code: def test_use_features_in_secondary_predict():
      np.random.seed(123)
      X, y = iris_data()
      meta = LogisticRegression(solver="liblinear", multi_class="ovr")
      clf1 = RandomForestClassifier(n_estimators=10)
      clf2 = GaussianNB()
      sclf = StackingClassifier(
          classifiers=[clf1, clf2],
          use_features_in_secondary=True,
          meta_classifier=meta,
      )
  
      scores = cross_val_score(sclf, X, y, cv=5, scoring="accuracy")
      scores_mean = round(scores.mean(), 2)
      assert scores_mean == 0.95, scores_mean
  
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 17 for calling a global function with example usage: test_use_features_in_secondary_predict() without return types