code: def eval_net(net, dataloader, device):
      net.eval()
  
      total = 0
      total_correct = 0
  
      for data in dataloader:
          graphs, labels = data
          graphs = graphs.to(device)
          labels = labels.to(device)
          # feat = graphs.ndata.pop('attr')
          total += len(labels)
          # outputs = net(graphs, feat)
          # outputs = net((graphs, labels))
          outputs = net(graphs)
          _, predicted = torch.max(outputs.data, 1)
  
          total_correct += (predicted == labels.data).sum().item()
  
      acc = 1.0 * total_correct / total
  
      net.train()
  
      return acc
  
imports: import os
  import pickle
  from dgl.dataloading import GraphDataLoader
  import numpy
  from tqdm import tqdm
  import random
  import torch
  import torch.nn
  import torch.optim
  from dgl.data import GINDataset
  import torch
  import torch.nn
  from autogl.module.model.dgl.gin import AutoGIN
  from autogl.module.model.dgl.topkpool import AutoTopkpool
  from autogl.solver.utils import set_seed
  import argparse
label_desc: Use image node_id 2 for calling a global function with example usage: eval_net(net, dataloader, device) and returns: acc