code: def test_lars_add_features():
      # assure that at least some features get added if necessary
      # test for 6d2b4c
      # Hilbert matrix
      n = 5
      H = 1.0 / (np.arange(1, n + 1) + np.arange(n)[:, np.newaxis])
      clf = linear_model.Lars(fit_intercept=False).fit(H, np.arange(n))
      assert np.all(np.isfinite(clf.coef_))
  
imports: import warnings
  import numpy
  import pytest
  from scipy import linalg
  from sklearn import datasets, linear_model
  from sklearn.base import clone
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.linear_model import Lars, LarsCV, LassoLars, LassoLarsCV, LassoLarsIC, lars_path
  from sklearn.linear_model._least_angle import _lars_path_residues
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.utils._testing import TempMemmap, assert_allclose, assert_array_almost_equal, ignore_warnings
label_desc: Use image node_id 21 for calling a global function with example usage: test_lars_add_features() without return types