code: def test_targets_in_y_space_net_regressor():
      """Raise an error when unique targets given in y are single."""
      iris = load_iris()
      X, _ = iris.data, iris.target
      y = np.ones(iris.target.shape)
  
      imgs, mask = to_niimgs(X, (2, 2, 2))
      regressor = SpaceNetRegressor(mask=mask, verbose=0)
  
      with pytest.raises(
          ValueError,
          match="The given input y must have at least 2 targets",
      ):
          regressor.fit(imgs, y)
  
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 24 for calling a global function with example usage: test_targets_in_y_space_net_regressor() without return types