code: def test_oof_pred_mode_verbose_1(self):
      model = LinearRegression()
      S_train_1 = cross_val_predict(
          model,
          X_train,
          y=y_train,
          cv=n_folds,
          n_jobs=1,
          verbose=0,
          method="predict",
      ).reshape(-1, 1)
      _ = model.fit(X_train, y_train)
      S_test_1 = model.predict(X_test).reshape(-1, 1)
  
      models = [LinearRegression()]
      S_train_2, S_test_2 = stacking(
          models,
          X_train,
          y_train,
          X_test,
          regression=True,
          n_folds=n_folds,
          shuffle=False,
          save_dir=temp_dir,
          mode="oof_pred",
          random_state=0,
          verbose=0,
      )
  
      models = [LinearRegression()]
      S_train_3, S_test_3 = stacking(
          models,
          X_train,
          y_train,
          X_test,
          regression=True,
          n_folds=n_folds,
          shuffle=False,
          save_dir=temp_dir,
          mode="oof_pred",
          random_state=0,
          verbose=1,
      )
  
      models = [LinearRegression()]
      S_train_4, S_test_4 = stacking(
          models,
          X_train,
          y_train,
          X_test,
          regression=True,
          n_folds=n_folds,
          shuffle=False,
          save_dir=temp_dir,
          mode="oof_pred",
          random_state=0,
          verbose=2,
      )
  
      models = [LinearRegression()]
      S_train_5, S_test_5 = stacking(
          models,
          X_train,
          y_train,
          X_test,
          regression=True,
          n_folds=n_folds,
          shuffle=False,
          mode="oof_pred",
          random_state=0,
          verbose=0,
      )
  
      models = [LinearRegression()]
      S_train_6, S_test_6 = stacking(
          models,
          X_train,
          y_train,
          X_test,
          regression=True,
          n_folds=n_folds,
          shuffle=False,
          mode="oof_pred",
          random_state=0,
          verbose=1,
      )
  
      models = [LinearRegression()]
      S_train_7, S_test_7 = stacking(
          models,
          X_train,
          y_train,
          X_test,
          regression=True,
          n_folds=n_folds,
          shuffle=False,
          mode="oof_pred",
          random_state=0,
          verbose=2,
      )
  
      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)
  
      assert_array_equal(S_train_1, S_train_4)
      assert_array_equal(S_test_1, S_test_4)
  
      assert_array_equal(S_train_1, S_train_5)
      assert_array_equal(S_test_1, S_test_5)
  
      assert_array_equal(S_train_1, S_train_6)
      assert_array_equal(S_test_1, S_test_6)
  
      assert_array_equal(S_train_1, S_train_7)
      assert_array_equal(S_test_1, S_test_7)
  
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
  from numpy.testing import assert_raises
  from numpy.testing import assert_warns
  import os
  import glob
  import numpy
  from scipy.sparse import csr_matrix
  from scipy.sparse import csc_matrix
  from scipy.sparse import coo_matrix
  from sklearn.model_selection import cross_val_predict
  from sklearn.model_selection import cross_val_score
  from sklearn.model_selection import KFold
  from sklearn.datasets import load_boston
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import make_scorer
  from sklearn.linear_model import LinearRegression
  from sklearn.linear_model import Ridge
  from vecstack import stacking
  from vecstack.core import model_action
label_desc: Use image node_id 14 for calling the TestFuncRegression obj's underlying member method code with example usage: obj.test_oof_pred_mode_verbose_1() without return types