code: def test_raises_value_error_if_positive_and_sparse(setup):
      error_msg = re.escape(
          "A sparse tensor was passed, but dense "
          "data is required. Use X.todense() to "
          "convert to a dense tensor."
      )
      # X must not be sparse if positive == True
      X = sparse.eye(10)
      y = np.ones(10)
  
      reg = LinearRegression(positive=True)
  
      with pytest.raises(TypeError, match=error_msg):
          reg.fit(X, y)
  
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 3 for calling a global function with example usage: test_raises_value_error_if_positive_and_sparse(setup) without return types