code: def reduce_dict(input_dict, average=True):
      """
      Args:
          input_dict (dict): all the values will be reduced
          average (bool): whether to do average or sum
      Reduce the values in the dictionary from all processes so that all processes
      have the averaged results. Returns a dict with the same fields as
      input_dict, after reduction.
      """
      world_size = get_world_size()
      if world_size < 2:
          return input_dict
      with torch.no_grad():
          names = []
          values = []
          # sort the keys so that they are consistent across processes
          for k in sorted(input_dict.keys()):
              names.append(k)
              values.append(input_dict[k])
          values = torch.stack(values, dim=0)
          dist.all_reduce(values)
          if average:
              values /= world_size
          reduced_dict = {k: v for k, v in zip(names, values)}
      return reduced_dict
  
imports: import os
  import subprocess
  import time
  from collections import defaultdict, deque
  import datetime
  import pickle
  from packaging import version
  from typing import Optional, List
  import torch
  import torch.distributed
  from torch import Tensor
  import torchvision
label_desc: Use image node_id 2 for calling a global function with example usage: reduce_dict(input_dict, average) and returns: reduced_dict, input_dict