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, batchSize=10):
      """
  
      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 zoo.orca.data.utils import *
  from zoo.orca import OrcaContext
  from zoo.common.nncontext import init_nncontext
  from zoo import ZooContext, get_node_and_core_number
  from zoo.util import nest
label_desc: Use image node_id 16 for calling the SparkXShards obj's underlying member method code with example usage: obj.save_pickle(path, batchSize) and returns: self