text: # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Primitive Neural Net (NN) Operations.
  
  ## Notes on padding
  
  Several neural network operations, such as `tf.nn.conv2d` and
  `tf.nn.max_pool2d`, take a `padding` parameter, which controls how the input is
  padded before running the operation. The input is padded by inserting values
  (typically zeros) before and after the tensor in each spatial dimension. The
  `padding` parameter can either be the string `'VALID'`, which means use no
  padding, or `'SAME'` which adds padding according to a formula which is
  described below. Certain ops also allow the amount of padding per dimension to
  be explicitly specified by passing a list to `padding`.
  
  In the case of convolutions, the input is padded with zeros. In case of pools,
  the padded input values are ignored. For example, in a max pool, the sliding
  window ignores padded values, which is equivalent to the padded values being
  `-infinity`.
  
  ### `'VALID'` padding
  
  Passing `padding='VALID'` to an op causes no padding to be used. This causes the
  output size to typically be smaller than the input size, even when the stride is
  one. In the 2D case, the output size is computed as:
  
  ```
  out_height = ceil((in_height - filter_height + 1) / stride_height)
  out_width  = ceil((in_width - filter_width + 1) / stride_width)
  ```
  
  The 1D and 3D cases are similar. Note `filter_height` and `filter_width` refer
  to the filter size after dilations (if any) for convolutions, and refer to the
  window size for pools.
  
  ### `'SAME'` padding
  
  With `'SAME'` padding, padding is applied to each spatial dimension. When the
  strides are 1, the input is padded such that the output size is the same as the
  input size. In the 2D case, the output size is computed as:
  
  ```
  out_height = ceil(in_height / stride_height)
  out_width  = ceil(in_width / stride_width)
  ```
  
  The amount of padding used is the smallest amount that results in the output
  size. The formula for the total amount of padding per dimension is:
  
  ```
  if (in_height % strides[1] == 0):
    pad_along_height = max(filter_height - stride_height, 0)
  else:
    pad_along_height = max(filter_height - (in_height % stride_height), 0)
  if (in_width % strides[2] == 0):
    pad_along_width = max(filter_width - stride_width, 0)
  else:
    pad_along_width = max(filter_width - (in_width % stride_width), 0)
  ```
  
  Finally, the padding on the top, bottom, left and right are:
  
  ```
  pad_top = pad_along_height // 2
  pad_bottom = pad_along_height - pad_top
  pad_left = pad_along_width // 2
  pad_right = pad_along_width - pad_left
  ```
  
  Note that the division by 2 means that there might be cases when the padding on
  both sides (top vs bottom, right vs left) are off by one. In this case, the
  bottom and right sides always get the one additional padded pixel. For example,
  when pad_along_height is 5, we pad 2 pixels at the top and 3 pixels at the
  bottom. Note that this is different from existing libraries such as PyTorch and
  Caffe, which explicitly specify the number of padded pixels and always pad the
  same number of pixels on both sides.
  
  Here is an example of `'SAME'` padding:
  
  >>> in_height = 5
  >>> filter_height = 3
  >>> stride_height = 2
  >>>
  >>> in_width = 2
  >>> filter_width = 2
  >>> stride_width = 1
  >>>
  >>> inp = tf.ones((2, in_height, in_width, 2))
  >>> filter = tf.ones((filter_height, filter_width, 2, 2))
  >>> strides = [stride_height, stride_width]
  >>> output = tf.nn.conv2d(inp, filter, strides, padding='SAME')
  >>> output.shape[1]  # output_height: ceil(5 / 2)
  3
  >>> output.shape[2] # output_width: ceil(2 / 1)
  2
  
  ### Explicit padding
  
  Certain ops, like `tf.nn.conv2d`, also allow a list of explicit padding amounts
  to be passed to the `padding` parameter. This list is in the same format as what
  is passed to `tf.pad`, except the padding must be a nested list, not a tensor.
  For example, in the 2D case, the list is in the format `[[0, 0], [pad_top,
  pad_bottom], [pad_left, pad_right], [0, 0]]` when `data_format` is its default
  value of `'NHWC'`. The two `[0, 0]` pairs  indicate the batch and channel
  dimensions have no padding, which is required, as only spatial dimensions can
  have padding.
  
  For example:
  
  >>> inp = tf.ones((1, 3, 3, 1))
  >>> filter = tf.ones((2, 2, 1, 1))
  >>> strides = [1, 1]
  >>> padding = [[0, 0], [1, 2], [0, 1], [0, 0]]
  >>> output = tf.nn.conv2d(inp, filter, strides, padding=padding)
  >>> tuple(output.shape)
  (1, 5, 3, 1)
  >>> # Equivalently, tf.pad can be used, since convolutions pad with zeros.
  >>> inp = tf.pad(inp, padding)
  >>> # 'VALID' means to use no padding in conv2d (we already padded inp)
  >>> output2 = tf.nn.conv2d(inp, filter, strides, padding='VALID')
  >>> tf.debugging.assert_equal(output, output2)
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import functools
  import numbers
  import os
  
  import numpy as np
  
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import graph_util
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import gen_math_ops
  from tensorflow.python.ops import gen_nn_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import variables as variables_lib
  # go/tf-wildcard-import
  # pylint: disable=wildcard-import
  from tensorflow.python.ops.gen_nn_ops import *
  # pylint: enable=wildcard-import
  from tensorflow.python.platform import device_context
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.compat import collections_abc
  from tensorflow.python.util.deprecation import deprecated_args
  from tensorflow.python.util.deprecation import deprecated_argument_lookup
  
  from tensorflow.python.util.tf_export import tf_export
  
  # Aliases for some automatically-generated names.
  local_response_normalization = gen_nn_ops.lrn
  
  # pylint: disable=protected-access
  
  # Acceptable channels last formats (robust to H, W, D order).
  _CHANNELS_LAST_FORMATS = frozenset({
      "NWC", "NHC", "NHWC", "NWHC", "NDHWC", "NDWHC", "NHDWC", "NHWDC", "NWDHC",
      "NWHDC"
  })
  
  
  def _get_sequence(value, n, channel_index, name):
    """Formats a value input for gen_nn_ops."""
    # Performance is fast-pathed for common cases:
    # `None`, `list`, `tuple` and `int`.
    if value is None:
      return [1] * (n + 2)
  
    # Always convert `value` to a `list`.
    if isinstance(value, list):
      pass
    elif isinstance(value, tuple):
      value = list(value)
    elif isinstance(value, int):
      value = [value]
    elif not isinstance(value, collections_abc.Sized):
      value = [value]
    else:
      value = list(value)  # Try casting to a list.
  
    len_value = len(value)
  
    # Fully specified, including batch and channel dims.
    if len_value == n + 2:
      return value
  
    # Apply value to spatial dims only.
    if len_value == 1:
      value = value * n  # Broadcast to spatial dimensions.
    elif len_value != n:
      raise ValueError("{} should be of length 1, {} or {} but was {}".format(
          name, n, n + 2, len_value))
  
    # Add batch and channel dims (always 1).
    if channel_index == 1:
      return [1, 1] + value
    else:
      return [1] + value + [1]
  
  
  def _non_atrous_convolution(
      input,  # pylint: disable=redefined-builtin
      filter,  # pylint: disable=redefined-builtin
      padding,
      data_format=None,  # pylint: disable=redefined-builtin
      strides=None,
      name=None):
    """Computes sums of N-D convolutions (actually cross correlation).
  
    It is required that 1 <= N <= 3.
  
    This is used to implement the more generic `convolution` function, which
    extends the interface of this function with a `dilation_rate` parameter.
  
    Args:
  
      input: Rank N+2 tensor of type T of shape
        `[batch_size] + input_spatial_shape + [in_channels]` if `data_format`
        does not start with `"NC"`, or
        `[batch_size, in_channels] + input_spatial_shape` if `data_format` starts
        with `"NC"`.
      filter: Rank N+2 tensor of type T of shape
        `filter_spatial_shape + [in_channels, out_channels]`.  Rank of either
        `input` or `filter` must be known.
      padding: Padding method to use, must be either "VALID" or "SAME".
      data_format: A string or None.  Specifies whether the channel dimension of
        the `input` and output is the last dimension (default, or if `data_format`
        does not start with "NC"), or the second dimension (if `data_format`
        starts with "NC").  For N=1, the valid values are "NWC" (default) and
        "NCW".  For N=2, the valid values are "NHWC" (default) and "NCHW".
        For N=3, the valid values are "NDHWC" (default) and "NCDHW".
      strides: Sequence of N positive integers, defaults to `[1] * N`.
      name: Name prefix to use.
  
    Returns:
      Rank N+2 tensor of type T of shape
      `[batch_size] + output_spatial_shape + [out_channels]`, where
      if padding == "SAME":
        output_spatial_shape = input_spatial_shape
      if padding == "VALID":
        output_spatial_shape = input_spatial_shape - filter_spatial_shape + 1.
  
    Raises:
      ValueError: if ranks are incompatible.
  
    """
    with ops.name_scope(name, "non_atrous_convolution", [input, filter]) as scope:
      input = ops.convert_to_tensor(input, name="input")  # pylint: disable=redefined-builtin
      input_shape = input.shape
      filter = ops.convert_to_tensor(filter, name="filter")  # pylint: disable=redefined-builtin
      filter_shape = filter.shape
      op = _NonAtrousConvolution(
          input_shape,
          filter_shape=filter_shape,
          padding=padding,
          data_format=data_format,
          strides=strides,
          name=scope)
      return op(input, filter)
  
  
  class _NonAtrousConvolution(object):
    """Helper class for _non_atrous_convolution.
  
    Note that this class assumes that shapes of input and filter passed to
    `__call__` are compatible with `input_shape` and filter_shape passed to the
    constructor.
  
    Args:
      input_shape: static input shape, i.e. input.shape.
      filter_shape: static filter shape, i.e. filter.shape.
      padding: see _non_atrous_convolution.
      data_format: see _non_atrous_convolution.
      strides: see _non_atrous_convolution.
      name: see _non_atrous_convolution.
      num_batch_dims: (Optional.)  The number of batch dimensions in the input;
       if not provided, the default of `1` is used.
    """
  
    def __init__(
        self,
        input_shape,
        filter_shape,
        padding,
        data_format=None,
        strides=None,
        name=None,
        num_batch_dims=1):
      # filter shape is always rank num_spatial_dims + 2
      # and num_spatial_dims == input_shape.ndims - num_batch_dims - 1
      if input_shape.ndims is not None:
        filter_shape = filter_shape.with_rank(
            input_shape.ndims - num_batch_dims + 1)
      self.padding = padding
      self.name = name
      # input shape is == num_spatial_dims + num_batch_dims + 1
      # and filter_shape is always rank num_spatial_dims + 2
      if filter_shape.ndims is not None:
        input_shape = input_shape.with_rank(
            filter_shape.ndims + num_batch_dims - 1)
      if input_shape.ndims is None:
        raise ValueError(
            "Rank of convolution must be known, but saw input_shape.ndims == {}"
            .format(input_shape.ndims))
      if input_shape.ndims < 3 or input_shape.ndims - num_batch_dims + 1 > 5:
        raise ValueError(
            "`input_shape.ndims - num_batch_dims + 1` must be at least 3 and at "
            "most 5 but saw `input_shape.ndims == {}` and `num_batch_dims == {}`"
            .format(input_shape.ndims, num_batch_dims))
      conv_dims = input_shape.ndims - num_batch_dims - 1
      if strides is None:
        strides = [1] * conv_dims
      elif len(strides) != conv_dims:
        raise ValueError("len(strides)=%d, but should be %d" % (len(strides),
                                                                conv_dims))
      if conv_dims == 1:
        # conv1d uses the 2-d data format names
        if data_format is None:
          data_format = "NWC"
        elif data_format not in {"NCW", "NWC", "NCHW", "NHWC"}:
          raise ValueError("data_format must be \"NWC\" or \"NCW\".")
        self.strides = strides[0]
        self.data_format = data_format
        self.conv_op = self._conv1d
      elif conv_dims == 2:
        if data_format is None or data_format == "NHWC":
          data_format = "NHWC"
          strides = [1] + list(strides) + [1]
        elif data_format == "NCHW":
          strides = [1, 1] + list(strides)
        else:
          raise ValueError("data_format must be \"NHWC\" or \"NCHW\".")
        self.strides = strides
        self.data_format = data_format
        self.conv_op = conv2d
      elif conv_dims == 3:
        if data_format is None or data_format == "NDHWC":
          strides = [1] + list(strides) + [1]
        elif data_format == "NCDHW":
          strides = [1, 1] + list(strides)
        else:
          raise ValueError("data_format must be \"NDHWC\" or \"NCDHW\". Have: %s"
                           % data_format)
        self.strides = strides
        self.data_format = data_format
        self.conv_op = _conv3d_expanded_batch
  
    # Note that we need this adapter since argument names for conv1d don't match
    # those for gen_nn_ops.conv2d and gen_nn_ops.conv3d.
    # pylint: disable=redefined-builtin
    def _conv1d(self, input, filter, strides, padding, data_format, name):
      return conv1d(
          value=input,
          filters=filter,
          stride=strides,
          padding=padding,
          data_format=data_format,
          name=name)
    # pylint: enable=redefined-builtin
  
    def __call__(self, inp, filter):  # pylint: disable=redefined-builtin
      return self.conv_op(
          input=inp,
          filter=filter,
          strides=self.strides,
          padding=self.padding,
          data_format=self.data_format,
          name=self.name)
  
  
  def squeeze_batch_dims(inp, op, inner_rank, name=None):
    """Returns `unsqueeze_batch(op(squeeze_batch(inp)))`.
  
    Where `squeeze_batch` reshapes `inp` to shape
    `[prod(inp.shape[:-inner_rank])] + inp.shape[-inner_rank:]`
    and `unsqueeze_batch` does the reverse reshape but on the output.
  
    Args:
      inp: A tensor with dims `batch_shape + inner_shape` where `inner_shape`
        is length `inner_rank`.
      op: A callable that takes a single input tensor and returns a single.
        output tensor.
      inner_rank: A python integer.
      name: A string.
  
    Returns:
      `unsqueeze_batch_op(squeeze_batch(inp))`.
    """
    with ops.name_scope(name, "squeeze_batch_dims", [inp]):
      inp = ops.convert_to_tensor(inp, name="input")
      shape = inp.shape
  
      inner_shape = shape[-inner_rank:]
      if not inner_shape.is_fully_defined():
        inner_shape = array_ops.shape(inp)[-inner_rank:]
  
      batch_shape = shape[:-inner_rank]
      if not batch_shape.is_fully_defined():
        batch_shape = array_ops.shape(inp)[:-inner_rank]
  
      if isinstance(inner_shape, tensor_shape.TensorShape):
        inp_reshaped = array_ops.reshape(inp, [-1] + inner_shape.as_list())
      else:
        inp_reshaped = array_ops.reshape(
            inp, array_ops.concat(([-1], inner_shape), axis=-1))
  
      out_reshaped = op(inp_reshaped)
  
      out_inner_shape = out_reshaped.shape[-inner_rank:]
      if not out_inner_shape.is_fully_defined():
        out_inner_shape = array_ops.shape(out_reshaped)[-inner_rank:]
  
      out = array_ops.reshape(
          out_reshaped, array_ops.concat((batch_shape, out_inner_shape), axis=-1))
  
      out.set_shape(inp.shape[:-inner_rank] + out.shape[-inner_rank:])
      return out
  
  
  @tf_export("nn.dilation2d", v1=[])
  @dispatch.add_dispatch_support
  def dilation2d_v2(
      input,   # pylint: disable=redefined-builtin
      filters,  # pylint: disable=redefined-builtin
      strides,
      padding,
      data_format,
      dilations,
      name=None):
    """Computes the grayscale dilation of 4-D `input` and 3-D `filters` tensors.
  
    The `input` tensor has shape `[batch, in_height, in_width, depth]` and the
    `filters` tensor has shape `[filter_height, filter_width, depth]`, i.e., each
    input channel is processed independently of the others with its own
    structuring function. The `output` tensor has shape
    `[batch, out_height, out_width, depth]`. The spatial dimensions of the output
    tensor depend on the `padding` algorithm. We currently only support the
    default "NHWC" `data_format`.
  
    In detail, the grayscale morphological 2-D dilation is the max-sum correlation
    (for consistency with `conv2d`, we use unmirrored filters):
  
        output[b, y, x, c] =
           max_{dy, dx} input[b,
                              strides[1] * y + rates[1] * dy,
                              strides[2] * x + rates[2] * dx,
                              c] +
                        filters[dy, dx, c]
  
    Max-pooling is a special case when the filter has size equal to the pooling
    kernel size and contains all zeros.
  
    Note on duality: The dilation of `input` by the `filters` is equal to the
    negation of the erosion of `-input` by the reflected `filters`.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `float32`, `float64`,
        `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`,
        `uint32`, `uint64`.
        4-D with shape `[batch, in_height, in_width, depth]`.
      filters: A `Tensor`. Must have the same type as `input`.
        3-D with shape `[filter_height, filter_width, depth]`.
      strides: A list of `ints` that has length `>= 4`.
        The stride of the sliding window for each dimension of the input
        tensor. Must be: `[1, stride_height, stride_width, 1]`.
      padding: A `string` from: `"SAME", "VALID"`.
        The type of padding algorithm to use.
      data_format: A `string`, only `"NHWC"` is currently supported.
      dilations: A list of `ints` that has length `>= 4`.
        The input stride for atrous morphological dilation. Must be:
        `[1, rate_height, rate_width, 1]`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
    """
    if data_format != "NHWC":
      raise ValueError("Data formats other than NHWC are not yet supported")
  
    return gen_nn_ops.dilation2d(input=input,
                                 filter=filters,
                                 strides=strides,
                                 rates=dilations,
                                 padding=padding,
                                 name=name)
  
  
  @tf_export(v1=["nn.dilation2d"])
  @dispatch.add_dispatch_support
  def dilation2d_v1(  # pylint: disable=missing-docstring
      input,  # pylint: disable=redefined-builtin
      filter=None,  # pylint: disable=redefined-builtin
      strides=None,
      rates=None,
      padding=None,
      name=None,
      filters=None,
      dilations=None):
    filter = deprecated_argument_lookup("filters", filters, "filter", filter)
    rates = deprecated_argument_lookup("dilations", dilations, "rates", rates)
    return gen_nn_ops.dilation2d(input, filter, strides, rates, padding, name)
  
  
  dilation2d_v1.__doc__ = gen_nn_ops.dilation2d.__doc__
  
  
  @tf_export("nn.with_space_to_batch")
  @dispatch.add_dispatch_support
  def with_space_to_batch(
      input,  # pylint: disable=redefined-builtin
      dilation_rate,
      padding,
      op,
      filter_shape=None,
      spatial_dims=None,
      data_format=None):
    """Performs `op` on the space-to-batch representation of `input`.
  
    This has the effect of transforming sliding window operations into the
    corresponding "atrous" operation in which the input is sampled at the
    specified `dilation_rate`.
  
    In the special case that `dilation_rate` is uniformly 1, this simply returns:
  
      op(input, num_spatial_dims, padding)
  
    Otherwise, it returns:
  
      batch_to_space_nd(
        op(space_to_batch_nd(input, adjusted_dilation_rate, adjusted_paddings),
           num_spatial_dims,
           "VALID")
        adjusted_dilation_rate,
        adjusted_crops),
  
    where:
  
      adjusted_dilation_rate is an int64 tensor of shape [max(spatial_dims)],
      adjusted_{paddings,crops} are int64 tensors of shape [max(spatial_dims), 2]
  
    defined as follows:
  
    We first define two int64 tensors `paddings` and `crops` of shape
    `[num_spatial_dims, 2]` based on the value of `padding` and the spatial
    dimensions of the `input`:
  
    If `padding = "VALID"`, then:
  
      paddings, crops = required_space_to_batch_paddings(
        input_shape[spatial_dims],
        dilation_rate)
  
    If `padding = "SAME"`, then:
  
      dilated_filter_shape =
        filter_shape + (filter_shape - 1) * (dilation_rate - 1)
  
      paddings, crops = required_space_to_batch_paddings(
        input_shape[spatial_dims],
        dilation_rate,
        [(dilated_filter_shape - 1) // 2,
         dilated_filter_shape - 1 - (dilated_filter_shape - 1) // 2])
  
    Because `space_to_batch_nd` and `batch_to_space_nd` assume that the spatial
    dimensions are contiguous starting at the second dimension, but the specified
    `spatial_dims` may not be, we must adjust `dilation_rate`, `paddings` and
    `crops` in order to be usable with these operations.  For a given dimension,
    if the block size is 1, and both the starting and ending padding and crop
    amounts are 0, then space_to_batch_nd effectively leaves that dimension alone,
    which is what is needed for dimensions not part of `spatial_dims`.
    Furthermore, `space_to_batch_nd` and `batch_to_space_nd` handle this case
    efficiently for any number of leading and trailing dimensions.
  
    For 0 <= i < len(spatial_dims), we assign:
  
      adjusted_dilation_rate[spatial_dims[i] - 1] = dilation_rate[i]
      adjusted_paddings[spatial_dims[i] - 1, :] = paddings[i, :]
      adjusted_crops[spatial_dims[i] - 1, :] = crops[i, :]
  
    All unassigned values of `adjusted_dilation_rate` default to 1, while all
    unassigned values of `adjusted_paddings` and `adjusted_crops` default to 0.
  
    Note in the case that `dilation_rate` is not uniformly 1, specifying "VALID"
    padding is equivalent to specifying `padding = "SAME"` with a filter_shape of
    `[1]*N`.
  
    Advanced usage. Note the following optimization: A sequence of
    `with_space_to_batch` operations with identical (not uniformly 1)
    `dilation_rate` parameters and "VALID" padding
  
      net = with_space_to_batch(net, dilation_rate, "VALID", op_1)
      ...
      net = with_space_to_batch(net, dilation_rate, "VALID", op_k)
  
    can be combined into a single `with_space_to_batch` operation as follows:
  
      def combined_op(converted_input, num_spatial_dims, _):
        result = op_1(converted_input, num_spatial_dims, "VALID")
        ...
        result = op_k(result, num_spatial_dims, "VALID")
  
      net = with_space_to_batch(net, dilation_rate, "VALID", combined_op)
  
    This eliminates the overhead of `k-1` calls to `space_to_batch_nd` and
    `batch_to_space_nd`.
  
    Similarly, a sequence of `with_space_to_batch` operations with identical (not
    uniformly 1) `dilation_rate` parameters, "SAME" padding, and odd filter
    dimensions
  
      net = with_space_to_batch(net, dilation_rate, "SAME", op_1, filter_shape_1)
      ...
      net = with_space_to_batch(net, dilation_rate, "SAME", op_k, filter_shape_k)
  
    can be combined into a single `with_space_to_batch` operation as follows:
  
      def combined_op(converted_input, num_spatial_dims, _):
        result = op_1(converted_input, num_spatial_dims, "SAME")
        ...
        result = op_k(result, num_spatial_dims, "SAME")
  
      net = with_space_to_batch(net, dilation_rate, "VALID", combined_op)
  
    Args:
      input: Tensor of rank > max(spatial_dims).
      dilation_rate: int32 Tensor of *known* shape [num_spatial_dims].
      padding: str constant equal to "VALID" or "SAME"
      op: Function that maps (input, num_spatial_dims, padding) -> output
      filter_shape: If padding = "SAME", specifies the shape of the convolution
        kernel/pooling window as an integer Tensor of shape [>=num_spatial_dims].
        If padding = "VALID", filter_shape is ignored and need not be specified.
      spatial_dims: Monotonically increasing sequence of `num_spatial_dims`
        integers (which are >= 1) specifying the spatial dimensions of `input`
        and output.  Defaults to: `range(1, num_spatial_dims+1)`.
      data_format: A string or None.  Specifies whether the channel dimension of
        the `input` and output is the last dimension (default, or if `data_format`
        does not start with "NC"), or the second dimension (if `data_format`
        starts with "NC").  For N=1, the valid values are "NWC" (default) and
        "NCW".  For N=2, the valid values are "NHWC" (default) and "NCHW".
        For N=3, the valid values are "NDHWC" (default) and "NCDHW".
  
    Returns:
      The output Tensor as described above, dimensions will vary based on the op
      provided.
  
    Raises:
      ValueError: if `padding` is invalid or the arguments are incompatible.
      ValueError: if `spatial_dims` are invalid.
  
    """
    input = ops.convert_to_tensor(input, name="input")  # pylint: disable=redefined-builtin
    input_shape = input.shape
  
    def build_op(num_spatial_dims, padding):
      return lambda inp, _: op(inp, num_spatial_dims, padding)
  
    new_op = _WithSpaceToBatch(
        input_shape,
        dilation_rate,
        padding,
        build_op,
        filter_shape=filter_shape,
        spatial_dims=spatial_dims,
        data_format=data_format)
    return new_op(input, None)
  
  
  class _WithSpaceToBatch(object):
    """Helper class for with_space_to_batch.
  
    Note that this class assumes that shapes of input and filter passed to
    `__call__` are compatible with `input_shape`, `filter_shape`, and
    `spatial_dims` passed to the constructor.
  
    Arguments
      input_shape: static shape of input. i.e. input.shape.
      dilation_rate: see `with_space_to_batch`.
      padding: see `with_space_to_batch`.
      build_op: Function that maps (num_spatial_dims, paddings) -> (function that
        maps (input, filter) -> output).
      filter_shape: see `with_space_to_batch`.
      spatial_dims: `see with_space_to_batch`.
      data_format: see `with_space_to_batch`.
      num_batch_dims: (Optional).  Number of batch dims in `input_shape`.
    """
  
    def __init__(self,
                 input_shape,
                 dilation_rate,
                 padding,
                 build_op,
                 filter_shape=None,
                 spatial_dims=None,
                 data_format=None,
                 num_batch_dims=1):
      """Helper class for _with_space_to_batch."""
      dilation_rate = ops.convert_to_tensor(
          dilation_rate, dtypes.int32, name="dilation_rate")
      if dilation_rate.shape.ndims not in (None, 1):
        raise ValueError(
            "rate must be rank 1 but saw {}".format(dilation_rate.shape.ndims))
  
      if not dilation_rate.shape.is_fully_defined():
        raise ValueError("rate must have known shape, but saw {}"
                         .format(dilation_rate.shape))
  
      num_spatial_dims = dilation_rate.shape.dims[0].value
  
      if data_format is not None and data_format.startswith("NC"):
        starting_spatial_dim = num_batch_dims + 1
      else:
        starting_spatial_dim = num_batch_dims
  
      if spatial_dims is None:
        spatial_dims = range(starting_spatial_dim,
                             num_spatial_dims + starting_spatial_dim)
      orig_spatial_dims = list(spatial_dims)
      spatial_dims = sorted(set(int(x) for x in orig_spatial_dims))
      if spatial_dims != orig_spatial_dims or any(x < 1 for x in spatial_dims):
        raise ValueError(
            "spatial_dims must be a monotonically increasing sequence of "
            "positive integers, but saw: {}".format(orig_spatial_dims))
  
      if data_format is not None and data_format.startswith("NC"):
        expected_input_rank = spatial_dims[-1]
      else:
        expected_input_rank = spatial_dims[-1] + 1
  
      try:
        input_shape.with_rank_at_least(expected_input_rank)
      except ValueError:
        raise ValueError(
            "input tensor must have rank at least {}, but saw rank {}"
            .format(expected_input_rank, input_shape.ndims))
  
      const_rate = tensor_util.constant_value(dilation_rate)
      rate_or_const_rate = dilation_rate
      if const_rate is not None:
        rate_or_const_rate = const_rate
        if np.any(const_rate < 1):
          raise ValueError("dilation_rate must be positive, but saw: {}"
                           .format(const_rate))
        if np.all(const_rate == 1):
          self.call = build_op(num_spatial_dims, padding)
          return
  
      padding, explicit_paddings = convert_padding(padding)
  
      # We have two padding contributions. The first is used for converting "SAME"
      # to "VALID". The second is required so that the height and width of the
      # zero-padded value tensor are multiples of rate.
  
