code: def get_feature_names(column_transformer):
      """
      Allow to extract all features names from encoders of the ColumnTransformer once it has been applied.
      If the right names aren't available, It creates a list with customized names.
  
      Parameters
      ----------
      column_transformer: sklearn ColumnTransformer
          The fitted ColumnTransformer containing the specific encoder.
  
      Returns
      -------
      feature_names: list
          List of returned features names when ColumnTransformer is applied.
      """
      feature_names = []
      l_transformers = list(column_transformer._iter(fitted=True))
  
      for name, trans, column, _ in l_transformers:
          feature_names.extend(
              get_names(name, trans, column, column_transformer)
          )
  
      return feature_names
  
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 7 for calling a global function with example usage: get_feature_names(column_transformer) and returns: feature_names