code: def test_stacking_classifier_stratify_default():
      # check that we stratify the classes for the default CV
      clf = StackingClassifier(
          estimators=[
              ("lr", LogisticRegression(max_iter=10_000)),
              ("svm", LinearSVC(dual="auto", max_iter=10_000)),
          ]
      )
      # since iris is not shuffled, a simple k-fold would not contain the
      # 3 classes during training
      clf.fit(X_iris, y_iris)
  
imports: from unittest.mock import Mock
  import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from scipy import sparse
  from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, clone
  from sklearn.datasets import load_breast_cancer, load_diabetes, load_iris, make_classification, make_multilabel_classification, make_regression
  from sklearn.dummy import DummyClassifier, DummyRegressor
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, StackingClassifier, StackingRegressor
  from sklearn.exceptions import ConvergenceWarning, NotFittedError
  from sklearn.linear_model import LinearRegression, LogisticRegression, Ridge, RidgeClassifier
  from sklearn.model_selection import KFold, StratifiedKFold, train_test_split
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.neural_network import MLPClassifier
  from sklearn.preprocessing import scale
  from sklearn.svm import SVC, LinearSVC, LinearSVR
  from sklearn.utils._mocking import CheckingClassifier
  from sklearn.utils._testing import assert_allclose, assert_allclose_dense_sparse, ignore_warnings
  from sklearn.utils.fixes import COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS
label_desc: Use image node_id 12 for calling a global function with example usage: test_stacking_classifier_stratify_default() without return types