code: def test_raises_value_error_if_positive_and_sparse():
      error_msg = (
          "Sparse data was passed for X, but dense data is required."
      )
      # 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 warnings
  import numpy
  import pytest
  from scipy import linalg, sparse
  from sklearn.datasets import load_iris, make_regression, make_sparse_uncorrelated
  from sklearn.linear_model import LinearRegression
  from sklearn.linear_model._base import _preprocess_data, _rescale_data, make_dataset
  from sklearn.preprocessing import add_dummy_feature
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS, LIL_CONTAINERS
label_desc: Use image node_id 3 for calling a global function with example usage: test_raises_value_error_if_positive_and_sparse() without return types