code: def check_features_name(columns_dict, features_dict, features):
      """
      Convert a list of feature names (string) or features ids into features ids.
      Features names can be part of columns_dict or features_dict.
  
      Parameters
      ----------
      features : List
          List of ints (columns ids) or of strings (business names)
      columns_dict: dict
      Dictionary mapping integer column number to technical feature names.
      features_dict: dict
      Dictionary mapping technical feature names to domain names.
  
      Returns
      -------
      list of ints
          Columns ids compatible with var_dict
      """
      if all(isinstance(f, int) for f in features):
          features_ids = features
  
      elif all(isinstance(f, str) for f in features):
          inv_columns_dict = {v: k for k, v in columns_dict.items()}
          inv_features_dict = {v: k for k, v in features_dict.items()}
  
          if features_dict and all(
              f in features_dict.values() for f in features
          ):
              columns_list = [inv_features_dict[f] for f in features]
              features_ids = [inv_columns_dict[c] for c in columns_list]
          elif inv_columns_dict and all(
              f in columns_dict.values() for f in features
          ):
              features_ids = [inv_columns_dict[f] for f in features]
          else:
              raise ValueError(
                  "All features must came from the same dict of features (technical names or domain names)."
              )
  
      else:
          raise ValueError(
              """
              features must be a list of ints (representing ids of columns)
              or a list of string from technical features names or from domain names.
              """
          )
      return features_ids
  
imports: import copy
  import numpy
  import pandas
  from shapash.utils.category_encoder_backend import no_dummies_category_encoder, supported_category_encoder, dummies_category_encoder
  from shapash.utils.columntransformer_backend import no_dummies_sklearn, supported_sklearn
  from shapash.utils.model import extract_features_model
  from shapash.utils.model_synoptic import dict_model_feature
  from shapash.utils.transform import preprocessing_tolist, check_transformers
  from shapash.utils.columntransformer_backend import columntransformer, get_feature_names, get_list_features_names
label_desc: Use image node_id 11 for calling a global function with example usage: check_features_name(columns_dict, features_dict, features) and returns: features_ids