code: def test_convergence_warning():
      X, y = build_dataset(n_samples=10, n_features=10)
      tol = 1e-16  # very small, not enough iterations below
      alpha_max = np.max(np.abs(X.T.dot(y))) / X.shape[0]
      clf = Lasso(alpha_max / 100, max_iter=1, max_epochs=1, tol=tol)
  
      with warnings.catch_warnings(record=True) as w:
          # Cause all warnings to always be triggered.
          warnings.simplefilter("always")
          clf.fit(X, y)
          assert len(w) >= 1
          assert issubclass(w[-1].category, ConvergenceWarning)
  
imports: import warnings
  from itertools import product
  import numpy
  from numpy.linalg import norm
  from numpy.testing import assert_allclose, assert_array_less, assert_array_equal
  import pytest
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.linear_model import LassoCV, Lasso, lasso_path
  from celer import celer_path
  from celer.dropin_sklearn import Lasso, LassoCV
  from celer.utils.testing import build_dataset
label_desc: Use image node_id 2 for calling a global function with example usage: test_convergence_warning() without return types