 1
    ];
    const padding = convInfo.padInfo.type;
    const dataFormat =
        convInfo.dataFormat === 'channelsLast' ? 'NDHWC' : 'NCDHW';

    if (!this.isGPUPackage && convInfo.dilationDepth > 1) {
      throw new Error('CPU Dilation depth must be 1');
    }
    const dilations = [
      1, convInfo.dilationDepth, convInfo.dilationHeight,
      convInfo.dilationWidth, 1
    ];
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding}, {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
      {name: 'dilations', type: this.binding.TF_ATTR_INT, value: dilations}
    ];
    const filterSizes = tensor1d(convInfo.filterShape, 'int32');
    return this.executeSingleOutput(
               'Conv3DBackpropFilterV2', opAttrs, [x, filterSizes, dY]) as
        Tensor5D;
  }

  maxPool(x: Tensor4D, convInfo: backend_util.Conv2DInfo): Tensor4D {
    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
      throw new Error(
          `TF Backend supports only 'valid' and 'same' padding ` +
          `while padding was ${convInfo.padInfo.type}`);
    }
    const ksize = [1, convInfo.filterHeight, convInfo.filterWidth, 1];
    const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
    const padding = convInfo.padInfo.type;
    const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      {name: 'ksize', type: this.binding.TF_ATTR_INT, value: ksize},
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding}, {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      }
    ];
    return this.executeSingleOutput('MaxPool', opAttrs, [x]) as Tensor4D;
  }

  maxPoolBackprop(
      dy: Tensor4D, x: Tensor4D, y: Tensor4D,
      convInfo: backend_util.Conv2DInfo): Tensor4D {
    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
      throw new Error(
          `TF Backend supports only 'valid' and 'same' padding ` +
          `while padding type was ${convInfo.padInfo.type}`);
    }
    const ksize = [1, convInfo.filterHeight, convInfo.filterWidth, 1];
    const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
    const padding = convInfo.padInfo.type;
    const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      {name: 'ksize', type: this.binding.TF_ATTR_INT, value: ksize},
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding},
      {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
    ];
    return this.executeSingleOutput('MaxPoolGrad', opAttrs, [x, y, dy]) as
        Tensor4D;
  }

  avgPool(x: Tensor4D, convInfo: backend_util.Conv2DInfo): Tensor4D {
    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
      throw new Error(
          `TF Backend supports only 'valid' and 'same' padding ` +
          `while padding was ${convInfo.padInfo.type}`);
    }
    const ksize = [1, convInfo.filterHeight, convInfo.filterWidth, 1];
    const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
    const padding = convInfo.padInfo.type;
    const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      {name: 'ksize', type: this.binding.TF_ATTR_INT, value: ksize},
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding},
      {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
    ];
    return this.executeSingleOutput('AvgPool', opAttrs, [x]) as Tensor4D;
  }

  avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: backend_util.Conv2DInfo):
      Tensor4D {
    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
      throw new Error(
          `TF Backend supports only 'valid' and 'same' padding ` +
          `while padding type was ${convInfo.padInfo.type}`);
    }
    const ksize = [1, convInfo.filterHeight, convInfo.filterWidth, 1];
    const strides = [1, convInfo.strideHeight, convInfo.strideWidth, 1];
    const padding = convInfo.padInfo.type;
    const dataFormat = convInfo.dataFormat === 'channelsLast' ? 'NHWC' : 'NCHW';
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      {name: 'ksize', type: this.binding.TF_ATTR_INT, value: ksize},
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding},
      {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
    ];
    const origInputShape = tensor1d(x.shape, 'int32');
    return this.executeSingleOutput(
               'AvgPoolGrad', opAttrs, [origInputShape, dy]) as Tensor4D;
  }

  avgPool3d(x: Tensor5D, convInfo: backend_util.Conv3DInfo): Tensor5D {
    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
      throw new Error(
          `TF Backend supports only 'valid' and 'same' padding ` +
          `while padding was ${convInfo.padInfo.type}`);
    }
    const ksize = [
      1, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, 1
    ];
    const strides = [
      1, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, 1
    ];
    const padding = convInfo.padInfo.type;
    const dataFormat =
        convInfo.dataFormat === 'channelsLast' ? 'NDHWC' : 'NCDHW';
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      {name: 'ksize', type: this.binding.TF_ATTR_INT, value: ksize},
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding},
      {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
    ];
    return this.executeSingleOutput('AvgPool3D', opAttrs, [x]) as Tensor5D;
  }

