class_docstr: A collection of data which can be pre-processed in parallel.
class_docstr_tok: ["A","collection","of","data","which","can","be","pre-processed","in","parallel","."]
docstr: Load XShards from pickle files.
  
  :param path: The pickle file path/directory
  :param minPartitions: The minimum partitions for the XShards
  :return: SparkXShards object
docstr_tok: ["Load","XShards","from","pickle","files",".",":","param","path",":","The","pickle","file","path\/directory",":","param","minPartitions",":","The","minimum","partitions","for","the","XShards",":","return",":","SparkXShards","object"]
code: def load_pickle(
      cls, path: str, minPartitions: Optional[int] = None
  ) -> "SparkXShards":
      """
  
      Load XShards from pickle files.
  
      :param path: The pickle file path/directory
      :param minPartitions: The minimum partitions for the XShards
      :return: SparkXShards object
      """
      sc = OrcaContext.get_spark_context()
      return SparkXShards(sc.pickleFile(path, minPartitions))
  
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 XShards obj's underlying member method code with example usage: obj.load_pickle(cls, path, minPartitions) and returns: SparkXShards