def conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None,
           data_format=None, name=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 vertices strides, `strides = [1, stride, stride, 1]`.

  Args:
    input: A `Tensor`. Must be one of the following types: `half`, `float32`.
      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: A list of `ints`.
      1-D tensor of length 4.  The stride of the sliding window for each
      dimension of `input`. The dimension order is determined by the value of
        `data_format`, see below for details.
    padding: A `string` from: `"SAME", "VALID"`.
      The type of padding algorithm to use.
    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].
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `input`.
    A 4-D tensor. The dimension order is determined by the value of
    `data_format`, see below for details.
  """
  result = _op_def_lib.apply_op("Conv2D", input=input, filter=filter,
                                strides=strides, padding=padding,
                                use_cudnn_on_gpu=use_cudnn_on_gpu,
                                data_format=data_format, name=name)
  return result


_conv2d_backprop_filter_outputs = ["output"]

input: A `Tensor`. Must be one of the following types: `half`, `float32`.
      A 4-D tensor. The dimension order is interpreted according to the value
      of `data_format`, see below for details.
input tensor of shape `[batch, in_height, in_width, in_channels]`

通过源码中的描述(如上)，我们可以知道input就是需要做卷积的图像（这里要求用Tensor来表示输入图像，并且Tensor(一个4维的Tensor，要求类型为half(half是什么东东？)或者float32)的shape为[batch, in_height, in_width, in_channels]具体含义[训练时一个batch图像的数量，图像高度，图像宽度， 图像通道数]）。

filter: A `Tensor`. Must have the same type as `input`.
      A 4-D tensor of shape
      `[filter_height, filter_width, in_channels, out_channels]`