code: def _broadcast_object_slow(
      obj: Any,
      src_rank: int,
      group: object,
      dist_device: torch.device,
  ) -> Any:
      if get_rank(group) == src_rank:
          # Emit data
          buffer = io.BytesIO()
          torch.save(obj, buffer)
          buffer = torch.ByteTensor(buffer.getbuffer()).to(dist_device)
          length = torch.LongTensor([len(buffer)]).to(dist_device)
          broadcast(length, src=src_rank, group=group)
          broadcast(buffer, src=src_rank, group=group)
      else:
          # Fetch from the source
          length = torch.LongTensor([0]).to(dist_device)
          broadcast(length, src=src_rank, group=group)
          buffer = torch.ByteTensor(int(length.item())).to(dist_device)
          broadcast(buffer, src=src_rank, group=group)
          buffer = io.BytesIO(buffer.cpu().numpy())
          obj = torch.load(buffer, map_location="cpu")
      return obj
  
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 34 for calling a global function with example usage: _broadcast_object_slow(obj, src_rank, group, dist_device) and returns: obj