code: def _compute_auc_stub(
      bn: BayesianNetwork,
      data: pd.DataFrame,
      var: str,
  ) -> float:
      """
      Helper function to compute AUC using all available information
  
      Args:
          bn: Bayesian network
          data: Input dataframe
          var: Target variable name
  
      Returns:
          Area under the curve (AUC)
      """
      y_true = _build_ground_truth(bn, data, var)
      y_pred = predict_using_all_nodes(bn, data, var, lv_name=None)
      _, auc = roc_auc_lv(y_true, y_pred)
      return auc
  
imports: import json
  import multiprocessing
  import os
  import warnings
  from collections import defaultdict
  from time import time
  from typing import List, Tuple
  import dataframe_image
  import networkx
  import numpy
  import pandas
  from IPython.core.display import display
  from IPython.display import Image
  from sklearn import metrics
  from sklearn.model_selection import KFold
  from causalnex.evaluation import roc_auc
  from causalnex.inference import InferenceEngine
  from causalnex.network import BayesianNetwork
  from causalnex.plots import plot_structure
  from causalnex.structure import StructureModel
label_desc: Use image node_id 12 for calling a global function with example usage: _compute_auc_stub(bn, data, var) and returns: auc