code: def test_train_size():
      X, y = iris_data()
      clf1 = LogisticRegression(solver="liblinear", multi_class="ovr")
      clf2 = DecisionTreeClassifier()
  
      expected_err_msg = (
          "train_size must be of type int or float. "
          "Got <class 'NoneType'>."
      )
  
      if sys.version_info < (3, 0):
          expected_err_msg = expected_err_msg.replace("<class", "<type")
  
      assert_raises(
          ValueError,
          expected_err_msg,
          paired_ttest_resampled,
          clf1,
          clf2,
          X,
          y,
          test_size=None,
      )
  
file_path: mlxtend/mlxtend/evaluate/tests/test_paired_ttest_resampled.py
imports: import sys
  from packaging.version import Version
  from sklearn import __version__
  from sklearn.linear_model import Lasso, LogisticRegression, Ridge
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeClassifier
  from mlxtend.data import boston_housing_data, iris_data
  from mlxtend.evaluate import paired_ttest_resampled
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 1 for calling a global function with example usage: test_train_size() without return types