code: def test_data_splitting_without_shape_mismatch(
      data_splitter: DataSourceSplitter, data: InputData
  ):
      """Checks if data split correctly into train test subsets: there are no new classes in test subset"""
      data_source = data_splitter.build(data=data)
      for fold_id, (train_data, test_data) in enumerate(data_source()):
          assert set(train_data.target) >= set(test_data.target)
  
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 13 for calling a global function with example usage: test_data_splitting_without_shape_mismatch(data_splitter, data) without return types