code: def _get_featurenames(
      subsets_dict, feature_idx, feature_names, n_features
  ):
      if feature_names is None:
          feature_names = [str(i) for i in range(n_features)]
  
      dict_keys = subsets_dict.keys()
      for key in dict_keys:
          subsets_dict[key]["feature_names"] = tuple(
              feature_names[idx]
              for idx in subsets_dict[key]["feature_idx"]
          )
  
      if feature_idx is None:
          feature_idx_names = None
      else:
          feature_idx_names = tuple(
              feature_names[idx] for idx in feature_idx
          )
  
      return subsets_dict, feature_idx_names
  
imports: from copy import deepcopy
  import numpy
  from sklearn.model_selection import cross_val_score
label_desc: Use image node_id 4 for calling a global function with example usage: _get_featurenames(subsets_dict, feature_idx, feature_names, n_features) and returns: subsets_dict,  feature_idx_names