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: Make this SparkXShards as non-persistent, and remove all blocks for it from memory
  
  :return:
docstr_tok: ["Make","this","SparkXShards","as","non-persistent",",","and","remove","all","blocks","for","it","from","memory",":","return",":"]
code: def uncache(self) -> "SparkXShards":
      """
  
      Make this SparkXShards as non-persistent, and remove all blocks for it from memory
  
      :return:
      """
      self.user_caching = False
      try:
          if self.is_cached():
              self.rdd.unpersist()
      except (Py4JError, TypeError):
          pass
      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 10 for calling the SparkXShards obj's underlying member method code with example usage: obj.uncache() and returns: self