code: def test_LassoCV(sparse_X, fit_intercept, positive):
      """Test that our LassoCV behaves like sklearn's LassoCV."""
  
      X, y = build_dataset(
          n_samples=20, n_features=30, sparse_X=sparse_X
      )
      params = dict(
          eps=0.05,
          n_alphas=10,
          tol=1e-10,
          cv=2,
          fit_intercept=fit_intercept,
          positive=positive,
          n_jobs=-1,
      )
  
      clf = LassoCV(**params)
      clf.fit(X, y)
  
      clf2 = sklearn_LassoCV(**params, max_iter=10000)
      clf2.fit(X, y)
  
      assert_allclose(
          clf.mse_path_, clf2.mse_path_, rtol=1e-3, atol=1e-4
      )
      assert_allclose(clf.alpha_, clf2.alpha_)
      assert_allclose(clf.coef_, clf2.coef_, atol=1e-5)
  
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 4 for calling a global function with example usage: test_LassoCV(sparse_X, fit_intercept, positive) without return types