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: Transform each shard in the XShards using specified function.
  
  :param func: pre-processing function
  :param args: arguments for the pre-processing function
  :return: DataShard
docstr_tok: ["Transform","each","shard","in","the","XShards","using","specified","function",".",":","param","func",":","pre-processing","function",":","param","args",":","arguments","for","the","pre-processing","function",":","return",":","DataShard"]
code: def transform_shard(self, func: Callable, *args):
      """
  
      Transform each shard in the XShards using specified function.
  
      :param func: pre-processing function
      :param args: arguments for the pre-processing function
      :return: DataShard
      """
      pass
  
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 1 for calling the XShards obj's underlying member method code with example usage: obj.transform_shard(func) without return types