docstr: Test inverse_transform having Nan in train and handle missing value expect returned with nan_Ordinal
docstr_tok: ["Test","inverse_transform","having","Nan","in","train","and","handle","missing","value","expect","returned","with","nan_Ordinal"]
code: def test_inverse_transform_5(self):
      """
      Test inverse_transform having Nan in train and handle missing value expect returned with nan_Ordinal
      """
      train = pd.DataFrame({"city": ["chicago", np.nan]})
      enc = ce.OrdinalEncoder(
          handle_missing="value", handle_unknown="value"
      )
      result = enc.fit_transform(train)
      original = inverse_transform(result, enc)
      pd.testing.assert_frame_equal(train, original)
  
imports: import unittest
  import pandas
  import numpy
  import category_encoders
  import catboost
  import lightgbm
  import xgboost
  from shapash.utils.transform import inverse_transform, apply_preprocessing, get_col_mapping_ce
  from sklearn.ensemble import GradientBoostingClassifier
label_desc: Use image node_id 5 for calling the TestInverseTransformCaterogyEncoder obj's underlying member method code with example usage: obj.test_inverse_transform_5() without return types