code: def test_no_weight_support():
      random.seed(87)
      w = np.array([random.random() for _ in range(len(y))])
      logi = LogisticRegression(solver="liblinear", multi_class="ovr")
      rf = RandomForestClassifier(n_estimators=10)
      gnb = GaussianNB()
      knn = KNeighborsClassifier()
      eclf = EnsembleVoteClassifier(
          clfs=[logi, rf, gnb, knn], voting="hard"
      )
      with pytest.raises(TypeError):
          eclf.fit(X, y, sample_weight=w)
  
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 5 for calling a global function with example usage: test_no_weight_support() without return types