code: def do_recv_multigpu(
      self,
      group_name="default",
      src_rank=0,
      src_gpu_index=0,
      dst_gpu_index=0,
  ):
      if dst_gpu_index == 0:
          col.recv_multigpu(
              self.buffer0, src_rank, src_gpu_index, group_name
          )
          cp.cuda.Device(0).synchronize()
          return self.buffer0
      elif dst_gpu_index == 1:
          col.recv_multigpu(
              self.buffer1, src_rank, src_gpu_index, group_name
          )
          cp.cuda.Device(1).synchronize()
          return self.buffer1
      else:
          raise RuntimeError()
  
imports: import cupy
  import logging
  import ray
  import ray.util.collective
  from ray.util.collective.types import Backend, ReduceOp
  from ray.util.collective.collective_group.nccl_util import get_num_gpus
  import torch
label_desc: Use image node_id 14 for calling the MultiGPUWorker obj's underlying member method code with example usage: obj.do_recv_multigpu(group_name, src_rank, src_gpu_index, dst_gpu_index) and returns: self, self