code: def roc_auc_lv(
      ground_truth: pd.DataFrame,
      predictions: pd.DataFrame,
  ) -> Tuple[List[Tuple[float, float]], float]:
      """
      Utility function to compute area under the curve (AUC)
  
      Args:
          ground_truth: Ground truth dataframe
          predictions: Prediction dataframe
  
      Returns:
          ROC curve and AUC
      """
      predictions = predictions[sorted(predictions.columns)]
  
      fpr, tpr, _ = metrics.roc_curve(
          ground_truth.values.ravel(), predictions.values.ravel()
      )
      roc = list(zip(fpr, tpr))
      auc = metrics.auc(fpr, tpr)
      return roc, 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 9 for calling a global function with example usage: roc_auc_lv(ground_truth, predictions) and returns: roc,  auc