# Codeblock 3b
        if self.add_channel or self.downsample:               #(1)
            self.projection = nn.Conv2d(in_channels=in_channels,    #(2) 
                                        out_channels=out_channels, 
                                        kernel_size=1, 
                                        stride=stride, 
                                        padding=0, 
                                        bias=False)
            nn.init.kaiming_normal_(self.projection.weight, nonlinearity='relu')
            self.bn_proj = nn.BatchNorm2d(num_features=out_channels)
        

        self.conv0 = nn.Conv2d(in_channels=in_channels,       #(3)
                               out_channels=mid_channels,     #(4)
                               kernel_size=1, 
                               stride=1, 
                               padding=0, 
                               bias=False)
        nn.init.kaiming_normal_(self.conv0.weight, nonlinearity='relu')
        self.bn0 = nn.BatchNorm2d(num_features=mid_channels)
        

        self.conv1 = nn.Conv2d(in_channels=mid_channels,      #(5)
                               out_channels=mid_channels, 
                               kernel_size=3, 
                               stride=stride,                 #(6)
                               padding=1, 
                               bias=False, 
                               groups=CARDINALITY)            #(7)
        nn.init.kaiming_normal_(self.conv1.weight, nonlinearity='relu')
        self.bn1 = nn.BatchNorm2d(num_features=mid_channels)
        

        self.conv2 = nn.Conv2d(in_channels=mid_channels,      #(8)
                               out_channels=out_channels,     #(9)
                               kernel_size=1, 
                               stride=1, 
                               padding=0, 
                               bias=False)
        nn.init.kaiming_normal_(self.conv2.weight, nonlinearity='relu')
        self.bn2 = nn.BatchNorm2d(num_features=out_channels)
        
        self.relu = nn.ReLU()