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 __init__(self, validation_size: float):
      BaseValidationSplitter.__init__(self)
      self.validation_size = 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 1 to create a new ValidationSplitter object from inherited base classes: BaseValidationSplitter with example: obj = ValidationSplitter(validation_size)