code: def test_singular_matrix():
      # Test when input is a singular matrix
      X1 = np.array([[1, 1.0], [1.0, 1.0]])
      y1 = np.array([1, 1])
      _, _, coef_path = linear_model.lars_path(X1, y1)
      assert_array_almost_equal(coef_path.T, [[0, 0], [1, 0]])
  
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 14 for calling a global function with example usage: test_singular_matrix() without return types