      # Padding required to reduce to "VALID" convolution
      if padding == "SAME":
        if filter_shape is None:
          raise ValueError("filter_shape must be specified for SAME padding")
        filter_shape = ops.convert_to_tensor(filter_shape, name="filter_shape")
        const_filter_shape = tensor_util.constant_value(filter_shape)
        if const_filter_shape is not None:
          filter_shape = const_filter_shape
          self.base_paddings = _with_space_to_batch_base_paddings(
              const_filter_shape, num_spatial_dims, rate_or_const_rate)
        else:
          self.num_spatial_dims = num_spatial_dims
          self.rate_or_const_rate = rate_or_const_rate
          self.base_paddings = None
      elif padding == "VALID":
        self.base_paddings = np.zeros([num_spatial_dims, 2], np.int32)
      elif padding == "EXPLICIT":
        base_paddings = (np.array(explicit_paddings)
                         .reshape([num_spatial_dims + 2, 2]))
        # Remove batch and channel dimensions
        if data_format is not None and data_format.startswith("NC"):
          self.base_paddings = base_paddings[2:]
        else:
          self.base_paddings = base_paddings[1:-1]
      else:
        raise ValueError("Invalid padding method %r" % padding)
  
      self.input_shape = input_shape
      self.spatial_dims = spatial_dims
      self.dilation_rate = dilation_rate
      self.data_format = data_format
      self.op = build_op(num_spatial_dims, "VALID")
      self.call = self._with_space_to_batch_call
  
    def _with_space_to_batch_call(self, inp, filter):  # pylint: disable=redefined-builtin
      """Call functionality for with_space_to_batch."""
      # Handle input whose shape is unknown during graph creation.
      input_spatial_shape = None
      input_shape = self.input_shape
      spatial_dims = self.spatial_dims
      if input_shape.ndims is not None:
        input_shape_list = input_shape.as_list()
        input_spatial_shape = [input_shape_list[i] for i in spatial_dims]
      if input_spatial_shape is None or None in input_spatial_shape:
        input_shape_tensor = array_ops.shape(inp)
        input_spatial_shape = array_ops.stack(
            [input_shape_tensor[i] for i in spatial_dims])
  
      base_paddings = self.base_paddings
      if base_paddings is None:
        # base_paddings could not be computed at build time since static filter
        # shape was not fully defined.
        filter_shape = array_ops.shape(filter)
        base_paddings = _with_space_to_batch_base_paddings(
            filter_shape, self.num_spatial_dims, self.rate_or_const_rate)
  
      paddings, crops = array_ops.required_space_to_batch_paddings(
          input_shape=input_spatial_shape,
          base_paddings=base_paddings,
          block_shape=self.dilation_rate)
  
      dilation_rate = _with_space_to_batch_adjust(self.dilation_rate, 1,
                                                  spatial_dims)
      paddings = _with_space_to_batch_adjust(paddings, 0, spatial_dims)
      crops = _with_space_to_batch_adjust(crops, 0, spatial_dims)
      input_converted = array_ops.space_to_batch_nd(
          input=inp, block_shape=dilation_rate, paddings=paddings)
  
      result = self.op(input_converted, filter)
  
      result_converted = array_ops.batch_to_space_nd(
          input=result, block_shape=dilation_rate, crops=crops)
  
      # Recover channel information for output shape if channels are not last.
      if self.data_format is not None and self.data_format.startswith("NC"):
        if not result_converted.shape.dims[1].value and filter is not None:
          output_shape = result_converted.shape.as_list()
          output_shape[1] = filter.shape[-1]
          result_converted.set_shape(output_shape)
  
      return result_converted
  
    def __call__(self, inp, filter):  # pylint: disable=redefined-builtin
      return self.call(inp, filter)
  
  
  def _with_space_to_batch_base_paddings(filter_shape, num_spatial_dims,
                                         rate_or_const_rate):
    """Helper function to compute base_paddings."""
    # Spatial dimensions of the filters and the upsampled filters in which we
    # introduce (rate - 1) zeros between consecutive filter values.
    filter_spatial_shape = filter_shape[:num_spatial_dims]
    pad_extra_shape = (filter_spatial_shape - 1) * rate_or_const_rate
  
    # When full_padding_shape is odd, we pad more at end, following the same
    # convention as conv2d.
    pad_extra_start = pad_extra_shape // 2
    pad_extra_end = pad_extra_shape - pad_extra_start
    base_paddings = array_ops.stack(
        [[pad_extra_start[i], pad_extra_end[i]] for i in range(num_spatial_dims)])
    return base_paddings
  
  
  def _with_space_to_batch_adjust(orig, fill_value, spatial_dims):
    """Returns an `adjusted` version of `orig` based on `spatial_dims`.
  
    Tensor of the same type as `orig` and with shape
    `[max(spatial_dims), ...]` where:
  
      adjusted[spatial_dims[i] - 1, ...] = orig[i, ...]
  
    for 0 <= i < len(spatial_dims), and
  
      adjusted[j, ...] = fill_value
  
    for j != spatial_dims[i] - 1 for some i.
  
    If `orig` is a constant value, then the result will be a constant value.
  
    Args:
      orig: Tensor of rank > max(spatial_dims).
      fill_value: Numpy scalar (of same data type as `orig) specifying the fill
        value for non-spatial dimensions.
      spatial_dims: See with_space_to_batch.
  
    Returns:
      `adjusted` tensor.
    """
    fill_dims = orig.get_shape().as_list()[1:]
    dtype = orig.dtype.as_numpy_dtype
    parts = []
    const_orig = tensor_util.constant_value(orig)
    const_or_orig = const_orig if const_orig is not None else orig
    prev_spatial_dim = 0
    i = 0
    while i < len(spatial_dims):
      start_i = i
      start_spatial_dim = spatial_dims[i]
      if start_spatial_dim > 1:
        # Fill in any gap from the previous spatial dimension (or dimension 1 if
        # this is the first spatial dimension) with `fill_value`.
        parts.append(
            np.full(
                [start_spatial_dim - 1 - prev_spatial_dim] + fill_dims,
                fill_value,
                dtype=dtype))
      # Find the largest value of i such that:
      #   [spatial_dims[start_i], ..., spatial_dims[i]]
      #     == [start_spatial_dim, ..., start_spatial_dim + i - start_i],
      # i.e. the end of a contiguous group of spatial dimensions.
      while (i + 1 < len(spatial_dims) and
             spatial_dims[i + 1] == spatial_dims[i] + 1):
        i += 1
      parts.append(const_or_orig[start_i:i + 1])
      prev_spatial_dim = spatial_dims[i]
      i += 1
    if const_orig is not None:
      return np.concatenate(parts)
    else:
      return array_ops.concat(parts, 0)
  
  
  def _get_strides_and_dilation_rate(num_spatial_dims, strides, dilation_rate):
    """Helper function for verifying strides and dilation_rate arguments.
  
    This is used by `convolution` and `pool`.
  
    Args:
      num_spatial_dims: int
      strides: Optional.  List of N ints >= 1.  Defaults to [1]*N.  If any value
        of strides is > 1, then all values of dilation_rate must be 1.
      dilation_rate: Optional.  List of N ints >= 1.  Defaults to [1]*N.  If any
        value of dilation_rate is > 1, then all values of strides must be 1.
  
    Returns:
      Normalized (strides, dilation_rate) as int32 numpy arrays of shape
      [num_spatial_dims].
  
    Raises:
      ValueError: if the parameters are invalid.
    """
    if dilation_rate is None:
      dilation_rate = [1] * num_spatial_dims
    elif len(dilation_rate) != num_spatial_dims:
      raise ValueError("len(dilation_rate)=%d but should be %d" %
                       (len(dilation_rate), num_spatial_dims))
    dilation_rate = np.array(dilation_rate, dtype=np.int32)
    if np.any(dilation_rate < 1):
      raise ValueError("all values of dilation_rate must be positive")
  
    if strides is None:
      strides = [1] * num_spatial_dims
    elif len(strides) != num_spatial_dims:
      raise ValueError("len(strides)=%d but should be %d" % (len(strides),
                                                             num_spatial_dims))
    strides = np.array(strides, dtype=np.int32)
    if np.any(strides < 1):
      raise ValueError("all values of strides must be positive")
  
    if np.any(strides > 1) and np.any(dilation_rate > 1):
      raise ValueError(
          "strides > 1 not supported in conjunction with dilation_rate > 1")
    return strides, dilation_rate
  
  
  @tf_export(v1=["nn.convolution"])
  @dispatch.add_dispatch_support
  def convolution(
      input,  # pylint: disable=redefined-builtin
      filter,  # pylint: disable=redefined-builtin
      padding,
      strides=None,
      dilation_rate=None,
      name=None,
      data_format=None,
      filters=None,
      dilations=None):  # pylint: disable=g-doc-args
    """Computes sums of N-D convolutions (actually cross-correlation).
  
    This also supports either output striding via the optional `strides` parameter
    or atrous convolution (also known as convolution with holes or dilated
    convolution, based on the French word "trous" meaning holes in English) via
    the optional `dilation_rate` parameter.  Currently, however, output striding
    is not supported for atrous convolutions.
  
    Specifically, in the case that `data_format` does not start with "NC", given
    a rank (N+2) `input` Tensor of shape
  
      [num_batches,
       input_spatial_shape[0],
       ...,
       input_spatial_shape[N-1],
       num_input_channels],
  
    a rank (N+2) `filter` Tensor of shape
  
      [spatial_filter_shape[0],
       ...,
       spatial_filter_shape[N-1],
       num_input_channels,
       num_output_channels],
  
    an optional `dilation_rate` tensor of shape [N] (defaulting to [1]*N)
    specifying the filter upsampling/input downsampling rate, and an optional list
    of N `strides` (defaulting [1]*N), this computes for each N-D spatial output
    position (x[0], ..., x[N-1]):
  
    ```
      output[b, x[0], ..., x[N-1], k] =
          sum_{z[0], ..., z[N-1], q}
              filter[z[0], ..., z[N-1], q, k] *
              padded_input[b,
                           x[0]*strides[0] + dilation_rate[0]*z[0],
                           ...,
                           x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1],
                           q]
    ```
    where b is the index into the batch, k is the output channel number, q is the
    input channel number, and z is the N-D spatial offset within the filter. Here,
    `padded_input` is obtained by zero padding the input using an effective
    spatial filter shape of `(spatial_filter_shape-1) * dilation_rate + 1` and
    output striding `strides`.
  
    In the case that `data_format` does start with `"NC"`, the `input` and output
    (but not the `filter`) are simply transposed as follows:
  
      convolution(input, data_format, **kwargs) =
        tf.transpose(convolution(tf.transpose(input, [0] + range(2,N+2) + [1]),
                                 **kwargs),
                     [0, N+1] + range(1, N+1))
  
    It is required that 1 <= N <= 3.
  
    Args:
      input: An (N+2)-D `Tensor` of type `T`, of shape
        `[batch_size] + input_spatial_shape + [in_channels]` if data_format does
        not start with "NC" (default), or
        `[batch_size, in_channels] + input_spatial_shape` if data_format starts
        with "NC".
      filter: An (N+2)-D `Tensor` with the same type as `input` and shape
        `spatial_filter_shape + [in_channels, out_channels]`.
      padding: A string, either `"VALID"` or `"SAME"`. The padding algorithm.
        `"valid"` means no padding. `"same"` results in padding evenly to
        the left/right or up/down of the input such that output has the same
        height/width dimension as the input.
      strides: Optional.  Sequence of N ints >= 1.  Specifies the output stride.
        Defaults to [1]*N.  If any value of strides is > 1, then all values of
        dilation_rate must be 1.
      dilation_rate: Optional.  Sequence of N ints >= 1.  Specifies the filter
        upsampling/input downsampling rate.  In the literature, the same parameter
        is sometimes called `input stride` or `dilation`.  The effective filter
        size used for the convolution will be `spatial_filter_shape +
        (spatial_filter_shape - 1) * (rate - 1)`, obtained by inserting
        (dilation_rate[i]-1) zeros between consecutive elements of the original
        filter in each spatial dimension i.  If any value of dilation_rate is > 1,
        then all values of strides must be 1.
      name: Optional name for the returned tensor.
      data_format: A string or None.  Specifies whether the channel dimension of
        the `input` and output is the last dimension (default, or if `data_format`
        does not start with "NC"), or the second dimension (if `data_format`
        starts with "NC").  For N=1, the valid values are "NWC" (default) and
        "NCW".  For N=2, the valid values are "NHWC" (default) and "NCHW".
        For N=3, the valid values are "NDHWC" (default) and "NCDHW".
  
    Returns:
      A `Tensor` with the same type as `input` of shape
  
          `[batch_size] + output_spatial_shape + [out_channels]`
  
      if data_format is None or does not start with "NC", or
  
          `[batch_size, out_channels] + output_spatial_shape`
  
      if data_format starts with "NC",
      where `output_spatial_shape` depends on the value of `padding`.
  
      If padding == "SAME":
        output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i])
  
      If padding == "VALID":
        output_spatial_shape[i] =
          ceil((input_spatial_shape[i] -
                (spatial_filter_shape[i]-1) * dilation_rate[i])
               / strides[i]).
  
    Raises:
      ValueError: If input/output depth does not match `filter` shape, if padding
        is other than `"VALID"` or `"SAME"`, or if data_format is invalid.
  
    """
    filter = deprecated_argument_lookup("filters", filters, "filter", filter)
    dilation_rate = deprecated_argument_lookup(
        "dilations", dilations, "dilation_rate", dilation_rate)
    return convolution_internal(
        input,
        filter,
        strides=strides,
        padding=padding,
        data_format=data_format,
        dilations=dilation_rate,
        name=name)
  
  
  @tf_export("nn.convolution", v1=[])
  @dispatch.add_dispatch_support
  def convolution_v2(  # pylint: disable=missing-docstring
      input,  # pylint: disable=redefined-builtin
      filters,
      strides=None,
      padding="VALID",
      data_format=None,
      dilations=None,
      name=None):
    return convolution_internal(
        input,  # pylint: disable=redefined-builtin
        filters,
        strides=strides,
        padding=padding,
        data_format=data_format,
        dilations=dilations,
        name=name)
  
  
  convolution_v2.__doc__ = deprecation.rewrite_argument_docstring(
      deprecation.rewrite_argument_docstring(
          convolution.__doc__, "dilation_rate", "dilations"),
      "filter", "filters")
  
  
  def convolution_internal(
      input,  # pylint: disable=redefined-builtin
      filters,
      strides=None,
      padding="VALID",
      data_format=None,
      dilations=None,
      name=None,
      call_from_convolution=True,
      num_spatial_dims=None):
    """Internal function which performs rank agnostic convolution.
  
    Args:
      input: See `convolution`.
      filters: See `convolution`.
      strides: See `convolution`.
      padding: See `convolution`.
      data_format: See `convolution`.
      dilations: See `convolution`.
      name: See `convolution`.
      call_from_convolution: See `convolution`.
      num_spatial_dims: (Optional.).  It is a integer describing the
        rank of the spatial dimensions.  For `1-D`, `2-D` and `3-D` convolutions,
        the value of `num_spatial_dims` is `1`, `2`, and `3`, respectively.
        This argument is only required to disambiguate the rank of `batch_shape`
        when `filter_shape.ndims is None` and `len(batch_shape) > 1`.  For
        backwards compatibility, if `num_spatial_dims is None` and
       `filter_shape.ndims is None`, then `len(batch_shape)` is assumed to be
       `1` (i.e., the input is expected to be
       `[batch_size, num_channels] + input_spatial_shape`
       or `[batch_size] + input_spatial_shape + [num_channels]`.
  
    Returns:
      A tensor of shape and dtype matching that of `input`.
  
    Raises:
      ValueError: If input and filter both have unknown shapes, or if
        `num_spatial_dims` is provided and incompatible with the value
        estimated from `filters.shape`.
    """
    if (not isinstance(filters, variables_lib.Variable) and
        not tensor_util.is_tf_type(filters)):
      with ops.name_scope("convolution_internal", None, [filters, input]):
        filters = ops.convert_to_tensor(filters, name='filters')
    if (not isinstance(input, ops.Tensor) and not tensor_util.is_tf_type(input)):
      with ops.name_scope("convolution_internal", None, [filters, input]):
        input = ops.convert_to_tensor(input, name="input")
  
    filters_rank = filters.shape.rank
    inputs_rank = input.shape.rank
    if num_spatial_dims is None:
      if filters_rank:
        num_spatial_dims = filters_rank - 2
      elif inputs_rank:
        num_spatial_dims = inputs_rank - 2
      else:
        raise ValueError("rank of input or filter must be known")
    elif filters_rank and filters_rank - 2 != num_spatial_dims:
      raise ValueError(
          "inconsistent estimate of spatial dims ({}) vs. actual passed "
          "num_spatial_dims ({}).  n was estimated as len(filters.shape) - 2, "
          "but filters shape is: {}".format(filters_rank, num_spatial_dims,
                                            filters.shape))
  
    if inputs_rank:
      num_batch_dims = inputs_rank - num_spatial_dims - 1  # Channel dimension.
    else:
      num_batch_dims = 1  # By default, assume single batch dimension.
  
    if num_spatial_dims not in {1, 2, 3}:
      raise ValueError(
          "num_spatial_dims (input.shape.ndims - num_batch_dims - 1) must be one "
          "of 1, 2 or 3 but saw {}.  num_batch_dims: {}.".format(
              num_spatial_dims, num_batch_dims))
  
    if data_format is None or data_format in _CHANNELS_LAST_FORMATS:
      channel_index = num_batch_dims + num_spatial_dims
    else:
      channel_index = num_batch_dims
  
    if dilations is None:
      dilations = _get_sequence(dilations, num_spatial_dims, channel_index,
                                "dilations")
      is_dilated_conv = False
    else:
      dilations = _get_sequence(dilations, num_spatial_dims, channel_index,
                                "dilations")
      is_dilated_conv = any(i != 1 for i in dilations)
  
    strides = _get_sequence(strides, num_spatial_dims, channel_index, "strides")
    has_tpu_context = device_context.enclosing_tpu_context() is not None
  
    if name:
      default_name = None
    elif not has_tpu_context or call_from_convolution:
      default_name = "convolution"
    elif num_spatial_dims == 2:  # Most common case.
      default_name = "Conv2D"
    elif num_spatial_dims == 3:
      default_name = "Conv3D"
    else:
      default_name = "conv1d"
  
    with ops.name_scope(name, default_name, [input, filters]) as name:
      # Fast path for TPU or if no dilation, as gradient only supported on TPU
      # for dilations.
      if not is_dilated_conv or has_tpu_context:
        if num_spatial_dims == 2:  # Most common case.
          op = _conv2d_expanded_batch
        elif num_spatial_dims == 3:
          op = _conv3d_expanded_batch
        else:
          op = conv1d
  
        return op(
            input,
            filters,
            strides,
            padding=padding,
            data_format=data_format,
            dilations=dilations,
            name=name)
      else:
        if channel_index == 1:
          strides = strides[2:]
          dilations = dilations[2:]
        else:
          strides = strides[1:-1]
          dilations = dilations[1:-1]
  
        op = Convolution(
            tensor_shape.as_shape(input.shape),
            tensor_shape.as_shape(filters.shape),
            padding,
            strides=strides,
            dilation_rate=dilations,
            name=name,
            data_format=data_format,
            num_spatial_dims=num_spatial_dims)
        return op(input, filters)
  
  
  class Convolution(object):
    """Helper class for convolution.
  
    Note that this class assumes that shapes of input and filter passed to
    `__call__` are compatible with `input_shape`, `filter_shape`, and
    `num_spatial_dims` passed to the constructor.
  
    Arguments
      input_shape: static shape of input. i.e. input.shape.  Its length is
        `batch_shape + input_spatial_shape + [num_channels]` if `data_format`
        does not start with `NC`, or
        `batch_shape + [num_channels] + input_spatial_shape` if `data_format`
        starts with `NC`.
      filter_shape: static shape of the filter. i.e. filter.shape.
      padding: The padding algorithm, must be "SAME" or "VALID".
      strides: see convolution.
      dilation_rate: see convolution.
      name: see convolution.
      data_format: A string or `None`.  Specifies whether the channel dimension of
        the `input` and output is the last dimension (if `data_format` is `None`
        or does not start with `NC`), or the first post-batch dimension (i.e. if
        `data_format` starts with `NC`).
      num_spatial_dims: (Usually optional.)  Python integer, the rank of the
        spatial and channel dimensions.  For `1-D`, `2-D` and `3-D` convolutions,
        the value of `num_spatial_dims` is `1`, `2`, and `3`, respectively.
        This argument is only required to disambiguate the rank of `batch_shape`
        when `filter_shape.ndims is None` and `len(batch_shape) > 1`.  For
        backwards compatibility, if `num_spatial_dims is None` and
        `filter_shape.ndims is None`, then `len(batch_shape)` is assumed to be
        `1` (i.e., the input is expected to be
        `[batch_size, num_channels] + input_spatial_shape`
        or `[batch_size] + input_spatial_shape + [num_channels]`.
    """
  
    def __init__(self,
                 input_shape,
                 filter_shape,
                 padding,
                 strides=None,
                 dilation_rate=None,
                 name=None,
                 data_format=None,
                 num_spatial_dims=None):
      """Helper function for convolution."""
      num_batch_dims = None
      filter_shape = tensor_shape.as_shape(filter_shape)
      input_shape = tensor_shape.as_shape(input_shape)
  
      if filter_shape.ndims is not None:
        if (num_spatial_dims is not None and
            filter_shape.ndims != num_spatial_dims + 2):
          raise ValueError(
              "Expected filter_shape.ndims == num_spatial_dims + 2, "
              "but saw filter_shape.ndims == {} and num_spatial_dims == {}"
              .format(filter_shape.ndims, num_spatial_dims))
        else:
          num_spatial_dims = filter_shape.ndims - 2
  
      if input_shape.ndims is not None and num_spatial_dims is not None:
        num_batch_dims = input_shape.ndims - num_spatial_dims - 1
  
      if num_spatial_dims is None:
        num_spatial_dims = input_shape.ndims - 2
      else:
        if input_shape.ndims is not None:
          if input_shape.ndims < num_spatial_dims + 2:
            raise ValueError(
                "Expected input_shape.ndims >= num_spatial_dims + 2, but saw "
                "input_shape.ndims == {} and num_spatial_dims == {}"
                .format(input_shape.ndims, num_spatial_dims))
          else:
            if num_batch_dims is None:
              num_batch_dims = input_shape.ndims - num_spatial_dims - 1
  
      if num_spatial_dims is None:
        raise ValueError(
            "Cannot estimate num_spatial_dims since input_shape.ndims is None, "
            "filter_shape.ndims is None, and argument num_spatial_dims is also "
            "None.")
  
      if num_batch_dims is None:
        num_batch_dims = 1
  
      if num_batch_dims < 1:
        raise ValueError(
            "num_batch_dims should be >= 1, but saw {}.  num_batch_dims was "
            "estimated as `input_shape.ndims - num_spatial_dims - 1` and "
            "num_spatial_dims was either provided or estimated as "
            "`filter_shape.ndims - 2`.  input_shape.ndims: {}, "
            "num_spatial_dims: {}, filter_shape.ndims: {}"
            .format(num_batch_dims, input_shape.ndims, num_spatial_dims,
                    filter_shape.ndims))
  
      if data_format is None or not data_format.startswith("NC"):
        input_channels_dim = tensor_shape.dimension_at_index(
            input_shape, num_spatial_dims + num_batch_dims)
        spatial_dims = range(num_batch_dims, num_spatial_dims + num_batch_dims)
      else:
        input_channels_dim = tensor_shape.dimension_at_index(
            input_shape, num_batch_dims)
        spatial_dims = range(
            num_batch_dims + 1, num_spatial_dims + num_batch_dims + 1)
  
      filter_dim = tensor_shape.dimension_at_index(filter_shape, num_spatial_dims)
      if not (input_channels_dim % filter_dim).is_compatible_with(0):
        raise ValueError("The number of input channels is not divisible by the "
                         "corresponding number of output filters. Received: "
                         "input channels={}, output filters={}".format(
                             input_channels_dim, filter_dim))
  
      strides, dilation_rate = _get_strides_and_dilation_rate(
          num_spatial_dims, strides, dilation_rate)
  
      self.input_shape = input_shape
      self.filter_shape = filter_shape
      self.data_format = data_format
      self.strides = strides
      self.padding = padding
      self.name = name
      self.dilation_rate = dilation_rate
      self.num_batch_dims = num_batch_dims
      self.num_spatial_dims = num_spatial_dims
      self.conv_op = _WithSpaceToBatch(
          input_shape,
          dilation_rate=dilation_rate,
          padding=padding,
          build_op=self._build_op,
          filter_shape=filter_shape,
          spatial_dims=spatial_dims,
          data_format=data_format,
          num_batch_dims=num_batch_dims)
  
    def _build_op(self, _, padding):
      return _NonAtrousConvolution(
          self.input_shape,
          filter_shape=self.filter_shape,
          padding=padding,
          data_format=self.data_format,
          strides=self.strides,
          name=self.name,
          num_batch_dims=self.num_batch_dims)
  
    def __call__(self, inp, filter):  # pylint: disable=redefined-builtin
      # TPU convolution supports dilations greater than 1.
      if device_context.enclosing_tpu_context() is not None:
        return convolution_internal(
            inp,
            filter,
            strides=self.strides,
            padding=self.padding,
            data_format=self.data_format,
            dilations=self.dilation_rate,
            name=self.name,
            call_from_convolution=False,
            num_spatial_dims=self.num_spatial_dims)
      else:
        return self.conv_op(inp, filter)
  
  
  @tf_export(v1=["nn.pool"])
  @dispatch.add_dispatch_support
  def pool(
      input,  # pylint: disable=redefined-builtin
      window_shape,
      pooling_type,
      padding,
      dilation_rate=None,
      strides=None,
      name=None,
      data_format=None,
      dilations=None):
    """Performs an N-D pooling operation.
  
    In the case that `data_format` does not start with "NC", computes for
        0 <= b < batch_size,
        0 <= x[i] < output_spatial_shape[i],
        0 <= c < num_channels:
  
    ```
      output[b, x[0], ..., x[N-1], c] =
        REDUCE_{z[0], ..., z[N-1]}
          input[b,
                x[0] * strides[0] - pad_before[0] + dilation_rate[0]*z[0],
                ...
                x[N-1]*strides[N-1] - pad_before[N-1] + dilation_rate[N-1]*z[N-1],
                c],
    ```
  
    where the reduction function REDUCE depends on the value of `pooling_type`,
    and pad_before is defined based on the value of `padding` as described in
    the "returns" section of `tf.nn.convolution` for details.
    The reduction never includes out-of-bounds positions.
  
    In the case that `data_format` starts with `"NC"`, the `input` and output are
    simply transposed as follows:
  
    ```
      pool(input, data_format, **kwargs) =
        tf.transpose(pool(tf.transpose(input, [0] + range(2,N+2) + [1]),
                          **kwargs),
                     [0, N+1] + range(1, N+1))
    ```
  
    Args:
      input: Tensor of rank N+2, of shape
        `[batch_size] + input_spatial_shape + [num_channels]` if data_format does
        not start with "NC" (default), or
        `[batch_size, num_channels] + input_spatial_shape` if data_format starts
        with "NC".  Pooling happens over the spatial dimensions only.
      window_shape: Sequence of N ints >= 1.
      pooling_type: Specifies pooling operation, must be "AVG" or "MAX".
      padding: The padding algorithm, must be "SAME" or "VALID".
        See the "returns" section of `tf.nn.convolution` for details.
      dilation_rate: Optional.  Dilation rate.  List of N ints >= 1.
        Defaults to [1]*N.  If any value of dilation_rate is > 1, then all values
        of strides must be 1.
      strides: Optional.  Sequence of N ints >= 1.  Defaults to [1]*N.
        If any value of strides is > 1, then all values of dilation_rate must be
        1.
      name: Optional. Name of the op.
      data_format: A string or None.  Specifies whether the channel dimension of
        the `input` and output is the last dimension (default, or if `data_format`
        does not start with "NC"), or the second dimension (if `data_format`
        starts with "NC").  For N=1, the valid values are "NWC" (default) and
        "NCW".  For N=2, the valid values are "NHWC" (default) and "NCHW".
        For N=3, the valid values are "NDHWC" (default) and "NCDHW".
      dilations: Alias for dilation_rate
  
    Returns:
      Tensor of rank N+2, of shape
        [batch_size] + output_spatial_shape + [num_channels]
  
      if data_format is None or does not start with "NC", or
  
        [batch_size, num_channels] + output_spatial_shape
  
      if data_format starts with "NC",
      where `output_spatial_shape` depends on the value of padding:
  
      If padding = "SAME":
        output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i])
  
      If padding = "VALID":
        output_spatial_shape[i] =
          ceil((input_spatial_shape[i] - (window_shape[i] - 1) * dilation_rate[i])
               / strides[i]).
  
    Raises:
      ValueError: if arguments are invalid.
  
