code: def _predict_spark_xshards(self, xshards, param):
      ray_xshards = RayXShards.from_spark_xshards(xshards)
  
      def transform_func(worker, shards_ref):
          data_creator = lambda config, batch_size: shards_ref
          return worker.predict.remote(data_creator, **param)
  
      pred_shards = ray_xshards.transform_shards_with_actors(
          self.remote_workers, transform_func
      )
      spark_xshards = pred_shards.to_spark_xshards()
      return spark_xshards
  
imports: import types
  import copy
  import logging
  import math
  from bigdl.orca.data.ray_xshards import RayXShards
  from bigdl.orca.learn.pytorch.pytorch_ray_worker import PytorchRayWorker
  from bigdl.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, update_predict_xshards, process_xshards_of_pandas_dataframe, add_predict_to_pd_xshards
  from bigdl.orca.ray import OrcaRayContext
  from bigdl.orca.learn.pytorch.core.base_ray_estimator import BaseRayEstimator
  from bigdl.orca.learn.pytorch.utils import process_stats, check_for_failure
  from bigdl.orca.learn.pytorch.callbacks.maincallback import make_only_mainCallback
  from bigdl.orca.learn.pytorch.callbacks.tqdm import TqdmCallback, is_tqdm_exists
  from bigdl.orca.learn.pytorch.callbacks.maxsteps import MaxstepsCallback
  import ray
  from bigdl.dllib.utils.log4Error import invalidInputError
  from typing import TYPE_CHECKING, Union, Optional, Callable, Dict, List
label_desc: Use image node_id 6 for calling the PyTorchRayEstimator obj's underlying member method code with example usage: obj._predict_spark_xshards(xshards, param) and returns: spark_xshards, worker