  avgPool3dBackprop(
      dy: Tensor5D, x: Tensor5D, convInfo: backend_util.Conv3DInfo): Tensor5D {
    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
      throw new Error(
          `TF Backend supports only 'valid' and 'same' padding ` +
          `while padding type was ${convInfo.padInfo.type}`);
    }
    const ksize = [
      1, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, 1
    ];
    const strides = [
      1, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, 1
    ];
    const padding = convInfo.padInfo.type;
    const dataFormat =
        convInfo.dataFormat === 'channelsLast' ? 'NDHWC' : 'NCDHW';
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      {name: 'ksize', type: this.binding.TF_ATTR_INT, value: ksize},
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding},
      {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
    ];
    const origInputShape = tensor1d(x.shape, 'int32');
    return this.executeSingleOutput(
               'AvgPool3DGrad', opAttrs, [origInputShape, dy]) as Tensor5D;
  }

  maxPool3d(x: Tensor5D, convInfo: backend_util.Conv3DInfo): Tensor5D {
    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
      throw new Error(
          `TF Backend supports only 'valid' and 'same' padding ` +
          `while padding was ${convInfo.padInfo.type}`);
    }
    const ksize = [
      1, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, 1
    ];
    const strides = [
      1, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, 1
    ];
    const padding = convInfo.padInfo.type;
    const dataFormat =
        convInfo.dataFormat === 'channelsLast' ? 'NDHWC' : 'NCDHW';
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      {name: 'ksize', type: this.binding.TF_ATTR_INT, value: ksize},
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding}, {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      }
    ];
    return this.executeSingleOutput('MaxPool3D', opAttrs, [x]) as Tensor5D;
  }

  maxPool3dBackprop(
      dy: Tensor5D, x: Tensor5D, y: Tensor5D,
      convInfo: backend_util.Conv3DInfo): Tensor5D {
    if (convInfo.padInfo.type !== 'VALID' && convInfo.padInfo.type !== 'SAME') {
      throw new Error(
          `TF Backend supports only 'valid' and 'same' padding ` +
          `while padding type was ${convInfo.padInfo.type}`);
    }
    const ksize = [
      1, convInfo.filterDepth, convInfo.filterHeight, convInfo.filterWidth, 1
    ];
    const strides = [
      1, convInfo.strideDepth, convInfo.strideHeight, convInfo.strideWidth, 1
    ];
    const padding = convInfo.padInfo.type;
    const dataFormat =
        convInfo.dataFormat === 'channelsLast' ? 'NDHWC' : 'NCDHW';
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      {name: 'ksize', type: this.binding.TF_ATTR_INT, value: ksize},
      {name: 'strides', type: this.binding.TF_ATTR_INT, value: strides},
      {name: 'padding', type: this.binding.TF_ATTR_STRING, value: padding},
      {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
    ];
    return this.executeSingleOutput('MaxPool3DGrad', opAttrs, [x, y, dy]) as
        Tensor5D;
  }

  reshape<T extends Tensor, R extends Rank>(x: T, shape: ShapeMap[R]):
      Tensor<R> {
    const shapeTensor = tensor1d(shape, 'int32');

    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      createTensorsTypeOpAttr('Tshape', shapeTensor.dtype)
    ];
    return this.executeSingleOutput('Reshape', opAttrs, [x, shapeTensor]) as
        Tensor<R>;
  }

  cast<T extends Tensor>(x: T, dtype: DataType): T {
    const opAttrs = [
      createTensorsTypeOpAttr('SrcT', x.dtype),
      createTensorsTypeOpAttr('DstT', dtype),
      {name: 'Truncate', type: this.binding.TF_ATTR_BOOL, value: false}
    ];
    return this.executeSingleOutput('Cast', opAttrs, [x]) as T;
  }

  tile<T extends Tensor>(x: T, reps: number[]): T {
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      createTensorsTypeOpAttr('Tmultiples', 'int32')
    ];
    const multiples = tensor1d(reps, 'int32');
    return this.executeSingleOutput('Tile', opAttrs, [x, multiples]) as T;
  }

  pad<T extends Tensor>(
      x: T, paddings: Array<[number, number]>, constantValue: number): T {
    // Bind tensor values
    const paddingsTensor = tensor2d(paddings, [paddings.length, 2], 'int32');
    const constantTensor = scalar(constantValue, x.dtype);

