code: def test_space_net_alpha_grid(
      rng, is_classif, l1_ratio, n_alphas, n_samples=4, n_features=3
  ):
      X = rng.standard_normal((n_samples, n_features))
      y = np.arange(n_samples)
  
      alpha_max = np.max(np.abs(np.dot(X.T, y))) / l1_ratio
  
      if n_alphas == 1:
          assert_almost_equal(
              _space_net_alpha_grid(
                  X,
                  y,
                  n_alphas=n_alphas,
                  l1_ratio=l1_ratio,
                  logistic=is_classif,
              ),
              alpha_max,
          )
  
      alphas = _space_net_alpha_grid(
          X,
          y,
          n_alphas=n_alphas,
          l1_ratio=l1_ratio,
          logistic=is_classif,
      )
  
      assert_almost_equal(alphas.max(), alpha_max)
      assert_almost_equal(n_alphas, len(alphas))
  
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 1 for calling a global function with example usage: test_space_net_alpha_grid(rng, is_classif, l1_ratio, n_alphas, n_samples, n_features) without return types