code: def send_multigpu(
      tensor,
      dst_rank: int,
      dst_gpu_index: int,
      group_name: str = "default",
      n_elements: int = 0,
  ):
      """Send a tensor to a remote GPU synchronously.
  
      The function asssume each process owns >1 GPUs, and the sender
      process and receiver process has equal nubmer of GPUs.
  
      Args:
          tensor: the tensor to send, located on a GPU.
          dst_rank: the rank of the destination process.
          dst_gpu_index: the destination gpu index.
          group_name: the name of the collective group.
          n_elements: if specified, send the next n elements
              from the starting address of tensor.
  
      Returns:
          None
      """
      if not types.cupy_available():
          raise RuntimeError("send_multigpu call requires NCCL.")
      _check_single_tensor_input(tensor)
      g = _check_and_get_group(group_name)
      _check_rank_valid(g, dst_rank)
      if dst_rank == g.rank:
          raise RuntimeError(
              "The dst_rank '{}' is self. Considering "
              "doing GPU to GPU memcpy instead?".format(dst_rank)
          )
      if n_elements < 0:
          raise RuntimeError(
              "The n_elements '{}' should >= 0.".format(n_elements)
          )
      opts = types.SendOptions()
      opts.dst_rank = dst_rank
      opts.dst_gpu_index = dst_gpu_index
      opts.n_elements = n_elements
      g.send([tensor], 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 21 for calling a global function with example usage: send_multigpu(tensor, dst_rank, dst_gpu_index, group_name, n_elements) without return types