code: def create_collective_workers(
      num_workers=2, group_name="default", backend="nccl"
  ):
      actors = [None] * num_workers
      for i in range(num_workers):
          actor = Worker.remote()
          ray.get([actor.init_tensors.remote()])
          actors[i] = actor
      world_size = num_workers
      init_results = ray.get(
          [
              actor.init_group.remote(
                  world_size, i, backend, group_name
              )
              for i, actor in enumerate(actors)
          ]
      )
      return actors, init_results
  
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 1 for calling a global function with example usage: create_collective_workers(num_workers, group_name, backend) and returns: actors,  init_results