    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      createTensorsTypeOpAttr('Tpaddings', paddingsTensor.dtype)
    ];

    return this.executeSingleOutput(
               'PadV2', opAttrs, [x, paddingsTensor, constantTensor]) as T;
  }

  transpose<T extends Tensor>(x: T, perm: number[]): T {
    const permTensor = tensor1d(perm, 'int32');
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      createTensorsTypeOpAttr('Tperm', 'int32')
    ];
    return this.executeSingleOutput('Transpose', opAttrs, [x, permTensor]) as T;
  }

  gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T {
    const axisTensor = scalar(axis, 'int32');
    const opAttrs = [
      createTensorsTypeOpAttr('Tparams', x.dtype),
      createTensorsTypeOpAttr('Tindices', indices.dtype),
      createTensorsTypeOpAttr('Taxis', 'int32')
    ];
    return this.executeSingleOutput(
               'GatherV2', opAttrs, [x, indices, axisTensor]) as T;
  }

  gatherND(x: Tensor, indices: Tensor): Tensor {
    const opAttrs = [
      createTensorsTypeOpAttr('Tparams', x.dtype),
      createTensorsTypeOpAttr('Tindices', 'int32')
    ];
    return this.executeSingleOutput('GatherNd', opAttrs, [x, indices]);
  }

  scatterND<R extends Rank>(
      indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor<R> {
    const opAttrs = [
      createTensorsTypeOpAttr('T', updates.dtype),
      createTensorsTypeOpAttr('Tindices', 'int32')
    ];
    const shapeTensor = tensor1d(shape, 'int32');
    return this.executeSingleOutput(
               'ScatterNd', opAttrs, [indices, updates, shapeTensor]) as
        Tensor<R>;
  }

  batchToSpaceND<T extends Tensor>(
      x: T, blockShape: number[], crops: number[][]): T {
    const blockShapeTensor = tensor1d(blockShape, 'int32');
    const cropsTensor =
        tensor2d(crops, [crops.length, crops[0].length], 'int32');
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      createTensorsTypeOpAttr('Tblock_shape', 'int32'),
      createTensorsTypeOpAttr('Tcrops', cropsTensor.dtype)
    ];
    return this.executeSingleOutput(
               'BatchToSpaceND', opAttrs, [x, blockShapeTensor, cropsTensor]) as
        T;
  }

  spaceToBatchND<T extends Tensor>(
      x: T, blockShape: number[], paddings: number[][]): T {
    const blockShapeTensor = tensor1d(blockShape, 'int32');
    const paddingsTensor =
        tensor2d(paddings, [paddings.length, paddings[0].length], 'int32');
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      createTensorsTypeOpAttr('Tblock_shape', 'int32'),
      createTensorsTypeOpAttr('Tpaddings', paddingsTensor.dtype)
    ];
    return this.executeSingleOutput(
               'SpaceToBatchND', opAttrs,
               [x, blockShapeTensor, paddingsTensor]) as T;
  }

  resizeBilinear(
      x: Tensor4D, newHeight: number, newWidth: number,
      alignCorners: boolean): Tensor4D {
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      {
        name: 'align_corners',
        type: this.binding.TF_ATTR_BOOL,
        value: alignCorners
      },
    ];
    const size = tensor1d([newHeight, newWidth], 'int32');
    return this.executeSingleOutput('ResizeBilinear', opAttrs, [x, size]) as
        Tensor4D;
  }

  resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean):
      Tensor4D {
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype), {
        name: 'align_corners',
        type: this.binding.TF_ATTR_BOOL,
        value: alignCorners
      }
    ];
    return this.executeSingleOutput('ResizeBilinearGrad', opAttrs, [dy, x]) as
        Tensor4D;
  }

  resizeNearestNeighbor(
      x: Tensor4D, newHeight: number, newWidth: number,
      alignCorners: boolean): Tensor4D {
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      {
        name: 'align_corners',
        type: this.binding.TF_ATTR_BOOL,
        value: alignCorners
      },
    ];
    const size = tensor1d([newHeight, newWidth], 'int32');
    return this.executeSingleOutput(
               'ResizeNearestNeighbor', opAttrs, [x, size]) as Tensor4D;
  }

