def METHOD_NAME(cls, logging_outputs) -> None:
    """Aggregate logging outputs from data parallel training."""
    super().METHOD_NAME(logging_outputs)
    sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
    rdrop_kl_loss = utils.item(
        sum(log.get("rdrop_kl_loss", 0) for log in logging_outputs)
        / sample_size
        / math.log(2)
    )
    if rdrop_kl_loss > 0:
        metrics.log_scalar("rdrop_kl_loss", rdrop_kl_loss)