code: def test_imputers_feature_names_out_pandas(imputer, add_indicator):
      """Check feature names out for imputers."""
      pd = pytest.importorskip("pandas")
      marker = np.nan
      imputer = imputer.set_params(
          add_indicator=add_indicator, missing_values=marker
      )
  
      X = np.array(
          [
              [marker, 1, 5, 3, marker, 1],
              [2, marker, 1, 4, marker, 2],
              [6, 3, 7, marker, marker, 3],
              [1, 2, 9, 8, marker, 4],
          ]
      )
      X_df = pd.DataFrame(X, columns=["a", "b", "c", "d", "e", "f"])
      imputer.fit(X_df)
  
      names = imputer.get_feature_names_out()
  
      if add_indicator:
          expected_names = [
              "a",
              "b",
              "c",
              "d",
              "f",
              "missingindicator_a",
              "missingindicator_b",
              "missingindicator_d",
              "missingindicator_e",
          ]
          assert_array_equal(expected_names, names)
      else:
          expected_names = ["a", "b", "c", "d", "f"]
          assert_array_equal(expected_names, names)
  
file_path: scikit-learn/sklearn/impute/tests/test_common.py
imports: import numpy
  import pytest
  from sklearn.experimental import enable_iterative_imputer
  from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer
  from sklearn.utils._testing import assert_allclose, assert_allclose_dense_sparse, assert_array_equal
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 7 for calling a global function with example usage: test_imputers_feature_names_out_pandas(imputer, add_indicator) without return types