code: def test_linear_regression_positive_vs_nonpositive(setup):
      # Test differences with LinearRegression when positive=False.
      X, y = make_sparse_uncorrelated(random_state=0)
  
      # reg = LinearRegression(positive=True)
      reg = sklearn_LR(positive=True)
      reg.fit(X, y)
      regn = LinearRegression(positive=False)
      regn.fit(X, y)
  
      assert np.mean(((reg.coef_ - regn.coef_) ** 2).to_numpy()) > 1e-3
  
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 10 for calling a global function with example usage: test_linear_regression_positive_vs_nonpositive(setup) without return types