class_docstr: Create a function that splits data with K-Fold Cross-Validation resampling.
  
  .. code-block:: python
  
      # create a kfold cross validation splitter with 2 kfold
      kfold_cross_validation_split(0.20)
  
  
  :param k_fold: number of folds.
  :return:
code: def _concat_fold_dact_data(
      self, arr1: DACTData, arr2: DACTData
  ) -> DACTData:
      if isinstance(arr1, (list, tuple)):
          return arr1 + arr2
      else:
          return np.concatenate((arr1, arr2), axis=0)
  
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 KFoldCrossValidationSplitter obj's underlying member method code with example usage: obj._concat_fold_dact_data(arr1, arr2) and returns: unknown, np