code: def test_multivariate_class():
      lr = LinearRegression()
      ridge = Ridge(random_state=1)
      meta = LinearRegression()
      stregr = StackingCVRegressor(
          regressors=[lr, ridge],
          meta_regressor=meta,
          multi_output=True,
          random_state=0,
      )
      stregr.fit(X2, y2).predict(X2)
      mse = 0.13
      got = np.mean((stregr.predict(X2) - y2) ** 2.0)
      assert round(got, 2) == mse, got
  
imports: import random
  import numpy
  import pytest
  from packaging.version import Version
  from scipy import sparse
  from sklearn import __version__
  from sklearn.base import clone
  from sklearn.linear_model import Lasso, LinearRegression, Ridge
  from sklearn.model_selection import GridSearchCV, KFold, train_test_split
  from sklearn.neighbors import KNeighborsRegressor
  from sklearn.svm import SVR
  from mlxtend.externals.estimator_checks import NotFittedError
  from mlxtend.regressor import StackingCVRegressor
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 4 for calling a global function with example usage: test_multivariate_class() without return types