  resizeNearestNeighborBackprop(
      dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D {
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype), {
        name: 'align_corners',
        type: this.binding.TF_ATTR_BOOL,
        value: alignCorners
      }
    ];
    const [, origHeight, origWidth, ] = x.shape;
    const size = tensor1d([origHeight, origWidth], 'int32');
    return this.executeSingleOutput(
               'ResizeNearestNeighborGrad', opAttrs, [dy, size]) as Tensor4D;
  }

  batchNorm(
      x: Tensor4D, mean: Tensor4D|Tensor1D, variance: Tensor4D|Tensor1D,
      offset?: Tensor4D|Tensor1D, scale?: Tensor4D|Tensor1D,
      varianceEpsilon?: number): Tensor4D {
    if (mean.rank > 1) {
      // Fused batch norm doesn't work with high-dim mean/var/scale/offset.
      let inv = rsqrt(variance.add(scalar(varianceEpsilon)));
      if (scale != null) {
        inv = inv.mul(scale);
      }
      const xNorm: Tensor4D = x.sub(mean).mul(inv);
      return offset != null ? xNorm.add(offset) : xNorm;
    }
    const dataFormat = 'NHWC';
    const depth = x.shape[3];
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      {
        name: 'epsilon',
        type: this.binding.TF_ATTR_FLOAT,
        value: varianceEpsilon
      },
      {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      },
      {name: 'is_training', type: this.binding.TF_ATTR_BOOL, value: false},
    ];
    const numOutputs = 5;
    if (scale == null) {
      scale = fill<Rank.R1>([depth], 1);
    }
    if (offset == null) {
      offset = fill<Rank.R1>([depth], 0);
    }
    return this.executeMultipleOutputs(
               'FusedBatchNorm', opAttrs, [x, scale, offset, mean, variance],
               numOutputs)[0] as Tensor4D;
  }

  localResponseNormalization4D(
      x: Tensor4D, radius: number, bias: number, alpha: number,
      beta: number): Tensor4D {
    const opAttrs = [
      createTensorsTypeOpAttr('T', x.dtype),
      {name: 'depth_radius', type: this.binding.TF_ATTR_INT, value: radius},
      {name: 'bias', type: this.binding.TF_ATTR_FLOAT, value: bias},
      {name: 'alpha', type: this.binding.TF_ATTR_FLOAT, value: alpha},
      {name: 'beta', type: this.binding.TF_ATTR_FLOAT, value: beta},
    ];
    return this.executeSingleOutput('LRN', opAttrs, [x]) as Tensor4D;
  }

  LRNGrad(
      dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D, radius: number,
      bias: number, alpha: number, beta: number): Tensor4D {
    const opAttrs = [
      createTensorsTypeOpAttr('T', dy.dtype),
      {name: 'depth_radius', type: this.binding.TF_ATTR_INT, value: radius},
      {name: 'bias', type: this.binding.TF_ATTR_FLOAT, value: bias},
      {name: 'alpha', type: this.binding.TF_ATTR_FLOAT, value: alpha},
      {name: 'beta', type: this.binding.TF_ATTR_FLOAT, value: beta},
    ];
    return this.executeSingleOutput(
               'LRNGrad', opAttrs, [dy, inputImage, outputImage]) as Tensor4D;
  }

  multinomial(
      logits: Tensor2D, normalized: boolean, numSamples: number,
      seed: number): Tensor2D {
    if (normalized) {
      throw new Error(
          'TF Node backend does not support normalized logits ' +
          'passed to multinomial');
    }
    const opAttrs = [
      createTensorsTypeOpAttr('T', logits.dtype),
      createTensorsTypeOpAttr('output_dtype', 'int32'),
      {name: 'seed', type: this.binding.TF_ATTR_INT, value: seed},
      {name: 'seed2', type: this.binding.TF_ATTR_INT, value: seed * seed},
    ];
    return this.executeSingleOutput(
               'Multinomial', opAttrs, [logits, scalar(numSamples, 'int32')]) as
        Tensor2D;
  }

  oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number):
      Tensor2D {
    const depthTensor = scalar(depth, 'int32');
    const onValueTensor = scalar(onValue, 'int32');
    const offValueTensor = scalar(offValue, 'int32');

    const opAttrs = [
      {name: 'axis', type: this.binding.TF_ATTR_INT, value: -1},
      createTensorsTypeOpAttr('T', indices.dtype),
      createTensorsTypeOpAttr('TI', indices.dtype)
    ];

