docstr: test inverse_transform with multiple baseN
docstr_tok: ["test","inverse_transform","with","multiple","baseN"]
code: def test_inverse_transform_17(self):
      """
      test inverse_transform with multiple baseN
      """
      train = pd.DataFrame(
          {"city": ["chicago", "paris"], "state": ["US", "FR"]}
      )
      test = pd.DataFrame(
          {
              "city_0": [0, 1],
              "city_1": [1, 0],
              "state_0": [0, 1],
              "state_1": [1, 0],
          }
      )
      enc = ce.BaseNEncoder(
          cols=["city", "state"],
          handle_missing="value",
          handle_unknown="return_nan",
      )
      enc.fit(train)
      original = inverse_transform(test, enc)
      pd.testing.assert_frame_equal(original, train)
  
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 17 for calling the TestInverseTransformCaterogyEncoder obj's underlying member method code with example usage: obj.test_inverse_transform_17() without return types