code: def test_tv_regression_3D_image_doesnt_crash(rng, l1_ratio):
      dim = (3, 4, 5)
      W_init = np.zeros(dim)
      W_init[2:3, 3:, 1:3] = 1
  
      n = 10
      p = dim[0] * dim[1] * dim[2]
      X = np.ones((n, 1)) + W_init.ravel().T
      X += rng.standard_normal((n, p))
      y = np.dot(X, W_init.ravel())
      alpha = 1.0
      X, mask = to_niimgs(X, dim)
  
      BaseSpaceNet(
          mask=mask,
          alphas=alpha,
          l1_ratios=l1_ratio,
          penalty="tv-l1",
          is_classif=False,
          max_iter=10,
          verbose=0,
      ).fit(X, y)
  
imports: from functools import partial
  import numpy
  import pytest
  from numpy.testing import assert_almost_equal, assert_array_equal
  from scipy import linalg
  from sklearn.datasets import load_iris
  from sklearn.linear_model import Lasso, LogisticRegression
  from sklearn.linear_model._coordinate_descent import _alpha_grid
  from sklearn.metrics import accuracy_score
  from nilearn._utils.param_validation import adjust_screening_percentile
  from nilearn.decoding.space_net import BaseSpaceNet, SpaceNetClassifier, SpaceNetRegressor, _crop_mask, _EarlyStoppingCallback, _space_net_alpha_grid, _univariate_feature_screening, path_scores
  from nilearn.decoding.space_net_solvers import graph_net_logistic, graph_net_squared_loss
  from nilearn.decoding.tests._testing import create_graph_net_simulation_data
  from nilearn.image import get_data
  from .test_same_api import to_niimgs
label_desc: Use image node_id 10 for calling a global function with example usage: test_tv_regression_3D_image_doesnt_crash(rng, l1_ratio) without return types