code: def test_multivariate_class():
      np.random.seed(123)
      meta = KNeighborsClassifier()
      clf1 = RandomForestClassifier(n_estimators=10)
      clf2 = KNeighborsClassifier()
      sclf = StackingClassifier(
          classifiers=[clf1, clf2], meta_classifier=meta
      )
      y_pred = sclf.fit(X, y2).predict(X)
      ca = 0.973
      assert round((y_pred == y2).mean(), 3) == ca
  
imports: import random
  import numpy
  import pytest
  from numpy.testing import assert_almost_equal
  from packaging.version import Version
  from scipy import sparse
  from sklearn import __version__
  from sklearn import exceptions
  from sklearn.base import clone
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LogisticRegression, PassiveAggressiveClassifier
  from sklearn.model_selection import GridSearchCV, 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 StackingClassifier
  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 11 for calling a global function with example usage: test_multivariate_class() without return types