# Codeblock 2
class Convolutional(nn.Module):
    def __init__(self, 
                 in_channels, 
                 out_channels, 
                 kernel_size, 
                 stride=1):
        super().__init__()
        
        self.conv = nn.Conv2d(in_channels=in_channels,
                              out_channels=out_channels, 
                              kernel_size=kernel_size, 
                              stride=stride,
                              bias=False,                            #(1)
                              padding=1 if kernel_size==3 else 0)    #(2)
        
        self.bn = nn.BatchNorm2d(num_features=out_channels)
        
        self.leaky_relu = nn.LeakyReLU(negative_slope=0.1)
        
    def forward(self, x):        #(3)
        print(f'original\t: {x.size()}')

        x = self.conv(x)
        print(f'after conv\t: {x.size()}')
        
        x = self.bn(x)
        print(f'after bn\t: {x.size()}')
        
        x = self.leaky_relu(x)
        print(f'after leaky relu: {x.size()}')
        
        return x