code: def reduce_dict(dictionary):
      world_size = get_world_size()
      if world_size < 2:
          return dictionary
  
      with torch.no_grad():
          if len(dictionary) == 0:
              return dictionary
  
          keys, values = zip(*sorted(dictionary.items()))
          values = torch.stack(values, dim=0)
  
          if is_xla():
              values = xm.all_reduce(
                  "sum", [values], scale=1.0 / world_size
              )[0]
          else:
              dist.reduce(values, dst=0)
              if dist.get_rank() == 0:
                  # only main process gets accumulated, so only divide by
                  # world_size in this case
                  values /= world_size
          reduced_dict = {k: v for k, v in zip(keys, values)}
      return reduced_dict
  
imports: import contextlib
  import logging
  import os
  import pickle
  import socket
  import subprocess
  import warnings
  from itertools import chain
  import torch
  from mmf.common.registry import registry
  from torch import distributed
label_desc: Use image node_id 14 for calling a global function with example usage: reduce_dict(dictionary) and returns: reduced_dict, dictionary, dictionary