code: def to_spark_xshards(self) -> "SparkXShards":
      from bigdl.orca.data import SparkXShards
  
      ray_ctx = OrcaRayContext.get()
      address = ray_ctx.redis_address  # type: ignore
      password = ray_ctx.redis_password  # type: ignore
      partition2store = self.partition2store_name
      rdd = self.rdd.mapPartitionsWithIndex(
          lambda idx, _: get_from_ray(
              idx, address, password, partition2store
          )
      )
  
      # the reason why we trigger computation here is to ensure we get the data
      # from ray before the RayXShards goes out of scope and the data get garbage collected
      rdd = rdd.cache()
      result_rdd = rdd.map(
          lambda x: x
      )  # sparkxshards will uncache the rdd when gc
      spark_xshards = SparkXShards(result_rdd)
      return spark_xshards
  
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 8 for calling the RayXShards obj's underlying member method code with example usage: obj.to_spark_xshards() and returns: spark_xshards