Given a query (which is typically the decoder hidden state), compute an attention over the
        output of the question encoder, and return a weighted sum of the question representations
        given this attention.  We also return the attention weights themselves.

        This is a simple computation, but we have it as a separate method so that the ``forward``
        method on the main parser module can call it on the initial hidden state, to simplify the
        logic in ``take_step``.

def attend_on_question(self,
                           query: torch.Tensor,
                           encoder_outputs: torch.Tensor,
                           encoder_output_mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Given a query (which is typically the decoder hidden state), compute an attention over the
        output of the question encoder, and return a weighted sum of the question representations
        given this attention.  We also return the attention weights themselves.

        This is a simple computation, but we have it as a separate method so that the ``forward``
        method on the main parser module can call it on the initial hidden state, to simplify the
        logic in ``take_step``.
        """
        # (group_size, question_length)
        question_attention_weights = self._input_attention(query,
                                                           encoder_outputs,
                                                           encoder_output_mask)
        # (group_size, encoder_output_dim)
        attended_question = util.weighted_sum(encoder_outputs, question_attention_weights)
        return attended_question, question_attention_weights