parameters: (  # pylint: disable=redefined-builtin,dangerous-default-value
      input_sizes,
      filter,
      out_backprop,
      strides,
      padding,
      data_format="NHWC",
      dilations=[1, 1, 1, 1],
      name=None)
return_statement: 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)
docstring: 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. See
        [here](https://www.tensorflow.org/api_docs/python/tf/nn#notes_on_padding_2)
        for more information. 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`.
docstring_summary: r"""Computes the gradients of depthwise convolution with respect to the input.
docstring_tokens: r Computes the gradients of depthwise convolution with respect to the input .
function: 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. See
        [here](https://www.tensorflow.org/api_docs/python/tf/nn#notes_on_padding_2)
        for more information. 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)
function_tokens: 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 ) : 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 )