class_docstr: This class defines the convolution layer with batch normalization and PReLU activation
docstr: :param nIn: number of input channels
  :param nOut: number of output channels
  :param kSize: kernel size
  :param stride: stride rate for down-sampling. Default is 1
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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: stride rate for down-sampling. Default is 1
      """
      super().__init__()
      padding = int((kSize - 1) / 2)
      # self.conv = nn.Conv2d(nIn, nOut, kSize, stride=stride, padding=padding, bias=False)
      self.conv = nn.Conv2d(
          nIn,
          nOut,
          (kSize, kSize),
          stride=stride,
          padding=(padding, padding),
          bias=False,
      )
      # self.conv1 = nn.Conv2d(nOut, nOut, (1, kSize), stride=1, padding=(0, padding), bias=False)
      self.bn = nn.BatchNorm2d(nOut, eps=1e-03)
      self.act = nn.PReLU(nOut)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new CBR object from inherited base classes: nn with example: obj = CBR(nIn, nOut, kSize, stride)