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