code: def test_weight_unsupported_no_weight():
      # This is okay since we do not pass sample weight
      np.random.seed(123)
      meta = LogisticRegression(solver="liblinear", multi_class="ovr")
      clf1 = RandomForestClassifier(n_estimators=10)
      clf2 = GaussianNB()
      clf3 = KNeighborsClassifier()
      sclf = StackingClassifier(
          classifiers=[clf1, clf2, clf3], meta_classifier=meta
      )
      sclf.fit(X, y)
  
imports: import random
  import numpy
  import pytest
  from numpy.testing import assert_almost_equal
  from packaging.version import Version
  from scipy import sparse
  from sklearn import __version__
  from sklearn import exceptions
  from sklearn.base import clone
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LogisticRegression, PassiveAggressiveClassifier
  from sklearn.model_selection import GridSearchCV, 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 StackingClassifier
  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 6 for calling a global function with example usage: test_weight_unsupported_no_weight() without return types