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"]
code: def __init__(self, rdd, transient=False):
      self.rdd = rdd
      self.user_cached = False
      if transient:
          self.eager = False
      else:
          self.eager = OrcaContext._eager_mode
          self.rdd.cache()
      if self.eager:
          self.compute()
      self.type = {}
  
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 1 to create a new SparkXShards object from inherited base classes: XShards with example: obj = SparkXShards(rdd, transient)