    """
    dilation_rate = deprecated_argument_lookup(
        "dilations", dilations, "dilation_rate", dilation_rate)
    # pylint: enable=line-too-long
    with ops.name_scope(name, "%s_pool" % (pooling_type.lower()),
                        [input]) as scope:
      input = ops.convert_to_tensor(input, name="input")  # pylint: disable=redefined-builtin
  
      num_spatial_dims = len(window_shape)
      if num_spatial_dims < 1 or num_spatial_dims > 3:
        raise ValueError("It is required that 1 <= num_spatial_dims <= 3.")
  
      input.get_shape().with_rank(num_spatial_dims + 2)
  
      strides, dilation_rate = _get_strides_and_dilation_rate(
          num_spatial_dims, strides, dilation_rate)
  
      if padding == "SAME" and np.any(dilation_rate > 1):
        raise ValueError(
            "pooling with SAME padding is not implemented for dilation_rate > 1")
  
      if np.any(strides > window_shape):
        raise ValueError(
            "strides > window_shape not supported due to inconsistency between "
            "CPU and GPU implementations")
  
      pooling_ops = {
          ("MAX", 1): max_pool,
          ("MAX", 2): max_pool,
          ("MAX", 3): max_pool3d,  # pylint: disable=undefined-variable
          ("AVG", 1): avg_pool,
          ("AVG", 2): avg_pool,
          ("AVG", 3): avg_pool3d,  # pylint: disable=undefined-variable
      }
      op_key = (pooling_type, num_spatial_dims)
      if op_key not in pooling_ops:
        raise ValueError("%d-D %s pooling is not supported." % (op_key[1],
                                                                op_key[0]))
  
      if data_format is None or not data_format.startswith("NC"):
        adjusted_window_shape = [1] + list(window_shape) + [1]
        adjusted_strides = [1] + list(strides) + [1]
        spatial_dims = range(1, num_spatial_dims + 1)
      else:
        adjusted_window_shape = [1, 1] + list(window_shape)
        adjusted_strides = [1, 1] + list(strides)
        spatial_dims = range(2, num_spatial_dims + 2)
  
      if num_spatial_dims == 1:
        if data_format is None or data_format == "NWC":
          data_format_kwargs = dict(data_format="NHWC")
        elif data_format == "NCW":
          data_format_kwargs = dict(data_format="NCHW")
        else:
          raise ValueError("data_format must be either \"NWC\" or \"NCW\".")
        adjusted_window_shape = [1] + adjusted_window_shape
        adjusted_strides = [1] + adjusted_strides
      else:
        data_format_kwargs = dict(data_format=data_format)
  
      def op(converted_input, _, converted_padding):  # pylint: disable=missing-docstring
        if num_spatial_dims == 1:
          converted_input = array_ops.expand_dims(converted_input,
                                                  spatial_dims[0])
        result = pooling_ops[op_key](
            converted_input,
            adjusted_window_shape,
            adjusted_strides,
            converted_padding,
            name=scope,
            **data_format_kwargs)
        if num_spatial_dims == 1:
          result = array_ops.squeeze(result, [spatial_dims[0]])
        return result
  
      return with_space_to_batch(
          input=input,
          dilation_rate=dilation_rate,
          padding=padding,
          op=op,
          spatial_dims=spatial_dims,
          filter_shape=window_shape)
  
  
  @tf_export("nn.pool", v1=[])
  @dispatch.add_dispatch_support
  def pool_v2(
      input,  # pylint: disable=redefined-builtin
      window_shape,
      pooling_type,
      strides=None,
      padding="VALID",
      data_format=None,
      dilations=None,
      name=None):
    # pylint: disable=line-too-long
    """Performs an N-D pooling operation.
  
    In the case that `data_format` does not start with "NC", computes for
        0 <= b < batch_size,
        0 <= x[i] < output_spatial_shape[i],
        0 <= c < num_channels:
  
    ```
      output[b, x[0], ..., x[N-1], c] =
        REDUCE_{z[0], ..., z[N-1]}
          input[b,
                x[0] * strides[0] - pad_before[0] + dilation_rate[0]*z[0],
                ...
                x[N-1]*strides[N-1] - pad_before[N-1] + dilation_rate[N-1]*z[N-1],
                c],
    ```
  
    where the reduction function REDUCE depends on the value of `pooling_type`,
    and pad_before is defined based on the value of `padding` as described in
    the "returns" section of `tf.nn.convolution` for details.
    The reduction never includes out-of-bounds positions.
  
    In the case that `data_format` starts with `"NC"`, the `input` and output are
    simply transposed as follows:
  
    ```
      pool(input, data_format, **kwargs) =
        tf.transpose(pool(tf.transpose(input, [0] + range(2,N+2) + [1]),
                          **kwargs),
                     [0, N+1] + range(1, N+1))
    ```
  
    Args:
      input: Tensor of rank N+2, of shape `[batch_size] + input_spatial_shape +
        [num_channels]` if data_format does not start with "NC" (default), or
        `[batch_size, num_channels] + input_spatial_shape` if data_format starts
        with "NC".  Pooling happens over the spatial dimensions only.
      window_shape: Sequence of N ints >= 1.
      pooling_type: Specifies pooling operation, must be "AVG" or "MAX".
      strides: Optional. Sequence of N ints >= 1.  Defaults to [1]*N. If any value of
        strides is > 1, then all values of dilation_rate must be 1.
      padding: The padding algorithm, must be "SAME" or "VALID". Defaults to "SAME".
        See the "returns" section of `tf.nn.convolution` for details.
      data_format: A string or None.  Specifies whether the channel dimension of
        the `input` and output is the last dimension (default, or if `data_format`
        does not start with "NC"), or the second dimension (if `data_format`
        starts with "NC").  For N=1, the valid values are "NWC" (default) and
        "NCW".  For N=2, the valid values are "NHWC" (default) and "NCHW". For
        N=3, the valid values are "NDHWC" (default) and "NCDHW".
      dilations: Optional.  Dilation rate.  List of N ints >= 1. Defaults to
        [1]*N.  If any value of dilation_rate is > 1, then all values of strides
        must be 1.
      name: Optional. Name of the op.
  
    Returns:
      Tensor of rank N+2, of shape
        [batch_size] + output_spatial_shape + [num_channels]
  
      if data_format is None or does not start with "NC", or
  
        [batch_size, num_channels] + output_spatial_shape
  
      if data_format starts with "NC",
      where `output_spatial_shape` depends on the value of padding:
  
      If padding = "SAME":
        output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i])
  
      If padding = "VALID":
        output_spatial_shape[i] =
          ceil((input_spatial_shape[i] - (window_shape[i] - 1) * dilation_rate[i])
               / strides[i]).
  
    Raises:
      ValueError: if arguments are invalid.
  
    """
    return pool(
        input=input,
        window_shape=window_shape,
        pooling_type=pooling_type,
        padding=padding,
        dilation_rate=dilations,
        strides=strides,
        name=name,
        data_format=data_format)
  
  
  @tf_export("nn.atrous_conv2d")
  @dispatch.add_dispatch_support
  def atrous_conv2d(value, filters, rate, padding, name=None):
    """Atrous convolution (a.k.a. convolution with holes or dilated convolution).
  
    This function is a simpler wrapper around the more general
    `tf.nn.convolution`, and exists only for backwards compatibility. You can
    use `tf.nn.convolution` to perform 1-D, 2-D, or 3-D atrous convolution.
  
  
    Computes a 2-D atrous convolution, also known as convolution with holes or
    dilated convolution, given 4-D `value` and `filters` tensors. If the `rate`
    parameter is equal to one, it performs regular 2-D convolution. If the `rate`
    parameter is greater than one, it performs convolution with holes, sampling
    the input values every `rate` pixels in the `height` and `width` dimensions.
    This is equivalent to convolving the input with a set of upsampled filters,
    produced by inserting `rate - 1` zeros between two consecutive values of the
    filters along the `height` and `width` dimensions, hence the name atrous
    convolution or convolution with holes (the French word trous means holes in
    English).
  
    More specifically:
  
    ```
    output[batch, height, width, out_channel] =
        sum_{dheight, dwidth, in_channel} (
            filters[dheight, dwidth, in_channel, out_channel] *
            value[batch, height + rate*dheight, width + rate*dwidth, in_channel]
        )
    ```
  
    Atrous convolution allows us to explicitly control how densely to compute
    feature responses in fully convolutional networks. Used in conjunction with
    bilinear interpolation, it offers an alternative to `conv2d_transpose` in
    dense prediction tasks such as semantic image segmentation, optical flow
    computation, or depth estimation. It also allows us to effectively enlarge
    the field of view of filters without increasing the number of parameters or
    the amount of computation.
  
    For a description of atrous convolution and how it can be used for dense
    feature extraction, please see: (Chen et al., 2015). The same operation is
    investigated further in (Yu et al., 2016). Previous works that effectively
    use atrous convolution in different ways are, among others,
    (Sermanet et al., 2014) and (Giusti et al., 2013).
    Atrous convolution is also closely related to the so-called noble identities
    in multi-rate signal processing.
  
    There are many different ways to implement atrous convolution (see the refs
    above). The implementation here reduces
  
    ```python
        atrous_conv2d(value, filters, rate, padding=padding)
    ```
  
    to the following three operations:
  
    ```python
        paddings = ...
        net = space_to_batch(value, paddings, block_size=rate)
        net = conv2d(net, filters, strides=[1, 1, 1, 1], padding="VALID")
        crops = ...
        net = batch_to_space(net, crops, block_size=rate)
    ```
  
    Advanced usage. Note the following optimization: A sequence of `atrous_conv2d`
    operations with identical `rate` parameters, 'SAME' `padding`, and filters
    with odd heights/ widths:
  
    ```python
        net = atrous_conv2d(net, filters1, rate, padding="SAME")
        net = atrous_conv2d(net, filters2, rate, padding="SAME")
        ...
        net = atrous_conv2d(net, filtersK, rate, padding="SAME")
    ```
  
    can be equivalently performed cheaper in terms of computation and memory as:
  
    ```python
        pad = ...  # padding so that the input dims are multiples of rate
        net = space_to_batch(net, paddings=pad, block_size=rate)
        net = conv2d(net, filters1, strides=[1, 1, 1, 1], padding="SAME")
        net = conv2d(net, filters2, strides=[1, 1, 1, 1], padding="SAME")
        ...
        net = conv2d(net, filtersK, strides=[1, 1, 1, 1], padding="SAME")
        net = batch_to_space(net, crops=pad, block_size=rate)
    ```
  
    because a pair of consecutive `space_to_batch` and `batch_to_space` ops with
    the same `block_size` cancel out when their respective `paddings` and `crops`
    inputs are identical.
  
    Args:
      value: A 4-D `Tensor` of type `float`. It needs to be in the default "NHWC"
        format. Its shape is `[batch, in_height, in_width, in_channels]`.
      filters: A 4-D `Tensor` with the same type as `value` and shape
        `[filter_height, filter_width, in_channels, out_channels]`. `filters`'
        `in_channels` dimension must match that of `value`. Atrous convolution is
        equivalent to standard convolution with upsampled filters with effective
        height `filter_height + (filter_height - 1) * (rate - 1)` and effective
        width `filter_width + (filter_width - 1) * (rate - 1)`, produced by
        inserting `rate - 1` zeros along consecutive elements across the
        `filters`' spatial dimensions.
      rate: A positive int32. The stride with which we sample input values across
        the `height` and `width` dimensions. Equivalently, the rate by which we
        upsample the filter values by inserting zeros across the `height` and
        `width` dimensions. In the literature, the same parameter is sometimes
        called `input stride` or `dilation`.
      padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
      name: Optional name for the returned tensor.
  
    Returns:
      A `Tensor` with the same type as `value`.
      Output shape with `'VALID'` padding is:
  
          [batch, height - 2 * (filter_width - 1),
           width - 2 * (filter_height - 1), out_channels].
  
      Output shape with `'SAME'` padding is:
  
          [batch, height, width, out_channels].
  
    Raises:
      ValueError: If input/output depth does not match `filters`' shape, or if
        padding is other than `'VALID'` or `'SAME'`.
  
    References:
      Multi-Scale Context Aggregation by Dilated Convolutions:
        [Yu et al., 2016](https://arxiv.org/abs/1511.07122)
        ([pdf](https://arxiv.org/pdf/1511.07122.pdf))
      Semantic Image Segmentation with Deep Convolutional Nets and Fully
      Connected CRFs:
        [Chen et al., 2015](http://arxiv.org/abs/1412.7062)
        ([pdf](https://arxiv.org/pdf/1412.7062))
      OverFeat - Integrated Recognition, Localization and Detection using
      Convolutional Networks:
        [Sermanet et al., 2014](https://arxiv.org/abs/1312.6229)
        ([pdf](https://arxiv.org/pdf/1312.6229.pdf))
      Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks:
        [Giusti et al., 2013]
        (https://ieeexplore.ieee.org/abstract/document/6738831)
        ([pdf](https://arxiv.org/pdf/1302.1700.pdf))
    """
    return convolution(
        input=value,
        filter=filters,
        padding=padding,
        dilation_rate=np.broadcast_to(rate, (2,)),
        name=name)
  
  
  def convert_padding(padding, expected_length=4):
    """Converts Python padding to C++ padding for ops which take EXPLICIT padding.
  
    Args:
      padding: the `padding` argument for a Python op which supports EXPLICIT
        padding.
      expected_length: Expected number of entries in the padding list when
        explicit padding is used.
  
    Returns:
      (padding, explicit_paddings) pair, which should be passed as attributes to a
      C++ op.
  
    Raises:
      ValueError: If padding is invalid.
    """
    explicit_paddings = []
    if padding == "EXPLICIT":
      # Give a better error message if EXPLICIT is passed.
      raise ValueError('"EXPLICIT" is not a valid value for the padding '
                       "parameter. To use explicit padding, the padding "
                       "parameter must be a list.")
    if isinstance(padding, (list, tuple)):
      for i, dim_paddings in enumerate(padding):
        if not isinstance(dim_paddings, (list, tuple)):
          raise ValueError("When padding is a list, each element of padding must "
                           "be a list/tuple of size 2. Element with index %d of "
                           "padding is not a list/tuple" % i)
        if len(dim_paddings) != 2:
          raise ValueError("When padding is a list, each element of padding must "
                           "be a list/tuple of size 2. Element with index %d of "
                           "padding has size %d" % (i, len(dim_paddings)))
        explicit_paddings.extend(dim_paddings)
      if len(padding) != expected_length:
        raise ValueError("When padding is a list, it must be of size %d. Got "
                         "padding of size: %d" % (expected_length, len(padding)))
      padding = "EXPLICIT"
    return padding, explicit_paddings
  
  
  @tf_export(v1=["nn.conv1d"])
  @dispatch.add_dispatch_support
  @deprecation.deprecated_arg_values(
      None,
      "`NCHW` for data_format is deprecated, use `NCW` instead",
      warn_once=True,
      data_format="NCHW")
  @deprecation.deprecated_arg_values(
      None,
      "`NHWC` for data_format is deprecated, use `NWC` instead",
      warn_once=True,
      data_format="NHWC")
  def conv1d(
      value=None,
      filters=None,
      stride=None,
      padding=None,
      use_cudnn_on_gpu=None,
      data_format=None,
      name=None,
      input=None,  # pylint: disable=redefined-builtin
      dilations=None):
    r"""Computes a 1-D convolution of input with rank `>=3` and a `3-D` filter.
  
    Given an input tensor of shape
      `batch_shape + [in_width, in_channels]`
    if `data_format` is `"NWC"`, or
      `batch_shape + [in_channels, in_width]`
    if `data_format` is `"NCW"`,
    and a filter / kernel tensor of shape
    `[filter_width, in_channels, out_channels]`, this op reshapes
    the arguments to pass them to `conv2d` to perform the equivalent
    convolution operation.
  
    Internally, this op reshapes the input tensors and invokes `tf.nn.conv2d`.
    For example, if `data_format` does not start with "NC", a tensor of shape
      `batch_shape + [in_width, in_channels]`
    is reshaped to
      `batch_shape + [1, in_width, in_channels]`,
    and the filter is reshaped to
      `[1, filter_width, in_channels, out_channels]`.
    The result is then reshaped back to
      `batch_shape + [out_width, out_channels]`
    \(where out_width is a function of the stride and padding as in conv2d\) and
    returned to the caller.
  
    Args:
      value: A Tensor of rank at least 3. Must be of type `float16`, `float32`, or
        `float64`.
      filters: A Tensor of rank at least 3.  Must have the same type as `value`.
      stride: An int or list of `ints` that has length `1` or `3`.  The number of
        entries by which the filter is moved right at each step.
      padding: 'SAME' or 'VALID'
      use_cudnn_on_gpu: An optional `bool`.  Defaults to `True`.
      data_format: An optional `string` from `"NWC", "NCW"`.  Defaults to `"NWC"`,
        the data is stored in the order of `batch_shape + [in_width,
        in_channels]`.  The `"NCW"` format stores data as `batch_shape +
        [in_channels, in_width]`.
      name: A name for the operation (optional).
      input: Alias for value.
      dilations: An int or list of `ints` that has length `1` or `3` which
        defaults to 1. The dilation factor for each dimension of input. If set to
        k > 1, there will be k-1 skipped cells between each filter element on that
        dimension. Dilations in the batch and depth dimensions must be 1.
  
    Returns:
      A `Tensor`.  Has the same type as input.
  
    Raises:
      ValueError: if `data_format` is invalid.
    """
    value = deprecation.deprecated_argument_lookup("input", input, "value", value)
    with ops.name_scope(name, "conv1d", [value, filters]) as name:
      # Reshape the input tensor to batch_shape + [1, in_width, in_channels]
      if data_format is None or data_format == "NHWC" or data_format == "NWC":
        data_format = "NHWC"
        spatial_start_dim = -3
        channel_index = 2
      elif data_format == "NCHW" or data_format == "NCW":
        data_format = "NCHW"
        spatial_start_dim = -2
        channel_index = 1
      else:
        raise ValueError("data_format must be \"NWC\" or \"NCW\".")
      strides = [1] + _get_sequence(stride, 1, channel_index, "stride")
      dilations = [1] + _get_sequence(dilations, 1, channel_index, "dilations")
  
      value = array_ops.expand_dims(value, spatial_start_dim)
      filters = array_ops.expand_dims(filters, 0)
      if value.shape.ndims in (4, 3, 2, 1, 0, None):
        result = gen_nn_ops.conv2d(
            value,
            filters,
            strides,
            padding,
            use_cudnn_on_gpu=use_cudnn_on_gpu,
            data_format=data_format,
            dilations=dilations,
            name=name)
      else:
        result = squeeze_batch_dims(
            value,
            functools.partial(
                gen_nn_ops.conv2d,
                filter=filters,
                strides=strides,
                padding=padding,
                use_cudnn_on_gpu=use_cudnn_on_gpu,
                data_format=data_format,
                dilations=dilations,
            ),
            inner_rank=3,
            name=name)
      return array_ops.squeeze(result, [spatial_start_dim])
  
  
  @tf_export("nn.conv1d", v1=[])
  @dispatch.add_dispatch_support
  def conv1d_v2(
      input,  # pylint: disable=redefined-builtin
      filters,
      stride,
      padding,
      data_format="NWC",
      dilations=None,
      name=None):
    r"""Computes a 1-D convolution given 3-D input and filter tensors.
  
    Given an input tensor of shape
      `batch_shape + [in_width, in_channels]`
    if `data_format` is `"NWC"`, or
      `batch_shape + [in_channels, in_width]`
    if `data_format` is `"NCW"`,
    and a filter / kernel tensor of shape
    `[filter_width, in_channels, out_channels]`, this op reshapes
    the arguments to pass them to `conv2d` to perform the equivalent
    convolution operation.
  
    Internally, this op reshapes the input tensors and invokes `tf.nn.conv2d`.
    For example, if `data_format` does not start with `"NC"`, a tensor of shape
      `batch_shape + [in_width, in_channels]`
    is reshaped to
      `batch_shape + [1, in_width, in_channels]`,
    and the filter is reshaped to
      `[1, filter_width, in_channels, out_channels]`.
    The result is then reshaped back to
      `batch_shape + [out_width, out_channels]`
    \(where out_width is a function of the stride and padding as in conv2d\) and
    returned to the caller.
  
    Args:
      input: A Tensor of rank at least 3. Must be of type `float16`, `float32`, or
        `float64`.
      filters: A Tensor of rank at least 3.  Must have the same type as `input`.
      stride: An int or list of `ints` that has length `1` or `3`.  The number of
        entries by which the filter is moved right at each step.
      padding: 'SAME' or 'VALID'
      data_format: An optional `string` from `"NWC", "NCW"`.  Defaults to `"NWC"`,
        the data is stored in the order of
        `batch_shape + [in_width, in_channels]`.  The `"NCW"` format stores data
        as `batch_shape + [in_channels, in_width]`.
      dilations: An int or list of `ints` that has length `1` or `3` which
        defaults to 1. The dilation factor for each dimension of input. If set to
        k > 1, there will be k-1 skipped cells between each filter element on that
        dimension. Dilations in the batch and depth dimensions must be 1.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`.  Has the same type as input.
  
    Raises:
      ValueError: if `data_format` is invalid.
    """
    return conv1d(
        input,  # pylint: disable=redefined-builtin
        filters,
        stride,
        padding,
        use_cudnn_on_gpu=True,
        data_format=data_format,
        name=name,
        dilations=dilations)
  
  
  @tf_export("nn.conv1d_transpose")
  @dispatch.add_dispatch_support
  def conv1d_transpose(
      input,  # pylint: disable=redefined-builtin
      filters,
      output_shape,
      strides,
      padding="SAME",
      data_format="NWC",
      dilations=None,
      name=None):
    """The transpose of `conv1d`.
  
    This operation is sometimes called "deconvolution" after
    (Zeiler et al., 2010), but is actually the transpose (gradient) of `conv1d`
    rather than an actual deconvolution.
  
    Args:
      input: A 3-D `Tensor` of type `float` and shape
        `[batch, in_width, in_channels]` for `NWC` data format or
        `[batch, in_channels, in_width]` for `NCW` data format.
      filters: A 3-D `Tensor` with the same type as `input` and shape
        `[filter_width, output_channels, in_channels]`.  `filter`'s
        `in_channels` dimension must match that of `input`.
      output_shape: A 1-D `Tensor`, containing three elements, representing the
        output shape of the deconvolution op.
      strides: An int or list of `ints` that has length `1` or `3`.  The number of
        entries by which the filter is moved right at each step.
      padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
        See the "returns" section of `tf.nn.convolution` for details.
      data_format: A string. `'NWC'` and `'NCW'` are supported.
      dilations: An int or list of `ints` that has length `1` or `3` which
        defaults to 1. The dilation factor for each dimension of input. If set to
        k > 1, there will be k-1 skipped cells between each filter element on that
        dimension. Dilations in the batch and depth dimensions must be 1.
      name: Optional name for the returned tensor.
  
    Returns:
      A `Tensor` with the same type as `input`.
  
    Raises:
      ValueError: If input/output depth does not match `filter`'s shape, if
        `output_shape` is not at 3-element vector, if `padding` is other than
        `'VALID'` or `'SAME'`, or if `data_format` is invalid.
  
    References:
      Deconvolutional Networks:
        [Zeiler et al., 2010]
        (https://ieeexplore.ieee.org/abstract/document/5539957)
        ([pdf]
        (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.232.4023&rep=rep1&type=pdf))
    """
    with ops.name_scope(name, "conv1d_transpose",
                        [input, filters, output_shape]) as name:
      # The format could be either NWC or NCW, map to NHWC or NCHW
      if data_format is None or data_format == "NWC":
        data_format = "NHWC"
        spatial_start_dim = 1
        channel_index = 2
      elif data_format == "NCW":
        data_format = "NCHW"
        spatial_start_dim = 2
        channel_index = 1
      else:
        raise ValueError("data_format must be \"NWC\" or \"NCW\".")
  
      # Reshape the input tensor to [batch, 1, in_width, in_channels]
      strides = [1] + _get_sequence(strides, 1, channel_index, "stride")
      dilations = [1] + _get_sequence(dilations, 1, channel_index, "dilations")
  
      input = array_ops.expand_dims(input, spatial_start_dim)
      filters = array_ops.expand_dims(filters, 0)
      output_shape = list(output_shape) if not isinstance(
          output_shape, ops.Tensor) else output_shape
      output_shape = array_ops.concat([output_shape[: spatial_start_dim], [1],
                                       output_shape[spatial_start_dim:]], 0)
  
      result = gen_nn_ops.conv2d_backprop_input(
          input_sizes=output_shape,
          filter=filters,
          out_backprop=input,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilations=dilations,
          name=name)
      return array_ops.squeeze(result, spatial_start_dim)
  
  
  @tf_export("nn.conv2d", v1=[])
  @dispatch.add_dispatch_support
  def conv2d_v2(input,  # pylint: disable=redefined-builtin
                filters,
                strides,
                padding,
                data_format="NHWC",
                dilations=None,
                name=None):
    # pylint: disable=line-too-long
    r"""Computes a 2-D convolution given `input` and 4-D `filters` tensors.
  
    The `input` tensor may have rank `4` or higher, where shape dimensions `[:-3]`
    are considered batch dimensions (`batch_shape`).
  
    Given an input tensor of shape
    `batch_shape + [in_height, in_width, in_channels]` and a filter / kernel
    tensor of shape `[filter_height, filter_width, in_channels, out_channels]`,
    this op performs the following:
  
    1. Flattens the filter to a 2-D matrix with shape
       `[filter_height * filter_width * in_channels, output_channels]`.
    2. Extracts image patches from the input tensor to form a *virtual*
       tensor of shape `[batch, out_height, out_width,
       filter_height * filter_width * in_channels]`.
    3. For each patch, right-multiplies the filter matrix and the image patch
       vector.
  
    In detail, with the default NHWC format,
  
        output[b, i, j, k] =
            sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
                            filter[di, dj, q, k]
  
    Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
    horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
  
    Usage Example:
  
    >>> x_in = np.array([[
    ...   [[2], [1], [2], [0], [1]],
    ...   [[1], [3], [2], [2], [3]],
    ...   [[1], [1], [3], [3], [0]],
    ...   [[2], [2], [0], [1], [1]],
    ...   [[0], [0], [3], [1], [2]], ]])
    >>> kernel_in = np.array([
    ...  [ [[2, 0.1]], [[3, 0.2]] ],
    ...  [ [[0, 0.3]],[[1, 0.4]] ], ])
    >>> x = tf.constant(x_in, dtype=tf.float32)
    >>> kernel = tf.constant(kernel_in, dtype=tf.float32)
    >>> tf.nn.conv2d(x, kernel, strides=[1, 1, 1, 1], padding='VALID')
    <tf.Tensor: shape=(1, 4, 4, 2), dtype=float32, numpy=..., dtype=float32)>
  
    Args:
      input: A `Tensor`. Must be one of the following types:
        `half`, `bfloat16`, `float32`, `float64`.
        A Tensor of rank at least 4. The dimension order is interpreted according
        to the value of `data_format`; with the all-but-inner-3 dimensions acting
        as batch dimensions. See below for details.
      filters: A `Tensor`. Must have the same type as `input`.
        A 4-D tensor of shape
        `[filter_height, filter_width, in_channels, out_channels]`
      strides: An int or list of `ints` that has length `1`, `2` or `4`.  The
        stride of the sliding window for each dimension of `input`. If a single
        value is given it is replicated in the `H` and `W` dimension. By default
        the `N` and `C` dimensions are set to 1. The dimension order is determined
        by the value of `data_format`, see below for details.
      padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
        padding algorithm to use, or a list indicating the explicit paddings at
        the start and end of each dimension. When explicit padding is used and
        data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,
        pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used
        and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0],
        [pad_top, pad_bottom], [pad_left, pad_right]]`.
      data_format: An optional `string` from: `"NHWC", "NCHW"`.
        Defaults to `"NHWC"`.
        Specify the data format of the input and output data. With the
        default format "NHWC", the data is stored in the order of:
            `batch_shape + [height, width, channels]`.
        Alternatively, the format could be "NCHW", the data storage order of:
            `batch_shape + [channels, height, width]`.
      dilations: An int or list of `ints` that has length `1`, `2` or `4`,
        defaults to 1. The dilation factor for each dimension of`input`. If a
        single value is given it is replicated in the `H` and `W` dimension. By
        default the `N` and `C` dimensions are set to 1. If set to k > 1, there
        will be k-1 skipped cells between each filter element on that dimension.
        The dimension order is determined by the value of `data_format`, see above
        for details. Dilations in the batch and depth dimensions if a 4-d tensor
        must be 1.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `input` and the same outer batch shape.
    """
    # pylint: enable=line-too-long
    return conv2d(input,  # pylint: disable=redefined-builtin
                  filters,
                  strides,
                  padding,
                  use_cudnn_on_gpu=True,
                  data_format=data_format,
                  dilations=dilations,
                  name=name)
  
  
  @tf_export(v1=["nn.conv2d"])
  @dispatch.add_dispatch_support
  def conv2d(  # pylint: disable=redefined-builtin,dangerous-default-value
      input,
      filter=None,
      strides=None,
      padding=None,
      use_cudnn_on_gpu=True,
      data_format="NHWC",
      dilations=[1, 1, 1, 1],
      name=None,
      filters=None):
    r"""Computes a 2-D convolution given 4-D `input` and `filter` tensors.
  
    Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
    and a filter / kernel tensor of shape
    `[filter_height, filter_width, in_channels, out_channels]`, this op
    performs the following:
  
    1. Flattens the filter to a 2-D matrix with shape
       `[filter_height * filter_width * in_channels, output_channels]`.
    2. Extracts image patches from the input tensor to form a *virtual*
       tensor of shape `[batch, out_height, out_width,
       filter_height * filter_width * in_channels]`.
    3. For each patch, right-multiplies the filter matrix and the image patch
       vector.
  
    In detail, with the default NHWC format,
  
        output[b, i, j, k] =
            sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q]
                            * filter[di, dj, q, k]
  
    Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
    horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
  
    Args:
      input: A `Tensor`. Must be one of the following types:
        `half`, `bfloat16`, `float32`, `float64`.
        A 4-D tensor. The dimension order is interpreted according to the value
        of `data_format`, see below for details.
      filter: A `Tensor`. Must have the same type as `input`.
        A 4-D tensor of shape
        `[filter_height, filter_width, in_channels, out_channels]`
      strides: An int or list of `ints` that has length `1`, `2` or `4`.  The
        stride of the sliding window for each dimension of `input`. If a single
        value is given it is replicated in the `H` and `W` dimension. By default
        the `N` and `C` dimensions are set to 1. The dimension order is determined
        by the value of `data_format`, see below for details.
      padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
        padding algorithm to use, or a list indicating the explicit paddings at
        the start and end of each dimension. When explicit padding is used and
        data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,
        pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used
        and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0],
        [pad_top, pad_bottom], [pad_left, pad_right]]`.
      use_cudnn_on_gpu: An optional `bool`. Defaults to `True`.
      data_format: An optional `string` from: `"NHWC", "NCHW"`.
        Defaults to `"NHWC"`.
        Specify the data format of the input and output data. With the
        default format "NHWC", the data is stored in the order of:
            [batch, height, width, channels].
        Alternatively, the format could be "NCHW", the data storage order of:
            [batch, channels, height, width].
      dilations: An int or list of `ints` that has length `1`, `2` or `4`,
        defaults to 1. The dilation factor for each dimension of`input`. If a
        single value is given it is replicated in the `H` and `W` dimension. By
        default the `N` and `C` dimensions are set to 1. If set to k > 1, there
        will be k-1 skipped cells between each filter element on that dimension.
        The dimension order is determined by the value of `data_format`, see above
        for details. Dilations in the batch and depth dimensions if a 4-d tensor
        must be 1.
      name: A name for the operation (optional).
      filters: Alias for filter.
  
    Returns:
      A `Tensor`. Has the same type as `input`.
    """
    filter = deprecation.deprecated_argument_lookup(
        "filters", filters, "filter", filter)
    padding, explicit_paddings = convert_padding(padding)
    if data_format is None:
      data_format = "NHWC"
    channel_index = 1 if data_format.startswith("NC") else 3
  
    strides = _get_sequence(strides, 2, channel_index, "strides")
    dilations = _get_sequence(dilations, 2, channel_index, "dilations")
  
    shape = input.shape
    # shape object may lack ndims, e.g., if input is an np.ndarray.  In that case,
    # we fall back to len(shape).
    ndims = getattr(shape, "ndims", -1)
    if ndims == -1:
      ndims = len(shape)
    if ndims in (4, 3, 2, 1, 0, None):
      # We avoid calling squeeze_batch_dims to reduce extra python function
      # call slowdown in eager mode.  This branch doesn't require reshapes.
      return gen_nn_ops.conv2d(
          input,
          filter=filter,
          strides=strides,
          padding=padding,
          use_cudnn_on_gpu=use_cudnn_on_gpu,
          explicit_paddings=explicit_paddings,
          data_format=data_format,
          dilations=dilations,
          name=name)
    return squeeze_batch_dims(
        input,
        functools.partial(
            gen_nn_ops.conv2d,
            filter=filter,
            strides=strides,
            padding=padding,
            use_cudnn_on_gpu=use_cudnn_on_gpu,
            explicit_paddings=explicit_paddings,
            data_format=data_format,
            dilations=dilations),
        inner_rank=3,
        name=name)
  
  
  @tf_export(v1=["nn.conv2d_backprop_filter"])
  @dispatch.add_dispatch_support
  def conv2d_backprop_filter(  # pylint: disable=redefined-builtin,dangerous-default-value
      input,
      filter_sizes,
      out_backprop,
      strides,
      padding,
      use_cudnn_on_gpu=True,
      data_format="NHWC",
      dilations=[1, 1, 1, 1],
      name=None):
    r"""Computes the gradients of convolution with respect to the filter.
  
    Args:
      input: A `Tensor`. Must be one of the following types:
        `half`, `bfloat16`, `float32`, `float64`.
        4-D with shape `[batch, in_height, in_width, in_channels]`.
      filter_sizes: A `Tensor` of type `int32`.
        An integer vector representing the tensor shape of `filter`,
        where `filter` is a 4-D
        `[filter_height, filter_width, in_channels, out_channels]` tensor.
      out_backprop: A `Tensor`. Must have the same type as `input`.
        4-D with shape `[batch, out_height, out_width, out_channels]`.
        Gradients w.r.t. the output of the convolution.
      strides: A list of `ints`.
        The stride of the sliding window for each dimension of the input
        of the convolution. Must be in the same order as the dimension specified
        with format.
      padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
        padding algorithm to use, or a list indicating the explicit paddings at
        the start and end of each dimension. When explicit padding is used and
        data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,
        pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used
        and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0],
        [pad_top, pad_bottom], [pad_left, pad_right]]`.
      use_cudnn_on_gpu: An optional `bool`. Defaults to `True`.
      data_format: An optional `string` from: `"NHWC", "NCHW"`.
        Defaults to `"NHWC"`.
        Specify the data format of the input and output data. With the
        default format "NHWC", the data is stored in the order of:
            [batch, in_height, in_width, in_channels].
        Alternatively, the format could be "NCHW", the data storage order of:
            [batch, in_channels, in_height, in_width].
      dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`.
        1-D tensor of length 4.  The dilation factor for each dimension of
        `input`. If set to k > 1, there will be k-1 skipped cells between each
        filter element on that dimension. The dimension order is determined by
        the value of `data_format`, see above for details. Dilations in the batch
        and depth dimensions must be 1.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
    """
    padding, explicit_paddings = convert_padding(padding)
    return gen_nn_ops.conv2d_backprop_filter(
        input, filter_sizes, out_backprop, strides, padding, use_cudnn_on_gpu,
        explicit_paddings, data_format, dilations, name)
  
  
  @tf_export(v1=["nn.conv2d_backprop_input"])
  @dispatch.add_dispatch_support
  def conv2d_backprop_input(  # pylint: disable=redefined-builtin,dangerous-default-value
      input_sizes,
      filter=None,
      out_backprop=None,
      strides=None,
      padding=None,
      use_cudnn_on_gpu=True,
      data_format="NHWC",
      dilations=[1, 1, 1, 1],
      name=None,
      filters=None):
    r"""Computes the gradients of convolution with respect to the input.
  
    Args:
      input_sizes: A `Tensor` of type `int32`.
        An integer vector representing the shape of `input`,
        where `input` is a 4-D `[batch, height, width, channels]` tensor.
      filter: A `Tensor`. Must be one of the following types:
        `half`, `bfloat16`, `float32`, `float64`.
        4-D with shape
        `[filter_height, filter_width, in_channels, out_channels]`.
      out_backprop: A `Tensor`. Must have the same type as `filter`.
        4-D with shape `[batch, out_height, out_width, out_channels]`.
        Gradients w.r.t. the output of the convolution.
      strides: A list of `ints`.
        The stride of the sliding window for each dimension of the input
        of the convolution. Must be in the same order as the dimension specified
        with format.
      padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
        padding algorithm to use, or a list indicating the explicit paddings at
        the start and end of each dimension. When explicit padding is used and
        data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,
        pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used
        and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0],
        [pad_top, pad_bottom], [pad_left, pad_right]]`.
      use_cudnn_on_gpu: An optional `bool`. Defaults to `True`.
      data_format: An optional `string` from: `"NHWC", "NCHW"`.
        Defaults to `"NHWC"`.
        Specify the data format of the input and output data. With the
        default format "NHWC", the data is stored in the order of:
            [batch, in_height, in_width, in_channels].
        Alternatively, the format could be "NCHW", the data storage order of:
            [batch, in_channels, in_height, in_width].
      dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`.
        1-D tensor of length 4.  The dilation factor for each dimension of
        `input`. If set to k > 1, there will be k-1 skipped cells between each
        filter element on that dimension. The dimension order is determined by
        the value of `data_format`, see above for details. Dilations in the batch
        and depth dimensions must be 1.
      name: A name for the operation (optional).
      filters: Alias for filter.
  
    Returns:
      A `Tensor`. Has the same type as `filter`.
    """
    filter = deprecation.deprecated_argument_lookup(
        "filters", filters, "filter", filter)
    padding, explicit_paddings = convert_padding(padding)
    return gen_nn_ops.conv2d_backprop_input(
        input_sizes, filter, out_backprop, strides, padding, use_cudnn_on_gpu,
        explicit_paddings, data_format, dilations, name)
  
  
  @tf_export(v1=["nn.conv2d_transpose"])
  @dispatch.add_dispatch_support
  def conv2d_transpose(
      value=None,
      filter=None,  # pylint: disable=redefined-builtin
      output_shape=None,
      strides=None,
      padding="SAME",
      data_format="NHWC",
      name=None,
      input=None,  # pylint: disable=redefined-builtin
      filters=None,
      dilations=None):
    """The transpose of `conv2d`.
  
    This operation is sometimes called "deconvolution" after
    (Zeiler et al., 2010), but is really the transpose (gradient) of `conv2d`
    rather than an actual deconvolution.
  
    Args:
      value: A 4-D `Tensor` of type `float` and shape
        `[batch, height, width, in_channels]` for `NHWC` data format or
        `[batch, in_channels, height, width]` for `NCHW` data format.
      filter: A 4-D `Tensor` with the same type as `value` and shape
        `[height, width, output_channels, in_channels]`.  `filter`'s
        `in_channels` dimension must match that of `value`.
      output_shape: A 1-D `Tensor` representing the output shape of the
        deconvolution op.
      strides: An int or list of `ints` that has length `1`, `2` or `4`.  The
        stride of the sliding window for each dimension of `input`. If a single
        value is given it is replicated in the `H` and `W` dimension. By default
        the `N` and `C` dimensions are set to 0. The dimension order is determined
        by the value of `data_format`, see below for details.
      padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
        See the "returns" section of `tf.nn.convolution` for details.
      data_format: A string. 'NHWC' and 'NCHW' are supported.
      name: Optional name for the returned tensor.
      input: Alias for value.
      filters: Alias for filter.
      dilations: An int or list of `ints` that has length `1`, `2` or `4`,
        defaults to 1. The dilation factor for each dimension of`input`. If a
        single value is given it is replicated in the `H` and `W` dimension. By
        default the `N` and `C` dimensions are set to 1. If set to k > 1, there
        will be k-1 skipped cells between each filter element on that dimension.
        The dimension order is determined by the value of `data_format`, see above
        for details. Dilations in the batch and depth dimensions if a 4-d tensor
        must be 1.
  
    Returns:
      A `Tensor` with the same type as `value`.
  
    Raises:
      ValueError: If input/output depth does not match `filter`'s shape, or if
        padding is other than `'VALID'` or `'SAME'`.
  
    References:
      Deconvolutional Networks:
        [Zeiler et al., 2010]
        (https://ieeexplore.ieee.org/abstract/document/5539957)
        ([pdf]
        (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.232.4023&rep=rep1&type=pdf))
    """
    value = deprecated_argument_lookup("input", input, "value", value)
    filter = deprecated_argument_lookup("filters", filters, "filter", filter)
    with ops.name_scope(name, "conv2d_transpose",
                        [value, filter, output_shape]) as name:
      return conv2d_transpose_v2(
          value,
          filter,
          output_shape,
          strides,
          padding=padding,
          data_format=data_format,
          dilations=dilations,
          name=name)
  
  
  @tf_export("nn.conv2d_transpose", v1=[])
  @dispatch.add_dispatch_support
  def conv2d_transpose_v2(
      input,  # pylint: disable=redefined-builtin
      filters,  # pylint: disable=redefined-builtin
      output_shape,
      strides,
      padding="SAME",
      data_format="NHWC",
      dilations=None,
      name=None):
    """The transpose of `conv2d`.
  
    This operation is sometimes called "deconvolution" after
    (Zeiler et al., 2010), but is really the transpose (gradient) of
    `atrous_conv2d` rather than an actual deconvolution.
  
    Args:
      input: A 4-D `Tensor` of type `float` and shape `[batch, height, width,
        in_channels]` for `NHWC` data format or `[batch, in_channels, height,
        width]` for `NCHW` data format.
      filters: A 4-D `Tensor` with the same type as `input` and shape `[height,
        width, output_channels, in_channels]`.  `filter`'s `in_channels` dimension
        must match that of `input`.
      output_shape: A 1-D `Tensor` representing the output shape of the
        deconvolution op.
      strides: An int or list of `ints` that has length `1`, `2` or `4`.  The
        stride of the sliding window for each dimension of `input`. If a single
        value is given it is replicated in the `H` and `W` dimension. By default
        the `N` and `C` dimensions are set to 0. The dimension order is determined
        by the value of `data_format`, see below for details.
      padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
        padding algorithm to use, or a list indicating the explicit paddings at
        the start and end of each dimension. When explicit padding is used and
        data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,
        pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used
        and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0],
        [pad_top, pad_bottom], [pad_left, pad_right]]`.
      data_format: A string. 'NHWC' and 'NCHW' are supported.
      dilations: An int or list of `ints` that has length `1`, `2` or `4`,
        defaults to 1. The dilation factor for each dimension of`input`. If a
        single value is given it is replicated in the `H` and `W` dimension. By
        default the `N` and `C` dimensions are set to 1. If set to k > 1, there
        will be k-1 skipped cells between each filter element on that dimension.
        The dimension order is determined by the value of `data_format`, see above
        for details. Dilations in the batch and depth dimensions if a 4-d tensor
        must be 1.
      name: Optional name for the returned tensor.
  
    Returns:
      A `Tensor` with the same type as `input`.
  
    Raises:
      ValueError: If input/output depth does not match `filter`'s shape, or if
        padding is other than `'VALID'` or `'SAME'`.
  
    References:
      Deconvolutional Networks:
        [Zeiler et al., 2010]
        (https://ieeexplore.ieee.org/abstract/document/5539957)
        ([pdf]
        (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.232.4023&rep=rep1&type=pdf))
    """
    with ops.name_scope(name, "conv2d_transpose",
                        [input, filter, output_shape]) as name:
      if data_format is None:
        data_format = "NHWC"
      channel_index = 1 if data_format.startswith("NC") else 3
  
      strides = _get_sequence(strides, 2, channel_index, "strides")
      dilations = _get_sequence(dilations, 2, channel_index, "dilations")
      padding, explicit_paddings = convert_padding(padding)
  
      return gen_nn_ops.conv2d_backprop_input(
          input_sizes=output_shape,
          filter=filters,
          out_backprop=input,
          strides=strides,
          padding=padding,
          explicit_paddings=explicit_paddings,
          data_format=data_format,
          dilations=dilations,
          name=name)
  
  
  def _conv2d_expanded_batch(
      input,  # pylint: disable=redefined-builtin
      filters,
      strides,
      padding,
      data_format,
      dilations,
      name):
    """Helper function for `convolution_internal`; handles expanded batches."""
    # Try really hard to avoid modifying the legacy name scopes - return early.
    input_rank = input.shape.rank
    if input_rank is None or input_rank < 5:
      # We avoid calling squeeze_batch_dims to reduce extra python function
      # call slowdown in eager mode.  This branch doesn't require reshapes.
      return gen_nn_ops.conv2d(
          input,
          filter=filters,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilations=dilations,
          name=name)
    return squeeze_batch_dims(
        input,
        functools.partial(
            gen_nn_ops.conv2d,
            filter=filters,
            strides=strides,
            padding=padding,
            data_format=data_format,
            dilations=dilations),
        inner_rank=3,
        name=name)
  
  
  @tf_export("nn.atrous_conv2d_transpose")
  @dispatch.add_dispatch_support
  def atrous_conv2d_transpose(value,
                              filters,
                              output_shape,
                              rate,
                              padding,
                              name=None):
    """The transpose of `atrous_conv2d`.
  
    This operation is sometimes called "deconvolution" after
    (Zeiler et al., 2010), but is really the transpose (gradient) of
    `atrous_conv2d` rather than an actual deconvolution.
  
    Args:
      value: A 4-D `Tensor` of type `float`. It needs to be in the default `NHWC`
        format. Its shape is `[batch, in_height, in_width, in_channels]`.
      filters: A 4-D `Tensor` with the same type as `value` and shape
        `[filter_height, filter_width, out_channels, in_channels]`. `filters`'
        `in_channels` dimension must match that of `value`. Atrous convolution is
        equivalent to standard convolution with upsampled filters with effective
        height `filter_height + (filter_height - 1) * (rate - 1)` and effective
        width `filter_width + (filter_width - 1) * (rate - 1)`, produced by
        inserting `rate - 1` zeros along consecutive elements across the
        `filters`' spatial dimensions.
      output_shape: A 1-D `Tensor` of shape representing the output shape of the
        deconvolution op.
      rate: A positive int32. The stride with which we sample input values across
        the `height` and `width` dimensions. Equivalently, the rate by which we
        upsample the filter values by inserting zeros across the `height` and
        `width` dimensions. In the literature, the same parameter is sometimes
        called `input stride` or `dilation`.
      padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
      name: Optional name for the returned tensor.
  
    Returns:
      A `Tensor` with the same type as `value`.
  
    Raises:
      ValueError: If input/output depth does not match `filters`' shape, or if
        padding is other than `'VALID'` or `'SAME'`, or if the `rate` is less
        than one, or if the output_shape is not a tensor with 4 elements.
  
    References:
      Deconvolutional Networks:
        [Zeiler et al., 2010]
        (https://ieeexplore.ieee.org/abstract/document/5539957)
        ([pdf]
        (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.232.4023&rep=rep1&type=pdf))
    """
    with ops.name_scope(name, "atrous_conv2d_transpose",
                        [value, filters, output_shape]) as name:
      value = ops.convert_to_tensor(value, name="value")
      filters = ops.convert_to_tensor(filters, name="filters")
      if not value.get_shape().dims[3].is_compatible_with(filters.get_shape()[3]):
        raise ValueError(
            "value's input channels does not match filters' input channels, "
            "{} != {}".format(value.get_shape()[3],
                              filters.get_shape()[3]))
      if rate < 1:
        raise ValueError("rate {} cannot be less than one".format(rate))
  
      if rate == 1:
        return conv2d_transpose(
            value,
            filters,
            output_shape,
            strides=[1, 1, 1, 1],
            padding=padding,
            data_format="NHWC")
  
      output_shape_ = ops.convert_to_tensor(output_shape, name="output_shape")
      if not output_shape_.get_shape().is_compatible_with(
          tensor_shape.TensorShape([4])):
        raise ValueError("output_shape must have shape (4,), got {}".format(
            output_shape_.get_shape()))
  
      if isinstance(output_shape, tuple):
        output_shape = list(output_shape)
  
      if isinstance(output_shape, (list, np.ndarray)):
        # output_shape's shape should be == [4] if reached this point.
        if not filters.get_shape().dims[2].is_compatible_with(output_shape[3]):
          raise ValueError(
              "output_shape does not match filter's output channels, "
              "{} != {}".format(output_shape[3],
                                filters.get_shape()[2]))
  
      # We have two padding contributions. The first is used for converting "SAME"
      # to "VALID". The second is required so that the height and width of the
      # zero-padded value tensor are multiples of rate.
  
      # Padding required to reduce to "VALID" convolution
      if padding == "SAME":
        # Handle filters whose shape is unknown during graph creation.
        if filters.get_shape().is_fully_defined():
          filter_shape = filters.get_shape().as_list()
        else:
          filter_shape = array_ops.shape(filters)
        filter_height, filter_width = filter_shape[0], filter_shape[1]
  
        # Spatial dimensions of the filters and the upsampled filters in which we
        # introduce (rate - 1) zeros between consecutive filter values.
        filter_height_up = filter_height + (filter_height - 1) * (rate - 1)
        filter_width_up = filter_width + (filter_width - 1) * (rate - 1)
  
        pad_height = filter_height_up - 1
        pad_width = filter_width_up - 1
  
        # When pad_height (pad_width) is odd, we pad more to bottom (right),
        # following the same convention as conv2d().
        pad_top = pad_height // 2
        pad_bottom = pad_height - pad_top
        pad_left = pad_width // 2
        pad_right = pad_width - pad_left
      elif padding == "VALID":
        pad_top = 0
        pad_bottom = 0
        pad_left = 0
        pad_right = 0
      else:
        raise ValueError("padding must be either VALID or SAME:"
                         " {}".format(padding))
  
      in_height = output_shape[1] + pad_top + pad_bottom
      in_width = output_shape[2] + pad_left + pad_right
  
      # More padding so that rate divides the height and width of the input.
      pad_bottom_extra = (rate - in_height % rate) % rate
      pad_right_extra = (rate - in_width % rate) % rate
  
      # The paddings argument to space_to_batch is just the extra padding
      # component.
      space_to_batch_pad = [[0, pad_bottom_extra], [0, pad_right_extra]]
  
      value = array_ops.space_to_batch(
          input=value, paddings=space_to_batch_pad, block_size=rate)
  
      input_sizes = [
          rate * rate * output_shape[0], (in_height + pad_bottom_extra) // rate,
          (in_width + pad_right_extra) // rate, output_shape[3]
      ]
  
      value = gen_nn_ops.conv2d_backprop_input(
          input_sizes=input_sizes,
          filter=filters,
          out_backprop=value,
          strides=[1, 1, 1, 1],
          padding="VALID",
          data_format="NHWC")
  
      # The crops argument to batch_to_space includes both padding components.
      batch_to_space_crop = [[pad_top, pad_bottom + pad_bottom_extra],
                             [pad_left, pad_right + pad_right_extra]]
  
      return array_ops.batch_to_space(
          input=value, crops=batch_to_space_crop, block_size=rate)
  
  
  @tf_export(v1=["nn.depthwise_conv2d_native"])
  @dispatch.add_dispatch_support
  @deprecation.deprecated_endpoints("nn.depthwise_conv2d_native")
  def depthwise_conv2d_native(  # pylint: disable=redefined-builtin,dangerous-default-value
      input,
      filter,
      strides,
      padding,
      data_format="NHWC",
      dilations=[1, 1, 1, 1],
      name=None):
    r"""Computes a 2-D depthwise convolution.
  
    Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
    and a filter / kernel tensor of shape
    `[filter_height, filter_width, in_channels, channel_multiplier]`, containing
    `in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies
    a different filter to each input channel (expanding from 1 channel to
    `channel_multiplier` channels for each), then concatenates the results
    together. Thus, the output has `in_channels * channel_multiplier` channels.
  
    ```
    for k in 0..in_channels-1
      for q in 0..channel_multiplier-1
        output[b, i, j, k * channel_multiplier + q] =
          sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
                            filter[di, dj, k, q]
    ```
  
    Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
    horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`,
        `float32`, `float64`.
      filter: A `Tensor`. Must have the same type as `input`.
      strides: A list of `ints`. 1-D of length 4.  The stride of the sliding
        window for each dimension of `input`.
      padding: Controls how to pad the image before applying the convolution. Can
        be the string `"SAME"` or `"VALID"` indicating the type of padding
        algorithm to use, or a list indicating the explicit paddings at the start
        and end of each dimension. When explicit padding is used and data_format
        is `"NHWC"`, this should be in the form `[[0, 0], [pad_top, pad_bottom],
        [pad_left, pad_right], [0, 0]]`. When explicit padding used and
        data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0],
        [pad_top, pad_bottom], [pad_left, pad_right]]`.
      data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to
        `"NHWC"`. Specify the data format of the input and output data. With the
        default format "NHWC", the data is stored in the order of: [batch, height,
          width, channels].
        Alternatively, the format could be "NCHW", the data storage order of:
          [batch, channels, height, width].
      dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D
        tensor of length 4.  The dilation factor for each dimension of `input`. If
        set to k > 1, there will be k-1 skipped cells between each filter element
        on that dimension. The dimension order is determined by the value of
        `data_format`, see above for details. Dilations in the batch and depth
        dimensions must be 1.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
    """
    padding, explicit_paddings = convert_padding(padding)
    return gen_nn_ops.depthwise_conv2d_native(
        input,
        filter,
        strides,
        padding,
        explicit_paddings=explicit_paddings,
        data_format=data_format,
        dilations=dilations,
        name=name)
  
  
  @tf_export(
      "nn.depthwise_conv2d_backprop_input",
      v1=[
          "nn.depthwise_conv2d_native_backprop_input",
          "nn.depthwise_conv2d_backprop_input"
      ])
  @dispatch.add_dispatch_support
  @deprecation.deprecated_endpoints("nn.depthwise_conv2d_native_backprop_input")
  def depthwise_conv2d_native_backprop_input(  # pylint: disable=redefined-builtin,dangerous-default-value
      input_sizes,
      filter,
      out_backprop,
      strides,
      padding,
      data_format="NHWC",
      dilations=[1, 1, 1, 1],
      name=None):
    r"""Computes the gradients of depthwise convolution with respect to the input.
  
