code: def test_lipschitz_constant_loss_mse(rng):
      X, _, _, mask = _make_data(rng=rng, masked=True)
      l1_ratio = 1.0
      alpha = 0.1
      mask = np.ones(X.shape[1]).astype(bool)
      grad_weight = alpha * X.shape[0] * (1.0 - l1_ratio)
  
      a = _squared_loss_derivative_lipschitz_constant(
          X, mask, grad_weight
      )
      b = spectral_norm_squared(X)
  
      assert_almost_equal(a, b)
  
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 4 for calling a global function with example usage: test_lipschitz_constant_loss_mse(rng) without return types