class_docstr: This class defines the dilated convolution.
docstr: :param nIn: number of input channels
  :param nOut: number of output channels
  :param kSize: kernel size
  :param stride: optional stride rate for down-sampling
  :param d: optional dilation rate
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code: def __init__(self, nIn, nOut, kSize, stride=1, d=1):
      """
      :param nIn: number of input channels
      :param nOut: number of output channels
      :param kSize: kernel size
      :param stride: optional stride rate for down-sampling
      :param d: optional dilation rate
      """
      super().__init__()
      padding = int((kSize - 1) / 2) * d
      self.conv = nn.Conv2d(
          nIn,
          nOut,
          (kSize, kSize),
          stride=stride,
          padding=(padding, padding),
          bias=False,
          dilation=d,
      )
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new CDilated object from inherited base classes: nn with example: obj = CDilated(nIn, nOut, kSize, stride, d)