code: def get_id_card_contrib(
      data: dict,
      index: int,
      features_dict: dict,
      columns_dict: dict,
      label_num: int = None,
  ) -> pd.DataFrame:
      """Get the contributions of the selected index for the identity card.
  
      Parameters
      ----------
      data : dict
          Data from the smart explainer
      index : int
          Index selected
      features_dict : dict
          Dictionary mapping technical feature names to domain names
      columns_dict : dict
          Dictionary mapping integer column number to technical feature names
      label_num : int, optional
          Label num, by default None
  
      Returns
      -------
      pd.DataFrame
          Dataframe of the contributions
      """
      if label_num is not None:
          contrib = (
              data["contrib_sorted"][label_num].loc[index, :].values
          )
          var_dict = data["var_dict"][label_num].loc[index, :].values
      else:
          contrib = data["contrib_sorted"].loc[index, :].values
          var_dict = data["var_dict"].loc[index, :].values
  
      var_dict = [features_dict[columns_dict[x]] for x in var_dict]
      selected_contrib = pd.DataFrame(
          [var_dict, contrib], index=["feature_name", "feature_contrib"]
      ).T
      selected_contrib["feature_contrib"] = selected_contrib[
          "feature_contrib"
      ].apply(lambda x: round(x, 4))
  
      return selected_contrib
  
imports: from typing import Optional, Tuple
  import numpy
  import pandas
  import datetime
  from dash import dcc, html
  import dash_bootstrap_components
label_desc: Use image node_id 15 for calling a global function with example usage: get_id_card_contrib(data, index, features_dict, columns_dict, label_num) and returns: selected_contrib