text: /**
   * @license
   * Copyright 2020 Google LLC. All Rights Reserved.
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT 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 { ENGINE } from '../engine';
  import { AvgPoolGrad } from '../kernel_names';
  import { convertToTensor } from '../tensor_util_env';
  import * as util from '../util';
  import { op } from './operation';
  import { reshape } from './reshape';
  /**
   * Computes the backprop of an 2D avg pool.
   *
   * @param dy The dy error, of rank 4 or rank 3 of shape
   *     [batchSize, height, width, channels]. If rank 3, batch of 1 is
   * assumed.
   * @param input The input image, of rank 4 or rank 3 of shape
   *     [batchSize, height, width, channels]. If rank 3, batch of 1 is
   * assumed.
   * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
   *     `filterSize` is a single number, then `filterHeight == filterWidth`.
   * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
   *     `strides` is a single number, then `strideHeight == strideWidth`.
   * @param pad The type of padding algorithm used in the forward prop of the op.
   *     'same', 'valid', for more info, see this guide:
   *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
   *         https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
   */
  function avgPoolGrad_(dy, input, filterSize, strides, pad) {
      const $dy = convertToTensor(dy, 'dy', 'avgPoolGrad');
      const $input = convertToTensor(input, 'input', 'avgPoolGrad');
      util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
      let input4D = $input;
      let dy4D = $dy;
      let reshapedTo4D = false;
      if ($input.rank === 3) {
          reshapedTo4D = true;
          input4D =
              reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);
          dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);
      }
      util.assert(dy4D.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ` +
          `${dy4D.rank}.`);
      util.assert(input4D.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ` +
          `${input4D.rank}.`);
      const inputs = { dy: dy4D, input: input4D };
      const attrs = { filterSize, strides, pad };
      // tslint:disable-next-line: no-unnecessary-type-assertion
      const res = ENGINE.runKernel(AvgPoolGrad, inputs, attrs);
      if (reshapedTo4D) {
          return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
      }
      return res;
  }
  export const avgPoolGrad = op({ avgPoolGrad_ });
  //# sourceMappingURL=avg_pool_grad.js.map