code: def recv_multigpu(
      tensor,
      src_rank: int,
      src_gpu_index: int,
      group_name: str = "default",
      n_elements: int = 0,
  ):
      """Receive a tensor from 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 received tensor, located on a GPU.
          src_rank: the rank of the source process.
          src_gpu_index (int)： the index of the source gpu on the src process.
          group_name: the name of the collective group.
  
      Returns:
          None
      """
      if not types.cupy_available():
          raise RuntimeError("recv_multigpu call requires NCCL.")
      _check_single_tensor_input(tensor)
      g = _check_and_get_group(group_name)
      _check_rank_valid(g, src_rank)
      if src_rank == g.rank:
          raise RuntimeError(
              "The dst_rank '{}' is self. Considering "
              "doing GPU to GPU memcpy instead?".format(src_rank)
          )
      if n_elements < 0:
          raise RuntimeError(
              "The n_elements '{}' should be >= 0.".format(n_elements)
          )
      opts = types.RecvOptions()
      opts.src_rank = src_rank
      opts.src_gpu_index = src_gpu_index
      opts.n_elements = n_elements
      g.recv([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 23 for calling a global function with example usage: recv_multigpu(tensor, src_rank, src_gpu_index, group_name, n_elements) without return types