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: Returns a list that contains all of the elements in this SparkXShards
  
  :return: a list of data elements.
docstr_tok: ["Returns","a","list","that","contains","all","of","the","elements","in","this","SparkXShards",":","return",":","a","list","of","data","elements","."]
code: def collect(self) -> List[Any]:
      """
  
      Returns a list that contains all of the elements in this SparkXShards
  
      :return: a list of data elements.
      """
      return self.rdd.collect()
  
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 6 for calling the SparkXShards obj's underlying member method code with example usage: obj.collect() and returns: self