def METHOD_NAME(logging_outputs) -> None:
    """Aggregate logging outputs from data parallel training."""
    lm_loss_sum = sum(log.get("lm_loss", 0) for log in logging_outputs)
    sentence_loss_sum = sum(log.get("sentence_loss", 0) for log in logging_outputs)
    ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
    nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
    sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
    agg_loss = sum(log.get("loss", 0) for log in logging_outputs)
    metrics.log_scalar(
        "loss",
        agg_loss / sample_size / math.log(2) if sample_size > 0 else 0.0,
        sample_size,
        round=3,
    )
    metrics.log_scalar(
        "lm_loss",
        lm_loss_sum / ntokens / math.log(2) if ntokens > 0 else 0.0,
        ntokens,
        round=3,
    )
    metrics.log_scalar(
        "sentence_loss",
        sentence_loss_sum / nsentences / math.log(2) if nsentences > 0 else 0.0,
        nsentences,
        round=3,
    )
    metrics.log_scalar(
        "nll_loss",
        lm_loss_sum / ntokens / math.log(2) if ntokens > 0 else 0.0,
        ntokens,
        round=3,
    )