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

        Arguments
        ---------
        x : torch.Tensor
            Input tensor of dimension [B, N, S], where B is the batch size,
            N is the number of channels (180 in this case), and S is the
            sequence length (e.g., time frames).

        Returns
        -------
        outputs : torch.Tensor
            Enhanced audio output tensor from the model.

        mask : torch.Tensor
            Mask tensor predicted by the model for speech separation.
        """
        outputs, mask = self.model(x)  # Get outputs and mask from TestNet
        return outputs, mask  # Return the outputs and mask

        Forward pass through the model.

        Arguments
        ---------
        x : torch.Tensor
            Input tensor of dimension [B, N, S], where B is the batch size,
            N is the number of channels (180 in this case), and S is the
            sequence length (e.g., time frames).

        Returns
        -------
        outputs : torch.Tensor
            Enhanced audio output tensor from the model.

        mask : torch.Tensor
            Mask tensor predicted by the model for speech separation.
        
clearvoice/clearvoice/models/mossformer2_se/mossformer2_se_wrapper.py