def METHOD_NAME(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)
    symm_kl_sum = sum(log.get("symm_kl", 0) for log in logging_outputs)
    metrics.log_scalar("symm_kl", symm_kl_sum / sample_size, sample_size, round=3)
    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)
    )