code: def test_rank_deficient_design():
      # consistency test that checks that LARS Lasso is handling rank
      # deficient input data (with n_features < rank) in the same way
      # as coordinate descent Lasso
      y = [5, 0, 5]
      for X in (
          [[5, 0], [0, 5], [10, 10]],
          [[10, 10, 0], [1e-32, 0, 0], [0, 0, 1]],
      ):
          # To be able to use the coefs to compute the objective function,
          # we need to turn off normalization
          lars = linear_model.LassoLars(0.1)
          coef_lars_ = lars.fit(X, y).coef_
          obj_lars = 1.0 / (2.0 * 3.0) * linalg.norm(
              y - np.dot(X, coef_lars_)
          ) ** 2 + 0.1 * linalg.norm(coef_lars_, 1)
          coord_descent = linear_model.Lasso(0.1, tol=1e-6)
          coef_cd_ = coord_descent.fit(X, y).coef_
          obj_cd = (1.0 / (2.0 * 3.0)) * linalg.norm(
              y - np.dot(X, coef_cd_)
          ) ** 2 + 0.1 * linalg.norm(coef_cd_, 1)
          assert obj_lars < obj_cd * (1.0 + 1e-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 15 for calling a global function with example usage: test_rank_deficient_design() without return types