code: def test_crop_mask(rng):
      mask = np.zeros((3, 4, 5), dtype=bool)
      box = mask[:2, :3, :4]
      box[rng.random(box.shape) < 3.0] = 1  # mask covers 30% of brain
      idx = np.where(mask)
  
      assert idx[1].max() < 3
      tight_mask = _crop_mask(mask)
      assert mask.sum() == tight_mask.sum()
      assert np.prod(tight_mask.shape) <= np.prod(box.shape)
  
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 14 for calling a global function with example usage: test_crop_mask(rng) without return types