code: def test_spca_feature_names_out(SPCA):
      """Check feature names out for *SparsePCA."""
      rng = np.random.RandomState(0)
      n_samples, n_features = 12, 10
      X = rng.randn(n_samples, n_features)
  
      model = SPCA(n_components=4).fit(X)
      names = model.get_feature_names_out()
  
      estimator_name = SPCA.__name__.lower()
      assert_array_equal(
          [f"{estimator_name}{i}" for i in range(4)], names
      )
  
imports: import sys
  import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from sklearn.decomposition import PCA, MiniBatchSparsePCA, SparsePCA
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, if_safe_multiprocessing_with_blas
label_desc: Use image node_id 15 for calling a global function with example usage: test_spca_feature_names_out(SPCA) without return types