    Args:
      input_sizes: A `Tensor` of type `int32`. An integer vector representing the
        shape of `input`, based on `data_format`.  For example, if `data_format`
        is 'NHWC' then `input` is a 4-D `[batch, height, width, channels]` tensor.
      filter: A `Tensor`. Must be one of the following types: `half`, `bfloat16`,
        `float32`, `float64`. 4-D with shape `[filter_height, filter_width,
        in_channels, depthwise_multiplier]`.
      out_backprop: A `Tensor`. Must have the same type as `filter`. 4-D with
        shape  based on `data_format`. For example, if `data_format` is 'NHWC'
        then out_backprop shape is `[batch, out_height, out_width, out_channels]`.
        Gradients w.r.t. the output of the convolution.
      strides: A list of `ints`. The stride of the sliding window for each
        dimension of the input of the convolution.
      padding: Controls how to pad the image before applying the convolution. Can
        be the string `"SAME"` or `"VALID"` indicating the type of padding
        algorithm to use, or a list indicating the explicit paddings at the start
        and end of each dimension. When explicit padding is used and data_format
        is `"NHWC"`, this should be in the form `[[0, 0], [pad_top, pad_bottom],
        [pad_left, pad_right], [0, 0]]`. When explicit padding used and
        data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0],
        [pad_top, pad_bottom], [pad_left, pad_right]]`.
      data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to
        `"NHWC"`. Specify the data format of the input and output data. With the
        default format "NHWC", the data is stored in the order of: [batch, height,
          width, channels].
        Alternatively, the format could be "NCHW", the data storage order of:
          [batch, channels, height, width].
      dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D
        tensor of length 4.  The dilation factor for each dimension of `input`. If
        set to k > 1, there will be k-1 skipped cells between each filter element
        on that dimension. The dimension order is determined by the value of
        `data_format`, see above for details. Dilations in the batch and depth
        dimensions must be 1.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `filter`.
    """
    padding, explicit_paddings = convert_padding(padding)
    return gen_nn_ops.depthwise_conv2d_native_backprop_input(
        input_sizes,
        filter,
        out_backprop,
        strides,
        padding,
        explicit_paddings=explicit_paddings,
        data_format=data_format,
        dilations=dilations,
        name=name)
  
  
  @tf_export(
      "nn.depthwise_conv2d_backprop_filter",
      v1=[
          "nn.depthwise_conv2d_native_backprop_filter",
          "nn.depthwise_conv2d_backprop_filter"
      ])
  @dispatch.add_dispatch_support
  @deprecation.deprecated_endpoints("nn.depthwise_conv2d_native_backprop_filter")
  def depthwise_conv2d_native_backprop_filter(  # pylint: disable=redefined-builtin,dangerous-default-value
      input,
      filter_sizes,
      out_backprop,
      strides,
      padding,
      data_format="NHWC",
      dilations=[1, 1, 1, 1],
      name=None):
    r"""Computes the gradients of depthwise convolution with respect to the filter.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`,
        `float32`, `float64`. 4-D with shape based on `data_format`.  For example,
        if `data_format` is 'NHWC' then `input` is a 4-D `[batch, in_height,
        in_width, in_channels]` tensor.
      filter_sizes: A `Tensor` of type `int32`. An integer vector representing the
        tensor shape of `filter`, where `filter` is a 4-D `[filter_height,
        filter_width, in_channels, depthwise_multiplier]` tensor.
      out_backprop: A `Tensor`. Must have the same type as `input`. 4-D with shape
        based on `data_format`. For example, if `data_format` is 'NHWC' then
        out_backprop shape is `[batch, out_height, out_width, out_channels]`.
        Gradients w.r.t. the output of the convolution.
      strides: A list of `ints`. The stride of the sliding window for each
        dimension of the input of the convolution.
      padding: Controls how to pad the image before applying the convolution. Can
        be the string `"SAME"` or `"VALID"` indicating the type of padding
        algorithm to use, or a list indicating the explicit paddings at the start
        and end of each dimension. When explicit padding is used and data_format
        is `"NHWC"`, this should be in the form `[[0, 0], [pad_top, pad_bottom],
        [pad_left, pad_right], [0, 0]]`. When explicit padding used and
        data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0],
        [pad_top, pad_bottom], [pad_left, pad_right]]`.
      data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to
        `"NHWC"`. Specify the data format of the input and output data. With the
        default format "NHWC", the data is stored in the order of: [batch, height,
          width, channels].
        Alternatively, the format could be "NCHW", the data storage order of:
          [batch, channels, height, width].
      dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D
        tensor of length 4.  The dilation factor for each dimension of `input`. If
        set to k > 1, there will be k-1 skipped cells between each filter element
        on that dimension. The dimension order is determined by the value of
        `data_format`, see above for details. Dilations in the batch and depth
        dimensions must be 1.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
    """
    padding, explicit_paddings = convert_padding(padding)
    return gen_nn_ops.depthwise_conv2d_native_backprop_filter(
        input,
        filter_sizes,
        out_backprop,
        strides,
        padding,
        explicit_paddings=explicit_paddings,
        data_format=data_format,
        dilations=dilations,
        name=name)
  
  
  def _conv3d_expanded_batch(
      input,  # pylint: disable=redefined-builtin
      filter,  # pylint: disable=redefined-builtin
      strides,
      padding,
      data_format,
      dilations=None,
      name=None):
    """Helper function for `conv3d`; handles expanded batches."""
    shape = input.shape
    # shape object may lack ndims, e.g., if input is an np.ndarray.  In that case,
    # we fall back to len(shape).
    ndims = getattr(shape, "ndims", -1)
    if ndims == -1:
      ndims = len(shape)
    if ndims in (5, 4, 3, 2, 1, 0, None):
      # We avoid calling squeeze_batch_dims to reduce extra python function
      # call slowdown in eager mode.  This branch doesn't require reshapes.
      return gen_nn_ops.conv3d(
          input,
          filter,
          strides,
          padding,
          data_format=data_format,
          dilations=dilations,
          name=name)
    else:
      return squeeze_batch_dims(
          input,
          functools.partial(
              gen_nn_ops.conv3d,
              filter=filter,
              strides=strides,
              padding=padding,
              data_format=data_format,
              dilations=dilations),
          inner_rank=4,
          name=name)
  
  
  @tf_export("nn.conv3d", v1=[])
  @dispatch.add_dispatch_support
  def conv3d_v2(input,  # pylint: disable=redefined-builtin,missing-docstring
                filters,
                strides,
                padding,
                data_format="NDHWC",
                dilations=None,
                name=None):
    if dilations is None:
      dilations = [1, 1, 1, 1, 1]
    return _conv3d_expanded_batch(input, filters, strides, padding, data_format,
                                  dilations, name)
  
  
  @tf_export(v1=["nn.conv3d"])
  @dispatch.add_dispatch_support
  def conv3d_v1(  # pylint: disable=missing-docstring,dangerous-default-value
      input,  # pylint: disable=redefined-builtin
      filter=None,  # pylint: disable=redefined-builtin
      strides=None,
      padding=None,
      data_format="NDHWC",
      dilations=[1, 1, 1, 1, 1],
      name=None,
      filters=None):
    filter = deprecated_argument_lookup("filters", filters, "filter", filter)
    return gen_nn_ops.conv3d(
        input, filter, strides, padding, data_format, dilations, name)
  
  
  conv3d_v2.__doc__ = deprecation.rewrite_argument_docstring(
      gen_nn_ops.conv3d.__doc__, "filter", "filters")
  conv3d_v1.__doc__ = gen_nn_ops.conv3d.__doc__
  
  
  @tf_export(v1=["nn.conv3d_transpose"])
  @dispatch.add_dispatch_support
  def conv3d_transpose(
      value,
      filter=None,  # pylint: disable=redefined-builtin
      output_shape=None,
      strides=None,
      padding="SAME",
      data_format="NDHWC",
      name=None,
      input=None,  # pylint: disable=redefined-builtin
      filters=None,
      dilations=None):
    """The transpose of `conv3d`.
  
    This operation is sometimes called "deconvolution" after
    (Zeiler et al., 2010), but is really the transpose (gradient) of `conv3d`
    rather than an actual deconvolution.
  
    Args:
      value: A 5-D `Tensor` of type `float` and shape
        `[batch, depth, height, width, in_channels]`.
      filter: A 5-D `Tensor` with the same type as `value` and shape
        `[depth, height, width, output_channels, in_channels]`.  `filter`'s
        `in_channels` dimension must match that of `value`.
      output_shape: A 1-D `Tensor` representing the output shape of the
        deconvolution op.
      strides: A list of ints. The stride of the sliding window for each
        dimension of the input tensor.
      padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
        See the "returns" section of `tf.nn.convolution` for details.
      data_format: A string, either `'NDHWC'` or `'NCDHW`' specifying the layout
        of the input and output tensors. Defaults to `'NDHWC'`.
      name: Optional name for the returned tensor.
      input: Alias of value.
      filters: Alias of filter.
      dilations: An int or list of `ints` that has length `1`, `3` or `5`,
        defaults to 1. The dilation factor for each dimension of`input`. If a
        single value is given it is replicated in the `D`, `H` and `W` dimension.
        By default the `N` and `C` dimensions are set to 1. If set to k > 1, there
        will be k-1 skipped cells between each filter element on that dimension.
        The dimension order is determined by the value of `data_format`, see above
        for details. Dilations in the batch and depth dimensions if a 5-d tensor
        must be 1.
  
    Returns:
      A `Tensor` with the same type as `value`.
  
    Raises:
      ValueError: If input/output depth does not match `filter`'s shape, or if
        padding is other than `'VALID'` or `'SAME'`.
  
    References:
      Deconvolutional Networks:
        [Zeiler et al., 2010]
        (https://ieeexplore.ieee.org/abstract/document/5539957)
        ([pdf]
        (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.232.4023&rep=rep1&type=pdf))
    """
    filter = deprecated_argument_lookup("filters", filters, "filter", filter)
    value = deprecated_argument_lookup("input", input, "value", value)
    return conv3d_transpose_v2(
        value,
        filter,
        output_shape,
        strides,
        padding=padding,
        data_format=data_format,
        dilations=dilations,
        name=name)
  
  
  @tf_export("nn.conv3d_transpose", v1=[])
  @dispatch.add_dispatch_support
  def conv3d_transpose_v2(input,  # pylint: disable=redefined-builtin
                          filters,
                          output_shape,
                          strides,
                          padding="SAME",
                          data_format="NDHWC",
                          dilations=None,
                          name=None):
    """The transpose of `conv3d`.
  
    This operation is sometimes called "deconvolution" after
    (Zeiler et al., 2010), but is really the transpose (gradient) of `conv3d`
    rather than an actual deconvolution.
  
    Args:
      input: A 5-D `Tensor` of type `float` and shape `[batch, depth, height,
        width, in_channels]` for `NDHWC` data format or `[batch, in_channels,
        depth, height, width]` for `NCDHW` data format.
      filters: A 5-D `Tensor` with the same type as `input` and shape `[depth,
        height, width, output_channels, in_channels]`.  `filter`'s `in_channels`
        dimension must match that of `input`.
      output_shape: A 1-D `Tensor` representing the output shape of the
        deconvolution op.
      strides: An int or list of `ints` that has length `1`, `3` or `5`.  The
        stride of the sliding window for each dimension of `input`. If a single
        value is given it is replicated in the `D`, `H` and `W` dimension. By
        default the `N` and `C` dimensions are set to 0. The dimension order is
        determined by the value of `data_format`, see below for details.
      padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. See
        the "returns" section of `tf.nn.convolution` for details.
      data_format: A string. 'NDHWC' and 'NCDHW' are supported.
      dilations: An int or list of `ints` that has length `1`, `3` or `5`,
        defaults to 1. The dilation factor for each dimension of`input`. If a
        single value is given it is replicated in the `D`, `H` and `W` dimension.
        By default the `N` and `C` dimensions are set to 1. If set to k > 1, there
        will be k-1 skipped cells between each filter element on that dimension.
        The dimension order is determined by the value of `data_format`, see above
        for details. Dilations in the batch and depth dimensions if a 5-d tensor
        must be 1.
      name: Optional name for the returned tensor.
  
    Returns:
      A `Tensor` with the same type as `input`.
  
    References:
      Deconvolutional Networks:
        [Zeiler et al., 2010]
        (https://ieeexplore.ieee.org/abstract/document/5539957)
        ([pdf]
        (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.232.4023&rep=rep1&type=pdf))
    """
    with ops.name_scope(name, "conv3d_transpose",
                        [input, filter, output_shape]) as name:
      if data_format is None:
        data_format = "NDHWC"
      channel_index = 1 if data_format.startswith("NC") else 4
  
      strides = _get_sequence(strides, 3, channel_index, "strides")
      dilations = _get_sequence(dilations, 3, channel_index, "dilations")
  
      return gen_nn_ops.conv3d_backprop_input_v2(
          input_sizes=output_shape,
          filter=filters,
          out_backprop=input,
          strides=strides,
          padding=padding,
          data_format=data_format,
          dilations=dilations,
          name=name)
  
  
  CONV_TRANSPOSE_OPS = (
      conv1d_transpose,
      conv2d_transpose_v2,
      conv3d_transpose_v2,
  )
  
  
  @tf_export("nn.conv_transpose")
  @dispatch.add_dispatch_support
  def conv_transpose(input,  # pylint: disable=redefined-builtin
                     filters,
                     output_shape,
                     strides,
                     padding="SAME",
                     data_format=None,
                     dilations=None,
                     name=None):
    """The transpose of `convolution`.
  
    This operation is sometimes called "deconvolution" after
    (Zeiler et al., 2010), but is really the transpose (gradient) of `conv3d`
    rather than an actual deconvolution.
  
    Args:
      input: An N+2 dimensional `Tensor` of shape
        `[batch_size] + input_spatial_shape + [in_channels]` if data_format does
        not start with "NC" (default), or
        `[batch_size, in_channels] + input_spatial_shape` if data_format starts
        with "NC". It must be one of the following types:
        `half`, `bfloat16`, `float32`, `float64`.
      filters: An N+2 dimensional `Tensor` with the same type as `input` and
        shape `spatial_filter_shape + [in_channels, out_channels]`.
      output_shape: A 1-D `Tensor` representing the output shape of the
        deconvolution op.
      strides: An int or list of `ints` that has length `1`, `N` or `N+2`.  The
        stride of the sliding window for each dimension of `input`. If a single
        value is given it is replicated in the spatial dimensions. By default
        the `N` and `C` dimensions are set to 0. The dimension order is determined
        by the value of `data_format`, see below for details.
      padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. See
        the "returns" section of `tf.nn.convolution` for details.
      data_format: A string or None.  Specifies whether the channel dimension of
        the `input` and output is the last dimension (default, or if `data_format`
        does not start with "NC"), or the second dimension (if `data_format`
        starts with "NC").  For N=1, the valid values are "NWC" (default) and
        "NCW".  For N=2, the valid values are "NHWC" (default) and "NCHW".
        For N=3, the valid values are "NDHWC" (default) and "NCDHW".
      dilations: An int or list of `ints` that has length `1`, `N` or `N+2`,
        defaults to 1. The dilation factor for each dimension of`input`. If a
        single value is given it is replicated in the spatial dimensions. By
        default the `N` and `C` dimensions are set to 1. If set to k > 1, there
        will be k-1 skipped cells between each filter element on that dimension.
        The dimension order is determined by the value of `data_format`, see above
        for details.
      name: A name for the operation (optional). If not specified "conv_transpose"
        is used.
  
    Returns:
      A `Tensor` with the same type as `value`.
  
    References:
      Deconvolutional Networks:
        [Zeiler et al., 2010]
        (https://ieeexplore.ieee.org/abstract/document/5539957)
        ([pdf]
        (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.232.4023&rep=rep1&type=pdf))
    """
    with ops.name_scope(name, "conv_transpose",
                        [input, filter, output_shape]) as name:
      if tensor_util.is_tf_type(output_shape):
        n = output_shape.shape[0] - 2
      elif isinstance(output_shape, collections_abc.Sized):
        n = len(output_shape) - 2
      else:
        raise ValueError("output_shape must be a tensor or sized collection.")
  
      if not 1 <= n <= 3:
        raise ValueError(
            "output_shape must be of length 3, 4 or 5 but was {}.".format(n + 2))
  
      op = CONV_TRANSPOSE_OPS[n-1]
      return op(
          input,
          filters,
          output_shape,
          strides,
          padding=padding,
          data_format=data_format,
          dilations=dilations,
          name=name)
  
  
  def _tf_deterministic_ops():
    if _tf_deterministic_ops.value is None:
      tf_deterministic_ops = os.environ.get("TF_DETERMINISTIC_OPS")
      if tf_deterministic_ops is not None:
        tf_deterministic_ops = tf_deterministic_ops.lower()
      _tf_deterministic_ops.value = (
          tf_deterministic_ops == "true" or tf_deterministic_ops == "1")
    return _tf_deterministic_ops.value
  
  
  _tf_deterministic_ops.value = None
  
  
  @tf_export("nn.bias_add")
  @dispatch.add_dispatch_support
  def bias_add(value, bias, data_format=None, name=None):
    """Adds `bias` to `value`.
  
    This is (mostly) a special case of `tf.add` where `bias` is restricted to 1-D.
    Broadcasting is supported, so `value` may have any number of dimensions.
    Unlike `tf.add`, the type of `bias` is allowed to differ from `value` in the
    case where both types are quantized.
  
    Args:
      value: A `Tensor` with type `float`, `double`, `int64`, `int32`, `uint8`,
        `int16`, `int8`, `complex64`, or `complex128`.
      bias: A 1-D `Tensor` with size matching the channel dimension of `value`.
        Must be the same type as `value` unless `value` is a quantized type,
        in which case a different quantized type may be used.
      data_format: A string. 'N...C' and 'NC...' are supported. If `None` (the
        default) is specified then 'N..C' is assumed.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` with the same type as `value`.
  
    Raises:
      ValueError if data format is unrecognized, if `value` has less than two
      dimensions when `data_format` is 'N..C'/`None` or `value` has less
      then three dimensions when `data_format` is `NC..`, if `bias` does not
      have exactly one dimension (is a vector), or if the size of `bias`
      does not match the size of the channel dimension of `value`.
    """
    with ops.name_scope(name, "BiasAdd", [value, bias]) as name:
      if data_format is not None:
        if data_format.startswith("NC"):
          data_format = "NCHW"
        elif data_format.startswith("N") and data_format.endswith("C"):
          data_format = "NHWC"
        else:
          raise ValueError("data_format must be of the form `N...C` or `NC...`")
  
      if not context.executing_eagerly():
        value = ops.convert_to_tensor(value, name="input")
        bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias")
  
      # TODO(duncanriach): Implement deterministic functionality at CUDA kernel
      #   level.
      if _tf_deterministic_ops():
        # Note that this code does not implement the same error checks as the
        # pre-existing C++ ops.
        if data_format == "NCHW":
          broadcast_shape_head = [1, array_ops.size(bias)]
          broadcast_shape_tail = array_ops.ones(
              array_ops.rank(value) - 2, dtype=dtypes.int32)
          broadcast_shape = array_ops.concat(
              [broadcast_shape_head, broadcast_shape_tail], 0)
          return math_ops.add(
              value, array_ops.reshape(bias, broadcast_shape), name=name)
        else:  # data_format == 'NHWC' or data_format == None
          return math_ops.add(value, bias, name=name)
      else:
        return gen_nn_ops.bias_add(
            value, bias, data_format=data_format, name=name)
  
  
  def bias_add_v1(value, bias, name=None):
    """Adds `bias` to `value`.
  
    This is a deprecated version of bias_add and will soon to be removed.
  
    This is (mostly) a special case of `tf.add` where `bias` is restricted to 1-D.
    Broadcasting is supported, so `value` may have any number of dimensions.
    Unlike `tf.add`, the type of `bias` is allowed to differ from `value` in the
    case where both types are quantized.
  
    Args:
      value: A `Tensor` with type `float`, `double`, `int64`, `int32`, `uint8`,
        `int16`, `int8`, `complex64`, or `complex128`.
      bias: A 1-D `Tensor` with size matching the last dimension of `value`.
        Must be the same type as `value` unless `value` is a quantized type,
        in which case a different quantized type may be used.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` with the same type as `value`.
    """
    with ops.name_scope(name, "BiasAddV1", [value, bias]) as name:
      value = ops.convert_to_tensor(value, name="input")
      bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias")
      return gen_nn_ops.bias_add_v1(value, bias, name=name)
  
  
  @tf_export(v1=["nn.crelu"])
  @dispatch.add_dispatch_support
  def crelu(features, name=None, axis=-1):
    """Computes Concatenated ReLU.
  
    Concatenates a ReLU which selects only the positive part of the activation
    with a ReLU which selects only the *negative* part of the activation.
    Note that as a result this non-linearity doubles the depth of the activations.
    Source: [Understanding and Improving Convolutional Neural Networks via
    Concatenated Rectified Linear Units. W. Shang, et
    al.](https://arxiv.org/abs/1603.05201)
  
    Args:
      features: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
        `int16`, or `int8`.
      name: A name for the operation (optional).
      axis: The axis that the output values are concatenated along. Default is -1.
  
    Returns:
      A `Tensor` with the same type as `features`.
  
    References:
      Understanding and Improving Convolutional Neural Networks via Concatenated
      Rectified Linear Units:
        [Shang et al., 2016](http://proceedings.mlr.press/v48/shang16)
        ([pdf](http://proceedings.mlr.press/v48/shang16.pdf))
    """
    with ops.name_scope(name, "CRelu", [features]) as name:
      features = ops.convert_to_tensor(features, name="features")
      c = array_ops.concat([features, -features], axis, name=name)
      return gen_nn_ops.relu(c)
  
  
  @tf_export("nn.crelu", v1=[])
  @dispatch.add_dispatch_support
  def crelu_v2(features, axis=-1, name=None):
    return crelu(features, name=name, axis=axis)
  crelu_v2.__doc__ = crelu.__doc__
  
  
  @tf_export("nn.relu6")
  @dispatch.add_dispatch_support
  def relu6(features, name=None):
    """Computes Rectified Linear 6: `min(max(features, 0), 6)`.
  
    Args:
      features: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
        `int16`, or `int8`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` with the same type as `features`.
  
    References:
      Convolutional Deep Belief Networks on CIFAR-10:
        Krizhevsky et al., 2010
        ([pdf](http://www.cs.utoronto.ca/~kriz/conv-cifar10-aug2010.pdf))
    """
    with ops.name_scope(name, "Relu6", [features]) as name:
      features = ops.convert_to_tensor(features, name="features")
      return gen_nn_ops.relu6(features, name=name)
  
  
  @tf_export("nn.leaky_relu")
  @dispatch.add_dispatch_support
  def leaky_relu(features, alpha=0.2, name=None):
    """Compute the Leaky ReLU activation function.
  
    Source: [Rectifier Nonlinearities Improve Neural Network Acoustic Models.
    AL Maas, AY Hannun, AY Ng - Proc. ICML, 2013]
    (https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf).
  
    Args:
      features: A `Tensor` representing preactivation values. Must be one of
        the following types: `float16`, `float32`, `float64`, `int32`, `int64`.
      alpha: Slope of the activation function at x < 0.
      name: A name for the operation (optional).
  
    Returns:
      The activation value.
  
    References:
      Rectifier Nonlinearities Improve Neural Network Acoustic Models:
        [Maas et al., 2013]
        (http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.693.1422)
        ([pdf]
        (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.693.1422&rep=rep1&type=pdf))
    """
    with ops.name_scope(name, "LeakyRelu", [features, alpha]) as name:
      features = ops.convert_to_tensor(features, name="features")
      if features.dtype.is_integer:
        features = math_ops.cast(features, dtypes.float32)
      if isinstance(alpha, np.ndarray):
        alpha = alpha.item()
      return gen_nn_ops.leaky_relu(features, alpha=alpha, name=name)
  
  
  @tf_export("nn.gelu", v1=[])
  @dispatch.add_dispatch_support
  def gelu(features, approximate=False, name=None):
    """Compute the Gaussian Error Linear Unit (GELU) activation function.
  
    Gaussian error linear unit (GELU) computes
    `x * P(X <= x)`, where `P(X) ~ N(0, 1)`.
    The (GELU) nonlinearity weights inputs by their value, rather than gates
    inputs by their sign as in ReLU.
  
    For example:
  
    >>> x = tf.constant([-3.0, -1.0, 0.0, 1.0, 3.0], dtype=tf.float32)
    >>> y = tf.nn.gelu(x)
    >>> y.numpy()
    array([-0.00404951, -0.15865529,  0.        ,  0.8413447 ,  2.9959507 ],
        dtype=float32)
    >>> y = tf.nn.gelu(x, approximate=True)
    >>> y.numpy()
    array([-0.00363752, -0.15880796,  0.        ,  0.841192  ,  2.9963627 ],
        dtype=float32)
  
    Args:
      features: A `Tensor` representing preactivation values.
      approximate: An optional `bool`. Defaults to `False`. Whether to enable
        approximation.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` with the same type as `features`.
  
    References:
      [Gaussian Error Linear Units (GELUs)](https://arxiv.org/abs/1606.08415).
    """
    with ops.name_scope(name, "Gelu", [features]):
      features = ops.convert_to_tensor(features, name="features")
      if approximate:
        coeff = math_ops.cast(0.044715, features.dtype)
        return 0.5 * features * (
            1.0 + math_ops.tanh(0.7978845608028654 *
                                (features + coeff * math_ops.pow(features, 3))))
      else:
        return 0.5 * features * (1.0 + math_ops.erf(
            features / math_ops.cast(1.4142135623730951, features.dtype)))
  
  
  def _flatten_outer_dims(logits):
    """Flattens logits' outer dimensions and keep its last dimension."""
    rank = array_ops.rank(logits)
    last_dim_size = array_ops.slice(
        array_ops.shape(logits), [math_ops.subtract(rank, 1)], [1])
    output = array_ops.reshape(logits, array_ops.concat([[-1], last_dim_size], 0))
  
    # Set output shape if known.
    if not context.executing_eagerly():
      shape = logits.get_shape()
      if shape is not None and shape.dims is not None:
        shape = shape.as_list()
        product = 1
        product_valid = True
        for d in shape[:-1]:
          if d is None:
            product_valid = False
            break
          else:
            product *= d
        if product_valid:
          output_shape = [product, shape[-1]]
          output.set_shape(output_shape)
  
    return output
  
  
  def _wrap_2d_function(inputs, compute_op, dim=-1, name=None):
    """Helper function for ops that accept and return 2d inputs of same shape.
  
    It reshapes and transposes the inputs into a 2-D Tensor and then invokes
    the given function. The output would be transposed and reshaped back.
    If the given function returns a tuple of tensors, each of them will be
    transposed and reshaped.
  
    Args:
      inputs: A non-empty `Tensor`. Must be one of the following types: `half`,
        `float32`, `float64`.
      compute_op: The function to wrap. Must accept the input tensor as its first
        arugment, and a second keyword argument `name`.
      dim: The dimension softmax would be performed on. The default is -1 which
        indicates the last dimension.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same shape as inputs. If compute_op returns multiple
        tensors, each of them have the same shape as the input.
    Raises:
      InvalidArgumentError: if `inputs` is empty or `dim` is beyond the last
        dimension of `inputs`.
    """
  
    def _swap_axis(input_tensor, dim_index, last_index, name=None):
      """Swaps logits's dim_index and last_index."""
      return array_ops.transpose(
          input_tensor,
          array_ops.concat([
              math_ops.range(dim_index), [last_index],
              math_ops.range(dim_index + 1, last_index), [dim_index]
          ], 0),
          name=name)
  
    inputs = ops.convert_to_tensor(inputs)
  
    # We need its original shape for shape inference.
    shape = inputs.get_shape()
    is_last_dim = (dim == -1) or (dim == shape.ndims - 1)
  
    if is_last_dim:
      return compute_op(inputs, name=name)
  
    dim_val = dim
    if isinstance(dim, ops.Tensor):
      dim_val = tensor_util.constant_value(dim)
    if dim_val is not None and not -shape.ndims <= dim_val < shape.ndims:
      raise errors_impl.InvalidArgumentError(
          None, None,
          "Dimension (%d) must be in the range [%d, %d) where %d is the number of"
          " dimensions in the input." % (dim_val, -shape.ndims, shape.ndims,
                                         shape.ndims))
  
    # If dim is not the last dimension, we have to do a transpose so that we can
    # still perform the op on its last dimension.
  
    # In case dim is negative (and is not last dimension -1), add shape.ndims
    ndims = array_ops.rank(inputs)
    if not isinstance(dim, ops.Tensor):
      if dim < 0:
        dim += ndims
    else:
      dim = array_ops.where(math_ops.less(dim, 0), dim + ndims, dim)
  
    # Swap logits' dimension of dim and its last dimension.
    input_rank = array_ops.rank(inputs)
    dim_axis = dim % shape.ndims
    inputs = _swap_axis(inputs, dim_axis, math_ops.subtract(input_rank, 1))
  
    # Do the actual call on its last dimension.
    def fix_output(output):
      output = _swap_axis(
          output, dim_axis, math_ops.subtract(input_rank, 1), name=name)
  
      # Make shape inference work since transpose may erase its static shape.
      output.set_shape(shape)
      return output
  
    outputs = compute_op(inputs)
    if isinstance(outputs, tuple):
      return tuple(fix_output(output) for output in outputs)
    else:
      return fix_output(outputs)
  
  
  @tf_export("nn.softmax", "math.softmax", v1=[])
  @dispatch.add_dispatch_support
  def softmax_v2(logits, axis=None, name=None):
    """Computes softmax activations.
  
    Used for multi-class predictions. The sum of all outputs generated by softmax
    is 1.
  
    This function performs the equivalent of
  
        softmax = tf.exp(logits) / tf.reduce_sum(tf.exp(logits), axis)
  
    Example usage:
  
    >>> softmax = tf.nn.softmax([-1, 0., 1.])
    >>> softmax
    <tf.Tensor: shape=(3,), dtype=float32,
    numpy=array([0.09003057, 0.24472848, 0.66524094], dtype=float32)>
    >>> sum(softmax)
    <tf.Tensor: shape=(), dtype=float32, numpy=1.0>
  
    Args:
      logits: A non-empty `Tensor`. Must be one of the following types: `half`,
        `float32`, `float64`.
      axis: The dimension softmax would be performed on. The default is -1 which
        indicates the last dimension.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type and shape as `logits`.
  
    Raises:
      InvalidArgumentError: if `logits` is empty or `axis` is beyond the last
        dimension of `logits`.
    """
    if axis is None:
      axis = -1
    return _wrap_2d_function(logits, gen_nn_ops.softmax, axis, name)
  
  
  @tf_export(v1=["nn.softmax", "math.softmax"])
  @dispatch.add_dispatch_support
  @deprecation.deprecated_args(None, "dim is deprecated, use axis instead", "dim")
  def softmax(logits, axis=None, name=None, dim=None):
    axis = deprecation.deprecated_argument_lookup("axis", axis, "dim", dim)
    if axis is None:
      axis = -1
    return _wrap_2d_function(logits, gen_nn_ops.softmax, axis, name)
  
  
  softmax.__doc__ = softmax_v2.__doc__
  
  
  @tf_export(v1=["nn.log_softmax", "math.log_softmax"])
  @dispatch.add_dispatch_support
  @deprecation.deprecated_args(None, "dim is deprecated, use axis instead", "dim")
  def log_softmax(logits, axis=None, name=None, dim=None):
    """Computes log softmax activations.
  
    For each batch `i` and class `j` we have
  
        logsoftmax = logits - log(reduce_sum(exp(logits), axis))
  
    Args:
      logits: A non-empty `Tensor`. Must be one of the following types: `half`,
        `float32`, `float64`.
      axis: The dimension softmax would be performed on. The default is -1 which
        indicates the last dimension.
      name: A name for the operation (optional).
      dim: Deprecated alias for `axis`.
  
    Returns:
      A `Tensor`. Has the same type as `logits`. Same shape as `logits`.
  
