code: def test_lassolarsic_alpha_selection(criterion):
      """Check that we properly compute the AIC and BIC score.
  
      In this test, we reproduce the example of the Fig. 2 of Zou et al.
      (reference [1] in LassoLarsIC) In this example, only 7 features should be
      selected.
      """
      model = make_pipeline(
          StandardScaler(), LassoLarsIC(criterion=criterion)
      )
      model.fit(X, y)
  
      best_alpha_selected = np.argmin(model[-1].criterion_)
      assert best_alpha_selected == 7
  
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 39 for calling a global function with example usage: test_lassolarsic_alpha_selection(criterion) without return types