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)
    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)
    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
        )
    if len(logging_outputs) > 0 and "ncorrect" in logging_outputs[0]:
        ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs)
        metrics.log_scalar(
            "accuracy", 100.0 * ncorrect / nsentences, nsentences, round=1
        )