code: def test_StackingClassifier_drop_proba_col():
      np.random.seed(123)
      lr1 = LogisticRegression(solver="liblinear", multi_class="ovr")
      sclf1 = StackingCVClassifier(
          classifiers=[lr1, lr1],
          use_probas=True,
          drop_proba_col=None,
          meta_classifier=lr1,
      )
  
      sclf1.fit(X_iris, y_iris)
      r1 = sclf1.predict_meta_features(X_iris[:2])
      assert r1.shape == (2, 6)
  
      sclf2 = StackingCVClassifier(
          classifiers=[lr1, lr1],
          use_probas=True,
          drop_proba_col="last",
          meta_classifier=lr1,
      )
  
      sclf2.fit(X_iris, y_iris)
      r2 = sclf2.predict_meta_features(X_iris[:2])
      assert r2.shape == (2, 4), r2.shape
  
      sclf4 = StackingCVClassifier(
          classifiers=[lr1, lr1],
          use_probas=True,
          drop_proba_col="first",
          meta_classifier=lr1,
      )
  
      sclf4.fit(X_iris, y_iris)
      r4 = sclf4.predict_meta_features(X_iris[:2])
      assert r4.shape == (2, 4), r4.shape
  
      sclf3 = StackingCVClassifier(
          classifiers=[lr1, lr1],
          use_probas=True,
          drop_proba_col="last",
          meta_classifier=lr1,
      )
  
      sclf3.fit(X_iris[0:100], y_iris[0:100])  # only 2 classes
      r3 = sclf3.predict_meta_features(X_iris[:2])
      assert r3.shape == (2, 2), r3.shape
  
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 24 for calling a global function with example usage: test_StackingClassifier_drop_proba_col() without return types