code: def test_use_features_in_secondary_predict_proba():
      np.random.seed(123)
      X, y = iris_data()
      meta = LogisticRegression(
          solver="liblinear", multi_class="ovr", random_state=1
      )
      clf1 = RandomForestClassifier(n_estimators=10, random_state=1)
      clf2 = GaussianNB()
      sclf = StackingClassifier(
          classifiers=[clf1, clf2],
          use_features_in_secondary=True,
          meta_classifier=meta,
      )
  
      sclf.fit(X, y)
      idx = [0, 1, 2]
      y_pred = sclf.predict_proba(X[idx])[:, 0]
      expect = np.array([0.916, 0.828, 0.889])
      np.testing.assert_almost_equal(y_pred, expect, 3)
  
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 18 for calling a global function with example usage: test_use_features_in_secondary_predict_proba() without return types