code: def get_feature_out(estimator, feature_in):
      """
      Returns estimator features out if it has get_feature_names_out method, else features_in
      """
      if hasattr(estimator, "get_feature_names_out") and hasattr(
          estimator, "categories_"
      ):
          return (
              estimator.get_feature_names_out(),
              estimator.categories_,
          )
      elif hasattr(estimator, "get_feature_names_out"):
          return estimator.get_feature_names_out(), []
      else:
          return feature_in, []
  
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 9 for calling a global function with example usage: get_feature_out(estimator, feature_in) and returns: estimator,  estimator, estimator,  list, feature_in,  list