custom/baseline_cross_entropy.py [80:89]:
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            'ntokens': ntokens,
            'nsentences': nsentences,
            'sample_size': sample_size,
        }
        from fairseq.custom.metrics import TrainingMetrics
        custom_output = TrainingMetrics.aggregate_and_normalize(logging_outputs)
        for k, v in custom_output.items():
            agg_output[k] = v

        if sample_size != ntokens:
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custom/candidate_penalty_ce_loss.py [110:120]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            'ntokens': ntokens,
            'nsentences': nsentences,
            'sample_size': sample_size,
        }

        from fairseq.custom.metrics import TrainingMetrics
        custom_output = TrainingMetrics.aggregate_and_normalize(logging_outputs)
        for k, v in custom_output.items():
            agg_output[k] = v

        if sample_size != ntokens:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -