code: def _gather_loss(self, loss):
      if self._world_size <= 1:
          return loss
  
      loss = torch.Tensor([loss]).cuda(self._args.device)
      if self._is_master():
          loss_list = [loss for _ in range(self._world_size)]
          dist.gather(loss, gather_list=loss_list, async_op=False)
          loss_sum = None
          for _l in loss_list:
              if loss_sum is None:
                  loss_sum = _l.item()
              else:
                  loss_sum += _l.item()
          return loss_sum / self._world_size
      else:
          dist.gather(loss, dst=0, async_op=False)
          return loss
  
imports: import logging
  import numpy
  import torch
  import torch.distributed
  from transformers.training_args import TrainingArguments
  from fate.ml.aggregator import AggregatorType, aggregator_map
label_desc: Use image node_id 7 for calling the AggregatorClientWrapper obj's underlying member method code with example usage: obj._gather_loss(loss) and returns: loss, unknown, loss