    return this.executeSingleOutput('OneHot', opAttrs, [
      indices, depthTensor, onValueTensor, offValueTensor
    ]) as Tensor2D;
  }

  cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean):
      Tensor {
    const axisTensor = scalar(axis, 'int32');
    const opAttrs = [
      {name: 'exclusive', type: this.binding.TF_ATTR_BOOL, value: exclusive},
      {name: 'reverse', type: this.binding.TF_ATTR_BOOL, value: reverse},
      createTensorsTypeOpAttr('T', x.dtype),
      createTensorsTypeOpAttr('Tidx', 'int32')
    ];
    return this.executeSingleOutput('Cumsum', opAttrs, [x, axisTensor]);
  }

  nonMaxSuppression(
      boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,
      iouThreshold?: number, scoreThreshold?: number): Tensor1D {
    const opAttrs = [createTensorsTypeOpAttr('T', boxes.dtype)];

    const maxOutputSizeTensor = scalar(maxOutputSize, 'int32');
    const iouThresholdTensor = scalar(iouThreshold);
    const scoreThresholdTensor = scalar(scoreThreshold);
    return this.executeSingleOutput('NonMaxSuppressionV3', opAttrs, [
      boxes, scores, maxOutputSizeTensor, iouThresholdTensor,
      scoreThresholdTensor
    ]) as Tensor1D;
  }

  fft(x: Tensor<Rank.R2>): Tensor<Rank.R2> {
    const opAttrs = [createTensorsTypeOpAttr('Tcomplex', x.dtype)];
    return this.executeSingleOutput('FFT', opAttrs, [x]) as Tensor<Rank.R2>;
  }

  ifft(x: Tensor2D): Tensor2D {
    const opAttrs = [createTensorsTypeOpAttr('Tcomplex', x.dtype)];
    return this.executeSingleOutput('IFFT', opAttrs, [x]) as Tensor2D;
  }

  complex<T extends Tensor>(real: T, imag: T): T {
    const opAttrs = [
      createTensorsTypeOpAttr('T', real),
      {
        name: 'Tout',
        type: this.binding.TF_ATTR_TYPE,
        value: this.binding.TF_COMPLEX64
      },
    ];
    const inputs = [real, imag];
    return this.executeSingleOutput('Complex', opAttrs, inputs) as T;
  }

  real<T extends Tensor>(input: T): T {
    const opAttrs = [
      createTensorsTypeOpAttr('T', input), {
        name: 'Tout',
        type: this.binding.TF_ATTR_TYPE,
        value: this.binding.TF_FLOAT
      }
    ];
    const inputs = [input];
    return this.executeSingleOutput('Real', opAttrs, inputs) as T;
  }

  imag<T extends Tensor>(input: T): T {
    const opAttrs = [
      {
        name: 'T',
        type: this.binding.TF_ATTR_TYPE,
        value: this.binding.TF_COMPLEX64
      },
      {
        name: 'Tout',
        type: this.binding.TF_ATTR_TYPE,
        value: this.binding.TF_FLOAT
      }
    ];
    const inputs = [input];
    return this.executeSingleOutput('Imag', opAttrs, inputs) as T;
  }

  cropAndResize(
      image: Tensor<Rank.R4>, boxes: Tensor<Rank.R2>, boxIndex: Tensor<Rank.R1>,
      cropSize: [number, number], method: 'bilinear'|'nearest',
      extrapolationValue: number): Tensor<Rank.R4> {
    const opAttrs = [
      createTensorsTypeOpAttr('T', image.dtype),
      {name: 'method', type: this.binding.TF_ATTR_STRING, value: method}, {
        name: 'extrapolation_value',
        type: this.binding.TF_ATTR_FLOAT,
        value: extrapolationValue
      }
    ];
    const cropSizeTensor = tensor1d(cropSize, 'int32');
    return this.executeSingleOutput(
               'CropAndResize', opAttrs,
               [image, boxes, boxIndex, cropSizeTensor]) as Tensor<Rank.R4>;
  }

  depthToSpace(x: Tensor<Rank.R4>, blockSize: number, dataFormat: string):
      Tensor<Rank.R4> {
    const opAttrs = [
      createTensorsTypeOpAttr('T', x), {
        name: 'block_size',
        type: this.binding.TF_ATTR_INT,
        value: blockSize < 2 ? 2 : blockSize
      },
      {
        name: 'data_format',
        type: this.binding.TF_ATTR_STRING,
        value: dataFormat
      }
    ];
    const inputs = [x];
    return this.executeSingleOutput('DepthToSpace', opAttrs, inputs) as
        Tensor<Rank.R4>;
  }

