# Codeblock 5
class ConvBlock(nn.Module):
    def __init__(self, 
                 in_channels,             #(1)
                 out_channels,            #(2)
                 kernel_size,             #(3)
                 stride,                  #(4)
                 padding,                 #(5)
                 groups=1,                #(6)
                 batchnorm=True,          #(7)
                 activation=nn.ReLU6()):  #(8)
        super().__init__()
        
        bias = False if batchnorm else True    #(9)
        
        self.conv = nn.Conv2d(in_channels=in_channels, 
                              out_channels=out_channels,
                              kernel_size=kernel_size, 
                              stride=stride, 
                              padding=padding, 
                              groups=groups,
                              bias=bias)
        self.bn = nn.BatchNorm2d(num_features=out_channels) if batchnorm else nn.Identity()  #(10)
        self.activation = activation
    
    def forward(self, x):    #(11)
        print(f'original\t\t: {x.size()}')
        
        x = self.conv(x)
        print(f'after conv\t\t: {x.size()}')
        
        x = self.bn(x)
        print(f'after bn\t\t: {x.size()}')
        
        x = self.activation(x)
        print(f'after activation\t: {x.size()}')
        
        return x