def forward(self, x):
        """
        Forward pass through the decoder.

        Args:
            x (torch.Tensor): Input tensor of shape (B, C, H, W) where B is batch size, 
                             C is the number of channels, H is height, and W is width.

        Returns:
            torch.Tensor: Output tensor after applying transposed convolution, batch normalization, and activation.
        """
        x = self.transconv(x)  # Apply transposed convolution
        x = self.bn(x)         # Apply batch normalization
        x = self.relu(x)       # Apply Leaky ReLU activation
        return x

        Forward pass through the decoder.

        Args:
            x (torch.Tensor): Input tensor of shape (B, C, H, W) where B is batch size, 
                             C is the number of channels, H is height, and W is width.

        Returns:
            torch.Tensor: Output tensor after applying transposed convolution, batch normalization, and activation.
        
clearvoice/clearvoice/models/frcrn_se/unet.py