Conv3d
class torch.nn.Conv3d(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 3D 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_{in}, D, H, W) and output (N, C_{out}, D_{out}, H_{out},
   W_{out}) can be precisely described as:
  out(N_i, C_{out_j}) = bias(C_{out_j}) +
  \sum_{k = 0}^{C_{in} - 1} weight(C_{out_j}, k) \star input(N_i,
  k)

where \star is the valid 3D cross-correlation operator
This module supports TensorFloat32.
On certain ROCm devices, when using float16 inputs this module will
   use different precision for backward.


"stride" controls the stride for the cross-correlation.


"padding" controls the amount of padding applied to the input. It
     can be either a string {'valid', 'same'} or a tuple of ints

