code: def test_no_weight_support():
      w = np.array([random.random() for _ in range(len(y_iris))])
      meta = LogisticRegression(multi_class="ovr", solver="liblinear")
      clf1 = RandomForestClassifier(n_estimators=10)
      clf2 = GaussianNB()
      clf3 = KNeighborsClassifier()
      sclf = StackingCVClassifier(
          classifiers=[clf1, clf2, clf3],
          meta_classifier=meta,
          shuffle=False,
      )
      with pytest.raises(TypeError):
          sclf.fit(X_iris, y_iris, sample_weight=w)
  
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 4 for calling a global function with example usage: test_no_weight_support() without return types