def forward(self, x, skpCn):
        """
        Returns output tensor after passing input `x` to the neural network
        block.

        Parameters
        ----------
            x : tensor
                input to the NN block.
            skpCn : tensor
                skip connection input to the NN block.

        Returns
        -------
            tensor
                output of the NN block.
        """

        # Bilinear interpolation with scaling 2.
        x = F.interpolate(x, scale_factor=2, mode='bilinear')
        # Convolution + Leaky ReLU
        x = F.leaky_relu(self.conv1(x), negative_slope = 0.1)
        # Convolution + Leaky ReLU on (`x`, `skpCn`)
        x = F.leaky_relu(self.conv2(torch.cat((x, skpCn), 1)), negative_slope = 0.1)
        return x

        Returns output tensor after passing input `x` to the neural network
        block.

        Parameters
        ----------
            x : tensor
                input to the NN block.
            skpCn : tensor
                skip connection input to the NN block.

        Returns
        -------
            tensor
                output of the NN block.
        
model.py