code: def test_raise_notimplemented_when_positive(setup):
      error_msg = re.escape("Does not support positive coefficients!")
  
      X = [[1], [2]]
      y = [1, 2]
  
      reg = LinearRegression(positive=True)
      with pytest.raises(NotImplementedError, 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 20 for calling a global function with example usage: test_raise_notimplemented_when_positive(setup) without return types