code: def evaluate_rocauc(y_pred_pos, y_pred_neg):
      results = {}
      rocauc = evaluator.eval(
          {
              "y_pred_pos": y_pred_pos,
              "y_pred_neg": y_pred_neg,
          }
      )["rocauc"]
  
      results["rocauc"] = rocauc
  
      return results
  
imports: import argparse
  import datetime
  import os
  import sys
  import time
  import dgl
  import torch
  from dgl.data.utils import load_graphs, save_graphs
  from dgl.dataloading import GraphDataLoader
  from ogb.linkproppred import DglLinkPropPredDataset, Evaluator
  from torch.nn import BCEWithLogitsLoss
  from torch.utils.data import Dataset
  from tqdm import tqdm
  from models import *
  from utils import *
label_desc: Use image node_id 6 for calling a global function with example usage: evaluate_rocauc(y_pred_pos, y_pred_neg) and returns: results