code: def test_coef_shape(penalty, cls):
      iris = load_iris()
      X, y = iris.data, iris.target
      X, mask = to_niimgs(X, (2, 2, 2))
  
      model = cls(
          mask=mask, max_iter=3, penalty=penalty, alphas=1.0, verbose=0
      ).fit(X, y)
  
      assert model.coef_.ndim == 2
  
imports: import numpy
  import pytest
  from nibabel import Nifti1Image
  from numpy.testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from sklearn.datasets import load_iris
  from nilearn.decoding._objective_functions import logistic_loss_lipschitz_constant, spectral_norm_squared, squared_loss, squared_loss_grad
  from nilearn.decoding.space_net import BaseSpaceNet, SpaceNetClassifier, SpaceNetRegressor
  from nilearn.decoding.space_net_solvers import _logistic_derivative_lipschitz_constant, _squared_loss_and_spatial_grad, _squared_loss_and_spatial_grad_derivative, _squared_loss_derivative_lipschitz_constant, graph_net_logistic, graph_net_squared_loss, tvl1_solver
  from nilearn.image import get_data
  from nilearn.masking import unmask_from_to_3d_array
label_desc: Use image node_id 11 for calling a global function with example usage: test_coef_shape(penalty, cls) without return types