code: def test_handle_missing_return_nan_test(self):
      X = pd.DataFrame({"city": ["chicago", "los angeles", "chicago"]})
      X_np = pd.DataFrame({"city": ["chicago", "los angeles", np.NaN]})
      X_pd = pd.DataFrame(
          {"city": ["chicago", "los angeles", pd.NA]}, dtype="string"
      )
      y = pd.Series([1, 0, 1])
  
      for encoder_name in set(encoders.__all__) - {
          "HashingEncoder"
      }:  # HashingEncoder supports new values by design -> excluded
          for X_na in (X_np, X_pd):
              with self.subTest(encoder_name=encoder_name):
                  enc = getattr(encoders, encoder_name)(
                      handle_missing="return_nan"
                  )
                  result = enc.fit(X, y).transform(X_na).iloc[2, :]
  
              if len(result) == 1:
                  self.assertTrue(result.isna().all())
              else:
                  self.assertTrue(result[1:].isna().all())
  
imports: import warnings
  from datetime import timedelta
  import numpy
  from numpy.testing import assert_array_equal
  import pandas
  import sklearn
  import tests.helpers
  from sklearn.utils.estimator_checks import check_transformer_general, check_transformers_unfitted, check_n_features_in
  from sklearn.compose import ColumnTransformer
  from unittest import TestCase
  from copy import deepcopy
  import category_encoders
label_desc: Use image node_id 11 for calling the TestEncoders obj's underlying member method code with example usage: obj.test_handle_missing_return_nan_test() without return types