code: def test_linear_regression_sparse_multiple_outcome(
      setup, random_state=0
  ):
      # Test multiple-outcome linear regressions with sparse data
      random_state = check_random_state(random_state)
      X, y = make_sparse_uncorrelated(random_state=random_state)
      X = sparse.coo_matrix(X)
      Y = np.vstack((y, y)).T
  
      ols = LinearRegression()
      error_msg = re.escape("Does not support sparse input!")
      with pytest.raises(NotImplementedError, match=error_msg):
          ols.fit(X, Y)
  
      error_msg = re.escape("Does not support sparse input!")
      with pytest.raises(NotImplementedError, match=error_msg):
          ols.fit(X, y.ravel())
  
imports: import re
  import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal, assert_array_equal, assert_almost_equal, assert_allclose
  from scipy import sparse
  from scipy import linalg
  from sklearn.datasets import make_sparse_uncorrelated, make_regression, load_iris
  from sklearn.linear_model import LinearRegression
  from sklearn.linear_model._base import make_dataset
  from sklearn.utils import check_random_state
  from ..None import LinearRegression
  from .._base import _preprocess_data, _rescale_data
label_desc: Use image node_id 9 for calling a global function with example usage: test_linear_regression_sparse_multiple_outcome(setup, random_state) without return types