parameters: (input,  # pylint: disable=redefined-builtin
                filters,
                strides,
                padding,
                data_format="NHWC",
                dilations=None,
                name=None)
return_statement: return conv2d(input,  # pylint: disable=redefined-builtin
                  filters,
                  strides,
                  padding,
                  use_cudnn_on_gpu=True,
                  data_format=data_format,
                  dilations=dilations,
                  name=name)
docstring: r"""Computes a 2-D convolution given 4-D `input` and `filters` 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 vertices 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.
      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, 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).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
docstring_summary: r"""Computes a 2-D convolution given 4-D `input` and `filters` tensors.
docstring_tokens: r Computes a 2 - D convolution given 4 - D input and filters tensors .
function: 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 4-D `input` and `filters` 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 vertices 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.
      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, 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).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
    """
    # 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)
function_tokens: def conv2d_v2 ( input , # pylint: disable=redefined-builtin filters , strides , padding , data_format = "NHWC" , dilations = None , name = None ) : # pylint: disable=line-too-long # 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 )