code: def test_lasso_gives_lstsq_solution():
      # Test that Lars Lasso gives least square solution at the end
      # of the path
      _, _, coef_path_ = linear_model.lars_path(X, y, method="lasso")
      coef_lstsq = np.linalg.lstsq(X, y)[0]
      assert_array_almost_equal(coef_lstsq, coef_path_[:, -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 8 for calling a global function with example usage: test_lasso_gives_lstsq_solution() without return types