pytorch_translate/ensemble_export.py [217:257]:
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            encoder_out = torch.jit._wait(future)
            # "primary" encoder output (vector representations per source token)
            encoder_outputs = encoder_out[0]
            outputs.append(encoder_outputs)
            output_names.append(f"encoder_output_{i}")
            if hasattr(model.decoder, "_init_prev_states"):
                states.extend(model.decoder._init_prev_states(encoder_out))
            if (
                self.enable_precompute_reduced_weights
                and hasattr(model.decoder, "_precompute_reduced_weights")
                and possible_translation_tokens is not None
            ):
                states.extend(torch.jit._wait(reduced_weights[i]))

        if possible_translation_tokens is not None:
            outputs.append(possible_translation_tokens)
            output_names.append("possible_translation_tokens")

        for i, state in enumerate(states):
            outputs.append(state)
            output_names.append(f"initial_state_{i}")

        self.output_names = output_names

        return tuple(outputs)

    @classmethod
    def build_from_checkpoints(
        cls,
        checkpoint_filenames,
        src_dict_filename,
        dst_dict_filename,
        lexical_dict_paths=None,
    ):
        models, src_dict, _ = load_models_from_checkpoints(
            checkpoint_filenames,
            src_dict_filename,
            dst_dict_filename,
            lexical_dict_paths,
        )
        return cls(models, src_dict=src_dict)
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pytorch_translate/ensemble_export.py [1398:1440]:
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            encoder_out = torch.jit._wait(future)

            # "primary" encoder output (vector representations per source token)
            encoder_outputs = encoder_out[0]
            outputs.append(encoder_outputs)
            output_names.append(f"encoder_output_{i}")

            if hasattr(model.decoder, "_init_prev_states"):
                states.extend(model.decoder._init_prev_states(encoder_out))
            if (
                self.enable_precompute_reduced_weights
                and hasattr(model.decoder, "_precompute_reduced_weights")
                and possible_translation_tokens is not None
            ):
                states.extend(torch.jit._wait(reduced_weights[i]))

        if possible_translation_tokens is not None:
            outputs.append(possible_translation_tokens)
            output_names.append("possible_translation_tokens")

        for i, state in enumerate(states):
            outputs.append(state)
            output_names.append(f"initial_state_{i}")

        self.output_names = output_names

        return tuple(outputs)

    @classmethod
    def build_from_checkpoints(
        cls,
        checkpoint_filenames,
        src_dict_filename,
        dst_dict_filename,
        lexical_dict_paths=None,
    ):
        models, src_dict, _ = load_models_from_checkpoints(
            checkpoint_filenames,
            src_dict_filename,
            dst_dict_filename,
            lexical_dict_paths,
        )
        return cls(models, src_dict=src_dict)
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