code: def test_one_iteration_alpha_max():
      n_samples, n_features = 100, 50
      X, y = build_dataset(n_samples, n_features)
  
      alpha_max = norm(X.T @ y, ord=np.inf) / n_samples
      m = 5
      model = Lasso(alpha=m * alpha_max, fit_intercept=False)
      model.fit(X, y)
  
      assert_array_equal(model.coef_, np.zeros(n_features))
      # solver exits right after computing first duality gap:
      np.testing.assert_equal(model.n_iter_, 1)
  
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 11 for calling a global function with example usage: test_one_iteration_alpha_max() without return types