code: def test_get_feature_names_out():
      """Check get_feature_names_out uses class name as prefix."""
  
      est = LinearDiscriminantAnalysis().fit(X, y)
      names_out = est.get_feature_names_out()
  
      class_name_lower = "LinearDiscriminantAnalysis".lower()
      expected_names_out = np.array(
          [
              f"{class_name_lower}{i}"
              for i in range(est.explained_variance_ratio_.shape[0])
          ],
          dtype=object,
      )
      assert_array_equal(names_out, expected_names_out)
  
imports: import numpy
  import pytest
  from scipy import linalg
  from sklearn.cluster import KMeans
  from sklearn.covariance import LedoitWolf, ShrunkCovariance, ledoit_wolf
  from sklearn.datasets import make_blobs
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis, _cov
  from sklearn.preprocessing import StandardScaler
  from sklearn.utils import _IS_WASM, check_random_state
  from sklearn.utils._testing import _convert_container, assert_allclose, assert_almost_equal, assert_array_almost_equal, assert_array_equal
label_desc: Use image node_id 23 for calling a global function with example usage: test_get_feature_names_out() without return types