code: def broadcast_object(
      obj: Any,
      src_rank: int,
      group: object,
      dist_device: Optional[torch.device] = None,
  ) -> Any:
      """Broadcast an arbitrary Python object to other workers."""
      if dist_device is None:
          if torch.distributed.get_backend(group) == "nccl":
              dist_device = torch.device("cuda")
          else:
              dist_device = torch.device("cpu")
  
      if get_rank(group) == src_rank:
          # split the tensors from the non-tensors so we can broadcast them
          # directly, avoiding unnecessary serialization/deserialization
          tensors = []
          obj = _split_tensors_from_obj(obj, tensors)
          obj = _broadcast_object_slow(
              obj, src_rank, group, dist_device
          )
          tensors = broadcast_tensors(
              tensors, src_rank, group, dist_device
          )
      else:
          obj = _broadcast_object_slow(
              None, src_rank, group, dist_device
          )
          tensors = broadcast_tensors(
              None, src_rank, group, dist_device
          )
      return _put_tensors_in_obj(obj, tensors)
  
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 33 for calling a global function with example usage: broadcast_object(obj, src_rank, group, dist_device) and returns: _put_tensors_in_obj