def __init__(self, nIn, nOut, kSize, stride=1, groups=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, stride=stride, padding=padding, bias=False,
                              groups=groups)
        self.bn = nn.BatchNorm2d(nOut)

        :param nIn: number of input channels
        :param nOut: number of output channels
        :param kSize: kernel size
        :param stride: optinal stide for down-sampling
        
model/ESPNet_v2/cnn_utils.py