code: def test_multivariate_time_series_splitting_correct():
      """Check if in-sample for multivariate time series work correctly"""
      multivar_ts = MultiModalData(
          {
              "series_1": get_ts_data_to_forecast_two_elements(),
              "series_2": get_ts_data_to_forecast_two_elements(),
          }
      )
  
      train_data, test_data = train_test_data_setup(
          multivar_ts, validation_blocks=2
      )
      for series_id, train_series_data in train_data.items():
          assert (
              len(train_series_data.features)
              == len(train_series_data.target)
              == 16
          )
  
      for series_id, test_series_data in test_data.items():
          assert len(test_series_data.features) == 20
          assert np.allclose(
              test_series_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 15 for calling a global function with example usage: test_multivariate_time_series_splitting_correct() without return types