code: def test_classifier_gridsearch():
      np.random.seed(123)
      clf1 = KNeighborsClassifier(n_neighbors=1)
      clf2 = RandomForestClassifier(n_estimators=10)
      clf3 = GaussianNB()
      lr = LogisticRegression(solver="liblinear", multi_class="ovr")
      sclf = StackingClassifier(
          classifiers=[clf1, clf2, clf3], meta_classifier=lr
      )
  
      params = {"classifiers": [[clf1, clf1, clf1], [clf2, clf3]]}
  
      grid = GridSearchCV(
          estimator=sclf, param_grid=params, cv=5, refit=True
      )
      grid.fit(X, y)
  
      assert len(grid.best_params_["classifiers"]) == 2
  
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 22 for calling a global function with example usage: test_classifier_gridsearch() without return types