code: def broadcast_parameters(params, root_rank=0):
      """
      Broadcasts the parameters from root rank to all other processes.
      Typical usage is to broadcast the `Module.get_params()`.
  
      Arguments:
          params: dict of parameters to broadcast
          root_rank: The rank of the process from which parameters will be
                     broadcasted to all other processes.
      """
      global parameter_index
  
      if isinstance(params, dict):
          tensors = [p for _, p in sorted(params.items())]
  
          # Run tensor initilization
          for i in range(len(tensors)):
              byteps_declare_tensor("parameter_" + str(parameter_index))
              # Broadcast is implemented as push + pull in BytePS
              # To broadcast: we should zero-out all non-root tensors, and disable push_pull average
              if rank() != root_rank:
                  tensors[i].__imul__(0)
              byteps_push_pull(
                  tensors[i],
                  version=0,
                  priority=0,
                  name="parameter_" + str(parameter_index),
                  is_average=False,
              )
              parameter_index += 1
  
          # Make sure tensors pushed to MXNet engine get processed such that all
          # workers are synced before starting training.
          for tensor in tensors:
              tensor.wait_to_read()
  
      elif isinstance(params, mx.gluon.parameter.ParameterDict):
          raise TypeError(
              "For gluon users, you should not call this function. "
              "DistributedTrainer will broadcast all parameters at "
              "the first training step."
          )
  
      else:
          raise ValueError("Invalid params of type: %s" % type(params))
  
imports: from __future__ import absolute_import, division, print_function
  import copy
  import os
  import struct
  import warnings
  import mxnet
  import mxnet.ndarray
  from byteps.mxnet.compression import Compression
  from byteps.mxnet.ops import byteps_declare_tensor, byteps_push_pull, init, local_rank, local_size, rank, resume, shutdown, size, suspend
label_desc: Use image node_id 1 for calling a global function with example usage: broadcast_parameters(params, root_rank) without return types