def forward(self, x):
        """
        Defines the forward pass for the SyncANet.

        Args:
            x (torch.Tensor): Input tensor of shape [batch_size, 2, height, width] representing complex signals.

        Returns:
            list: List containing the real and imaginary parts of the output tensor.
        """
        out_list = []  # List to store outputs
        mag = torch.sqrt(x[:, 0, :, :]**2 + x[:, 1, :, :]**2).unsqueeze(1)  # Calculate magnitude
        noisy_phase = torch.angle(torch.complex(x[:, 0, :, :], x[:, 1, :, :])).unsqueeze(1)  # Calculate phase
        x_in = torch.cat([mag, x], dim=1)  # Concatenate magnitude and input for processing

        x = self.dense_encoder(x_in)  # Feature extraction using dense encoder
        for ii in range(self.n_layers):
            x = self.blocks[ii](x)  # Pass through attention blocks

        mask = self.mask_decoder(x)  # Estimate mask from features
        out_mag = mask * mag  # Apply mask to magnitude

        complex_out = self.complex_decoder(x)  # Generate complex output
        mag_real = out_mag * torch.cos(noisy_phase)  # Real part of the output
        mag_imag = out_mag * torch.sin(noisy_phase)  # Imaginary part of the output
        final_real = mag_real + complex_out[:, 0, :, :].unsqueeze(1)  # Final real output
        final_imag = mag_imag + complex_out[:, 1, :, :].unsqueeze(1)  # Final imaginary output
        out_list.append(final_real)  # Append real output to list
        out_list.append(final_imag)  # Append imaginary output to list

        return out_list  # Return list of outputs

        Defines the forward pass for the SyncANet.

        Args:
            x (torch.Tensor): Input tensor of shape [batch_size, 2, height, width] representing complex signals.

        Returns:
            list: List containing the real and imaginary parts of the output tensor.
        
clearvoice/clearvoice/models/mossformer_gan_se/generator.py