code: def test_predict_meta_features():
      knn = KNeighborsClassifier()
      lr = LogisticRegression(
          solver="liblinear", multi_class="ovr", random_state=1
      )
      gnb = GaussianNB()
      X_train, X_test, y_train, _ = train_test_split(
          X, y, test_size=0.3
      )
  
      #  test default (class labels)
      stclf = StackingClassifier(
          classifiers=[knn, gnb],
          meta_classifier=lr,
          store_train_meta_features=True,
      )
      stclf.fit(X_train, y_train)
      test_meta_features = stclf.predict(X_test)
      assert test_meta_features.shape == (X_test.shape[0],)
  
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 24 for calling a global function with example usage: test_predict_meta_features() without return types