def forward(self,  # pylint: disable=arguments-differ
                inputs: torch.Tensor,
                word_inputs: torch.Tensor = None) -> Dict[str, Union[torch.Tensor, List[torch.Tensor]]]:
        """
        Parameters
        ----------
        inputs: ``torch.Tensor``, required.
            Shape ``(batch_size, timesteps, 50)`` of character ids representing the current batch.
        word_inputs : ``torch.Tensor``, required.
            If you passed a cached vocab, you can in addition pass a tensor of shape ``(batch_size, timesteps)``,
            which represent word ids which have been pre-cached.

        Returns
        -------
        Dict with keys:

        ``'activations'``: ``List[torch.Tensor]``
            A list of activations at each layer of the network, each of shape
            ``(batch_size, timesteps + 2, embedding_dim)``
        ``'mask'``:  ``torch.Tensor``
            Shape ``(batch_size, timesteps + 2)`` long tensor with sequence mask.

        Note that the output tensors all include additional special begin and end of sequence
        markers.
        """
        if self._word_embedding is not None and word_inputs is not None:
            try:
                mask_without_bos_eos = (word_inputs > 0).long()
                # The character cnn part is cached - just look it up.
                embedded_inputs = self._word_embedding(word_inputs) # type: ignore
                # shape (batch_size, timesteps + 2, embedding_dim)
                type_representation, mask = add_sentence_boundary_token_ids(
                        embedded_inputs,
                        mask_without_bos_eos,
                        self._bos_embedding,
                        self._eos_embedding
                )
            except RuntimeError:
                # Back off to running the character convolutions,
                # as we might not have the words in the cache.
                token_embedding = self._token_embedder(inputs)
                mask = token_embedding['mask']
                type_representation = token_embedding['token_embedding']
        else:
            token_embedding = self._token_embedder(inputs)
            mask = token_embedding['mask']
            type_representation = token_embedding['token_embedding']
        lstm_outputs = self._elmo_lstm(type_representation, mask)

        # Prepare the output.  The first layer is duplicated.
        # Because of minor differences in how masking is applied depending
        # on whether the char cnn layers are cached, we'll be defensive and
        # multiply by the mask here. It's not strictly necessary, as the
        # mask passed on is correct, but the values in the padded areas
        # of the char cnn representations can change.
        output_tensors = [
                torch.cat([type_representation, type_representation], dim=-1) * mask.float().unsqueeze(-1)
        ]
        for layer_activations in torch.chunk(lstm_outputs, lstm_outputs.size(0), dim=0):
            output_tensors.append(layer_activations.squeeze(0))

        return {
                'activations': output_tensors,
                'mask': mask,
        }
Parameters
        ----------
        inputs: ``torch.Tensor``, required.
            Shape ``(batch_size, timesteps, 50)`` of character ids representing the current batch.
        word_inputs : ``torch.Tensor``, required.
            If you passed a cached vocab, you can in addition pass a tensor of shape ``(batch_size, timesteps)``,
            which represent word ids which have been pre-cached.

        Returns
        -------
        Dict with keys:

        ``'activations'``: ``List[torch.Tensor]``
            A list of activations at each layer of the network, each of shape
            ``(batch_size, timesteps + 2, embedding_dim)``
        ``'mask'``:  ``torch.Tensor``
            Shape ``(batch_size, timesteps + 2)`` long tensor with sequence mask.

        Note that the output tensors all include additional special begin and end of sequence
        markers.