code: def test_meta_feat_reordering():
      knn = KNeighborsClassifier()
      lr = LogisticRegression(multi_class="ovr", solver="liblinear")
      gnb = GaussianNB()
      stclf = StackingCVClassifier(
          classifiers=[knn, gnb],
          meta_classifier=lr,
          shuffle=True,
          random_state=42,
          store_train_meta_features=True,
      )
      X_train, _, y_train, _ = train_test_split(
          X_breast, y_breast, random_state=0, test_size=0.3
      )
      stclf.fit(X_train, y_train)
  
      if Version(sklearn_version) < Version("0.21"):
          expected_value = 0.86
      elif Version(sklearn_version) < Version("0.22"):
          expected_value = 0.87
      else:
          expected_value = 0.85
  
      assert (
          round(
              roc_auc_score(y_train, stclf.train_meta_features_[:, 1]),
              2,
          )
          == expected_value
      ), round(
          roc_auc_score(y_train, stclf.train_meta_features_[:, 1]), 2
      )
  
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 20 for calling a global function with example usage: test_meta_feat_reordering() without return types