code: def test_screening_space_net():
      size = 4
      X_, *_ = create_graph_net_simulation_data(
          snr=1.0, n_samples=10, size=size, n_points=5, random_state=42
      )
      _, mask = to_niimgs(X_, [size] * 3)
  
      for verbose in [0, 2]:
          with pytest.warns(UserWarning):
              screening_percentile = adjust_screening_percentile(
                  10, mask, verbose
              )
      with pytest.warns(UserWarning):
          screening_percentile = adjust_screening_percentile(10, mask)
      # We gave here a very small mask, judging by standards of brain size
      # thus the screening_percentile_ corrected for brain size should
      # be 100%
      assert screening_percentile == 100
  
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 6 for calling a global function with example usage: test_screening_space_net() without return types