code: def test_variant_A_2_estimators_labels(self):
      # Estimator 1
      S_test_temp_e1 = np.zeros((X_test.shape[0], n_folds))
      # Using StratifiedKFold because by defauld cross_val_predict uses StratifiedKFold
      kf = StratifiedKFold(
          n_splits=n_folds, shuffle=False, random_state=0
      )
      for fold_counter, (tr_index, te_index) in enumerate(
          kf.split(X_train, y_train)
      ):
          # Split data and target
          X_tr = X_train[tr_index]
          y_tr = y_train[tr_index]
          # X_te = X_train[te_index]
          # y_te = y_train[te_index]
          model = LogisticRegression(
              random_state=0, solver="liblinear", multi_class="ovr"
          )
          model = model.fit(X_tr, y_tr)
          S_test_temp_e1[:, fold_counter] = model.predict(X_test)
      S_test_1_e1 = st.mode(S_test_temp_e1, axis=1)[0]
  
      model = LogisticRegression(
          random_state=0, solver="liblinear", multi_class="ovr"
      )
      S_train_1_e1 = cross_val_predict(
          model,
          X_train,
          y=y_train,
          cv=n_folds,
          n_jobs=1,
          verbose=0,
          method="predict",
      ).reshape(-1, 1)
  
      # Estimator 2
      S_test_temp_e2 = np.zeros((X_test.shape[0], n_folds))
      # Using StratifiedKFold because by defauld cross_val_predict uses StratifiedKFold
      kf = StratifiedKFold(
          n_splits=n_folds, shuffle=False, random_state=0
      )
      for fold_counter, (tr_index, te_index) in enumerate(
          kf.split(X_train, y_train)
      ):
          # Split data and target
          X_tr = X_train[tr_index]
          y_tr = y_train[tr_index]
          # X_te = X_train[te_index]
          # y_te = y_train[te_index]
          model = GaussianNB()
          model = model.fit(X_tr, y_tr)
          S_test_temp_e2[:, fold_counter] = model.predict(X_test)
      S_test_1_e2 = st.mode(S_test_temp_e2, axis=1)[0]
  
      model = GaussianNB()
      S_train_1_e2 = cross_val_predict(
          model,
          X_train,
          y=y_train,
          cv=n_folds,
          n_jobs=1,
          verbose=0,
          method="predict",
      ).reshape(-1, 1)
  
      S_train_1 = np.c_[S_train_1_e1, S_train_1_e2]
      S_test_1 = np.c_[S_test_1_e1, S_test_1_e2]
  
      # fit then transform
      estimators = [
          (
              "logit",
              LogisticRegression(
                  random_state=0, solver="liblinear", multi_class="ovr"
              ),
          ),
          ("bayes", GaussianNB()),
      ]
      stack = StackingTransformer(
          estimators,
          regression=False,
          n_folds=n_folds,
          shuffle=False,
          variant="A",
          random_state=0,
          stratified=True,
          verbose=0,
      )
      stack = stack.fit(X_train, y_train)
      S_train_2 = stack.transform(X_train)
      S_test_2 = stack.transform(X_test)
  
      # fit_transform
      # also check refitting already fitted transformer
      S_train_3 = stack.fit_transform(X_train, y_train)
      S_test_3 = stack.transform(X_test)
  
      # compare
      assert_array_equal(S_train_1, S_train_2)
      assert_array_equal(S_test_1, S_test_2)
  
      assert_array_equal(S_train_1, S_train_3)
      assert_array_equal(S_test_1, S_test_3)
  
imports: from __future__ import print_function
  from __future__ import division
  import unittest
  from numpy.testing import assert_array_equal
  from numpy.testing import assert_equal
  import numpy
  import scipy.stats
  from sklearn.model_selection import cross_val_predict
  from sklearn.model_selection import cross_val_score
  from sklearn.model_selection import StratifiedKFold
  from sklearn.datasets import make_classification
  from sklearn.preprocessing import OneHotEncoder
  from sklearn.metrics import accuracy_score
  from sklearn.metrics import zero_one_loss
  from sklearn.metrics import log_loss
  from sklearn.metrics import roc_auc_score
  from sklearn.metrics import make_scorer
  from sklearn.linear_model import LogisticRegression
  from sklearn.naive_bayes import GaussianNB
  from sklearn.dummy import DummyClassifier
  from vecstack import StackingTransformer
label_desc: Use image node_id 12 for calling the TestSklearnClassificationMulticlass obj's underlying member method code with example usage: obj.test_variant_A_2_estimators_labels() without return types