code: def evaluate_mrr(y_pred_pos, y_pred_neg):
      y_pred_neg = y_pred_neg.view(y_pred_pos.shape[0], -1)
      results = {}
      mrr = (
          evaluator.eval(
              {
                  "y_pred_pos": y_pred_pos,
                  "y_pred_neg": y_pred_neg,
              }
          )["mrr_list"]
          .mean()
          .item()
      )
  
      results["mrr"] = mrr
  
      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 5 for calling a global function with example usage: evaluate_mrr(y_pred_pos, y_pred_neg) and returns: results