  split<T extends Tensor>(value: T, sizeSplits: number[], axis: number): T[] {
    const opAttrs = [
      {
        name: 'num_split',
        type: this.binding.TF_ATTR_INT,
        value: sizeSplits.length
      },
      createTensorsTypeOpAttr('T', value), {
        name: 'Tlen',
        type: this.binding.TF_ATTR_TYPE,
        value: this.binding.TF_INT32
      }
    ];
    const inputs = [value];
    inputs.push(tensor1d(sizeSplits, 'int32') as T);
    inputs.push(scalar(axis, 'int32') as T);
    return this.executeMultipleOutputs(
               'SplitV', opAttrs, inputs, sizeSplits.length) as T[];
  }

  sparseToDense<R extends Rank>(
      sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R],
      defaultValue: Tensor<Rank.R0>): Tensor<R> {
    const opAttrs = [
      {name: 'validate_indices', type: this.binding.TF_ATTR_BOOL, value: true},
      createTensorsTypeOpAttr('T', sparseValues.dtype),
      createTensorsTypeOpAttr('Tindices', sparseIndices.dtype)
    ];
    const outputShapeTensor = tensor1d(outputShape, 'int32');
    return this.executeSingleOutput('SparseToDense', opAttrs, [
      sparseIndices, outputShapeTensor, sparseValues, defaultValue
    ]) as Tensor<R>;
  }

  linspace(start: number, stop: number, num: number): Tensor1D {
    const opAttrs = [
      createTensorsTypeOpAttr('T', 'float32'),
      createTensorsTypeOpAttr('Tidx', 'int32')
    ];
    const inputs = [
      scalar(start, 'float32'), scalar(stop, 'float32'), scalar(num, 'int32')
    ];
    return this.executeSingleOutput('LinSpace', opAttrs, inputs) as Tensor1D;
  }

  decodeJpeg(
      contents: Uint8Array, channels: number, ratio: number,
      fancyUpscaling: boolean, tryRecoverTruncated: boolean,
      acceptableFraction: number, dctMethod: string): Tensor3D {
    const opAttrs = [
      {name: 'channels', type: this.binding.TF_ATTR_INT, value: channels},
      {name: 'ratio', type: this.binding.TF_ATTR_INT, value: ratio}, {
        name: 'fancy_upscaling',
        type: this.binding.TF_ATTR_BOOL,
        value: fancyUpscaling
      },
      {
        name: 'try_recover_truncated',
        type: this.binding.TF_ATTR_BOOL,
        value: tryRecoverTruncated
      },
      {
        name: 'acceptable_fraction',
        type: this.binding.TF_ATTR_FLOAT,
        value: acceptableFraction
      },
      {name: 'dct_method', type: this.binding.TF_ATTR_STRING, value: dctMethod}
    ];
    const inputArgs = [scalar(contents, 'string')];
    return this.executeSingleOutput('DecodeJpeg', opAttrs, inputArgs) as
        Tensor<Rank.R3>;
  }

  decodePng(contents: Uint8Array, channels: number): Tensor3D {
    const opAttrs =
        [{name: 'channels', type: this.binding.TF_ATTR_INT, value: channels}];
    const inputArgs = [scalar(contents, 'string')];

    return this.executeSingleOutput('DecodePng', opAttrs, inputArgs) as
        Tensor<Rank.R3>;
  }

  decodeBmp(contents: Uint8Array, channels: number): Tensor3D {
    const opAttrs =
        [{name: 'channels', type: this.binding.TF_ATTR_INT, value: channels}];
    const inputArgs = [scalar(contents, 'string')];
    return this.executeSingleOutput('DecodeBmp', opAttrs, inputArgs) as
        Tensor<Rank.R3>;
  }

  decodeGif(contents: Uint8Array): Tensor4D {
    const inputArgs = [scalar(contents, 'string')];
    return this.executeSingleOutput('DecodeGif', [], inputArgs) as
        Tensor<Rank.R4>;
  }

