code: def test_mfista_solver_graph_net_no_l1_term():
      w = np.zeros(2)
      X = np.array([[1, 0], [0, 4]])
      y = np.array([-10, 20])
  
      def f1(w):
          return 0.5 * np.dot(np.dot(X, w) - y, np.dot(X, w) - y)
  
      def f1_grad(w):
          return np.dot(X.T, np.dot(X, w) - y)
  
      def f2_prox(w, step_size, *args, **kwargs):
          return w, dict(converged=True)
  
      lipschitz_constant = _squared_loss_derivative_lipschitz_constant(
          X, (np.eye(2) == 1).astype(bool), 1
      )
      estimate_solution, _, _ = mfista(
          f1_grad,
          f2_prox,
          f1,
          lipschitz_constant,
          w.size,
          tol=1e-8,
          verbose=0,
      )
  
      solution = np.array([-10, 5])
  
      assert_almost_equal(estimate_solution, solution, decimal=4)
  
imports: import numpy
  import pytest
  import scipy
  from nibabel import Nifti1Image
  from numpy.testing import assert_almost_equal
  from scipy import linalg
  from nilearn.decoding._objective_functions import divergence, gradient
  from nilearn.decoding.space_net import BaseSpaceNet
  from nilearn.decoding.space_net_solvers import _graph_net_adjoint_data_function, _graph_net_data_function, _logistic_data_loss_and_spatial_grad, _logistic_data_loss_and_spatial_grad_derivative, _logistic_derivative_lipschitz_constant, _squared_loss_and_spatial_grad, _squared_loss_and_spatial_grad_derivative, _squared_loss_derivative_lipschitz_constant, mfista
  from nilearn.decoding.tests._testing import create_graph_net_simulation_data
  from .test_same_api import to_niimgs
label_desc: Use image node_id 13 for calling a global function with example usage: test_mfista_solver_graph_net_no_l1_term() and returns: unknown, np, w,  dict