code: def test_classifier_gridsearch():
      clf1 = KNeighborsClassifier(n_neighbors=1)
      clf2 = RandomForestClassifier(random_state=1, n_estimators=10)
      clf3 = GaussianNB()
      eclf = EnsembleVoteClassifier(clfs=[clf1])
  
      params = {"clfs": [[clf1, clf1, clf1], [clf2, clf3]]}
  
      if Version(sklearn_version) < Version("0.24.1"):
          grid = GridSearchCV(
              estimator=eclf,
              param_grid=params,
              iid=False,
              cv=5,
              refit=True,
          )
      else:
          grid = GridSearchCV(
              estimator=eclf, param_grid=params, cv=5, refit=True
          )
      grid.fit(X, y)
  
      assert len(grid.best_params_["clfs"]) == 2
  
imports: import random
  import numpy
  import pytest
  from packaging.version import Version
  from sklearn import __version__
  from sklearn import exceptions
  from sklearn.base import clone
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import GridSearchCV, cross_val_score
  from sklearn.naive_bayes import GaussianNB
  from sklearn.neighbors import KNeighborsClassifier
  from mlxtend.classifier import EnsembleVoteClassifier
  from mlxtend.data import iris_data
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 13 for calling a global function with example usage: test_classifier_gridsearch() without return types