def forward(self,  # pylint: disable=arguments-differ
                text_field_input: Dict[str, torch.Tensor],
                num_wrapping_dims: int = 0) -> torch.Tensor:
        """
        Parameters
        ----------
        text_field_input : ``Dict[str, torch.Tensor]``
            A dictionary that was the output of a call to ``TextField.as_tensor``.  Each tensor in
            here is assumed to have a shape roughly similar to ``(batch_size, sequence_length)``
            (perhaps with an extra trailing dimension for the characters in each token).
        num_wrapping_dims : ``int``, optional (default=0)
            If you have a ``ListField[TextField]`` that created the ``text_field_input``, you'll
            end up with tensors of shape ``(batch_size, wrapping_dim1, wrapping_dim2, ...,
            sequence_length)``.  This parameter tells us how many wrapping dimensions there are, so
            that we can correctly ``TimeDistribute`` the embedding of each named representation.
        """
        raise NotImplementedError
def forward ( self , text_field_input : Dict [ str , torch . Tensor ] , num_wrapping_dims : int = 0 ) -> torch . Tensor : raise NotImplementedError
Parameters
        ----------
        text_field_input : ``Dict[str, torch.Tensor]``
            A dictionary that was the output of a call to ``TextField.as_tensor``.  Each tensor in
            here is assumed to have a shape roughly similar to ``(batch_size, sequence_length)``
            (perhaps with an extra trailing dimension for the characters in each token).
        num_wrapping_dims : ``int``, optional (default=0)
            If you have a ``ListField[TextField]`` that created the ``text_field_input``, you'll
            end up with tensors of shape ``(batch_size, wrapping_dim1, wrapping_dim2, ...,
            sequence_length)``.  This parameter tells us how many wrapping dimensions there are, so
            that we can correctly ``TimeDistribute`` the embedding of each named representation.