code: def test_multivariate_class():
      lr = LinearRegression()
      ridge = Ridge(random_state=1)
      meta = LinearRegression()
      stregr = StackingRegressor(
          regressors=[lr, ridge], meta_regressor=meta, multi_output=True
      )
      stregr.fit(X2, y2).predict(X2)
      mse = 0.12
      got = np.mean((stregr.predict(X2) - y2) ** 2.0)
      assert round(got, 2) == mse, got
  
imports: import numpy
  import pytest
  from numpy.testing import assert_almost_equal
  from packaging.version import Version
  from scipy import sparse
  from sklearn import __version__
  from sklearn.base import clone
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.linear_model import LinearRegression, Ridge
  from sklearn.model_selection import GridSearchCV, train_test_split
  from sklearn.neighbors import KNeighborsRegressor
  from sklearn.svm import SVR
  from mlxtend.externals.estimator_checks import NotFittedError
  from mlxtend.regressor import StackingRegressor
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 3 for calling a global function with example usage: test_multivariate_class() without return types