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 mel bins (80 in this case), and S is the
            sequence length (e.g., time frames).

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

        """
        x = self.model_m(x)  # Get outputs and mask from TestNet
        outpus = self.model_g(x)
        return outputs  # Return the outputs

        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 mel bins (80 in this case), and S is the
            sequence length (e.g., time frames).

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

        
train/speech_super_resolution/models/mossformer2_sr/mossformer2_sr_wrapper.py