code: def test_all_precomputed():
      # Test that lars_path with precomputed Gram and Xy gives the right answer
      G = np.dot(X.T, X)
      Xy = np.dot(X.T, y)
      for method in "lar", "lasso":
          output = linear_model.lars_path(X, y, method=method)
          output_pre = linear_model.lars_path(
              X, y, Gram=G, Xy=Xy, method=method
          )
          for expected, got in zip(output, output_pre):
              assert_array_almost_equal(expected, got)
  
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 6 for calling a global function with example usage: test_all_precomputed() without return types