code: def zip_reduce_shards_with_actors(
      self,
      xshards: "RayXShards",
      actors: List["ActorHandle"],
      reduce_partitions_func: Callable,
      return_refs: bool = False,
  ) -> List[Any]:
      invalidInputError(
          self.num_partitions() == xshards.num_partitions(),
          "the rdds to be zipped must have the same number of partitions",
      )
      invalidInputError(
          self.num_partitions() >= len(actors),
          f"Get number of partitions ({self.num_partitions()}) smaller than"
          f" number of actors ({len(actors)}). Please submit an issue"
          f" to BigDL.",
      )
      assigned_partitions, _, _ = self.assign_partitions_to_actors(
          actors
      )
      result_refs = []
      for actor, part_ids in zip(actors, assigned_partitions):
          assigned_partition_refs = self._get_multiple_partition_refs(
              part_ids
          )
          assigned_partition_refs_other = (
              xshards._get_multiple_partition_refs(part_ids)
          )
          result_ref = reduce_partitions_func(
              actor,
              assigned_partition_refs,
              assigned_partition_refs_other,
          )
          result_refs.append(result_ref)
      if return_refs:
          return result_refs
      results = ray.get(result_refs)
      return results
  
imports: from __future__ import annotations
  from collections import defaultdict
  import ray
  import ray._private.services
  import uuid
  import random
  from bigdl.orca.data import XShards
  from bigdl.orca.ray import OrcaRayContext
  import logging
  from bigdl.dllib.utils.log4Error import invalidInputError
  from numpy import ndarray
  from typing import TYPE_CHECKING, Any, Callable, DefaultDict, Dict, List, Tuple, Union, no_type_check
label_desc: Use image node_id 12 for calling the RayXShards obj's underlying member method code with example usage: obj.zip_reduce_shards_with_actors(xshards, actors, reduce_partitions_func, return_refs) and returns: results, result_refs