code: def test_MultiTaskLassoCV():
      """Test that our MultitaskLassoCV behaves like sklearn's."""
      X, y = build_dataset(n_samples=30, n_features=50, n_targets=3)
  
      params = dict(
          eps=1e-2,
          n_alphas=10,
          tol=1e-12,
          cv=2,
          n_jobs=1,
          fit_intercept=False,
          verbose=0,
      )
  
      clf = MultiTaskLassoCV(**params)
      clf.fit(X, y)
  
      clf2 = sklearn_MultiTaskLassoCV(**params)
      clf2.max_iter = 10000  # increase max_iter bc of low tol
      clf2.fit(X, y)
  
      np.testing.assert_allclose(
          clf.mse_path_, clf2.mse_path_, atol=1e-4, rtol=1e-04
      )
      np.testing.assert_allclose(
          clf.alpha_, clf2.alpha_, atol=1e-4, rtol=1e-04
      )
      np.testing.assert_allclose(
          clf.coef_, clf2.coef_, atol=1e-4, rtol=1e-04
      )
  
imports: import pytest
  import itertools
  import numpy
  from numpy.linalg import norm
  from numpy.testing import assert_allclose, assert_array_less, assert_array_equal
  from sklearn.utils.estimator_checks import check_estimator
  from sklearn.linear_model import MultiTaskLassoCV
  from sklearn.linear_model import MultiTaskLasso
  from sklearn.linear_model import lasso_path
  from celer import Lasso, GroupLasso, GroupLassoCV, MultiTaskLasso, MultiTaskLassoCV
  from celer.homotopy import celer_path, mtl_path, _grp_converter
  from celer.group_fast import dnorm_grp
  from celer.utils.testing import build_dataset
label_desc: Use image node_id 5 for calling a global function with example usage: test_MultiTaskLassoCV() without return types