code: def reduce_log(self, logging_outputs, sample_size):
      with metrics.aggregate() as agg:
          if logging_outputs is not None:
              self.task.reduce_metrics(logging_outputs, self.criterion)
              del logging_outputs
      logging_output = agg.get_smoothed_values()
      logging_output["sample_size"] = sample_size
      return logging_output
  
imports: import math
  import torch
  from torch import nn
  import torch.distributed
  import deepspeed
  from deepspeed.utils import log_dist
  from fairseq import tasks, distributed_utils
  from fairseq.logging import metrics
  from ds_fairseq_data import BatchIterator
  from ds_fairseq_argument import gen_ds_fairseq_arg
label_desc: Use image node_id 3 for calling the DsFairseqTrainer obj's underlying member method code with example usage: obj.reduce_log(logging_outputs, sample_size) and returns: logging_output