class_docstr: This class groups the convolution and batch normalization
docstr: :param nIn: number of input channels
  :param nOut: number of output channels
  :param kSize: kernel size
  :param stride: optinal stide for down-sampling
docstr_tok: [":","param","nIn",":","number","of","input","channels",":","param","nOut",":","number","of","output","channels",":","param","kSize",":","kernel","size",":","param","stride",":","optinal","stide","for","down-sampling"]
code: def __init__(self, nIn, nOut, kSize, stride=1):
      """
      :param nIn: number of input channels
      :param nOut: number of output channels
      :param kSize: kernel size
      :param stride: optinal stide for down-sampling
      """
      super().__init__()
      padding = int((kSize - 1) / 2)
      self.conv = nn.Conv2d(
          nIn,
          nOut,
          (kSize, kSize),
          stride=stride,
          padding=(padding, padding),
          bias=False,
      )
      self.bn = nn.BatchNorm2d(nOut, eps=1e-03)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new CB object from inherited base classes: nn with example: obj = CB(nIn, nOut, kSize, stride)