code: def test_cross_validation_technique():
      # This is like the `test_do_not_stratify` but instead
      # autogenerating the cross validation strategy it provides
      # a pre-created object
      cv = KFold(n_splits=2, shuffle=True)
      meta = LogisticRegression(multi_class="ovr", solver="liblinear")
      clf1 = RandomForestClassifier(n_estimators=10, random_state=42)
      clf2 = GaussianNB()
      sclf = StackingCVClassifier(
          classifiers=[clf1, clf2],
          meta_classifier=meta,
          cv=cv,
          random_state=42,
      )
  
      scores = cross_val_score(
          sclf, X_iris, y_iris, cv=5, scoring="accuracy"
      )
      scores_mean = round(scores.mean(), 2)
      assert scores_mean == 0.92, scores.mean()
  
imports: import random
  import numpy
  import pandas
  import pytest
  from packaging.version import Version
  from scipy import sparse
  from sklearn import __version__
  from sklearn import datasets, exceptions
  from sklearn.base import clone
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LogisticRegression, PassiveAggressiveClassifier
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import GridSearchCV, KFold, cross_val_score, train_test_split
  from sklearn.naive_bayes import GaussianNB
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.svm import SVC
  from mlxtend.classifier import StackingCVClassifier
  from mlxtend.data import iris_data
  from mlxtend.externals.estimator_checks import NotFittedError
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 13 for calling a global function with example usage: test_cross_validation_technique() without return types