Conv2d
class torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None)
Applies a 2D convolution over an input signal composed of several
   input planes.
In the simplest case, the output value of the layer with input size
   (N, C_{\text{in}}, H, W) and output (N, C_{\text{out}},
   H_{\text{out}}, W_{\text{out}}) can be precisely described as:
  \text{out}(N_i, C_{\text{out}_j}) =
  \text{bias}(C_{\text{out}_j}) + \sum_{k = 0}^{C_{\text{in}} - 1}
  \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k)

where \star is the valid 2D cross-correlation operator, N is a
   batch size, C denotes a number of channels, H is a height of input
   planes in pixels, and W is width in pixels.
This module supports TensorFloat32.
On certain ROCm devices, when using float16 inputs this module will
   use different precision for backward.