docstr: test get_col_mapping_ct with PowerTransformer Encoder Sklearn and passthrough option
docstr_tok: ["test","get_col_mapping_ct","with","PowerTransformer","Encoder","Sklearn","and","passthrough","option"]
code: def test_get_col_mapping_ct_10(self):
      """
      test get_col_mapping_ct with PowerTransformer Encoder Sklearn and passthrough option
      """
      enc = ColumnTransformer(
          transformers=[
              ("power", skp.PowerTransformer(), ["num1", "num2"])
          ],
          remainder="passthrough",
      )
  
      test = pd.DataFrame(
          {
              "num1": [0, 1, 1],
              "num2": [0, 2, 3],
              "other": ["A", "B", "C"],
          }
      )
  
      test_encoded = pd.DataFrame(enc.fit_transform(test))
      mapping = get_col_mapping_ct(enc, test_encoded)
      expected_mapping = {
          "power_num1": [0],
          "power_num2": [1],
          "other": [2],
      }
  
      self.assertDictEqual(mapping, expected_mapping)
  
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 44 for calling the TestInverseTransformColumnsTransformer obj's underlying member method code with example usage: obj.test_get_col_mapping_ct_10() without return types