code: def test_lars_with_jitter(est):
      # Test that a small amount of jitter helps stability,
      # using example provided in issue #2746
  
      X = np.array(
          [[0.0, 0.0, 0.0, -1.0, 0.0], [0.0, -1.0, 0.0, 0.0, 0.0]]
      )
      y = [-2.5, -2.5]
      expected_coef = [0, 2.5, 0, 2.5, 0]
  
      # set to fit_intercept to False since target is constant and we want check
      # the value of coef. coef would be all zeros otherwise.
      est.set_params(fit_intercept=False)
      est_jitter = clone(est).set_params(jitter=10e-8, random_state=0)
  
      est.fit(X, y)
      est_jitter.fit(X, y)
  
      assert np.mean((est.coef_ - est_jitter.coef_) ** 2) > 0.1
      np.testing.assert_allclose(
          est_jitter.coef_, expected_coef, rtol=1e-3
      )
  
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 34 for calling a global function with example usage: test_lars_with_jitter(est) without return types