def forward(self, x):
        """Return the decoded output.

        Arguments
        ---------
        x : torch.Tensor
            Input tensor with dimensionality [B, N, L].
                where, B = Batchsize,
                       N = number of filters
                       L = time points
        """

        if x.dim() not in [2, 3]:
            raise RuntimeError(
                "{} accept 3/4D tensor as input".format(self.__name__)
            )
        x = super().forward(x if x.dim() == 3 else torch.unsqueeze(x, 1))

        if torch.squeeze(x).dim() == 1:
            x = torch.squeeze(x, dim=1)
        else:
            x = torch.squeeze(x)
        return x
Return the decoded output.

        Arguments
        ---------
        x : torch.Tensor
            Input tensor with dimensionality [B, N, L].
                where, B = Batchsize,
                       N = number of filters
                       L = time points
        
clearvoice/clearvoice/models/mossformer2_se/mossformer2.py