    def forward(self, x, incremental_state=None, unfold=False):
        """Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C
        args:
            x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size)
            incremental_state: A dict to keep the state
            unfold: unfold the input or not. If not, we use the matrix trick instead
        """
        unfold = unfold or (incremental_state is not None)

        if unfold:
            output = self._forward_unfolded(x, incremental_state)
        else:
            output = self._forward_expanded(x, incremental_state)

        if self.bias is not None:
            output = output + self.bias.view(1, 1, -1)
        return output
Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C
args:
x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size)
incremental_state: A dict to keep the state
unfold: unfold the input or not. If not, we use the matrix trick instead
fairseq/modules/lightweight_convolution.py