code: def test_lars_precompute(classifier):
      # Check for different values of precompute
      G = np.dot(X.T, X)
  
      clf = classifier(precompute=G)
      output_1 = ignore_warnings(clf.fit)(X, y).coef_
      for precompute in [True, False, "auto", None]:
          clf = classifier(precompute=precompute)
          output_2 = clf.fit(X, y).coef_
          assert_array_almost_equal(output_1, output_2, decimal=8)
  
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 13 for calling a global function with example usage: test_lars_precompute(classifier) without return types