code: def test_classifier_gridsearch():
      clf1 = KNeighborsClassifier(n_neighbors=1)
      clf2 = RandomForestClassifier(n_estimators=10, random_state=42)
      clf3 = GaussianNB()
      lr = LogisticRegression(multi_class="ovr", solver="liblinear")
      sclf = StackingCVClassifier(
          classifiers=[clf1], meta_classifier=lr, random_state=42
      )
  
      params = {"classifiers": [[clf1], [clf1, clf2, clf3]]}
  
      if Version(sklearn_version) < Version("0.24.1"):
          grid = GridSearchCV(
              estimator=sclf,
              param_grid=params,
              cv=5,
              iid=False,
              refit=True,
          )
      else:
          grid = GridSearchCV(
              estimator=sclf, param_grid=params, cv=5, refit=True
          )
      grid.fit(X_iris, y_iris)
  
      assert len(grid.best_params_["classifiers"]) == 3, len(
          grid.best_params_["classifiers"]
      )
  
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 17 for calling a global function with example usage: test_classifier_gridsearch() without return types