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

        Arguments
        ---------
        input : torch.Tensor
            Input tensor of dimension [B, N, S], where B is the batch size,
            N is the number of input channels (180), and S is the sequence length.

        Returns
        -------
        out_list : list
            List containing the mask tensor predicted by the MossFormer_MaskNet.
        """
        out_list = []  # Initialize output list to store outputs
        # Transpose input to match expected shape for MaskNet
        x = input.transpose(1, 2)  # Change shape from [B, N, S] to [B, S, N]
        
        # Get the mask from the MossFormer MaskNet
        mask = self.mossformer(x)  # Forward pass through the MossFormer_MaskNet
        out_list.append(mask)  # Append the mask to the output list

        return out_list  # Return the list containing the mask

        Forward pass through the TestNet model.

        Arguments
        ---------
        input : torch.Tensor
            Input tensor of dimension [B, N, S], where B is the batch size,
            N is the number of input channels (180), and S is the sequence length.

        Returns
        -------
        out_list : list
            List containing the mask tensor predicted by the MossFormer_MaskNet.
        
clearvoice/clearvoice/models/mossformer2_se/mossformer2_se_wrapper.py