def METHOD_NAME(cls, logging_outputs) -> None:
    """Aggregate logging outputs from data parallel training."""
    loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
    nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs)
    ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
    sample_size = 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_derived(
        "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
    )
    total = utils.item(sum(log.get("total", 0) for log in logging_outputs))
    if total > 0:
        metrics.log_scalar("total", total)
        n_correct = utils.item(
            sum(log.get("n_correct", 0) for log in logging_outputs)
        )
        metrics.log_scalar("n_correct", n_correct)
        metrics.log_derived(
            "accuracy",
            lambda meters: round(
                meters["n_correct"].sum * 100.0 / meters["total"].sum, 3
            )
            if meters["total"].sum > 0
            else float("nan"),
        )