  executeEncodeImageOp(
      name: string, opAttrs: TFEOpAttr[], imageData: Uint8Array,
      imageShape: number[]): Tensor {
    const inputTensorId =
        this.binding.createTensor(imageShape, this.binding.TF_UINT8, imageData);
    const outputMetadata =
        this.binding.executeOp(name, opAttrs, [inputTensorId], 1);
    const outputTensorInfo = outputMetadata[0];
    // prevent the tensor data from being converted to a UTF8 string, since
    // the encoded data is not valid UTF8
    outputTensorInfo.dtype = this.binding.TF_UINT8;
    return this.createOutputTensor(outputTensorInfo);
  }

  encodeJpeg(
      imageData: Uint8Array, imageShape: number[], format: ''|'grayscale'|'rgb',
      quality: number, progressive: boolean, optimizeSize: boolean,
      chromaDownsampling: boolean, densityUnit: 'in'|'cm', xDensity: number,
      yDensity: number, xmpMetadata: string): Tensor {
    const opAttrs = [
      {name: 'format', type: this.binding.TF_ATTR_STRING, value: format},
      {name: 'quality', type: this.binding.TF_ATTR_INT, value: quality}, {
        name: 'progressive',
        type: this.binding.TF_ATTR_BOOL,
        value: progressive
      },
      {
        name: 'optimize_size',
        type: this.binding.TF_ATTR_BOOL,
        value: optimizeSize
      },
      {
        name: 'chroma_downsampling',
        type: this.binding.TF_ATTR_BOOL,
        value: chromaDownsampling
      },
      {
        name: 'density_unit',
        type: this.binding.TF_ATTR_STRING,
        value: densityUnit
      },
      {name: 'x_density', type: this.binding.TF_ATTR_INT, value: xDensity},
      {name: 'y_density', type: this.binding.TF_ATTR_INT, value: yDensity}, {
        name: 'xmp_metadata',
        type: this.binding.TF_ATTR_STRING,
        value: xmpMetadata
      }
    ];
    return this.executeEncodeImageOp(
        'EncodeJpeg', opAttrs, imageData, imageShape);
  }

  encodePng(imageData: Uint8Array, imageShape: number[], compression: number):
      Tensor {
    const opAttrs = [
      {name: 'compression', type: this.binding.TF_ATTR_INT, value: compression}
    ];
    return this.executeEncodeImageOp(
        'EncodePng', opAttrs, imageData, imageShape);
  }

  deleteSavedModel(id: number): void {
    this.binding.deleteSavedModel(id);
  }

  loadSavedModelMetaGraph(path: string, tags: string): number {
    return this.binding.loadSavedModel(path, tags);
  }

  runSavedModel(
      id: number, inputs: Tensor[], inputOpNames: string[],
      outputOpNames: string[]): Tensor[] {
    const outputMetadata = this.binding.runSavedModel(
        id, this.getInputTensorIds(inputs), inputOpNames.join(','),
        outputOpNames.join(','));
    return outputMetadata.map(m => this.createOutputTensor(m));
  }

  // ------------------------------------------------------------
  // TensorBoard-related (tfjs-node-specific) backend kernels.

  summaryWriter(logdir: string): Tensor1D {
    const opAttrs = [
      {
        name: 'shared_name',
        type: this.binding.TF_ATTR_STRING,
        value: `logdir:${logdir}`
      },
      {name: 'container', type: this.binding.TF_ATTR_STRING, value: ''}
    ];
    const writerResource =
        this.executeSingleOutput('SummaryWriter', opAttrs, []);
    return writerResource as Tensor1D;
  }

  createSummaryFileWriter(
      resourceHandle: Tensor, logdir: string, maxQueue?: number,
      flushMillis?: number, filenameSuffix?: string): void {
    const inputArgs = [
      resourceHandle, scalar(logdir),
      scalar(maxQueue == null ? 10 : maxQueue, 'int32'),
      scalar(flushMillis == null ? 2 * 60 * 1000 : flushMillis, 'int32'),
      scalar(filenameSuffix == null ? '.v2' : filenameSuffix)
    ];
    this.executeMultipleOutputs('CreateSummaryFileWriter', [], inputArgs, 0);
  }

  writeScalarSummary(
      resourceHandle: Tensor, step: number, name: string,
      value: Scalar|number): void {
    tidy(() => {
      util.assert(
          Number.isInteger(step),
          () => `step is expected to be an integer, but is instead ${step}`);
      const inputArgs: Array<Tensor|Int64Scalar> =
          [resourceHandle, new Int64Scalar(step), scalar(name, 'string')];

