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: Create an instance of SparkXShards after transformation.
docstr_tok: ["Create","an","instance","of","SparkXShards","after","transformation","."]
code: def _create(
      self, rdd: Union["PipelinedRDD", "RDD"], class_name: str = None
  ) -> "SparkXShards":
      """
      Create an instance of SparkXShards after transformation.
      """
      if self.is_lazy:
          return SparkXShards(
              rdd, transient=True, class_name=class_name
          ).to_lazy()
      else:
          return SparkXShards(rdd, class_name=class_name)
  
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 3 for calling the SparkXShards obj's underlying member method code with example usage: obj._create(rdd, class_name) and returns: SparkXShards, SparkXShards