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

        Arguments
        ---------
        x : torch.Tensor
            Input tensor with dimensionality [B, L].
        Return
        ------
        x : torch.Tensor
            Encoded tensor with dimensionality [B, N, T_out].

        where B = Batchsize
              L = Number of timepoints
              N = Number of filters
              T_out = Number of timepoints at the output of the encoder
        """
        # B x L -> B x 1 x L
        if self.in_channels == 1:
            x = torch.unsqueeze(x, dim=1)
        # B x 1 x L -> B x N x T_out
        x = self.conv1d(x)
        x = F.relu(x)

        return x
Return the encoded output.

        Arguments
        ---------
        x : torch.Tensor
            Input tensor with dimensionality [B, L].
        Return
        ------
        x : torch.Tensor
            Encoded tensor with dimensionality [B, N, T_out].

        where B = Batchsize
              L = Number of timepoints
              N = Number of filters
              T_out = Number of timepoints at the output of the encoder
        
clearvoice/clearvoice/models/mossformer2_se/mossformer2.py