code: def test_fit_and_process_using_meta_feature_generator(self):
      X, y = load_iris(return_X_y=True)
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, random_state=8
      )
  
      self.stacked_ensemble.fit(X_train, y_train)
  
      preds = (
          self.stacked_ensemble._process_using_meta_feature_generator(
              X_test, "predict"
          )
      )
      assert np.round(accuracy_score(y_test, preds), 3) == 0.868
  
      probas = (
          self.stacked_ensemble._process_using_meta_feature_generator(
              X_test, "predict_proba"
          )
      )
      preds = np.argmax(probas, axis=1)
      assert np.round(accuracy_score(y_test, preds), 3) == 0.868
  
file_path: xcessiv/xcessiv/tests/test_stacker.py
imports: from __future__ import absolute_import, print_function, division, unicode_literals
  import unittest
  import numpy
  from xcessiv import stacker
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import StratifiedKFold, train_test_split
  from sklearn.datasets import load_iris
  from sklearn.metrics import accuracy_score
label_desc: Use image node_id 2 for calling the TestStacker obj's underlying member method code with example usage: obj.test_fit_and_process_using_meta_feature_generator() without return types