code: def test_MultiTaskLasso(fit_intercept):
      """Test that our MultiTaskLasso behaves as sklearn's."""
      X, Y = build_dataset(n_samples=20, n_features=30, n_targets=10)
      alpha_max = np.max(norm(X.T.dot(Y), axis=1)) / X.shape[0]
  
      alpha = alpha_max / 2.0
      params = dict(alpha=alpha, fit_intercept=fit_intercept, tol=1e-10)
      clf = MultiTaskLasso(**params)
      clf.verbose = 2
      clf.fit(X, Y)
  
      clf2 = sklearn_MultiTaskLasso(**params)
      clf2.fit(X, Y)
      np.testing.assert_allclose(clf.coef_, clf2.coef_, rtol=1e-5)
      if fit_intercept:
          np.testing.assert_allclose(clf.intercept_, clf2.intercept_)
  
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 6 for calling a global function with example usage: test_MultiTaskLasso(fit_intercept) without return types