docstr: test inv_transform_ct with multiple encoding and passthrough option
docstr_tok: ["test","inv_transform_ct","with","multiple","encoding","and","passthrough","option"]
code: def test_inv_transform_ct_2(self):
      """
      test inv_transform_ct with multiple encoding and passthrough option
      """
      train = pd.DataFrame(
          {
              "city": ["chicago", "paris"],
              "state": ["US", "FR"],
              "other": ["A", "B"],
          },
          index=["index1", "index2"],
      )
  
      enc = ColumnTransformer(
          transformers=[
              ("onehot_ce", ce.OneHotEncoder(), ["city", "state"]),
              ("onehot_skp", skp.OneHotEncoder(), ["city", "state"]),
          ],
          remainder="passthrough",
      )
      enc.fit(train)
      test = pd.DataFrame(
          {
              "city": ["chicago", "chicago", "paris"],
              "state": ["US", "FR", "FR"],
              "other": ["A", "B", "C"],
          },
          index=["index1", "index2", "index3"],
      )
  
      expected = pd.DataFrame(
          {
              "onehot_ce_city": ["chicago", "chicago", "paris"],
              "onehot_ce_state": ["US", "FR", "FR"],
              "onehot_skp_city": ["chicago", "chicago", "paris"],
              "onehot_skp_state": ["US", "FR", "FR"],
              "other": ["A", "B", "C"],
          },
          index=["index1", "index2", "index3"],
      )
  
      result = pd.DataFrame(enc.transform(test))
      result.columns = [
          "col1_0",
          "col1_1",
          "col2_0",
          "col2_1",
          "col3_0",
          "col3_1",
          "col4_0",
          "col4_1",
          "other",
      ]
      result.index = ["index1", "index2", "index3"]
      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 2 for calling the TestInverseTransformColumnsTransformer obj's underlying member method code with example usage: obj.test_inv_transform_ct_2() without return types