code: def test_predict_meta_features():
      lr = LinearRegression()
      svr_rbf = SVR(kernel="rbf", gamma="auto")
      ridge = Ridge(random_state=1)
      stregr = StackingCVRegressor(
          regressors=[lr, ridge], meta_regressor=svr_rbf
      )
      X_train, X_test, y_train, y_test = train_test_split(
          X2, y, test_size=0.3
      )
      stregr.fit(X_train, y_train)
      test_meta_features = stregr.predict(X_test)
      assert test_meta_features.shape[0] == X_test.shape[0]
  
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 9 for calling a global function with example usage: test_predict_meta_features() without return types