code: def all_reduce_dict(
      data: Mapping[str, Any], device, group
  ) -> Dict[str, Any]:
      """
      AllReduce a dictionary of values across workers. We separately
      reduce items that are already on the device and items on CPU for
      better performance.
  
      Args:
          data (Mapping[str, Any]): dictionary of data to all-reduce, but
              cannot be a nested dictionary
          device (torch.device): device for the reduction
          group: group of the collective
      """
      data_keys = list(data.keys())
  
      # We want to separately reduce items that are already on the
      # device and items on CPU for performance reasons.
      cpu_data = OrderedDict()
      device_data = OrderedDict()
      for k in data_keys:
          t = data[k]
          if not torch.is_tensor(t):
              cpu_data[k] = torch.tensor(t, dtype=torch.double)
          elif t.device.type != device.type:
              cpu_data[k] = t.to(dtype=torch.double)
          else:
              device_data[k] = t.to(dtype=torch.double)
  
      def _all_reduce_dict(data: OrderedDict):
          if len(data) == 0:
              return data
          buf = torch.cat([t.view(-1) for t in data.values()]).to(
              device=device
          )
          all_reduce(buf, group=group)
          split_buf = torch.split(
              buf.clone(), [t.numel() for t in data.values()]
          )
          reduced_data = [
              t.view_as(orig)
              for t, orig in zip(split_buf, data.values())
          ]
          return OrderedDict(zip(data.keys(), reduced_data))
  
      cpu_data = _all_reduce_dict(cpu_data)
      device_data = _all_reduce_dict(device_data)
  
      def get_from_stack(key):
          if key in cpu_data:
              return cpu_data[key]
          elif key in device_data:
              return device_data[key]
          raise KeyError
  
      return OrderedDict(
          [(key, get_from_stack(key)) for key in data_keys]
      )
  
imports: import io
  import logging
  import os
  import pickle
  import random
  import socket
  import struct
  import subprocess
  import warnings
  from argparse import Namespace
  from collections import OrderedDict
  from dataclasses import dataclass
  from typing import Any, Dict, List, Mapping, Optional
  import torch
  import torch.distributed
  from fairseq.dataclass.configs import DistributedTrainingConfig, FairseqConfig
  from omegaconf import open_dict
label_desc: Use image node_id 31 for calling a global function with example usage: all_reduce_dict(data, device, group) and returns: OrderedDict, OrderedDict, data, cpu_data, device_data