code: def test_lassolarsic_noise_variance(fit_intercept):
      """Check the behaviour when `n_samples` < `n_features` and that one needs
      to provide the noise variance."""
      rng = np.random.RandomState(0)
      X, y = datasets.make_regression(
          n_samples=10, n_features=11 - fit_intercept, random_state=rng
      )
  
      model = make_pipeline(
          StandardScaler(), LassoLarsIC(fit_intercept=fit_intercept)
      )
  
      err_msg = (
          "You are using LassoLarsIC in the case where the number of samples is smaller"
          " than the number of features"
      )
      with pytest.raises(ValueError, match=err_msg):
          model.fit(X, y)
  
      model.set_params(lassolarsic__noise_variance=1.0)
      model.fit(X, y).predict(X)
  
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 40 for calling a global function with example usage: test_lassolarsic_noise_variance(fit_intercept) without return types