code: def test_predictions_from_sparse_matrix():
      lr = LinearRegression()
      svr_lin = SVR(kernel="linear", gamma="auto")
      ridge = Ridge(random_state=1)
      stregr = StackingRegressor(
          regressors=[svr_lin, lr], meta_regressor=ridge
      )
  
      # dense
      stregr.fit(X1, y)
      print(stregr.score(X1, y))
      assert round(stregr.score(X1, y), 2) == 0.61
  
      # sparse
      stregr.fit(sparse.csr_matrix(X1), y)
      print(stregr.score(X1, y))
      assert round(stregr.score(X1, y), 2) == 0.61
  
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 21 for calling a global function with example usage: test_predictions_from_sparse_matrix() without return types