code: def broadcast_tensor(tensor, src=0):
      world_size = get_world_size()
      if world_size < 2:
          return tensor
  
      with torch.no_grad():
          if is_xla():
              tensor = xm.all_to_all(
                  tensor.repeat([world_size] + [1] * tensor.dim()),
                  split_dimension=0,
                  concat_dimension=0,
                  split_count=world_size,
              )[src]
          else:
              dist.broadcast(tensor, src=0)
  
      return tensor
  
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 8 for calling a global function with example usage: broadcast_tensor(tensor, src) and returns: tensor, tensor