code: def test_ElasticNetCV(fit_intercept):
      n_samples, n_features = 30, 100
      X, y = build_dataset(n_samples, n_features, sparse_X=False)
  
      params = dict(
          l1_ratio=[0.7, 0.8, 0.5],
          eps=0.05,
          n_alphas=10,
          tol=1e-10,
          cv=2,
          fit_intercept=fit_intercept,
          n_jobs=-1,
      )
  
      clf = ElasticNetCV(**params)
      clf.fit(X, y)
  
      clf2 = sk_ElasticNetCV(**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)
      assert_allclose(clf.l1_ratio_, clf2.l1_ratio_, atol=1e-5)
  
file_path: celer/celer/tests/test_enet.py
imports: from itertools import product
  import pytest
  import numpy
  from numpy.linalg import norm
  from numpy.testing import assert_allclose, assert_array_less, assert_equal
  from sklearn.linear_model import enet_path, ElasticNet, ElasticNetCV
  from celer import Lasso, ElasticNet, celer_path, ElasticNetCV
  from celer.utils.testing import build_dataset
label_desc: Use image node_id 7 for calling a global function with example usage: test_ElasticNetCV(fit_intercept) without return types