pytorch_translate/beam_search_and_decode_v2.py [45:71]:
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        (
            log_probs_per_model,
            attn_weights_per_model,
            state_outputs,
            beam_axis_per_state,
            possible_translation_tokens,
        ) = self._get_decoder_outputs(
            input_tokens, prev_scores, timestep, *inputs, src_tuple=src_tuple
        )

        average_log_probs = torch.mean(
            torch.cat(log_probs_per_model, dim=1), dim=1, keepdim=True
        )

        if possible_translation_tokens is None:
            word_rewards = self.word_rewards
        else:
            word_rewards = self.word_rewards.index_select(
                0, possible_translation_tokens
            )
        word_rewards = word_rewards.unsqueeze(dim=0).unsqueeze(dim=0)

        average_log_probs_with_rewards = average_log_probs + word_rewards

        average_attn_weights = torch.mean(
            torch.cat(attn_weights_per_model, dim=1), dim=1, keepdim=True
        )
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pytorch_translate/ensemble_export.py [303:329]:
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        (
            log_probs_per_model,
            attn_weights_per_model,
            state_outputs,
            beam_axis_per_state,
            possible_translation_tokens,
        ) = self._get_decoder_outputs(
            input_tokens, prev_scores, timestep, *inputs, src_tuple=src_tuple
        )

        average_log_probs = torch.mean(
            torch.cat(log_probs_per_model, dim=1), dim=1, keepdim=True
        )

        if possible_translation_tokens is None:
            word_rewards = self.word_rewards
        else:
            word_rewards = self.word_rewards.index_select(
                0, possible_translation_tokens
            )
        word_rewards = word_rewards.unsqueeze(dim=0).unsqueeze(dim=0)

        average_log_probs_with_rewards = average_log_probs + word_rewards

        average_attn_weights = torch.mean(
            torch.cat(attn_weights_per_model, dim=1), dim=1, keepdim=True
        )
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