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:
code: def _get_index_split(self, data_inputs: DACTData) -> int:
      if self.validation_size < 0 or self.validation_size > 1:
          raise ValueError(
              "validation_size must be a float in the range [0, 1]."
          )
      return math.floor(len(data_inputs) * (1 - self.validation_size))
  
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 6 for calling the ValidationSplitter obj's underlying member method code with example usage: obj._get_index_split(data_inputs) and returns: math