code: def broadcast_parameters(params, root_rank, prefix="Parameter."):
      """
      Broadcasts the parameters from root rank to all other processes.
      Typical usage is to broadcast the `model.state_dict()`,
      `model.named_parameters()`, or `model.parameters()`.
      Arguments:
          params: One of the following:
              - list of parameters to broadcast
              - dict of parameters to broadcast
          root_rank: The rank of the process from which parameters will be
                     broadcasted to all other processes.
      """
      if isinstance(params, dict):
          params = sorted(params.items())
      elif isinstance(params, list):
          # support both named_parameters() and regular parameters()
          params = [
              p if isinstance(p, tuple) else (None, p) for p in params
          ]
      else:
          raise ValueError("invalid params of type: %s" % type(params))
  
      # Run synchronous broadcasts.
      for name, p in params:
          # Broadcast is implemented as push + pull in BytePS
          # To make it a real broadcast, we set the non-root tensors all 0.
          if rank() != root_rank:
              p.fill_(0)
          # Remember to disable averaging because we are doing broadcast
          if name:
              handle = byteps_push_pull(
                  p, average=False, name=prefix + name
              )
          else:
              handle = byteps_push_pull(p, average=False)
          synchronize(handle)
  
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 2 for calling a global function with example usage: broadcast_parameters(params, root_rank, prefix) without return types