code: def broadcast_multigpu(
      tensor_list,
      src_rank: int = 0,
      src_tensor: int = 0,
      group_name: str = "default",
  ):
      """Broadcast the tensor from a source GPU to all other GPUs.
  
      Args:
          tensor_list: the tensors to broadcast (src) or receive (dst).
          src_rank: the rank of the source process.
          src_tensor: the index of the source GPU on the source process.
          group_name: the collective group name to perform broadcast.
  
      Returns:
          None
      """
      if not types.cupy_available():
          raise RuntimeError("Multigpu calls requires NCCL and Cupy.")
      _check_tensor_list_input(tensor_list)
      g = _check_and_get_group(group_name)
  
      # check src rank
      _check_rank_valid(g, src_rank)
      _check_root_tensor_valid(len(tensor_list), src_tensor)
      opts = types.BroadcastOptions()
      opts.root_rank = src_rank
      opts.root_tensor = src_tensor
      g.broadcast(tensor_list, opts)
  
imports: import logging
  import os
  from typing import List
  import numpy
  import ray
  from ray.util.collective import types
label_desc: Use image node_id 15 for calling a global function with example usage: broadcast_multigpu(tensor_list, src_rank, src_tensor, group_name) without return types