code: def AUC(all_item_scores, dataset, test_data):
      """
      design for a single user
      """
      dataset: BasicDataset
      r_all = np.zeros((dataset.m_items,))
      r_all[test_data] = 1
      r = r_all[all_item_scores >= 0]
      test_item_scores = all_item_scores[all_item_scores >= 0]
      return roc_auc_score(r, test_item_scores)
  
imports: import paddle
  from paddle import nn, optimizer
  import numpy
  from paddle import log
  from dataloader import BasicDataset
  from paddle.io import Dataset
  from time import time
  from sklearn.metrics import roc_auc_score
  import random
  import os
  from tqdm import tqdm
label_desc: Use image node_id 7 for calling a global function with example usage: AUC(all_item_scores, dataset, test_data) and returns: roc_auc_score