def forward(self, x):
        """
        Forward pass through the ComplexUniDeepFsmn_L1 model.

        Parameters:
        - x (Tensor): Input tensor of shape (batch_size, channels, height, time, 2).

        Returns:
        - Tensor: Output tensor after processing.
        """
        b, c, h, T, d = x.size()
        x = torch.transpose(x, 1, 3)  # Shape: [b, T, h, c, 2]
        x = torch.reshape(x, (b * T, h, c, d))  # Reshape to process

        # Process the real and imaginary parts
        real = self.fsmn_re_L1(x[..., 0]) - self.fsmn_im_L1(x[..., 1])
        imaginary = self.fsmn_re_L1(x[..., 1]) + self.fsmn_im_L1(x[..., 0])

        # Combine results and reshape back to original dimensions
        output = torch.stack((real, imaginary), dim=-1)  # Shape: [b*T, h, c, 2]
        output = torch.reshape(output, (b, T, h, c, d))  # Restore shape to [b, T, h, c, 2]
        output = torch.transpose(output, 1, 3)  # Shape: [b, c, h, T, 2]

        return output

        Forward pass through the ComplexUniDeepFsmn_L1 model.

        Parameters:
        - x (Tensor): Input tensor of shape (batch_size, channels, height, time, 2).

        Returns:
        - Tensor: Output tensor after processing.
        
clearvoice/clearvoice/models/frcrn_se/complex_nn.py