code: def broadcast_xla_master_model_param(model):
      logger.info(
          "Broadcasting XLA model parameters and buffers from master process ..."
      )
  
      parameters_and_buffers = []
      for p in chain(model.parameters(), model.buffers()):
          # Set all params in non-master devices to zero so that all_reduce is equivalent
          # to broadcasting parameters from master to other devices.
          if not is_main():
              zero = torch.tensor(
                  0, dtype=p.data.dtype, device=p.data.device
              )
              p.data.mul_(zero)
          parameters_and_buffers.append(p.data)
      xm.wait_device_ops()
      xm.all_reduce(xm.REDUCE_SUM, parameters_and_buffers)
      xm.mark_step()
      xm.rendezvous(
          "mmf.trainers.core.device.broadcast_xla_master_model_param"
      )
      logger.info("Done!")
  
imports: import contextlib
  import logging
  import os
  import pickle
  import socket
  import subprocess
  import warnings
  from itertools import chain
  import torch
  from mmf.common.registry import registry
  from torch import distributed
label_desc: Use image node_id 22 for calling a global function with example usage: broadcast_xla_master_model_param(model) without return types