code: def hybrid_forward(self, F, inputs):
          # pylint: disable=arguments-differ
          
          # the character id embedding
          # (batch_size * sequence_length, max_chars_per_token, embed_dim)
          character_embedding = self._char_embedding(inputs.reshape((-1, self._max_chars_per_token)))
  
          character_embedding = F.transpose(character_embedding, axes=(1, 0, 2))
          token_embedding = self._convolutions(character_embedding)
  
          out_shape_ref = inputs.slice_axis(axis=-1, begin=0, end=1)
          out_shape_ref = out_shape_ref.broadcast_axes(axis=(2,),
                                                       size=(self._output_size))
  
          return token_embedding.reshape_like(out_shape_ref)
docs: Compute context insensitive token embeddings for ELMo representations.
  
          Parameters
          ----------
          inputs : NDArray
              Shape (batch_size, sequence_length, max_character_per_token)
              of character ids representing the current batch.
  
          Returns
          -------
          token_embedding : NDArray
              Shape (batch_size, sequence_length, embedding_size) with context
              insensitive token representations.