code: def _broadcast_object(
      obj: Any,
      src_rank: int,
      group: object = dist.group.WORLD,
      dist_device: torch.device = torch.device("cpu"),
  ) -> Any:
      """
      Either broadcast from master to the fleet (default),
      or use the src setting as the original rank.
  
      This is only needed for some older GPUs where dist.broadcast_object_list seems to hang. Also
      the hang behavior persist across processes once it happens. I.e. once we call dist.broadcast_object_list,
      subsequent calls with _broadcast_object also hang.
      """
  
      if dist.get_rank() == src_rank:
          # Emit data
          buffer = io.BytesIO()
          torch.save(obj, buffer)
          data = bytearray(buffer.getbuffer())
          length_tensor = torch.LongTensor([len(data)]).to(dist_device)
          data_send_tensor = torch.ByteTensor(data).to(dist_device)
          dist.broadcast(
              length_tensor, src=src_rank, group=group, async_op=False
          )
          dist.broadcast(
              data_send_tensor,
              src=src_rank,
              group=group,
              async_op=False,
          )
      else:
          # Fetch from the source
          length_tensor = torch.LongTensor([0]).to(dist_device)
          dist.broadcast(
              length_tensor, src=src_rank, group=group, async_op=False
          )
          data_recv_tensor = torch.empty(
              [int(length_tensor.item())],
              dtype=torch.uint8,
              device=dist_device,
          )
          dist.broadcast(
              data_recv_tensor,
              src=src_rank,
              group=group,
              async_op=False,
          )
          buffer = io.BytesIO(data_recv_tensor.cpu().numpy())
          obj = torch.load(buffer, map_location=dist_device)
      return obj
  
imports: from collections import OrderedDict
  import copy
  import io
  from itertools import chain
  import logging
  from math import inf
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Type, Union
  import torch
  from torch.autograd import profiler
  import torch.distributed
  from torch.nn import Parameter
  from torch.optim import SGD, Optimizer
  from fairscale.internal.params import calc_grad_norm, get_global_rank, recursive_copy_to_device
  from fairscale.nn.misc import ParamBucket
label_desc: Use image node_id 2 for calling a global function with example usage: _broadcast_object(obj, src_rank, group, dist_device) and returns: obj