      let typeAttr: number;
      if (typeof value === 'number') {
        inputArgs.push(scalar(value));
        typeAttr = this.binding.TF_FLOAT;
      } else {
        // `value` is a Scalar.
        util.assert(
            value.rank === 0,
            () => `A non-scalar tensor (rank ${value.rank}) is passed to ` +
                `writeScalarSummary()`);
        inputArgs.push(value);
        typeAttr = this.typeAttributeFromTensor(value);
      }
      const opAttrs: TFEOpAttr[] =
          [{name: 'T', type: this.binding.TF_ATTR_TYPE, value: typeAttr}];

      this.binding.executeOp(
          'WriteScalarSummary', opAttrs, this.getInputTensorIds(inputArgs), 0);
    });
  }

  flushSummaryWriter(resourceHandle: Tensor): void {
    const inputArgs: Tensor[] = [resourceHandle];
    this.executeMultipleOutputs('FlushSummaryWriter', [], inputArgs, 0);
  }

  // ~ TensorBoard-related (tfjs-node-specific) backend kernels.
  // ------------------------------------------------------------

  memory() {
    // Due to automatic garbage collection, the numbers are unreliable.
    // TODO(kreeger): Since there is finalization in C, count the true
    // number of undisposed tensors.
    return {unreliable: true};
  }

  async time(f: () => void): Promise<BackendTimingInfo> {
    const start = process.hrtime();
    f();
    // hrtime() returns tuple of [seconds, nanoseconds], and we need to return
    // milliseconds.
    const elapsed = process.hrtime(start);
    return {kernelMs: elapsed[0] * 1000 + elapsed[1] / 1000000};
  }

  getNumOfSavedModels() {
    return this.binding.getNumOfSavedModels();
  }
}

/** Returns an instance of the Node.js backend. */
export function nodeBackend(): NodeJSKernelBackend {
  return tf.findBackend('tensorflow') as NodeJSKernelBackend;
}

/** Returns the TF dtype for a given DataType. */
export function getTFDType(dataType: tf.DataType): number {
  const binding = nodeBackend().binding;
  switch (dataType) {
    case 'float32':
      return binding.TF_FLOAT;
    case 'int32':
      return binding.TF_INT32;
    case 'bool':
      return binding.TF_BOOL;
    case 'complex64':
      return binding.TF_COMPLEX64;
    case 'string':
      return binding.TF_STRING;
    // tslint:disable-next-line:no-any
    case 'int64' as any:
      // int64 is not a generally supported dtype in TensorFlow.js
      // (tfjs-core). However, it needs to be included here for the purpose of
      // writing the `step` value to TensorBoard via WriteScalarSummary and
      // other op kernels.
      return binding.TF_INT64;
    default:
      const errorMessage = `Unknown dtype: ${dataType}`;
      throw new Error(errorMessage);
  }
}

/**
 * Creates a TFEOpAttr for a 'type' OpDef attribute from a Tensor or list of
 * Tensors.
 */
export function createTensorsTypeOpAttr(
    attrName: string,
    tensorsOrDtype: tf.Tensor|tf.Tensor[]|tf.DataType): TFEOpAttr {
  if (isNullOrUndefined(tensorsOrDtype)) {
    throw new Error('Invalid input tensors value.');
  }
  return {
    name: attrName,
    type: nodeBackend().binding.TF_ATTR_TYPE,
    value:
        (tensorsOrDtype instanceof tf.Tensor || Array.isArray(tensorsOrDtype)) ?
        getTFDTypeForInputs(tensorsOrDtype) :
        getTFDType(tensorsOrDtype)
  };
}

/** Returns the dtype number for a single or list of input Tensors. */
function getTFDTypeForInputs(tensors: tf.Tensor|tf.Tensor[]): number {
  if (isNullOrUndefined(tensors)) {
    throw new Error('Invalid input tensors value.');
  }
  if (isArray(tensors)) {
    for (let i = 0; i < tensors.length; i++) {
      return getTFDType(tensors[i].dtype);
    }
    return -1;
  } else {
    return getTFDType(tensors.dtype);
  }
}

export function ensureTensorflowBackend() {
  tf.util.assert(
      tf.getBackend() === 'tensorflow',
      () => `Expect the current backend to be "tensorflow", but got "${
          tf.getBackend()}"`);
}