code: def get_names(name, trans, column, column_transformer):
      """
      Allow to extract features names from one encoder of the ColumnTransformer.
      If the right names aren't available, It creates a list with customized names.
  
      Parameters
      ----------
      name: string
          String which indicates the name of the transformer.
      trans: sklearn encoder or category_encoders
          One of the encoder fitted through the ColumnTransformer.
      column: list
          List of features impacted by the specific transformer.
      column_transformer: sklearn ColumnTransformer
          The fitted ColumnTransformer containing the specific encoder.
  
      Returns
      -------
      list:
          List of returned features when specific transformer is applied.
      """
      if trans == "drop" or (
          hasattr(column, "__len__") and not len(column)
      ):
          return []
      if trans == "passthrough":
          if hasattr(column_transformer, "_df_columns"):
              if (not isinstance(column, slice)) and all(
                  isinstance(col, str) for col in column
              ):
                  return column
              else:
                  return column_transformer._df_columns[column]
          else:
              indices = np.arange(column_transformer._n_features)
              return ["x%d" % i for i in indices[column]]
      if not hasattr(trans, "get_feature_names_out"):
          if column is None:
              return []
          else:
              return [name + "__" + f for f in column]
  
      return [name + "__" + f for f in trans.get_feature_names_out()]
  
file_path: shapash/shapash/utils/columntransformer_backend.py
imports: import pandas
  import numpy
  from shapash.utils.category_encoder_backend import inv_transform_ordinal
  from shapash.utils.category_encoder_backend import inv_transform_ce
  from shapash.utils.category_encoder_backend import supported_category_encoder
  from shapash.utils.category_encoder_backend import dummies_category_encoder
  from shapash.utils.category_encoder_backend import category_encoder_binary
  from shapash.utils.category_encoder_backend import transform_ordinal, get_col_mapping_ce
  from shapash.utils.model_synoptic import simple_tree_model_sklearn, catboost_model, linear_model, svm_model, xgboost_model, lightgbm_model, dict_model_feature
  from shapash.utils.model import extract_features_model
label_desc: Use image node_id 6 for calling a global function with example usage: get_names(name, trans, column, column_transformer) and returns: unknown, list, unknown, list, unknown, column, column_transformer