class_docstr: Perform time series splitting for cross validation on InputData structures.
  The difference between TimeSeriesSplit (sklearn) and TsInputDataSplit can be
  demonstrated by an example:
  The time series [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] must be splitted into 3
  parts, where the size of each fold for validation will be 2 elements.
  TimeSeriesSplit (return indices)
      train - [0, 1, 2, 3] test - [4, 5]
      train - [0, 1, 2, 3, 4, 5] test - [6, 7]
      train - [0, 1, 2, 3, 4, 5, 6, 7] test - [8, 9]
  TsInputDataSplit (return values of time series)
      train - [1, 2, 3, 4] test - [1, 2, 3, 4, 5, 6]
      train - [1, 2, 3, 4, 5, 6] test - [1, 2, 3, 4, 5, 6, 7, 8]
      train - [1, 2, 3, 4, 5, 6, 7, 8] test - [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
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code: def __init__(self, n_splits: int, test_size: int):
      super().__init__(gap=0, n_splits=n_splits, test_size=test_size)
  
file_path: FEDOT/fedot/core/data/cv_folds.py
imports: from typing import Iterator, Optional, Tuple, Union
  import numpy
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.repository.tasks import TaskTypesEnum
  from sklearn.model_selection import KFold, TimeSeriesSplit
  from sklearn.model_selection._split import StratifiedKFold
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import _split_input_data_by_indexes
label_desc: Use image node_id 1 to create a new TsInputDataSplit object from inherited base classes: TimeSeriesSplit with example: obj = TsInputDataSplit(n_splits, test_size)