code: def test_no_weight_support_with_no_weight():
      logit = LogisticRegression(multi_class="ovr", solver="liblinear")
      rf = RandomForestClassifier(n_estimators=10)
      gnb = GaussianNB()
      knn = KNeighborsClassifier()
      sclf = StackingCVClassifier(
          classifiers=[logit, rf, gnb],
          meta_classifier=knn,
          shuffle=False,
      )
      sclf.fit(X_iris, y_iris)
  
      sclf = StackingCVClassifier(
          classifiers=[logit, knn, gnb],
          meta_classifier=rf,
          shuffle=False,
      )
      sclf.fit(X_iris, y_iris)
  
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 6 for calling a global function with example usage: test_no_weight_support_with_no_weight() without return types