code: def test_EnsembleVoteClassifier_gridsearch_enumerate_names():
      clf1 = LogisticRegression(
          solver="liblinear", multi_class="ovr", random_state=1
      )
      clf2 = RandomForestClassifier(random_state=1)
      eclf = EnsembleVoteClassifier(clfs=[clf1, clf1, clf2])
  
      params = {
          "logisticregression-1__C": [1.0, 100.0],
          "logisticregression-2__C": [1.0, 100.0],
          "randomforestclassifier__n_estimators": [5, 20],
          "voting": ["hard", "soft"],
      }
  
      if Version(sklearn_version) < Version("0.24.1"):
          grid = GridSearchCV(
              estimator=eclf, param_grid=params, cv=5, iid=False
          )
      else:
          grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)
  
      X, y = iris_data()
      grid = grid.fit(X, y)
  
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 11 for calling a global function with example usage: test_EnsembleVoteClassifier_gridsearch_enumerate_names() without return types