docstr: Train/Test split data inputs and expected outputs.
  
  :param data_inputs: data inputs
  :param ids: id associated with each data entry (optional)
  :param expected_outputs: expected outputs (optional)
  :param context: execution context (optional)
  :return: train_di, train_eo, train_ids, valid_di, valid_eo, valid_ids
docstr_tok: ["Train\/Test","split","data","inputs","and","expected","outputs",".",":","param","data_inputs",":","data","inputs",":","param","ids",":","id","associated","with","each","data","entry","(","optional",")",":","param","expected_outputs",":","expected","outputs","(","optional",")",":","param","context",":","execution","context","(","optional",")",":","return",":","train_di",",","train_eo",",","train_ids",",","valid_di",",","valid_eo",",","valid_ids"]
code: def split(
      self,
      data_inputs: DIT,
      ids: Optional[IDT] = None,
      expected_outputs: Optional[EOT] = None,
      context: Optional[CX] = None,
  ) -> Tuple[
      FoldsList[DIT],
      FoldsList[EOT],
      FoldsList[IDT],
      FoldsList[DIT],
      FoldsList[EOT],
      FoldsList[IDT],
  ]:
      """
      Train/Test split data inputs and expected outputs.
  
      :param data_inputs: data inputs
      :param ids: id associated with each data entry (optional)
      :param expected_outputs: expected outputs (optional)
      :param context: execution context (optional)
      :return: train_di, train_eo, train_ids, valid_di, valid_eo, valid_ids
      """
      pass
  
imports: import copy
  import math
  from abc import ABC, abstractmethod
  from typing import Any, List, Optional, Set, Tuple
  import numpy
  from neuraxle.base import ExecutionContext
  from neuraxle.data_container import DIT, EOT, IDT, DACTData
  from neuraxle.data_container import DataContainer
  from neuraxle.data_container import TrainDACT, ValidDACT
label_desc: Use image node_id 4 for calling the BaseValidationSplitter obj's underlying member method code with example usage: obj.split(data_inputs, ids, expected_outputs, context) without return types