code: def broadcast_object(obj, root_rank=0, name=None):
      """
      Serializes and broadcasts an object from root rank to all other processes.
      Typical usage is to broadcast the `optimizer.state_dict()`, for example:
  
      .. code-block:: python
  
          state_dict = broadcast_object(optimizer.state_dict(), 0)
          if bps.rank() > 0:
              optimizer.load_state_dict(state_dict)
  
      Arguments:
          obj: An object capable of being serialized without losing any context.
          root_rank: The rank of the process from which parameters will be
                     broadcasted to all other processes.
          name: Optional name to use during broadcast, will default to the class
                type.
      Returns:
          The object that was broadcast from the `root_rank`.
      """
      if name is None:
          name = type(obj).__name__
  
      if rank() == root_rank:
          b = io.BytesIO()
          cloudpickle.dump(obj, b)
          t = torch.ByteTensor(bytearray(b.getvalue()))
          sz = torch.IntTensor([t.shape[0]])
          broadcast_parameters(
              [(name + ".sz", sz)], root_rank, prefix="Size."
          )
      else:
          sz = torch.IntTensor([0])
          broadcast_parameters(
              [(name + ".sz", sz)], root_rank, prefix="Size."
          )
          t = torch.ByteTensor(sz.tolist()[0])
  
      broadcast_parameters(
          [(name + ".t", t)], root_rank, prefix="Parameter."
      )
  
      if rank() != root_rank:
          buf = io.BytesIO(t.numpy().tobytes())
          obj = cloudpickle.load(buf)
  
      return obj
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from contextlib import contextmanager
  from byteps.torch.compression import Compression
  from byteps.torch.ops import push_pull_async_inplace
  from byteps.torch.ops import push_pull
  from byteps.torch.ops import poll, synchronize, declare
  from byteps.torch.ops import init, shutdown, suspend, resume
  from byteps.torch.ops import size, local_size, rank, local_rank
  import os
  import torch
  import collections
  import io
  import cloudpickle
label_desc: Use image node_id 4 for calling a global function with example usage: broadcast_object(obj, root_rank, name) and returns: obj