code: def test_graph_net_and_tv_same_for_pure_l1_another_test(
      standardize, decimal=1
  ):
      """Check that graph_net_solver and tvl1_solver give same results \
      when l1_ratio = 1."""
      dim = (3, 3, 3)
      X, y, _, mask = _make_data(masked=True, dim=dim)
      X, mask = to_niimgs(X, dim)
      alpha = 0.1
      l1_ratio = 1.0
      max_iter = 20
  
      sl = BaseSpaceNet(
          alphas=alpha,
          l1_ratios=l1_ratio,
          penalty="graph-net",
          max_iter=max_iter,
          mask=mask,
          is_classif=False,
          standardize=standardize,
          verbose=0,
      ).fit(X, y)
      tvl1 = BaseSpaceNet(
          alphas=alpha,
          l1_ratios=l1_ratio,
          penalty="tv-l1",
          max_iter=max_iter,
          mask=mask,
          is_classif=False,
          standardize=standardize,
          verbose=0,
      ).fit(X, y)
  
      assert_array_almost_equal(sl.coef_, tvl1.coef_, decimal=decimal)
  
imports: import numpy
  import pytest
  from nibabel import Nifti1Image
  from numpy.testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from sklearn.datasets import load_iris
  from nilearn.decoding._objective_functions import logistic_loss_lipschitz_constant, spectral_norm_squared, squared_loss, squared_loss_grad
  from nilearn.decoding.space_net import BaseSpaceNet, SpaceNetClassifier, SpaceNetRegressor
  from nilearn.decoding.space_net_solvers import _logistic_derivative_lipschitz_constant, _squared_loss_and_spatial_grad, _squared_loss_and_spatial_grad_derivative, _squared_loss_derivative_lipschitz_constant, graph_net_logistic, graph_net_squared_loss, tvl1_solver
  from nilearn.image import get_data
  from nilearn.masking import unmask_from_to_3d_array
label_desc: Use image node_id 10 for calling a global function with example usage: test_graph_net_and_tv_same_for_pure_l1_another_test(standardize, decimal) without return types