    Raises:
      InvalidArgumentError: if `logits` is empty or `axis` is beyond the last
        dimension of `logits`.
    """
    axis = deprecation.deprecated_argument_lookup("axis", axis, "dim", dim)
    if axis is None:
      axis = -1
    return _wrap_2d_function(logits, gen_nn_ops.log_softmax, axis, name)
  
  
  @tf_export("nn.log_softmax", "math.log_softmax", v1=[])
  @dispatch.add_dispatch_support
  def log_softmax_v2(logits, axis=None, name=None):
    """Computes log softmax activations.
  
    For each batch `i` and class `j` we have
  
        logsoftmax = logits - log(reduce_sum(exp(logits), axis))
  
    Args:
      logits: A non-empty `Tensor`. Must be one of the following types: `half`,
        `float32`, `float64`.
      axis: The dimension softmax would be performed on. The default is -1 which
        indicates the last dimension.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `logits`. Same shape as `logits`.
  
    Raises:
      InvalidArgumentError: if `logits` is empty or `axis` is beyond the last
        dimension of `logits`.
    """
    if axis is None:
      axis = -1
    return _wrap_2d_function(logits, gen_nn_ops.log_softmax, axis, name)
  
  
  def _ensure_xent_args(name, sentinel, labels, logits):
    # Make sure that all arguments were passed as named arguments.
    if sentinel is not None:
      raise ValueError("Only call `%s` with "
                       "named arguments (labels=..., logits=..., ...)" % name)
    if labels is None or logits is None:
      raise ValueError("Both labels and logits must be provided.")
  
  
  @tf_export("nn.softmax_cross_entropy_with_logits", v1=[])
  @dispatch.add_dispatch_support
  def softmax_cross_entropy_with_logits_v2(labels, logits, axis=-1, name=None):
    """Computes softmax cross entropy between `logits` and `labels`.
  
    Measures the probability error in discrete classification tasks in which the
    classes are mutually exclusive (each entry is in exactly one class).  For
    example, each CIFAR-10 image is labeled with one and only one label: an image
    can be a dog or a truck, but not both.
  
    **NOTE:**  While the classes are mutually exclusive, their probabilities
    need not be.  All that is required is that each row of `labels` is
    a valid probability distribution.  If they are not, the computation of the
    gradient will be incorrect.
  
    If using exclusive `labels` (wherein one and only
    one class is true at a time), see `sparse_softmax_cross_entropy_with_logits`.
  
    Usage:
  
    >>> logits = [[4.0, 2.0, 1.0], [0.0, 5.0, 1.0]]
    >>> labels = [[1.0, 0.0, 0.0], [0.0, 0.8, 0.2]]
    >>> tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits)
    <tf.Tensor: shape=(2,), dtype=float32,
    numpy=array([0.16984604, 0.82474494], dtype=float32)>
  
    **WARNING:** This op expects unscaled logits, since it performs a `softmax`
    on `logits` internally for efficiency.  Do not call this op with the
    output of `softmax`, as it will produce incorrect results.
  
    A common use case is to have logits and labels of shape
    `[batch_size, num_classes]`, but higher dimensions are supported, with
    the `axis` argument specifying the class dimension.
  
    `logits` and `labels` must have the same dtype (either `float16`, `float32`,
    or `float64`).
  
    Backpropagation will happen into both `logits` and `labels`.  To disallow
    backpropagation into `labels`, pass label tensors through `tf.stop_gradient`
    before feeding it to this function.
  
    **Note that to avoid confusion, it is required to pass only named arguments to
    this function.**
  
    Args:
      labels: Each vector along the class dimension should hold a valid
        probability distribution e.g. for the case in which labels are of shape
        `[batch_size, num_classes]`, each row of `labels[i]` must be a valid
        probability distribution.
      logits: Per-label activations, typically a linear output. These activation
        energies are interpreted as unnormalized log probabilities.
      axis: The class dimension. Defaulted to -1 which is the last dimension.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` that contains the softmax cross entropy loss. Its type is the
      same as `logits` and its shape is the same as `labels` except that it does
      not have the last dimension of `labels`.
    """
    return softmax_cross_entropy_with_logits_v2_helper(
        labels=labels, logits=logits, axis=axis, name=name)
  
  
  @tf_export(v1=["nn.softmax_cross_entropy_with_logits_v2"])
  @dispatch.add_dispatch_support
  @deprecated_args(None, "dim is deprecated, use axis instead", "dim")
  def softmax_cross_entropy_with_logits_v2_helper(
      labels, logits, axis=None, name=None, dim=None):
    """Computes softmax cross entropy between `logits` and `labels`.
  
    Measures the probability error in discrete classification tasks in which the
    classes are mutually exclusive (each entry is in exactly one class).  For
    example, each CIFAR-10 image is labeled with one and only one label: an image
    can be a dog or a truck, but not both.
  
    **NOTE:**  While the classes are mutually exclusive, their probabilities
    need not be.  All that is required is that each row of `labels` is
    a valid probability distribution.  If they are not, the computation of the
    gradient will be incorrect.
  
    If using exclusive `labels` (wherein one and only
    one class is true at a time), see `sparse_softmax_cross_entropy_with_logits`.
  
    **WARNING:** This op expects unscaled logits, since it performs a `softmax`
    on `logits` internally for efficiency.  Do not call this op with the
    output of `softmax`, as it will produce incorrect results.
  
    A common use case is to have logits and labels of shape
    `[batch_size, num_classes]`, but higher dimensions are supported, with
    the `axis` argument specifying the class dimension.
  
    `logits` and `labels` must have the same dtype (either `float16`, `float32`,
    or `float64`).
  
    Backpropagation will happen into both `logits` and `labels`.  To disallow
    backpropagation into `labels`, pass label tensors through `tf.stop_gradient`
    before feeding it to this function.
  
    **Note that to avoid confusion, it is required to pass only named arguments to
    this function.**
  
    Args:
      labels: Each vector along the class dimension should hold a valid
        probability distribution e.g. for the case in which labels are of shape
        `[batch_size, num_classes]`, each row of `labels[i]` must be a valid
        probability distribution.
      logits: Unscaled log probabilities.
      axis: The class dimension. Defaulted to -1 which is the last dimension.
      name: A name for the operation (optional).
      dim: Deprecated alias for axis.
  
    Returns:
      A `Tensor` that contains the softmax cross entropy loss. Its type is the
      same as `logits` and its shape is the same as `labels` except that it does
      not have the last dimension of `labels`.
    """
    # TODO(pcmurray) Raise an error when the labels do not sum to 1. Note: This
    # could break users who call this with bad labels, but disregard the bad
    # results.
    axis = deprecated_argument_lookup("axis", axis, "dim", dim)
    del dim
    if axis is None:
      axis = -1
  
    with ops.name_scope(name, "softmax_cross_entropy_with_logits",
                        [logits, labels]) as name:
      logits = ops.convert_to_tensor(logits, name="logits")
      labels = ops.convert_to_tensor(labels, name="labels")
      convert_to_float32 = (
          logits.dtype == dtypes.float16 or logits.dtype == dtypes.bfloat16)
      precise_logits = math_ops.cast(
          logits, dtypes.float32) if convert_to_float32 else logits
      # labels and logits must be of the same type
      labels = math_ops.cast(labels, precise_logits.dtype)
      input_rank = array_ops.rank(precise_logits)
      # For shape inference.
      shape = logits.get_shape()
  
      # Move the dim to the end if dim is not the last dimension.
      if axis != -1:
  
        def _move_dim_to_end(tensor, dim_index, rank):
          return array_ops.transpose(
              tensor,
              array_ops.concat([
                  math_ops.range(dim_index),
                  math_ops.range(dim_index + 1, rank), [dim_index]
              ], 0))
  
        precise_logits = _move_dim_to_end(precise_logits, axis, input_rank)
        labels = _move_dim_to_end(labels, axis, input_rank)
  
      input_shape = array_ops.shape(precise_logits)
  
      # Make precise_logits and labels into matrices.
      precise_logits = _flatten_outer_dims(precise_logits)
      labels = _flatten_outer_dims(labels)
  
      # Do the actual op computation.
      # The second output tensor contains the gradients.  We use it in
      # CrossEntropyGrad() in nn_grad but not here.
      cost, unused_backprop = gen_nn_ops.softmax_cross_entropy_with_logits(
          precise_logits, labels, name=name)
  
      # The output cost shape should be the input minus axis.
      output_shape = array_ops.slice(input_shape, [0],
                                     [math_ops.subtract(input_rank, 1)])
      cost = array_ops.reshape(cost, output_shape)
  
      # Make shape inference work since reshape and transpose may erase its static
      # shape.
      if not context.executing_eagerly(
      ) and shape is not None and shape.dims is not None:
        shape = shape.as_list()
        del shape[axis]
        cost.set_shape(shape)
  
      if convert_to_float32:
        return math_ops.cast(cost, logits.dtype)
      else:
        return cost
  
  
  _XENT_DEPRECATION = """
  Future major versions of TensorFlow will allow gradients to flow
  into the labels input on backprop by default.
  
  See `tf.nn.softmax_cross_entropy_with_logits_v2`.
  """
  
  
  @tf_export(v1=["nn.softmax_cross_entropy_with_logits"])
  @dispatch.add_dispatch_support
  @deprecation.deprecated(date=None, instructions=_XENT_DEPRECATION)
  def softmax_cross_entropy_with_logits(
      _sentinel=None,  # pylint: disable=invalid-name
      labels=None,
      logits=None,
      dim=-1,
      name=None,
      axis=None):
    """Computes softmax cross entropy between `logits` and `labels`.
  
    Measures the probability error in discrete classification tasks in which the
    classes are mutually exclusive (each entry is in exactly one class).  For
    example, each CIFAR-10 image is labeled with one and only one label: an image
    can be a dog or a truck, but not both.
  
    **NOTE:**  While the classes are mutually exclusive, their probabilities
    need not be.  All that is required is that each row of `labels` is
    a valid probability distribution.  If they are not, the computation of the
    gradient will be incorrect.
  
    If using exclusive `labels` (wherein one and only
    one class is true at a time), see `sparse_softmax_cross_entropy_with_logits`.
  
    **WARNING:** This op expects unscaled logits, since it performs a `softmax`
    on `logits` internally for efficiency.  Do not call this op with the
    output of `softmax`, as it will produce incorrect results.
  
    A common use case is to have logits and labels of shape
    `[batch_size, num_classes]`, but higher dimensions are supported, with
    the `dim` argument specifying the class dimension.
  
    Backpropagation will happen only into `logits`.  To calculate a cross entropy
    loss that allows backpropagation into both `logits` and `labels`, see
    `tf.nn.softmax_cross_entropy_with_logits_v2`.
  
    **Note that to avoid confusion, it is required to pass only named arguments to
    this function.**
  
    Args:
      _sentinel: Used to prevent positional parameters. Internal, do not use.
      labels: Each vector along the class dimension should hold a valid
        probability distribution e.g. for the case in which labels are of shape
        `[batch_size, num_classes]`, each row of `labels[i]` must be a valid
        probability distribution.
      logits: Per-label activations, typically a linear output. These activation
        energies are interpreted as unnormalized log probabilities.
      dim: The class dimension. Defaulted to -1 which is the last dimension.
      name: A name for the operation (optional).
      axis: Alias for dim.
  
    Returns:
      A `Tensor` that contains the softmax cross entropy loss. Its type is the
      same as `logits` and its shape is the same as `labels` except that it does
      not have the last dimension of `labels`.
    """
    dim = deprecated_argument_lookup("axis", axis, "dim", dim)
    _ensure_xent_args("softmax_cross_entropy_with_logits", _sentinel, labels,
                      logits)
  
    with ops.name_scope(name, "softmax_cross_entropy_with_logits_sg",
                        [logits, labels]) as name:
      labels = array_ops.stop_gradient(labels, name="labels_stop_gradient")
  
    return softmax_cross_entropy_with_logits_v2(
        labels=labels, logits=logits, axis=dim, name=name)
  
  
  @tf_export(v1=["nn.sparse_softmax_cross_entropy_with_logits"])
  @dispatch.add_dispatch_support
  def sparse_softmax_cross_entropy_with_logits(
      _sentinel=None,  # pylint: disable=invalid-name
      labels=None,
      logits=None,
      name=None):
    """Computes sparse softmax cross entropy between `logits` and `labels`.
  
    Measures the probability error in discrete classification tasks in which the
    classes are mutually exclusive (each entry is in exactly one class).  For
    example, each CIFAR-10 image is labeled with one and only one label: an image
    can be a dog or a truck, but not both.
  
    **NOTE:**  For this operation, the probability of a given label is considered
    exclusive.  That is, soft classes are not allowed, and the `labels` vector
    must provide a single specific index for the true class for each row of
    `logits` (each minibatch entry).  For soft softmax classification with
    a probability distribution for each entry, see
    `softmax_cross_entropy_with_logits_v2`.
  
    **WARNING:** This op expects unscaled logits, since it performs a `softmax`
    on `logits` internally for efficiency.  Do not call this op with the
    output of `softmax`, as it will produce incorrect results.
  
    A common use case is to have logits of shape
    `[batch_size, num_classes]` and have labels of shape
    `[batch_size]`, but higher dimensions are supported, in which
    case the `dim`-th dimension is assumed to be of size `num_classes`.
    `logits` must have the dtype of `float16`, `float32`, or `float64`, and
    `labels` must have the dtype of `int32` or `int64`.
  
    **Note that to avoid confusion, it is required to pass only named arguments to
    this function.**
  
    Args:
      _sentinel: Used to prevent positional parameters. Internal, do not use.
      labels: `Tensor` of shape `[d_0, d_1, ..., d_{r-1}]` (where `r` is rank of
        `labels` and result) and dtype `int32` or `int64`. Each entry in `labels`
        must be an index in `[0, num_classes)`. Other values will raise an
        exception when this op is run on CPU, and return `NaN` for corresponding
        loss and gradient rows on GPU.
      logits: Per-label activations (typically a linear output) of shape
        `[d_0, d_1, ..., d_{r-1}, num_classes]` and dtype `float16`, `float32`, or
        `float64`. These activation energies are interpreted as unnormalized log
        probabilities.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of the same shape as `labels` and of the same type as `logits`
      with the softmax cross entropy loss.
  
    Raises:
      ValueError: If logits are scalars (need to have rank >= 1) or if the rank
        of the labels is not equal to the rank of the logits minus one.
    """
    _ensure_xent_args("sparse_softmax_cross_entropy_with_logits", _sentinel,
                      labels, logits)
  
    # TODO(pcmurray) Raise an error when the label is not an index in
    # [0, num_classes). Note: This could break users who call this with bad
    # labels, but disregard the bad results.
  
    # Reshape logits and labels to rank 2.
    with ops.name_scope(name, "SparseSoftmaxCrossEntropyWithLogits",
                        [labels, logits]):
      labels = ops.convert_to_tensor(labels)
      logits = ops.convert_to_tensor(logits)
      precise_logits = math_ops.cast(logits, dtypes.float32) if (dtypes.as_dtype(
          logits.dtype) == dtypes.float16) else logits
  
      # Store label shape for result later.
      labels_static_shape = labels.get_shape()
      labels_shape = array_ops.shape(labels)
      static_shapes_fully_defined = (
          labels_static_shape.is_fully_defined() and
          logits.get_shape()[:-1].is_fully_defined())
      if logits.get_shape().ndims is not None and logits.get_shape().ndims == 0:
        raise ValueError(
            "Logits cannot be scalars - received shape %s." % logits.get_shape())
      if logits.get_shape().ndims is not None and (
          labels_static_shape.ndims is not None and
          labels_static_shape.ndims != logits.get_shape().ndims - 1):
        raise ValueError("Rank mismatch: Rank of labels (received %s) should "
                         "equal rank of logits minus 1 (received %s)." %
                         (labels_static_shape.ndims, logits.get_shape().ndims))
      if (static_shapes_fully_defined and
          labels_static_shape != logits.get_shape()[:-1]):
        raise ValueError("Shape mismatch: The shape of labels (received %s) "
                         "should equal the shape of logits except for the last "
                         "dimension (received %s)." % (labels_static_shape,
                                                       logits.get_shape()))
      # Check if no reshapes are required.
      if logits.get_shape().ndims == 2:
        cost, _ = gen_nn_ops.sparse_softmax_cross_entropy_with_logits(
            precise_logits, labels, name=name)
        if logits.dtype == dtypes.float16:
          return math_ops.cast(cost, dtypes.float16)
        else:
          return cost
  
      # Perform a check of the dynamic shapes if the static shapes are not fully
      # defined.
      shape_checks = []
      if not static_shapes_fully_defined:
        shape_checks.append(
            check_ops.assert_equal(
                array_ops.shape(labels),
                array_ops.shape(logits)[:-1]))
      with ops.control_dependencies(shape_checks):
        # Reshape logits to 2 dim, labels to 1 dim.
        num_classes = array_ops.shape(logits)[array_ops.rank(logits) - 1]
        precise_logits = array_ops.reshape(precise_logits, [-1, num_classes])
        labels = array_ops.reshape(labels, [-1])
        # The second output tensor contains the gradients.  We use it in
        # _CrossEntropyGrad() in nn_grad but not here.
        cost, _ = gen_nn_ops.sparse_softmax_cross_entropy_with_logits(
            precise_logits, labels, name=name)
        cost = array_ops.reshape(cost, labels_shape)
        cost.set_shape(labels_static_shape)
        if logits.dtype == dtypes.float16:
          return math_ops.cast(cost, dtypes.float16)
        else:
          return cost
  
  
  @tf_export("nn.sparse_softmax_cross_entropy_with_logits", v1=[])
  @dispatch.add_dispatch_support
  def sparse_softmax_cross_entropy_with_logits_v2(labels, logits, name=None):
    """Computes sparse softmax cross entropy between `logits` and `labels`.
  
    Measures the probability error in discrete classification tasks in which the
    classes are mutually exclusive (each entry is in exactly one class).  For
    example, each CIFAR-10 image is labeled with one and only one label: an image
    can be a dog or a truck, but not both.
  
    Note:  For this operation, the probability of a given label is considered
    exclusive.  That is, soft classes are not allowed, and the `labels` vector
    must provide a single specific index for the true class for each row of
    `logits` (each minibatch entry).  For soft softmax classification with
    a probability distribution for each entry, see
    `softmax_cross_entropy_with_logits_v2`.
  
    Warning: This op expects unscaled logits, since it performs a `softmax`
    on `logits` internally for efficiency.  Do not call this op with the
    output of `softmax`, as it will produce incorrect results.
  
    A common use case is to have logits of shape
    `[batch_size, num_classes]` and have labels of shape
    `[batch_size]`, but higher dimensions are supported, in which
    case the `dim`-th dimension is assumed to be of size `num_classes`.
    `logits` must have the dtype of `float16`, `float32`, or `float64`, and
    `labels` must have the dtype of `int32` or `int64`.
  
    >>> logits = tf.constant([[2., -5., .5, -.1],
    ...                       [0., 0., 1.9, 1.4],
    ...                       [-100., 100., -100., -100.]])
    >>> labels = tf.constant([0, 3, 1])
    >>> tf.nn.sparse_softmax_cross_entropy_with_logits(
    ...     labels=labels, logits=logits).numpy()
    array([0.29750752, 1.1448325 , 0.        ], dtype=float32)
  
    To avoid confusion, passing only named arguments to this function is
    recommended.
  
    Args:
      labels: `Tensor` of shape `[d_0, d_1, ..., d_{r-1}]` (where `r` is rank of
        `labels` and result) and dtype `int32` or `int64`. Each entry in `labels`
        must be an index in `[0, num_classes)`. Other values will raise an
        exception when this op is run on CPU, and return `NaN` for corresponding
        loss and gradient rows on GPU.
      logits: Unscaled log probabilities of shape `[d_0, d_1, ..., d_{r-1},
        num_classes]` and dtype `float16`, `float32`, or `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of the same shape as `labels` and of the same type as `logits`
      with the softmax cross entropy loss.
  
    Raises:
      ValueError: If logits are scalars (need to have rank >= 1) or if the rank
        of the labels is not equal to the rank of the logits minus one.
    """
    return sparse_softmax_cross_entropy_with_logits(
        labels=labels, logits=logits, name=name)
  
  
  @tf_export("nn.avg_pool", v1=["nn.avg_pool_v2"])
  @dispatch.add_dispatch_support
  def avg_pool_v2(input, ksize, strides, padding, data_format=None, name=None):  # pylint: disable=redefined-builtin
    """Performs the avg pooling on the input.
  
    Each entry in `output` is the mean of the corresponding size `ksize`
    window in `value`.
  
    Args:
      input:  Tensor of rank N+2, of shape `[batch_size] + input_spatial_shape +
        [num_channels]` if `data_format` does not start with "NC" (default), or
        `[batch_size, num_channels] + input_spatial_shape` if data_format starts
        with "NC". Pooling happens over the spatial dimensions only.
      ksize: An int or list of `ints` that has length `1`, `N` or `N+2`. The size
        of the window for each dimension of the input tensor.
      strides: An int or list of `ints` that has length `1`, `N` or `N+2`. The
        stride of the sliding window for each dimension of the input tensor.
      padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. See
        the "returns" section of `tf.nn.convolution` for details.
      data_format: A string. Specifies the channel dimension. For N=1 it can be
        either "NWC" (default) or "NCW", for N=2 it can be either "NHWC" (default)
        or "NCHW" and for N=3 either "NDHWC" (default) or "NCDHW".
      name: Optional name for the operation.
  
    Returns:
      A `Tensor` of format specified by `data_format`.
      The average pooled output tensor.
    """
    if input.shape is not None:
      n = len(input.shape) - 2
    elif data_format is not None:
      n = len(data_format) - 2
    else:
      raise ValueError(
          "The input must have a rank or a data format must be given.")
    if not 1 <= n <= 3:
      raise ValueError(
          "Input tensor must be of rank 3, 4 or 5 but was {}.".format(n + 2))
  
    if data_format is None:
      channel_index = n + 1
    else:
      channel_index = 1 if data_format.startswith("NC") else n + 1
  
    ksize = _get_sequence(ksize, n, channel_index, "ksize")
    strides = _get_sequence(strides, n, channel_index, "strides")
  
    avg_pooling_ops = {
        1: avg_pool1d,
        2: gen_nn_ops.avg_pool,
        3: gen_nn_ops.avg_pool3d
    }
  
    op = avg_pooling_ops[n]
    return op(
        input,
        ksize=ksize,
        strides=strides,
        padding=padding,
        data_format=data_format,
        name=name)
  
  
  @tf_export(v1=["nn.avg_pool", "nn.avg_pool2d"])
  @dispatch.add_dispatch_support
  def avg_pool(value, ksize, strides, padding, data_format="NHWC",
               name=None, input=None):  # pylint: disable=redefined-builtin
    """Performs the average pooling on the input.
  
    Each entry in `output` is the mean of the corresponding size `ksize`
    window in `value`.
  
    Args:
      value: A 4-D `Tensor` of shape `[batch, height, width, channels]` and type
        `float32`, `float64`, `qint8`, `quint8`, or `qint32`.
      ksize: An int or list of `ints` that has length `1`, `2` or `4`. The size of
        the window for each dimension of the input tensor.
      strides: An int or list of `ints` that has length `1`, `2` or `4`. The
        stride of the sliding window for each dimension of the input tensor.
      padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
        See the "returns" section of `tf.nn.convolution` for details.
      data_format: A string. 'NHWC' and 'NCHW' are supported.
      name: Optional name for the operation.
      input: Alias for value.
  
    Returns:
      A `Tensor` with the same type as `value`.  The average pooled output tensor.
    """
    with ops.name_scope(name, "AvgPool", [value]) as name:
      value = deprecation.deprecated_argument_lookup(
          "input", input, "value", value)
  
      if data_format is None:
        data_format = "NHWC"
      channel_index = 1 if data_format.startswith("NC") else 3
  
      ksize = _get_sequence(ksize, 2, channel_index, "ksize")
      strides = _get_sequence(strides, 2, channel_index, "strides")
  
      return gen_nn_ops.avg_pool(
          value,
          ksize=ksize,
          strides=strides,
          padding=padding,
          data_format=data_format,
          name=name)
  
  
  @tf_export("nn.avg_pool2d", v1=[])
  @dispatch.add_dispatch_support
  def avg_pool2d(input, ksize, strides, padding, data_format="NHWC", name=None):  # pylint: disable=redefined-builtin
    """Performs the average pooling on the input.
  
    Each entry in `output` is the mean of the corresponding size `ksize`
    window in `value`.
  
    Args:
      input: A 4-D `Tensor` of shape `[batch, height, width, channels]` and type
        `float32`, `float64`, `qint8`, `quint8`, or `qint32`.
      ksize: An int or list of `ints` that has length `1`, `2` or `4`. The size of
        the window for each dimension of the input tensor.
      strides: An int or list of `ints` that has length `1`, `2` or `4`. The
        stride of the sliding window for each dimension of the input tensor.
      padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
        See the "returns" section of `tf.nn.convolution` for details.
      data_format: A string. 'NHWC' and 'NCHW' are supported.
      name: Optional name for the operation.
  
    Returns:
      A `Tensor` with the same type as `value`.  The average pooled output tensor.
    """
    with ops.name_scope(name, "AvgPool2D", [input]) as name:
      if data_format is None:
        data_format = "NHWC"
      channel_index = 1 if data_format.startswith("NC") else 3
  
      ksize = _get_sequence(ksize, 2, channel_index, "ksize")
      strides = _get_sequence(strides, 2, channel_index, "strides")
  
      return gen_nn_ops.avg_pool(
          input,
          ksize=ksize,
          strides=strides,
          padding=padding,
          data_format=data_format,
          name=name)
  
  
  @tf_export("nn.avg_pool1d")
  @dispatch.add_dispatch_support
  def avg_pool1d(input, ksize, strides, padding, data_format="NWC", name=None):  # pylint: disable=redefined-builtin
    """Performs the average pooling on the input.
  
    Each entry in `output` is the mean of the corresponding size `ksize`
    window in `value`.
  
    Note internally this op reshapes and uses the underlying 2d operation.
  
    Args:
      input: A 3-D `Tensor` of the format specified by `data_format`.
      ksize: An int or list of `ints` that has length `1` or `3`. The size of the
        window for each dimension of the input tensor.
      strides: An int or list of `ints` that has length `1` or `3`. The stride of
        the sliding window for each dimension of the input tensor.
      padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. See
        the "returns" section of `tf.nn.convolution` for details.
      data_format: An optional string from: "NWC", "NCW". Defaults to "NWC".
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of format specified by `data_format`.
      The max pooled output tensor.
    """
    with ops.name_scope(name, "AvgPool1D", [input]) as name:
      if data_format is None:
        data_format = "NWC"
      channel_index = 1 if data_format.startswith("NC") else 2
      ksize = [1] + _get_sequence(ksize, 1, channel_index, "ksize")
      strides = [1] + _get_sequence(strides, 1, channel_index, "strides")
  
      expanding_dim = 1 if data_format == "NWC" else 2
      data_format = "NHWC" if data_format == "NWC" else "NCHW"
  
      input = array_ops.expand_dims_v2(input, expanding_dim)
      result = gen_nn_ops.avg_pool(
          input,
          ksize=ksize,
          strides=strides,
          padding=padding,
          data_format=data_format,
          name=name)
      return array_ops.squeeze(result, expanding_dim)
  
  
  @tf_export("nn.avg_pool3d")
  @dispatch.add_dispatch_support
  def avg_pool3d(input, ksize, strides, padding, data_format="NDHWC", name=None):  # pylint: disable=redefined-builtin
    """Performs the average pooling on the input.
  
    Each entry in `output` is the mean of the corresponding size `ksize`
    window in `value`.
  
    Args:
      input: A 5-D `Tensor` of shape `[batch, height, width, channels]` and type
        `float32`, `float64`, `qint8`, `quint8`, or `qint32`.
      ksize: An int or list of `ints` that has length `1`, `3` or `5`. The size of
        the window for each dimension of the input tensor.
      strides: An int or list of `ints` that has length `1`, `3` or `5`. The
        stride of the sliding window for each dimension of the input tensor.
      padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
        See the "returns" section of `tf.nn.convolution` for details.
      data_format: A string. 'NDHWC' and 'NCDHW' are supported.
      name: Optional name for the operation.
  
    Returns:
      A `Tensor` with the same type as `value`.  The average pooled output tensor.
    """
    with ops.name_scope(name, "AvgPool3D", [input]) as name:
      if data_format is None:
        data_format = "NDHWC"
      channel_index = 1 if data_format.startswith("NC") else 3
  
      ksize = _get_sequence(ksize, 3, channel_index, "ksize")
      strides = _get_sequence(strides, 3, channel_index, "strides")
  
      return gen_nn_ops.avg_pool3d(
          input,
          ksize=ksize,
          strides=strides,
          padding=padding,
          data_format=data_format,
          name=name)
  
  
  # pylint: disable=redefined-builtin
  @tf_export("nn.max_pool", v1=["nn.max_pool_v2"])
  @dispatch.add_dispatch_support
  def max_pool_v2(input, ksize, strides, padding, data_format=None, name=None):
    """Performs max pooling on the input.
  
