code: def test_space_net_alpha_grid_pure_spatial(rng, is_classif):
      X = rng.standard_normal((10, 100))
      y = np.arange(X.shape[0])
  
      assert not np.any(
          np.isnan(
              _space_net_alpha_grid(
                  X, y, l1_ratio=0.0, logistic=is_classif
              )
          )
      )
  
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 18 for calling a global function with example usage: test_space_net_alpha_grid_pure_spatial(rng, is_classif) without return types