code: def test_Lasso(sparse_X, fit_intercept, positive):
      """Test that our Lasso class behaves as sklearn's Lasso."""
      X, y = build_dataset(
          n_samples=20, n_features=30, sparse_X=sparse_X
      )
      if not positive:
          alpha_max = norm(X.T.dot(y), ord=np.inf) / X.shape[0]
      else:
          alpha_max = X.T.dot(y).max() / X.shape[0]
  
      alpha = alpha_max / 2.0
      params = dict(
          alpha=alpha,
          fit_intercept=fit_intercept,
          tol=1e-10,
          positive=positive,
      )
      clf = Lasso(**params)
      clf.fit(X, y)
  
      clf2 = sklearn_Lasso(**params)
      clf2.fit(X, y)
      assert_allclose(clf.coef_, clf2.coef_, rtol=1e-5)
      if fit_intercept:
          assert_allclose(clf.intercept_, clf2.intercept_)
  
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 5 for calling a global function with example usage: test_Lasso(sparse_X, fit_intercept, positive) without return types