code: def test_linear_regression_positive_vs_nonpositive_when_positive(
      global_random_seed,
  ):
      # Test LinearRegression fitted coefficients
      # when the problem is positive.
      rng = np.random.RandomState(global_random_seed)
      n_samples = 200
      n_features = 4
      X = rng.rand(n_samples, n_features)
      y = X[:, 0] + 2 * X[:, 1] + 3 * X[:, 2] + 1.5 * X[:, 3]
  
      reg = LinearRegression(positive=True)
      reg.fit(X, y)
      regn = LinearRegression(positive=False)
      regn.fit(X, y)
  
      assert np.mean((reg.coef_ - regn.coef_) ** 2) < 1e-6
  
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 13 for calling a global function with example usage: test_linear_regression_positive_vs_nonpositive_when_positive(global_random_seed) without return types