class_docstr: A collection of data which can be pre-processed in parallel on Spark.
class_docstr_tok: ["A","collection","of","data","which","can","be","pre-processed","in","parallel","on","Spark","."]
docstr: Save this SparkXShards as a SequenceFile of serialized objects.
  The serializer used is pyspark.serializers.PickleSerializer, default batch size is 10.
  
  :param path: target path.
  :param batchSize: batch size for each sequence file chunk.
docstr_tok: ["Save","this","SparkXShards","as","a","SequenceFile","of","serialized","objects",".","The","serializer","used","is","pyspark.serializers.PickleSerializer",",","default","batch","size","is","10.",":","param","path",":","target","path",".",":","param","batchSize",":","batch","size","for","each","sequence","file","chunk","."]
code: def save_pickle(
      self, path: str, batchSize: int = 10
  ) -> "SparkXShards":
      """
  
      Save this SparkXShards as a SequenceFile of serialized objects.
      The serializer used is pyspark.serializers.PickleSerializer, default batch size is 10.
  
      :param path: target path.
      :param batchSize: batch size for each sequence file chunk.
      """
      self.rdd.saveAsPickleFile(path, batchSize)
      return self
  
imports: from py4j.protocol import Py4JError
  from bigdl.orca.data.utils import *
  from bigdl.orca import OrcaContext
  from bigdl.dllib.utils.common import get_node_and_core_number, get_spark_sql_context, get_spark_context
  from bigdl.dllib.utils import nest
  from bigdl.dllib.utils.log4Error import invalidInputError
  import numpy
  from typing import TYPE_CHECKING, Any
  from typing import Callable, Dict, List, Optional, Tuple, Union
label_desc: Use image node_id 30 for calling the SparkXShards obj's underlying member method code with example usage: obj.save_pickle(path, batchSize) and returns: self