code: def test_tlsplitter(rndstate, cv):
      """Test wrapper for cross-validation in transfer learning"""
      X, y_enc = make_classification_transfer(
          n_matrices=25,
          class_sep=5,
          class_disp=1.0,
          random_state=rndstate,
      )
      cv = TLSplitter(
          target_domain="target_domain",
          cv=cv,
      )
      train_idx, test_idx = next(cv.split(X, y_enc))
      assert (
          len(train_idx) == 90
      )  # 50 from source and 4/5*100 from target
      assert len(test_idx) == 10  # 1/5*100 from target
      assert cv.get_n_splits() == 5
  
imports: import pytest
  from pytest import approx
  import numpy
  from numpy.testing import assert_array_equal
  from sklearn.model_selection import KFold, StratifiedShuffleSplit
  from sklearn.pipeline import make_pipeline, Pipeline
  from pyriemann.datasets.simulated import make_classification_transfer, make_matrices
  from pyriemann.classification import MDM, FgMDM, KNearestNeighbor, TSclassifier, SVC, MeanField
  from pyriemann.regression import KNearestNeighborRegressor, SVR
  from pyriemann.transfer import decode_domains, encode_domains, TLCenter, TLStretch, TLRotate, TLSplitter, TLClassifier, TLRegressor, MDWM
  from pyriemann.utils.distance import distance, distance_riemann
  from pyriemann.utils.mean import mean_covariance, mean_riemann
  from pyriemann.utils.utils import check_weights
label_desc: Use image node_id 5 for calling a global function with example usage: test_tlsplitter(rndstate, cv) without return types