code: def test_train_test_split(features):
      X, y = make_classification(
          n_samples=4000,
          n_features=features,
          n_informative=features,
          n_redundant=0,
          n_clusters_per_class=8,
          random_state=0,
      )
      (
          baseline_X_train,
          baseline_X_test,
          baseline_y_train,
          baseline_y_test,
      ) = train_test_split(X, y, test_size=0.33, random_state=0)
      baseline = [
          baseline_X_train,
          baseline_X_test,
          baseline_y_train,
          baseline_y_test,
      ]
      for _ in range(10):
          X_train, X_test, y_train, y_test = train_test_split(
              X, y, test_size=0.33, random_state=0
          )
          res = [X_train, X_test, y_train, y_test]
          for a, b in zip(res, baseline):
              np.testing.assert_allclose(
                  a,
                  b,
                  rtol=0.0,
                  atol=0.0,
                  err_msg=str("train_test_split is incorrect"),
              )
  
imports: import random
  import numpy
  import pytest
  import daal4py
  from sklearnex import patch_sklearn
  from scipy import sparse
  from sklearn.cluster import DBSCAN, KMeans
  from sklearn.datasets import load_breast_cancer, load_diabetes, load_iris, make_classification, make_regression
  from sklearn.decomposition import PCA
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  from sklearn.linear_model import ElasticNet, Lasso, LinearRegression, LogisticRegression, LogisticRegressionCV, Ridge
  from sklearn.manifold import TSNE
  from sklearn.metrics import pairwise_distances, roc_auc_score
  from sklearn.model_selection import train_test_split
  from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor, LocalOutlierFactor, NearestNeighbors
  from sklearn.svm import SVC, SVR, NuSVC, NuSVR
  from daal4py.sklearn._utils import daal_check_version
label_desc: Use image node_id 6 for calling a global function with example usage: test_train_test_split(features) without return types