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)

        :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.py