code: def check_consistency_model_label(columns_dict, label_dict=None):
      """
      Check the matching between attributes, features names are same, or include
  
      Parameters
      ----------
      columns_dict: dict
          Dictionary mapping integer column number (in the same order of the trained dataset) to technical feature names.
      label_dict: dict (optional)
          Dictionary mapping integer labels to domain names (classification - target values).
      """
  
      if label_dict is not None:
          if not all(feat in columns_dict for feat in label_dict):
              raise ValueError(
                  "All features of label_dict must be in model"
              )
  
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 9 for calling a global function with example usage: check_consistency_model_label(columns_dict, label_dict) without return types