code: def test_simple_precomputed():
      # The same, with precomputed Gram matrix
  
      _, _, coef_path_ = linear_model.lars_path(
          X, y, Gram=G, method="lar"
      )
  
      for i, coef_ in enumerate(coef_path_.T):
          res = y - np.dot(X, coef_)
          cov = np.dot(X.T, res)
          C = np.max(abs(cov))
          eps = 1e-3
          ocur = len(cov[C - eps < abs(cov)])
          if i < X.shape[1]:
              assert ocur == i + 1
          else:
              # no more than max_pred variables can go into the active set
              assert ocur == X.shape[1]
  
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 2 for calling a global function with example usage: test_simple_precomputed() without return types