code: def test_advanced_time_series_splitting():
      """Check how data prepared for time series in-sample validation"""
      validation_blocks = 2
      time_series = get_ts_data_to_forecast_two_elements()
      train_data, test_data = train_test_data_setup(
          time_series, validation_blocks=validation_blocks
      )
  
      assert len(train_data.features) == len(train_data.target) == 16
      # For in sample validation it is required to have all length of time series
      assert len(test_data.features) == 20
      assert np.allclose(test_data.target, np.array([16, 17, 18, 19]))
  
imports: from functools import partial
  from typing import Callable
  import numpy
  import pandas
  import pytest
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.optimisers.objective.data_source_splitter import DataSourceSplitter
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import split_data
  from fedot.core.data.cv_folds import cv_generator
  from test.unit.pipelines.test_decompose_pipelines import get_classification_data
  from test.unit.tasks.test_forecasting import get_ts_data
label_desc: Use image node_id 12 for calling a global function with example usage: test_advanced_time_series_splitting() without return types