code: def test_lasso_lars_vs_R_implementation():
      # Test that sklearn LassoLars implementation agrees with the LassoLars
      # implementation available in R (lars library) when fit_intercept=False.
  
      # Let's generate the data used in the bug report 7778
      y = np.array(
          [
              -6.45006793,
              -3.51251449,
              -8.52445396,
              6.12277822,
              -19.42109366,
          ]
      )
      x = np.array(
          [
              [0.47299829, 0, 0, 0, 0],
              [0.08239882, 0.85784863, 0, 0, 0],
              [0.30114139, -0.07501577, 0.80895216, 0, 0],
              [-0.01460346, -0.1015233, 0.0407278, 0.80338378, 0],
              [
                  -0.69363927,
                  0.06754067,
                  0.18064514,
                  -0.0803561,
                  0.40427291,
              ],
          ]
      )
  
      X = x.T
  
      # The R result was obtained using the following code:
      #
      # library(lars)
      # model_lasso_lars = lars(X, t(y), type="lasso", intercept=FALSE,
      #                         trace=TRUE, normalize=FALSE)
      # r = t(model_lasso_lars$beta)
      #
  
      r = np.array(
          [
              [
                  0,
                  0,
                  0,
                  0,
                  0,
                  -79.810362809499026,
                  -83.528788732782829,
                  -83.777653739190711,
                  -83.784156932888934,
                  -84.033390591756657,
              ],
              [
                  0,
                  0,
                  0,
                  0,
                  -0.476624256777266,
                  0,
                  0,
                  0,
                  0,
                  0.025219751009936,
              ],
              [
                  0,
                  -3.577397088285891,
                  -4.702795355871871,
                  -7.016748621359461,
                  -7.614898471899412,
                  -0.336938391359179,
                  0,
                  0,
                  0.001213370600853,
                  0.048162321585148,
              ],
              [
                  0,
                  0,
                  0,
                  2.231558436628169,
                  2.723267514525966,
                  2.811549786389614,
                  2.813766976061531,
                  2.817462468949557,
                  2.817368178703816,
                  2.816221090636795,
              ],
              [
                  0,
                  0,
                  -1.218422599914637,
                  -3.457726183014808,
                  -4.021304522060710,
                  -45.827461592423745,
                  -47.776608869312305,
                  -47.911561610746404,
                  -47.914845922736234,
                  -48.039562334265717,
              ],
          ]
      )
  
      model_lasso_lars = linear_model.LassoLars(
          alpha=0, fit_intercept=False
      )
      model_lasso_lars.fit(X, y)
      skl_betas = model_lasso_lars.coef_path_
  
      assert_array_almost_equal(r, skl_betas, decimal=12)
  
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 31 for calling a global function with example usage: test_lasso_lars_vs_R_implementation() without return types