    For a given window of `ksize`, takes the maximum value within that window.
    Used for reducing computation and preventing overfitting.
  
    Consider an example of pooling with 2x2, non-overlapping windows:
  
    >>> matrix = tf.constant([
    ...     [0, 0, 1, 7],
    ...     [0, 2, 0, 0],
    ...     [5, 2, 0, 0],
    ...     [0, 0, 9, 8],
    ... ])
    >>> reshaped = tf.reshape(matrix, (1, 4, 4, 1))
    >>> tf.nn.max_pool(reshaped, ksize=2, strides=2, padding="SAME")
    <tf.Tensor: shape=(1, 2, 2, 1), dtype=int32, numpy=
    array([[[[2],
             [7]],
            [[5],
             [9]]]], dtype=int32)>
  
    We can adjust the window size using the `ksize` parameter. For example, if we
    were to expand the window to 3:
  
    >>> tf.nn.max_pool(reshaped, ksize=3, strides=2, padding="SAME")
    <tf.Tensor: shape=(1, 2, 2, 1), dtype=int32, numpy=
    array([[[[5],
             [7]],
            [[9],
             [9]]]], dtype=int32)>
  
    We've now picked up two additional large numbers (5 and 9) in two of the
    pooled spots.
  
    Note that our windows are now overlapping, since we're still moving by 2 units
    on each iteration. This is causing us to see the same 9 repeated twice, since
    it is part of two overlapping windows.
  
    We can adjust how far we move our window with each iteration using the
    `strides` parameter. Updating this to the same value as our window size
    eliminates the overlap:
  
    >>> tf.nn.max_pool(reshaped, ksize=3, strides=3, padding="SAME")
    <tf.Tensor: shape=(1, 2, 2, 1), dtype=int32, numpy=
    array([[[[2],
             [7]],
            [[5],
             [9]]]], dtype=int32)>
  
    Because the window does not neatly fit into our input, padding is added around
    the edges, giving us the same result as when we used a 2x2 window. We can skip
    padding altogether and simply drop the windows that do not fully fit into our
    input by instead passing `"VALID"` to the `padding` argument:
  
    >>> tf.nn.max_pool(reshaped, ksize=3, strides=3, padding="VALID")
    <tf.Tensor: shape=(1, 1, 1, 1), dtype=int32, numpy=array([[[[5]]]],
     dtype=int32)>
  
    Now we've grabbed the largest value in the 3x3 window starting from the upper-
    left corner. Since no other windows fit in our input, they are dropped.
  
    Args:
      input:  Tensor of rank N+2, of shape `[batch_size] + input_spatial_shape +
        [num_channels]` if `data_format` does not start with "NC" (default), or
        `[batch_size, num_channels] + input_spatial_shape` if data_format starts
        with "NC". Pooling happens over the spatial dimensions only.
      ksize: An int or list of `ints` that has length `1`, `N` or `N+2`. The size
        of the window for each dimension of the input tensor.
      strides: An int or list of `ints` that has length `1`, `N` or `N+2`. The
        stride of the sliding window for each dimension of the input tensor.
      padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
        padding algorithm to use, or a list indicating the explicit paddings at
        the start and end of each dimension. When explicit padding is used and
        data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,
        pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used
        and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0],
        [pad_top, pad_bottom], [pad_left, pad_right]]`. When using explicit
        padding, the size of the paddings cannot be greater than the sliding
        window size.
      data_format: A string. Specifies the channel dimension. For N=1 it can be
        either "NWC" (default) or "NCW", for N=2 it can be either "NHWC" (default)
        or "NCHW" and for N=3 either "NDHWC" (default) or "NCDHW".
      name: Optional name for the operation.
  
    Returns:
      A `Tensor` of format specified by `data_format`.
      The max pooled output tensor.
    """
    if input.shape is not None:
      n = len(input.shape) - 2
    elif data_format is not None:
      n = len(data_format) - 2
    else:
      raise ValueError(
          "The input must have a rank or a data format must be given.")
    if not 1 <= n <= 3:
      raise ValueError(
          "Input tensor must be of rank 3, 4 or 5 but was {}.".format(n + 2))
  
    if data_format is None:
      channel_index = n + 1
    else:
      channel_index = 1 if data_format.startswith("NC") else n + 1
  
    if isinstance(padding, (list, tuple)) and data_format == "NCHW_VECT_C":
      raise ValueError("Data formats NCHW_VECT_C is not yet supported with "
                       "explicit padding")
  
    ksize = _get_sequence(ksize, n, channel_index, "ksize")
    strides = _get_sequence(strides, n, channel_index, "strides")
  
    if (isinstance(padding, (list, tuple)) and n == 3):
      raise ValueError("Explicit padding is not yet supported with an input "
                       "tensor of rank 5")
  
    max_pooling_ops = {
        1: max_pool1d,
        2: max_pool2d,
        3: gen_nn_ops.max_pool3d
    }
  
    op = max_pooling_ops[n]
    return op(
        input,
        ksize=ksize,
        strides=strides,
        padding=padding,
        data_format=data_format,
        name=name)
  # pylint: enable=redefined-builtin
  
  
  @tf_export(v1=["nn.max_pool"])
  @dispatch.add_dispatch_support
  def max_pool(value,
               ksize,
               strides,
               padding,
               data_format="NHWC",
               name=None,
               input=None):  # pylint: disable=redefined-builtin
    """Performs the max pooling on the input.
  
    Args:
      value: A 4-D `Tensor` of the format specified by `data_format`.
      ksize: An int or list of `ints` that has length `1`, `2` or `4`.
        The size of the window for each dimension of the input tensor.
      strides: An int or list of `ints` that has length `1`, `2` or `4`.
        The stride of the sliding window for each dimension of the input tensor.
      padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
        padding algorithm to use, or a list indicating the explicit paddings at
        the start and end of each dimension. When explicit padding is used and
        data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,
        pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used
        and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0],
        [pad_top, pad_bottom], [pad_left, pad_right]]`. When using explicit
        padding, the size of the paddings cannot be greater than the sliding
        window size.
      data_format: A string. 'NHWC', 'NCHW' and 'NCHW_VECT_C' are supported.
      name: Optional name for the operation.
      input: Alias for value.
  
    Returns:
      A `Tensor` of format specified by `data_format`.
      The max pooled output tensor.
    """
    value = deprecation.deprecated_argument_lookup("input", input, "value", value)
    with ops.name_scope(name, "MaxPool", [value]) as name:
      if data_format is None:
        data_format = "NHWC"
      channel_index = 1 if data_format.startswith("NC") else 3
  
      ksize = _get_sequence(ksize, 2, channel_index, "ksize")
      strides = _get_sequence(strides, 2, channel_index, "strides")
      if isinstance(padding, (list, tuple)) and data_format == "NCHW_VECT_C":
        raise ValueError("Data formats NCHW_VECT_C is not yet supported with "
                         "explicit padding")
      padding, explicit_paddings = convert_padding(padding)
      if ((np.isscalar(ksize) and ksize == 0) or
          (isinstance(ksize,
                      (list, tuple, np.ndarray)) and any(v == 0 for v in ksize))):
        raise ValueError("ksize cannot be zero.")
  
      return gen_nn_ops.max_pool(
          value,
          ksize=ksize,
          strides=strides,
          padding=padding,
          explicit_paddings=explicit_paddings,
          data_format=data_format,
          name=name)
  
  
  # pylint: disable=redefined-builtin
  @tf_export("nn.max_pool1d")
  @dispatch.add_dispatch_support
  def max_pool1d(input, ksize, strides, padding, data_format="NWC", name=None):
    """Performs the max pooling on the input.
  
    Note internally this op reshapes and uses the underlying 2d operation.
  
    Args:
      input: A 3-D `Tensor` of the format specified by `data_format`.
      ksize: An int or list of `ints` that has length `1` or `3`. The size of the
        window for each dimension of the input tensor.
      strides: An int or list of `ints` that has length `1` or `3`. The stride of
        the sliding window for each dimension of the input tensor.
      padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
        padding algorithm to use, or a list indicating the explicit paddings at
        the start and end of each dimension. When explicit padding is used and
        data_format is `"NWC"`, this should be in the form `[[0, 0], [pad_left,
        pad_right], [0, 0]]`. When explicit padding used and data_format is
        `"NCW"`, this should be in the form `[[0, 0], [0, 0], [pad_left,
        pad_right]]`. When using explicit padding, the size of the paddings cannot
        be greater than the sliding window size.
      data_format: An optional string from: "NWC", "NCW". Defaults to "NWC".
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of format specified by `data_format`.
      The max pooled output tensor.
    """
    with ops.name_scope(name, "MaxPool1d", [input]) as name:
      if isinstance(padding, (list, tuple)) and data_format == "NCHW_VECT_C":
        raise ValueError("Data formats NCHW_VECT_C is not yet supported with "
                         "explicit padding")
      if data_format is None:
        data_format = "NWC"
      channel_index = 1 if data_format.startswith("NC") else 2
      ksize = [1] + _get_sequence(ksize, 1, channel_index, "ksize")
      strides = [1] + _get_sequence(strides, 1, channel_index, "strides")
      padding, explicit_paddings = convert_padding(padding, 3)
      if padding == "EXPLICIT":
        explicit_paddings = [0, 0] + explicit_paddings
  
      expanding_dim = 1 if data_format == "NWC" else 2
      data_format = "NHWC" if data_format == "NWC" else "NCHW"
  
      input = array_ops.expand_dims_v2(input, expanding_dim)
      result = gen_nn_ops.max_pool(
          input,
          ksize=ksize,
          strides=strides,
          padding=padding,
          explicit_paddings=explicit_paddings,
          data_format=data_format,
          name=name)
      return array_ops.squeeze(result, expanding_dim)
  # pylint: enable=redefined-builtin
  
  
  # pylint: disable=redefined-builtin
  @tf_export("nn.max_pool2d")
  @dispatch.add_dispatch_support
  def max_pool2d(input, ksize, strides, padding, data_format="NHWC", name=None):
    """Performs the max pooling on the input.
  
    Args:
      input: A 4-D `Tensor` of the format specified by `data_format`.
      ksize: An int or list of `ints` that has length `1`, `2` or `4`. The size of
        the window for each dimension of the input tensor.
      strides: An int or list of `ints` that has length `1`, `2` or `4`. The
        stride of the sliding window for each dimension of the input tensor.
      padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
        padding algorithm to use, or a list indicating the explicit paddings at
        the start and end of each dimension. When explicit padding is used and
        data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,
        pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used
        and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0],
        [pad_top, pad_bottom], [pad_left, pad_right]]`. When using explicit
        padding, the size of the paddings cannot be greater than the sliding
        window size.
      data_format: A string. 'NHWC', 'NCHW' and 'NCHW_VECT_C' are supported.
      name: Optional name for the operation.
  
    Returns:
      A `Tensor` of format specified by `data_format`.
      The max pooled output tensor.
    """
    with ops.name_scope(name, "MaxPool2d", [input]) as name:
      if data_format is None:
        data_format = "NHWC"
      channel_index = 1 if data_format.startswith("NC") else 3
  
      ksize = _get_sequence(ksize, 2, channel_index, "ksize")
      strides = _get_sequence(strides, 2, channel_index, "strides")
      if isinstance(padding, (list, tuple)) and data_format == "NCHW_VECT_C":
        raise ValueError("Data formats NCHW_VECT_C is not yet supported with "
                         "explicit padding")
      padding, explicit_paddings = convert_padding(padding)
  
      return gen_nn_ops.max_pool(
          input,
          ksize=ksize,
          strides=strides,
          padding=padding,
          explicit_paddings=explicit_paddings,
          data_format=data_format,
          name=name)
  # pylint: enable=redefined-builtin
  
  
  # pylint: disable=redefined-builtin
  @tf_export("nn.max_pool3d")
  @dispatch.add_dispatch_support
  def max_pool3d(input, ksize, strides, padding, data_format="NDHWC", name=None):
    """Performs the max pooling on the input.
  
    Args:
      input: A 5-D `Tensor` of the format specified by `data_format`.
      ksize: An int or list of `ints` that has length `1`, `3` or `5`. The size of
        the window for each dimension of the input tensor.
      strides: An int or list of `ints` that has length `1`, `3` or `5`. The
        stride of the sliding window for each dimension of the input tensor.
      padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. See
        the "returns" section of `tf.nn.convolution` for details.
      data_format: An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC".
        The data format of the input and output data. With the default format
        "NDHWC", the data is stored in the order of: [batch, in_depth, in_height,
          in_width, in_channels]. Alternatively, the format could be "NCDHW", the
        data storage order is: [batch, in_channels, in_depth, in_height,
          in_width].
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of format specified by `data_format`.
      The max pooled output tensor.
    """
    with ops.name_scope(name, "MaxPool3D", [input]) as name:
      if data_format is None:
        data_format = "NDHWC"
      channel_index = 1 if data_format.startswith("NC") else 4
  
      ksize = _get_sequence(ksize, 3, channel_index, "ksize")
      strides = _get_sequence(strides, 3, channel_index, "strides")
  
      return gen_nn_ops.max_pool3d(
          input,
          ksize=ksize,
          strides=strides,
          padding=padding,
          data_format=data_format,
          name=name)
  # pylint: enable=redefined-builtin
  
  
  @tf_export("nn.max_pool_with_argmax", v1=[])
  @dispatch.add_dispatch_support
  def max_pool_with_argmax_v2(
      input,  # pylint: disable=redefined-builtin
      ksize,
      strides,
      padding,
      data_format="NHWC",
      output_dtype=dtypes.int64,
      include_batch_in_index=False,
      name=None):
    """Performs max pooling on the input and outputs both max values and indices.
  
    The indices in `argmax` are flattened, so that a maximum value at position
    `[b, y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if
    `include_batch_in_index` is False;
    `((b * height + y) * width + x) * channels + c`
    if `include_batch_in_index` is True.
  
    The indices returned are always in `[0, height) x [0, width)` before
    flattening, even if padding is involved and the mathematically correct answer
    is outside (either negative or too large).  This is a bug, but fixing it is
    difficult to do in a safe backwards compatible way, especially due to
    flattening.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `float32`, `float64`,
        `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`,
        `uint32`, `uint64`.
        4-D with shape `[batch, height, width, channels]`.  Input to pool over.
      ksize: An int or list of `ints` that has length `1`, `2` or `4`.
        The size of the window for each dimension of the input tensor.
      strides: An int or list of `ints` that has length `1`, `2` or `4`.
        The stride of the sliding window for each dimension of the
        input tensor.
      padding: A `string` from: `"SAME", "VALID"`.
        The type of padding algorithm to use.
      data_format: An optional `string`, must be set to `"NHWC"`. Defaults to
        `"NHWC"`.
        Specify the data format of the input and output data.
      output_dtype: An optional `tf.DType` from: `tf.int32, tf.int64`.
        Defaults to `tf.int64`.
        The dtype of the returned argmax tensor.
      include_batch_in_index: An optional `boolean`. Defaults to `False`.
        Whether to include batch dimension in flattened index of `argmax`.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (output, argmax).
  
      output: A `Tensor`. Has the same type as `input`.
      argmax: A `Tensor` of type `output_dtype`.
    """
  
    if data_format != "NHWC":
      raise ValueError("Data formats other than 'NHWC' are not yet supported")
  
    ksize = _get_sequence(ksize, 2, 3, "ksize")
    strides = _get_sequence(strides, 2, 3, "strides")
  
    return gen_nn_ops.max_pool_with_argmax(
        input=input,
        ksize=ksize,
        strides=strides,
        padding=padding,
        Targmax=output_dtype,
        include_batch_in_index=include_batch_in_index,
        name=name)
  
  
  @tf_export(v1=["nn.max_pool_with_argmax"])
  @dispatch.add_dispatch_support
  def max_pool_with_argmax_v1(  # pylint: disable=missing-docstring,invalid-name
      input,  # pylint: disable=redefined-builtin
      ksize,
      strides,
      padding,
      data_format="NHWC",
      Targmax=None,
      name=None,
      output_dtype=None,
      include_batch_in_index=False):
    if data_format != "NHWC":
      raise ValueError("Data formats other than 'NHWC' are not yet supported")
  
    Targmax = deprecated_argument_lookup(
        "output_dtype", output_dtype, "Targmax", Targmax)
    if Targmax is None:
      Targmax = dtypes.int64
    return gen_nn_ops.max_pool_with_argmax(
        input=input,
        ksize=ksize,
        strides=strides,
        padding=padding,
        Targmax=Targmax,
        include_batch_in_index=include_batch_in_index,
        name=name)
  
  
  max_pool_with_argmax_v1.__doc__ = gen_nn_ops.max_pool_with_argmax.__doc__
  
  
  @ops.RegisterStatistics("Conv3D", "flops")
  def _calc_conv3d_flops(graph, node):
    """Calculates the compute resources needed for Conv3D."""
    input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
    input_shape.assert_is_fully_defined()
    filter_shape = graph_util.tensor_shape_from_node_def_name(
        graph, node.input[1])
    filter_shape.assert_is_fully_defined()
    output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
    output_shape.assert_is_fully_defined()
    filter_time = int(filter_shape[0])
    filter_height = int(filter_shape[1])
    filter_width = int(filter_shape[2])
    filter_in_depth = int(filter_shape[3])
    output_count = np.prod(output_shape.as_list(), dtype=np.int64)
    return ops.OpStats("flops", (output_count * filter_in_depth * filter_time *
                                 filter_height * filter_width * 2))
  
  
  @ops.RegisterStatistics("Conv2D", "flops")
  def _calc_conv_flops(graph, node):
    """Calculates the compute resources needed for Conv2D."""
    input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
    input_shape.assert_is_fully_defined()
    filter_shape = graph_util.tensor_shape_from_node_def_name(
        graph, node.input[1])
    filter_shape.assert_is_fully_defined()
    output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
    output_shape.assert_is_fully_defined()
    filter_height = int(filter_shape[0])
    filter_width = int(filter_shape[1])
    filter_in_depth = int(filter_shape[2])
    output_count = np.prod(output_shape.as_list(), dtype=np.int64)
    return ops.OpStats(
        "flops",
        (output_count * filter_in_depth * filter_height * filter_width * 2))
  
  
  @ops.RegisterStatistics("DepthwiseConv2dNative", "flops")
  def _calc_depthwise_conv_flops(graph, node):
    """Calculates the compute resources needed for DepthwiseConv2dNative."""
    input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
    input_shape.assert_is_fully_defined()
    filter_shape = graph_util.tensor_shape_from_node_def_name(
        graph, node.input[1])
    filter_shape.assert_is_fully_defined()
    output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
    output_shape.assert_is_fully_defined()
    filter_height = int(filter_shape[0])
    filter_width = int(filter_shape[1])
    output_count = np.prod(output_shape.as_list(), dtype=np.int64)
    return ops.OpStats("flops", (output_count * filter_height * filter_width * 2))
  
  
  @ops.RegisterStatistics("BiasAdd", "flops")
  def _calc_bias_add_flops(graph, node):
    """Calculates the computing needed for BiasAdd."""
    input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
    input_shape.assert_is_fully_defined()
    input_count = np.prod(input_shape.as_list())
    return ops.OpStats("flops", input_count)
  
  
  @tf_export(v1=["nn.xw_plus_b"])
  @dispatch.add_dispatch_support
  def xw_plus_b(x, weights, biases, name=None):  # pylint: disable=invalid-name
    """Computes matmul(x, weights) + biases.
  
    Args:
      x: a 2D tensor.  Dimensions typically: batch, in_units
      weights: a 2D tensor.  Dimensions typically: in_units, out_units
      biases: a 1D tensor.  Dimensions: out_units
      name: A name for the operation (optional).  If not specified
        "xw_plus_b" is used.
  
    Returns:
      A 2-D Tensor computing matmul(x, weights) + biases.
      Dimensions typically: batch, out_units.
    """
    with ops.name_scope(name, "xw_plus_b", [x, weights, biases]) as name:
      x = ops.convert_to_tensor(x, name="x")
      weights = ops.convert_to_tensor(weights, name="weights")
      biases = ops.convert_to_tensor(biases, name="biases")
      mm = math_ops.matmul(x, weights)
      return bias_add(mm, biases, name=name)
  
  
  def xw_plus_b_v1(x, weights, biases, name=None):
    """Computes matmul(x, weights) + biases.
  
    This is a deprecated version of that will soon be removed.
  
    Args:
      x: a 2D tensor.  Dimensions typically: batch, in_units
      weights: a 2D tensor.  Dimensions typically: in_units, out_units
      biases: a 1D tensor.  Dimensions: out_units
      name: A name for the operation (optional).  If not specified
        "xw_plus_b_v1" is used.
  
    Returns:
      A 2-D Tensor computing matmul(x, weights) + biases.
      Dimensions typically: batch, out_units.
    """
    with ops.name_scope(name, "xw_plus_b_v1", [x, weights, biases]) as name:
      x = ops.convert_to_tensor(x, name="x")
      weights = ops.convert_to_tensor(weights, name="weights")
      biases = ops.convert_to_tensor(biases, name="biases")
      mm = math_ops.matmul(x, weights)
      return bias_add_v1(mm, biases, name=name)
  
  
  def _get_noise_shape(x, noise_shape):
    # If noise_shape is none return immediately.
    if noise_shape is None:
      return array_ops.shape(x)
  
    try:
      # Best effort to figure out the intended shape.
      # If not possible, let the op to handle it.
      # In eager mode exception will show up.
      noise_shape_ = tensor_shape.as_shape(noise_shape)
    except (TypeError, ValueError):
      return noise_shape
  
    if x.shape.dims is not None and len(x.shape.dims) == len(noise_shape_.dims):
      new_dims = []
      for i, dim in enumerate(x.shape.dims):
        if noise_shape_.dims[i].value is None and dim.value is not None:
          new_dims.append(dim.value)
        else:
          new_dims.append(noise_shape_.dims[i].value)
      return tensor_shape.TensorShape(new_dims)
  
    return noise_shape
  
  
  @tf_export(v1=["nn.dropout"])
  @dispatch.add_dispatch_support
  @deprecation.deprecated_args(None, "Please use `rate` instead of `keep_prob`. "
                               "Rate should be set to `rate = 1 - keep_prob`.",
                               "keep_prob")
  def dropout(x, keep_prob=None, noise_shape=None, seed=None, name=None,
              rate=None):
    """Computes dropout.
  
    For each element of `x`, with probability `rate`, outputs `0`, and otherwise
    scales up the input by `1 / (1-rate)`. The scaling is such that the expected
    sum is unchanged.
  
    By default, each element is kept or dropped independently.  If `noise_shape`
    is specified, it must be
    [broadcastable](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
    to the shape of `x`, and only dimensions with `noise_shape[i] == shape(x)[i]`
    will make independent decisions.  For example, if `shape(x) = [k, l, m, n]`
    and `noise_shape = [k, 1, 1, n]`, each batch and channel component will be
    kept independently and each row and column will be kept or not kept together.
  
    Args:
      x: A floating point tensor.
      keep_prob: (deprecated) A deprecated alias for `(1-rate)`.
      noise_shape: A 1-D `Tensor` of type `int32`, representing the
        shape for randomly generated keep/drop flags.
      seed: A Python integer. Used to create random seeds. See
        `tf.random.set_seed` for behavior.
      name: A name for this operation (optional).
      rate: A scalar `Tensor` with the same type as `x`. The probability that each
        element of `x` is discarded.
  
    Returns:
      A Tensor of the same shape of `x`.
  
    Raises:
      ValueError: If `rate` is not in `[0, 1)` or if `x` is not a floating
        point tensor.
    """
    try:
      keep = 1. - keep_prob if keep_prob is not None else None
    except TypeError:
      raise ValueError("keep_prob must be a floating point number or Tensor "
                       "(got %r)" % keep_prob)
  
    rate = deprecation.deprecated_argument_lookup(
        "rate", rate,
        "keep_prob", keep)
  
    if rate is None:
      raise ValueError("You must provide a rate to dropout.")
  
    return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)
  
  
  @tf_export("nn.dropout", v1=[])
  @dispatch.add_dispatch_support
  def dropout_v2(x, rate, noise_shape=None, seed=None, name=None):
    """Computes dropout: randomly sets elements to zero to prevent overfitting.
  
    Note: The behavior of dropout has changed between TensorFlow 1.x and 2.x.
    When converting 1.x code, please use named arguments to ensure behavior stays
    consistent.
  
    See also: `tf.keras.layers.Dropout` for a dropout layer.
  
    [Dropout](https://arxiv.org/abs/1207.0580) is useful for regularizing DNN
    models. Inputs elements are randomly set to zero (and the other elements are
    rescaled). This encourages each node to be independently useful, as it cannot
    rely on the output of other nodes.
  
    More precisely: With probability `rate` elements of `x` are set to `0`.
    The remaining elements are scaled up by `1.0 / (1 - rate)`, so that the
    expected value is preserved.
  
    >>> tf.random.set_seed(0)
    >>> x = tf.ones([3,5])
    >>> tf.nn.dropout(x, rate = 0.5, seed = 1).numpy()
    array([[2., 0., 0., 2., 2.],
         [2., 2., 2., 2., 2.],
         [2., 0., 2., 0., 2.]], dtype=float32)
  
    >>> tf.random.set_seed(0)
    >>> x = tf.ones([3,5])
    >>> tf.nn.dropout(x, rate = 0.8, seed = 1).numpy()
    array([[0., 0., 0., 5., 5.],
         [0., 5., 0., 5., 0.],
         [5., 0., 5., 0., 5.]], dtype=float32)
  
    >>> tf.nn.dropout(x, rate = 0.0) == x
    <tf.Tensor: shape=(3, 5), dtype=bool, numpy=
      array([[ True,  True,  True,  True,  True],
             [ True,  True,  True,  True,  True],
             [ True,  True,  True,  True,  True]])>
  
  
    By default, each element is kept or dropped independently.  If `noise_shape`
    is specified, it must be
    [broadcastable](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
    to the shape of `x`, and only dimensions with `noise_shape[i] == shape(x)[i]`
    will make independent decisions. This is useful for dropping whole
    channels from an image or sequence. For example:
  
    >>> tf.random.set_seed(0)
    >>> x = tf.ones([3,10])
    >>> tf.nn.dropout(x, rate = 2/3, noise_shape=[1,10], seed=1).numpy()
    array([[0., 0., 0., 3., 3., 0., 3., 3., 3., 0.],
         [0., 0., 0., 3., 3., 0., 3., 3., 3., 0.],
         [0., 0., 0., 3., 3., 0., 3., 3., 3., 0.]], dtype=float32)
  
    Args:
      x: A floating point tensor.
      rate: A scalar `Tensor` with the same type as x. The probability
        that each element is dropped. For example, setting rate=0.1 would drop
        10% of input elements.
      noise_shape: A 1-D `Tensor` of type `int32`, representing the
        shape for randomly generated keep/drop flags.
      seed: A Python integer. Used to create random seeds. See
        `tf.random.set_seed` for behavior.
      name: A name for this operation (optional).
  
    Returns:
      A Tensor of the same shape of `x`.
  
    Raises:
      ValueError: If `rate` is not in `[0, 1)` or if `x` is not a floating point
        tensor. `rate=1` is disallowed, because the output would be all zeros,
        which is likely not what was intended.
    """
    with ops.name_scope(name, "dropout", [x]) as name:
      is_rate_number = isinstance(rate, numbers.Real)
      if is_rate_number and (rate < 0 or rate >= 1):
        raise ValueError("rate must be a scalar tensor or a float in the "
                         "range [0, 1), got %g" % rate)
      x = ops.convert_to_tensor(x, name="x")
      x_dtype = x.dtype
      if not x_dtype.is_floating:
        raise ValueError("x has to be a floating point tensor since it's going "
                         "to be scaled. Got a %s tensor instead." % x_dtype)
      if is_rate_number and rate == 0:
        # Fast-path: Return the input immediately if rate is non-tensor & is `0`.
        # We trigger this after all error checking
        # and after `x` has been converted to a tensor, to prevent inconsistent
        # tensor conversions/error raising if rate is changed to/from 0.
        #
        # We also explicitly call `random_seed.get_seed` to make sure
        # we don't change the random number generation behavior of
        # stateful random ops by entering a fastpath,
        # despite not generating a random tensor in the fastpath
        random_seed.get_seed(seed)
        return x
  
      is_executing_eagerly = context.executing_eagerly()
      if not tensor_util.is_tf_type(rate):
        if is_rate_number:
          keep_prob = 1 - rate
          scale = 1 / keep_prob
          scale = ops.convert_to_tensor(scale, dtype=x_dtype)
          ret = gen_math_ops.mul(x, scale)
        else:
          raise ValueError("rate is neither scalar nor scalar tensor %r" % rate)
      else:
        rate.get_shape().assert_has_rank(0)
        rate_dtype = rate.dtype
        if rate_dtype != x_dtype:
          if not rate_dtype.is_compatible_with(x_dtype):
            raise ValueError(
                "Tensor dtype %s is incomptaible with Tensor dtype %s: %r" %
                (x_dtype.name, rate_dtype.name, rate))
          rate = gen_math_ops.cast(rate, x_dtype, name="rate")
        one_tensor = constant_op.constant(1, dtype=x_dtype)
        ret = gen_math_ops.real_div(x, gen_math_ops.sub(one_tensor, rate))
  
      noise_shape = _get_noise_shape(x, noise_shape)
      # Sample a uniform distribution on [0.0, 1.0) and select values larger
      # than rate.
      #
      # NOTE: Random uniform can only generate 2^23 floats on [1.0, 2.0)
      # and subtract 1.0.
      random_tensor = random_ops.random_uniform(
          noise_shape, seed=seed, dtype=x_dtype)
      # NOTE: if (1.0 + rate) - 1 is equal to rate, then that float is selected,
      # hence a >= comparison is used.
      keep_mask = random_tensor >= rate
      ret = gen_math_ops.mul(ret, gen_math_ops.cast(keep_mask, x_dtype))
      if not is_executing_eagerly:
        ret.set_shape(x.get_shape())
      return ret
  
  
  @tf_export("math.top_k", "nn.top_k")
  @dispatch.add_dispatch_support
  def top_k(input, k=1, sorted=True, name=None):  # pylint: disable=redefined-builtin
    """Finds values and indices of the `k` largest entries for the last dimension.
  
