docstr: test inv_tranform_ct with ordinal Encoder sklearn and drop option
docstr_tok: ["test","inv_tranform_ct","with","ordinal","Encoder","sklearn","and","drop","option"]
code: def test_inv_transform_ct_16(self):
      """
      test inv_tranform_ct with ordinal Encoder sklearn and drop option
      """
      y = pd.DataFrame(data=[0, 1], columns=["y"])
  
      train = pd.DataFrame(
          {
              "city": ["chicago", "paris"],
              "state": ["US", "FR"],
              "other": ["A", "B"],
          }
      )
  
      enc = ColumnTransformer(
          transformers=[
              ("ordinal", skp.OrdinalEncoder(), ["city", "state"])
          ],
          remainder="drop",
      )
      enc.fit(train, y)
      test = pd.DataFrame(
          {
              "city": ["chicago", "chicago", "paris"],
              "state": ["US", "FR", "FR"],
              "other": ["A", "B", "C"],
          }
      )
  
      expected = pd.DataFrame(
          {
              "ordinal_city": ["chicago", "chicago", "paris"],
              "ordinal_state": ["US", "FR", "FR"],
          }
      )
  
      result = pd.DataFrame(enc.transform(test))
      result.columns = ["col1", "col2"]
      original = inverse_transform(result, enc)
      pd.testing.assert_frame_equal(original, expected)
  
imports: import unittest
  import pandas
  import numpy
  import category_encoders
  from sklearn.compose import ColumnTransformer
  import sklearn.preprocessing
  import catboost
  import sklearn
  import lightgbm
  import xgboost
  from shapash.utils.transform import inverse_transform
  from shapash.utils.transform import apply_preprocessing
  from shapash.utils.columntransformer_backend import get_feature_names, get_names, get_list_features_names, get_col_mapping_ct
  from sklearn.ensemble import GradientBoostingClassifier
label_desc: Use image node_id 16 for calling the TestInverseTransformColumnsTransformer obj's underlying member method code with example usage: obj.test_inv_transform_ct_16() without return types