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: Making the current SparkXShards lazy won't change the behavior of the current SparkXShards.
  Cached data won't be uncached unless uncache is explicitly invoked.
  After being marked as lazy, future operations would be performed lazily.
  
  :return: An instance of SparkXShards that computes lazily.
docstr_tok: ["Making","the","current","SparkXShards","lazy","wo","n't","change","the","behavior","of","the","current","SparkXShards",".","Cached","data","wo","n't","be","uncached","unless","uncache","is","explicitly","invoked",".","After","being","marked","as","lazy",",","future","operations","would","be","performed","lazily",".",":","return",":","An","instance","of","SparkXShards","that","computes","lazily","."]
code: def to_lazy(self) -> "SparkXShards":
      """
      Making the current SparkXShards lazy won't change the behavior of the current SparkXShards.
      Cached data won't be uncached unless uncache is explicitly invoked.
      After being marked as lazy, future operations would be performed lazily.
  
      :return: An instance of SparkXShards that computes lazily.
      """
      self.eager = False
      self.is_lazy = True
      self.user_caching = True
      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 4 for calling the SparkXShards obj's underlying member method code with example usage: obj.to_lazy() and returns: self