docstr: test inverse_transform having Nan in train and handle missing expected a result with Nan
docstr_tok: ["test","inverse_transform","having","Nan","in","train","and","handle","missing","expected","a","result","with","Nan"]
code: def test_inverse_transform_14(self):
      """
      test inverse_transform having Nan in train and handle missing expected a result with Nan
      """
      train = pd.DataFrame({"city": ["chicago", np.nan]})
  
      enc = ce.BaseNEncoder(
          handle_missing="return_nan", 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 14 for calling the TestInverseTransformCaterogyEncoder obj's underlying member method code with example usage: obj.test_inverse_transform_14() without return types