docstr: Aggregate logging outputs from data parallel training.
code: def aggregate_logging_outputs(cls, logging_outputs):
      """Aggregate logging outputs from data parallel training."""
      return (
          LabelSmoothedCrossEntropyCriterion.aggregate_logging_outputs(
              logging_outputs
          )
      )
  
file_path: translate/pytorch_translate/weighted_criterions.py
imports: from fairseq import utils
  from fairseq.criterions import LegacyFairseqCriterion, register_criterion
  from fairseq.criterions.label_smoothed_cross_entropy import LabelSmoothedCrossEntropyCriterion
label_desc: Use image node_id 4 for calling the WeightedLabelSmoothedCrossEntropyCriterion obj's underlying member method code with example usage: obj.aggregate_logging_outputs(cls, logging_outputs) and returns: LabelSmoothedCrossEntropyCriterion