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))
    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
    )
    if sample_size != ntokens:
        metrics.log_scalar(
            "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3
        )
        metrics.log_derived(
            "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
        )
    else:
        metrics.log_derived(
            "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg)
        )