class_docstr: Create a function that splits data into a training, and a validation set.
  
  .. code-block:: python
  
      # create a validation splitter function with 80% train, and 20% validation
      validation_splitter(0.20)
  
  
  :param test_size: test size in float
  :return:
docstr: Split validation set.
  
  :param data_inputs: data inputs to split
  :return: validation_data_inputs
docstr_tok: ["Split","validation","set",".",":","param","data_inputs",":","data","inputs","to","split",":","return",":","validation_data_inputs"]
code: def _validation_split(self, data_inputs: DACTData) -> DACTData:
      """
      Split validation set.
  
      :param data_inputs: data inputs to split
      :return: validation_data_inputs
      """
      if data_inputs is None:
          return None
      return data_inputs[self._get_index_split(data_inputs) :]
  
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 5 for calling the ValidationSplitter obj's underlying member method code with example usage: obj._validation_split(data_inputs) and returns: data_inputs, None