code: def test_GroupLasso_Lasso_equivalence(sparse_X, fit_intercept):
      """Check that GroupLasso with groups of size 1 gives Lasso."""
      n_features = 1000
      X, y = build_dataset(
          n_samples=100, n_features=n_features, sparse_X=sparse_X
      )
      alpha_max = norm(X.T @ y, ord=np.inf) / len(y)
      alpha = alpha_max / 10
      clf = Lasso(
          alpha, tol=1e-12, fit_intercept=fit_intercept, verbose=0
      )
      clf.fit(X, y)
      # take groups of size 1:
      clf1 = GroupLasso(
          alpha=alpha,
          groups=1,
          tol=1e-12,
          fit_intercept=fit_intercept,
          verbose=0,
      )
      clf1.fit(X, y)
  
      np.testing.assert_allclose(clf1.coef_, clf.coef_, atol=1e-6)
      np.testing.assert_allclose(
          clf1.intercept_, clf.intercept_, rtol=1e-4
      )
  
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 1 for calling a global function with example usage: test_GroupLasso_Lasso_equivalence(sparse_X, fit_intercept) without return types