def __init__(self, nIn, nOut, kSize, stride=1, d=1):
        """
        args:
           nIn: number of input channels
           nOut: number of output channels
           kSize: kernel size
           stride: optional stride rate for down-sampling
           d: 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)

        args:
           nIn: number of input channels
           nOut: number of output channels
           kSize: kernel size
           stride: optional stride rate for down-sampling
           d: dilation rate
        
model/CGNet.py