code: def test_warm_start():
      """Test Lasso path convergence."""
      X, y = build_dataset(n_samples=100, n_features=100, sparse_X=True)
      n_samples, n_features = X.shape
      alpha_max = np.max(np.abs(X.T.dot(y))) / n_samples
      n_alphas = 10
      alphas = alpha_max * np.logspace(0, -2, n_alphas)
  
      reg1 = Lasso(tol=1e-6, warm_start=True, p0=10)
      reg1.coef_ = np.zeros(n_features)
  
      for alpha in alphas:
          reg1.set_params(alpha=alpha)
          reg1.fit(X, y)
          # refitting with warm start should take less than 2 iters:
          reg1.fit(X, y)
          # hack because assert_array_less does strict comparison...
          assert_array_less(reg1.n_iter_, 2.01)
  
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 8 for calling a global function with example usage: test_warm_start() without return types