def METHOD_NAME(logging_outputs) -> None:
    """Aggregate logging outputs from data parallel training."""
    loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs))
    nll_loss_sum = utils.item(
        sum(log.get("nll_loss", 0) for log in logging_outputs)
    )
    alignment_loss_sum = utils.item(
        sum(log.get("alignment_loss", 0) for log in logging_outputs)
    )
    ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs))
    sample_size = utils.item(
        sum(log.get("sample_size", 0) for log in logging_outputs)
    )
    metrics.log_scalar(
        "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
    )
    metrics.log_scalar(
        "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3
    )
    metrics.log_scalar(
        "alignment_loss",
        alignment_loss_sum / sample_size / math.log(2),
        sample_size,
        round=3,
    )
    metrics.log_derived(
        "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
    )