code: def evaluate(net, test_loader, dev):
      net.eval()
  
      total_correct = 0
      count = 0
      start_time = time.time()
      with torch.no_grad():
          with tqdm.tqdm(test_loader, ascii=True) as tq:
              for data, label in tq:
                  label = label[:, 0]
                  num_examples = label.shape[0]
                  data, label = (
                      data.to(dev),
                      label.to(dev).squeeze().long(),
                  )
                  logits = net(data)
                  _, preds = logits.max(1)
  
                  correct = (preds == label).sum().item()
                  total_correct += correct
                  count += num_examples
  
                  tq.set_postfix(
                      {"AvgAcc": "%.5f" % (total_correct / count)}
                  )
      print(
          "[Test]  AvgAcc: {:.5}, Time: {:.5}s".format(
              total_correct / count, time.time() - start_time
          )
      )
      return total_correct / count
  
file_path: dgl/examples/pytorch/pointcloud/pct/train_cls.py
imports: import argparse
  import os
  import time
  from functools import partial
  import provider
  import torch
  import torch.nn
  import tqdm
  from dgl.data.utils import download, get_download_dir
  from ModelNetDataLoader import ModelNetDataLoader
  from pct import PointTransformerCLS
  from torch.utils.data import DataLoader
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(net, test_loader, dev) and returns: unknown