    If the input is a vector (rank=1), finds the `k` largest entries in the vector
    and outputs their values and indices as vectors.  Thus `values[j]` is the
    `j`-th largest entry in `input`, and its index is `indices[j]`.
  
    >>> result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
    ...                         k=3)
    >>> result.values.numpy()
    array([99, 98, 96], dtype=int32)
    >>> result.indices.numpy()
    array([5, 2, 9], dtype=int32)
  
    For matrices (resp. higher rank input), computes the top `k` entries in each
    row (resp. vector along the last dimension).  Thus,
  
    >>> input = tf.random.normal(shape=(3,4,5,6))
    >>> k = 2
    >>> values, indices  = tf.math.top_k(input, k=k)
    >>> values.shape.as_list()
    [3, 4, 5, 2]
    >>>
    >>> values.shape == indices.shape == input.shape[:-1] + [k]
    True
  
    The indices can be used to `gather` from a tensor who's shape matches `input`.
  
    >>> gathered_values = tf.gather(input, indices, batch_dims=-1)
    >>> assert tf.reduce_all(gathered_values == values)
  
    If two elements are equal, the lower-index element appears first.
  
    >>> result = tf.math.top_k([1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0],
    ...                        k=3)
    >>> result.indices.numpy()
    array([0, 1, 3], dtype=int32)
  
    Args:
      input: 1-D or higher `Tensor` with last dimension at least `k`.
      k: 0-D `int32` `Tensor`.  Number of top elements to look for along the last
        dimension (along each row for matrices).
      sorted: If true the resulting `k` elements will be sorted by the values in
        descending order.
      name: Optional name for the operation.
  
    Returns:
      A tuple with two named fields:
      values: The `k` largest elements along each last dimensional slice.
      indices: The indices of `values` within the last dimension of `input`.
    """
    return gen_nn_ops.top_kv2(input, k=k, sorted=sorted, name=name)
  
  
  def nth_element(input, n, reverse=False, name=None):  # pylint: disable=redefined-builtin
    r"""Finds values of the `n`-th smallest value for the last dimension.
  
    Note that n is zero-indexed.
  
    If the input is a vector (rank-1), finds the entries which is the nth-smallest
    value in the vector and outputs their values as scalar tensor.
  
    For matrices (resp. higher rank input), computes the entries which is the
    nth-smallest value in each row (resp. vector along the last dimension). Thus,
  
        values.shape = input.shape[:-1]
  
    Args:
      input: 1-D or higher `Tensor` with last dimension at least `n+1`.
      n: A `Tensor` of type `int32`.
        0-D. Position of sorted vector to select along the last dimension (along
        each row for matrices). Valid range of n is `[0, input.shape[:-1])`
      reverse: An optional `bool`. Defaults to `False`.
        When set to True, find the nth-largest value in the vector and vice
        versa.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
      The `n`-th order statistic along each last dimensional slice.
    """
    return gen_nn_ops.nth_element(input, n, reverse=reverse, name=name)
  
  
  @tf_export(v1=["nn.fractional_max_pool"])
  @dispatch.add_dispatch_support
  @deprecation.deprecated(date=None, instructions="`seed2` and `deterministic` "
                          "args are deprecated.  Use fractional_max_pool_v2.")
  def fractional_max_pool(value,
                          pooling_ratio,
                          pseudo_random=False,
                          overlapping=False,
                          deterministic=False,
                          seed=0,
                          seed2=0,
                          name=None):   # pylint: disable=redefined-builtin
    r"""Performs fractional max pooling on the input.
  
    This is a deprecated version of `fractional_max_pool`.
  
    Fractional max pooling is slightly different than regular max pooling.  In
    regular max pooling, you downsize an input set by taking the maximum value of
    smaller N x N subsections of the set (often 2x2), and try to reduce the set by
    a factor of N, where N is an integer.  Fractional max pooling, as you might
    expect from the word "fractional", means that the overall reduction ratio N
    does not have to be an integer.
  
    The sizes of the pooling regions are generated randomly but are fairly
    uniform.  For example, let's look at the height dimension, and the constraints
    on the list of rows that will be pool boundaries.
  
    First we define the following:
  
    1.  input_row_length : the number of rows from the input set
    2.  output_row_length : which will be smaller than the input
    3.  alpha = input_row_length / output_row_length : our reduction ratio
    4.  K = floor(alpha)
    5.  row_pooling_sequence : this is the result list of pool boundary rows
  
    Then, row_pooling_sequence should satisfy:
  
    1.  a[0] = 0 : the first value of the sequence is 0
    2.  a[end] = input_row_length : the last value of the sequence is the size
    3.  K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size
    4.  length(row_pooling_sequence) = output_row_length+1
  
    Args:
      value: A `Tensor`. 4-D with shape `[batch, height, width, channels]`.
      pooling_ratio: A list of `floats` that has length >= 4.  Pooling ratio for
        each dimension of `value`, currently only supports row and col dimension
        and should be >= 1.0. For example, a valid pooling ratio looks like [1.0,
        1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't
        allow pooling on batch and channels dimensions.  1.44 and 1.73 are pooling
        ratio on height and width dimensions respectively.
      pseudo_random: An optional `bool`.  Defaults to `False`. When set to `True`,
        generates the pooling sequence in a pseudorandom fashion, otherwise, in a
        random fashion. Check (Graham, 2015) for difference between
        pseudorandom and random.
      overlapping: An optional `bool`.  Defaults to `False`.  When set to `True`,
        it means when pooling, the values at the boundary of adjacent pooling
        cells are used by both cells. For example:
        `index  0  1  2  3  4`
        `value  20 5  16 3  7`
        If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used
        twice.  The result would be [20, 16] for fractional max pooling.
      deterministic: An optional `bool`.  Deprecated; use `fractional_max_pool_v2`
        instead.
      seed: An optional `int`.  Defaults to `0`.  If set to be non-zero, the
        random number generator is seeded by the given seed.  Otherwise it is
        seeded by a random seed.
      seed2: An optional `int`.  Deprecated; use `fractional_max_pool_v2` instead.
      name: A name for the operation (optional).
  
    Returns:
    A tuple of `Tensor` objects (`output`, `row_pooling_sequence`,
    `col_pooling_sequence`).
      output: Output `Tensor` after fractional max pooling.  Has the same type as
        `value`.
      row_pooling_sequence: A `Tensor` of type `int64`.
      col_pooling_sequence: A `Tensor` of type `int64`.
  
    References:
      Fractional Max-Pooling:
        [Graham, 2015](https://arxiv.org/abs/1412.6071)
        ([pdf](https://arxiv.org/pdf/1412.6071.pdf))
    """
    return gen_nn_ops.fractional_max_pool(value, pooling_ratio, pseudo_random,
                                          overlapping, deterministic, seed, seed2,
                                          name)
  
  
  @tf_export("nn.fractional_max_pool", v1=[])
  @dispatch.add_dispatch_support
  def fractional_max_pool_v2(value,
                             pooling_ratio,
                             pseudo_random=False,
                             overlapping=False,
                             seed=0,
                             name=None):  # pylint: disable=redefined-builtin
    r"""Performs fractional max pooling on the input.
  
    Fractional max pooling is slightly different than regular max pooling.  In
    regular max pooling, you downsize an input set by taking the maximum value of
    smaller N x N subsections of the set (often 2x2), and try to reduce the set by
    a factor of N, where N is an integer.  Fractional max pooling, as you might
    expect from the word "fractional", means that the overall reduction ratio N
    does not have to be an integer.
  
    The sizes of the pooling regions are generated randomly but are fairly
    uniform.  For example, let's look at the height dimension, and the constraints
    on the list of rows that will be pool boundaries.
  
    First we define the following:
  
    1.  input_row_length : the number of rows from the input set
    2.  output_row_length : which will be smaller than the input
    3.  alpha = input_row_length / output_row_length : our reduction ratio
    4.  K = floor(alpha)
    5.  row_pooling_sequence : this is the result list of pool boundary rows
  
    Then, row_pooling_sequence should satisfy:
  
    1.  a[0] = 0 : the first value of the sequence is 0
    2.  a[end] = input_row_length : the last value of the sequence is the size
    3.  K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size
    4.  length(row_pooling_sequence) = output_row_length+1
  
    Args:
      value: A `Tensor`. 4-D with shape `[batch, height, width, channels]`.
      pooling_ratio: An int or list of `ints` that has length `1`, `2` or `4`.
        Pooling ratio for each dimension of `value`, currently only supports row
        and col dimension and should be >= 1.0. For example, a valid pooling ratio
        looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0
        because we don't allow pooling on batch and channels dimensions.  1.44 and
        1.73 are pooling ratio on height and width dimensions respectively.
      pseudo_random: An optional `bool`.  Defaults to `False`. When set to `True`,
        generates the pooling sequence in a pseudorandom fashion, otherwise, in a
        random fashion. Check paper (Graham, 2015) for difference between
        pseudorandom and random.
      overlapping: An optional `bool`.  Defaults to `False`.  When set to `True`,
        it means when pooling, the values at the boundary of adjacent pooling
        cells are used by both cells. For example:
        `index  0  1  2  3  4`
        `value  20 5  16 3  7`
        If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used
        twice.  The result would be [20, 16] for fractional max pooling.
      seed: An optional `int`.  Defaults to `0`.  If set to be non-zero, the
        random number generator is seeded by the given seed.  Otherwise it is
        seeded by a random seed.
      name: A name for the operation (optional).
  
    Returns:
    A tuple of `Tensor` objects (`output`, `row_pooling_sequence`,
    `col_pooling_sequence`).
      output: Output `Tensor` after fractional max pooling.  Has the same type as
        `value`.
      row_pooling_sequence: A `Tensor` of type `int64`.
      col_pooling_sequence: A `Tensor` of type `int64`.
  
    References:
      Fractional Max-Pooling:
        [Graham, 2015](https://arxiv.org/abs/1412.6071)
        ([pdf](https://arxiv.org/pdf/1412.6071.pdf))
    """
    if (isinstance(pooling_ratio, (list, tuple))):
      if (pooling_ratio[0] != 1.0 or pooling_ratio[-1] != 1.0):
        raise ValueError(
            "The first and last elements of pooling ratio must be 1.0.")
      for element in pooling_ratio:
        if element < 1.0:
          raise ValueError("pooling_ratio should be >= 1.0.")
    elif (isinstance(pooling_ratio, (int, float))):
      if pooling_ratio < 1.0:
        raise ValueError("pooling_ratio should be >= 1.0.")
    else:
      raise ValueError("pooling_ratio should be an int or a list of ints.")
  
    pooling_ratio = _get_sequence(pooling_ratio, 2, 3, "pooling_ratio")
  
    if seed == 0:
      return gen_nn_ops.fractional_max_pool(value, pooling_ratio, pseudo_random,
                                            overlapping, deterministic=False,
                                            seed=0, seed2=0, name=name)
    else:
      seed1, seed2 = random_seed.get_seed(seed)
      return gen_nn_ops.fractional_max_pool(value, pooling_ratio, pseudo_random,
                                            overlapping, deterministic=True,
                                            seed=seed1, seed2=seed2, name=name)
  
  
  @tf_export(v1=["nn.fractional_avg_pool"])
  @dispatch.add_dispatch_support
  @deprecation.deprecated(date=None, instructions="`seed2` and `deterministic` "
                          "args are deprecated.  Use fractional_avg_pool_v2.")
  def fractional_avg_pool(value,
                          pooling_ratio,
                          pseudo_random=False,
                          overlapping=False,
                          deterministic=False,
                          seed=0,
                          seed2=0,
                          name=None):  # pylint: disable=redefined-builtin
    r"""Performs fractional average pooling on the input.
  
    This is a deprecated version of `fractional_avg_pool`.
  
    Fractional average pooling is similar to Fractional max pooling in the pooling
    region generation step. The only difference is that after pooling regions are
    generated, a mean operation is performed instead of a max operation in each
    pooling region.
  
    Args:
      value: A `Tensor`. 4-D with shape `[batch, height, width, channels]`.
      pooling_ratio: A list of `floats` that has length >= 4.  Pooling ratio for
        each dimension of `value`, currently only supports row and col dimension
        and should be >= 1.0. For example, a valid pooling ratio looks like [1.0,
        1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't
        allow pooling on batch and channels dimensions.  1.44 and 1.73 are pooling
        ratio on height and width dimensions respectively.
      pseudo_random: An optional `bool`.  Defaults to `False`. When set to `True`,
        generates the pooling sequence in a pseudorandom fashion, otherwise, in a
        random fashion. Check paper (Graham, 2015) for difference between
        pseudorandom and random.
      overlapping: An optional `bool`.  Defaults to `False`.  When set to `True`,
        it means when pooling, the values at the boundary of adjacent pooling
        cells are used by both cells. For example:
        `index  0  1  2  3  4`
        `value  20 5  16 3  7`
        If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used
        twice.  The result would be [20, 16] for fractional avg pooling.
      deterministic: An optional `bool`.  Deprecated; use `fractional_avg_pool_v2`
        instead.
      seed: An optional `int`.  Defaults to `0`.  If set to be non-zero, the
        random number generator is seeded by the given seed.  Otherwise it is
        seeded by a random seed.
      seed2: An optional `int`.  Deprecated; use `fractional_avg_pool_v2` instead.
      name: A name for the operation (optional).
  
    Returns:
    A tuple of `Tensor` objects (`output`, `row_pooling_sequence`,
    `col_pooling_sequence`).
      output: Output `Tensor` after fractional avg pooling.  Has the same type as
        `value`.
      row_pooling_sequence: A `Tensor` of type `int64`.
      col_pooling_sequence: A `Tensor` of type `int64`.
  
    References:
      Fractional Max-Pooling:
        [Graham, 2015](https://arxiv.org/abs/1412.6071)
        ([pdf](https://arxiv.org/pdf/1412.6071.pdf))
    """
    return gen_nn_ops.fractional_avg_pool(value, pooling_ratio, pseudo_random,
                                          overlapping, deterministic, seed, seed2,
                                          name=name)
  
  
  @tf_export("nn.fractional_avg_pool", v1=[])
  @dispatch.add_dispatch_support
  def fractional_avg_pool_v2(value,
                             pooling_ratio,
                             pseudo_random=False,
                             overlapping=False,
                             seed=0,
                             name=None):  # pylint: disable=redefined-builtin
    r"""Performs fractional average pooling on the input.
  
    Fractional average pooling is similar to Fractional max pooling in the pooling
    region generation step. The only difference is that after pooling regions are
    generated, a mean operation is performed instead of a max operation in each
    pooling region.
  
    Args:
      value: A `Tensor`. 4-D with shape `[batch, height, width, channels]`.
      pooling_ratio: A list of `floats` that has length >= 4.  Pooling ratio for
        each dimension of `value`, currently only supports row and col dimension
        and should be >= 1.0. For example, a valid pooling ratio looks like [1.0,
        1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't
        allow pooling on batch and channels dimensions.  1.44 and 1.73 are pooling
        ratio on height and width dimensions respectively.
      pseudo_random: An optional `bool`.  Defaults to `False`. When set to `True`,
        generates the pooling sequence in a pseudorandom fashion, otherwise, in a
        random fashion. Check paper (Graham, 2015) for difference between
        pseudorandom and random.
      overlapping: An optional `bool`.  Defaults to `False`.  When set to `True`,
        it means when pooling, the values at the boundary of adjacent pooling
        cells are used by both cells. For example:
        `index  0  1  2  3  4`
        `value  20 5  16 3  7`
        If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used
        twice.  The result would be [20, 16] for fractional avg pooling.
      seed: An optional `int`.  Defaults to `0`.  If set to be non-zero, the
        random number generator is seeded by the given seed.  Otherwise it is
        seeded by a random seed.
      name: A name for the operation (optional).
  
    Returns:
    A tuple of `Tensor` objects (`output`, `row_pooling_sequence`,
    `col_pooling_sequence`).
      output: Output `Tensor` after fractional avg pooling.  Has the same type as
        `value`.
      row_pooling_sequence: A `Tensor` of type `int64`.
      col_pooling_sequence: A `Tensor` of type `int64`.
  
    References:
      Fractional Max-Pooling:
        [Graham, 2015](https://arxiv.org/abs/1412.6071)
        ([pdf](https://arxiv.org/pdf/1412.6071.pdf))
    """
    if seed == 0:
      return gen_nn_ops.fractional_avg_pool(value, pooling_ratio, pseudo_random,
                                            overlapping, deterministic=False,
                                            seed=0, seed2=0, name=name)
    else:
      seed1, seed2 = random_seed.get_seed(seed)
      return gen_nn_ops.fractional_avg_pool(value, pooling_ratio, pseudo_random,
                                            overlapping, deterministic=True,
                                            seed=seed1, seed2=seed2, name=name)
  
  
  @ops.RegisterStatistics("Dilation2D", "flops")
  def _calc_dilation2d_flops(graph, node):
    """Calculates the compute resources needed for Dilation2D."""
    input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
    input_shape.assert_is_fully_defined()
    filter_shape = graph_util.tensor_shape_from_node_def_name(
        graph, node.input[1])
    filter_shape.assert_is_fully_defined()
    output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name)
    output_shape.assert_is_fully_defined()
    filter_height = int(filter_shape[0])
    filter_width = int(filter_shape[1])
    output_count = np.prod(output_shape.as_list(), dtype=np.int64)
    return ops.OpStats("flops", (output_count * filter_height * filter_width * 2))
  
  
  @tf_export(v1=["nn.erosion2d"])
  @dispatch.add_dispatch_support
  def erosion2d(value, kernel, strides, rates, padding, name=None):
    """Computes the grayscale erosion of 4-D `value` and 3-D `kernel` tensors.
  
    The `value` tensor has shape `[batch, in_height, in_width, depth]` and the
    `kernel` tensor has shape `[kernel_height, kernel_width, depth]`, i.e.,
    each input channel is processed independently of the others with its own
    structuring function. The `output` tensor has shape
    `[batch, out_height, out_width, depth]`. The spatial dimensions of the
    output tensor depend on the `padding` algorithm. We currently only support the
    default "NHWC" `data_format`.
  
    In detail, the grayscale morphological 2-D erosion is given by:
  
        output[b, y, x, c] =
           min_{dy, dx} value[b,
                              strides[1] * y - rates[1] * dy,
                              strides[2] * x - rates[2] * dx,
                              c] -
                        kernel[dy, dx, c]
  
    Duality: The erosion of `value` by the `kernel` is equal to the negation of
    the dilation of `-value` by the reflected `kernel`.
  
    Args:
      value: A `Tensor`. 4-D with shape `[batch, in_height, in_width, depth]`.
      kernel: A `Tensor`. Must have the same type as `value`.
        3-D with shape `[kernel_height, kernel_width, depth]`.
      strides: A list of `ints` that has length `>= 4`.
        1-D of length 4. The stride of the sliding window for each dimension of
        the input tensor. Must be: `[1, stride_height, stride_width, 1]`.
      rates: A list of `ints` that has length `>= 4`.
        1-D of length 4. The input stride for atrous morphological dilation.
        Must be: `[1, rate_height, rate_width, 1]`.
      padding: A `string` from: `"SAME", "VALID"`.
        The type of padding algorithm to use.
      name: A name for the operation (optional). If not specified "erosion2d"
        is used.
  
    Returns:
      A `Tensor`. Has the same type as `value`.
      4-D with shape `[batch, out_height, out_width, depth]`.
    Raises:
      ValueError: If the `value` depth does not match `kernel`' shape, or if
        padding is other than `'VALID'` or `'SAME'`.
    """
    with ops.name_scope(name, "erosion2d", [value, kernel]) as name:
      # Reduce erosion to dilation by duality.
      return math_ops.negative(
          gen_nn_ops.dilation2d(
              input=math_ops.negative(value),
              filter=array_ops.reverse_v2(kernel, [0, 1]),
              strides=strides,
              rates=rates,
              padding=padding,
              name=name))
  
  
  @tf_export("nn.erosion2d", v1=[])
  @dispatch.add_dispatch_support
  def erosion2d_v2(value,
                   filters,
                   strides,
                   padding,
                   data_format,
                   dilations,
                   name=None):
    """Computes the grayscale erosion of 4-D `value` and 3-D `filters` tensors.
  
    The `value` tensor has shape `[batch, in_height, in_width, depth]` and the
    `filters` tensor has shape `[filters_height, filters_width, depth]`, i.e.,
    each input channel is processed independently of the others with its own
    structuring function. The `output` tensor has shape
    `[batch, out_height, out_width, depth]`. The spatial dimensions of the
    output tensor depend on the `padding` algorithm. We currently only support the
    default "NHWC" `data_format`.
  
    In detail, the grayscale morphological 2-D erosion is given by:
  
        output[b, y, x, c] =
           min_{dy, dx} value[b,
                              strides[1] * y - dilations[1] * dy,
                              strides[2] * x - dilations[2] * dx,
                              c] -
                        filters[dy, dx, c]
  
    Duality: The erosion of `value` by the `filters` is equal to the negation of
    the dilation of `-value` by the reflected `filters`.
  
    Args:
      value: A `Tensor`. 4-D with shape `[batch, in_height, in_width, depth]`.
      filters: A `Tensor`. Must have the same type as `value`.
        3-D with shape `[filters_height, filters_width, depth]`.
      strides: A list of `ints` that has length `>= 4`.
        1-D of length 4. The stride of the sliding window for each dimension of
        the input tensor. Must be: `[1, stride_height, stride_width, 1]`.
      padding: A `string` from: `"SAME", "VALID"`.
        The type of padding algorithm to use.
      data_format: A `string`, only `"NHWC"` is currently supported.
      dilations: A list of `ints` that has length `>= 4`.
        1-D of length 4. The input stride for atrous morphological dilation.
        Must be: `[1, rate_height, rate_width, 1]`.
      name: A name for the operation (optional). If not specified "erosion2d"
        is used.
  
    Returns:
      A `Tensor`. Has the same type as `value`.
      4-D with shape `[batch, out_height, out_width, depth]`.
  
    Raises:
      ValueError: If the `value` depth does not match `filters`' shape, or if
        padding is other than `'VALID'` or `'SAME'`.
    """
    if data_format != "NHWC":
      raise ValueError("Data formats other than NHWC are not yet supported")
  
    with ops.name_scope(name, "erosion2d", [value, filters]) as name:
      # Reduce erosion to dilation by duality.
      return math_ops.negative(
          gen_nn_ops.dilation2d(
              input=math_ops.negative(value),
              filter=array_ops.reverse_v2(filters, [0, 1]),
              strides=strides,
              rates=dilations,
              padding=padding,
              name=name))
  
  
  @tf_export(v1=["math.in_top_k", "nn.in_top_k"])
  @dispatch.add_dispatch_support
  def in_top_k(predictions, targets, k, name=None):
    r"""Says whether the targets are in the top `K` predictions.
  
    This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the
    prediction for the target class is finite (not inf, -inf, or nan) and among
    the top `k` predictions among all predictions for example `i`. Note that the
    behavior of `InTopK` differs from the `TopK` op in its handling of ties; if
    multiple classes have the same prediction value and straddle the top-`k`
    boundary, all of those classes are considered to be in the top `k`.
  
    More formally, let
  
      \\(predictions_i\\) be the predictions for all classes for example `i`,
      \\(targets_i\\) be the target class for example `i`,
      \\(out_i\\) be the output for example `i`,
  
    $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$
  
    Args:
      predictions: A `Tensor` of type `float32`.
        A `batch_size` x `classes` tensor.
      targets: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A `batch_size` vector of class ids.
      k: An `int`. Number of top elements to look at for computing precision.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`. Computed Precision at `k` as a `bool Tensor`.
    """
    with ops.name_scope(name, "in_top_k"):
      return gen_nn_ops.in_top_kv2(predictions, targets, k, name=name)
  
  
  @tf_export("math.in_top_k", "nn.in_top_k", v1=[])
  @dispatch.add_dispatch_support
  def in_top_k_v2(targets, predictions, k, name=None):
    return in_top_k(predictions, targets, k, name)
  
  
  in_top_k_v2.__doc__ = in_top_k.__doc__
  
  
  tf_export(v1=["nn.quantized_avg_pool"])(
      dispatch.add_dispatch_support(gen_nn_ops.quantized_avg_pool))
  tf_export(v1=["nn.quantized_conv2d"])(
      dispatch.add_dispatch_support(gen_nn_ops.quantized_conv2d))
  tf_export(v1=["nn.quantized_relu_x"])(
      dispatch.add_dispatch_support(gen_nn_ops.quantized_relu_x))
  tf_export(v1=["nn.quantized_max_pool"])(
      dispatch.add_dispatch_support(gen_nn_ops.quantized_max_pool))
  
  
  @tf_export("nn.isotonic_regression", v1=[])
  @dispatch.add_dispatch_support
  def isotonic_regression(inputs, decreasing=True, axis=-1):
    r"""Solves isotonic regression problems along the given axis.
  
    For each vector x, the problem solved is
  
    $$\argmin_{y_1 >= y_2 >= ... >= y_n} \sum_i (x_i - y_i)^2.$$
  
    As the solution is component-wise constant, a second tensor is returned that
    encodes the segments. The problems are solved over the given axis.
  
    Consider the following example, where we solve a batch of two problems. The
    first input is [3, 1, 2], while the second [1, 3, 4] (as the axis is 1).
    >>> x = tf.constant([[3, 1, 2], [1, 3, 4]], dtype=tf.float32)
    >>> y, segments = tf.nn.isotonic_regression(x, axis=1)
    >>> y  # The solution.
    <tf.Tensor: shape=(2, 3), dtype=float32, numpy=
    array([[3.       , 1.5      , 1.5      ],
           [2.6666667, 2.6666667, 2.6666667]], dtype=float32)>
  
    Note that the first solution has two blocks [2] and [1.5, 1.5]. The second
    solution is constant, and thus has a single segment. These segments are
    exactly what the second returned tensor encodes:
  
    >>> segments
    <tf.Tensor: shape=(2, 3), dtype=int32, numpy=
    array([[0, 1, 1],
           [0, 0, 0]], dtype=int32)>
  
  
    Args:
      inputs: A tensor holding the inputs.
      decreasing: If set to False, the inequalities in the optimizing constrained
        are flipped.
      axis: The axis along which the problems should be solved.
  
    Returns:
      output: The solutions, same shape as type as the input.
      segments: An int32 tensor, same shape as the input indicating the segments
        that have the same value. Specifically, those positions that have the same
        value correspond to the same segment. These values start at zero, and are
        monotonously increasing for each solution.
    """
    type_promotions = {
        # Float types get mapped to themselves, int8/16 to float32, rest to double
        dtypes.float32:
            dtypes.float32,
        dtypes.half:
            dtypes.half,
        dtypes.bfloat16:
            dtypes.bfloat16,
        dtypes.int8:
            dtypes.float32,
        dtypes.int16:
            dtypes.float32,
    }
    inputs = ops.convert_to_tensor(inputs)
    try:
      output_dtype = type_promotions[inputs.dtype]
    except KeyError:
      output_dtype = dtypes.float64
  
    def compute_on_matrix(matrix, name=None):
      iso_fn = functools.partial(
          gen_nn_ops.isotonic_regression, output_dtype=output_dtype, name=name)
      if decreasing:
        return iso_fn(matrix)
      else:
        output, segments = iso_fn(-matrix)
        return -output, segments
  
    return _wrap_2d_function(inputs, compute_on_matrix, axis)
  