code: def evaluate_wikikg2(model, loader, mode, save_path):
      from ogb.linkproppred import Evaluator
  
      evaluator = Evaluator(name="ogbl-wikikg2")
      model.eval()
      with paddle.no_grad():
          y_pred_pos = []
          y_pred_neg = []
          for h, r, t, neg_h, neg_t in tqdm(loader):
              pos_h = model._get_ent_embedding(h)
              pos_r = model._get_rel_embedding(r)
              pos_t = model._get_ent_embedding(t)
              y_pred_pos.append(model(pos_h, pos_r, pos_t).numpy())
              y_neg_head = model.predict(
                  t, r, neg_h, mode="head"
              ).numpy()
              y_neg_tail = model.predict(
                  h, r, neg_t, mode="tail"
              ).numpy()
              y_pred_neg.append(
                  np.concatenate([y_neg_head, y_neg_tail], axis=1)
              )
          y_pred_pos = np.concatenate(y_pred_pos, axis=0)
          y_pred_neg = np.concatenate(y_pred_neg, axis=0)
          input_dict = {
              "y_pred_pos": y_pred_pos,
              "y_pred_neg": y_pred_neg,
          }
          result = evaluator.eval(input_dict)
      logging.info("-- %s results ------------" % mode)
      logging.info(
          " "
          + " ".join(
              ["{}: {}".format(k, v.mean()) for k, v in result.items()]
          )
      )
  
imports: import os
  import csv
  import math
  import json
  import time
  import random
  import logging
  import functools
  import traceback
  from collections import defaultdict
  from _thread import start_new_thread
  from multiprocessing import Queue, Process
  import numpy
  from tqdm import tqdm
  import paddle
  import paddle.distributed
label_desc: Use image node_id 7 for calling a global function with example usage: evaluate_wikikg2(model, loader, mode, save_path) without return types