tf.compat.v1.nn.conv2d Computes a 2-D convolution given 4-D input and filter tensors. 
tf.compat.v1.nn.conv2d(
    input, filter=None, strides=None, padding=None, use_cudnn_on_gpu=True,
    data_format='NHWC', dilations=[1, 1, 1, 1], name=None, filters=None
)
 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:  Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 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]. 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 ofinput. 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.