code: def evaluate(model, data_loader, device):
      model.eval()
  
      # 用于存储预测正确的样本个数
      sum_num = torch.zeros(1).to(device)
      # 统计验证集样本总数目
      num_samples = len(data_loader.dataset)
  
      # 打印验证进度
      data_loader = tqdm(
          data_loader, desc="validation...", file=sys.stdout
      )
  
      for step, data in enumerate(data_loader):
          images, labels = data
          pred = model(images.to(device))
          pred = torch.max(pred, dim=1)[1]
          sum_num += torch.eq(pred, labels.to(device)).sum()
  
      # 计算预测正确的比例
      acc = sum_num.item() / num_samples
  
      return acc
  
imports: import sys
  from tqdm import tqdm
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, data_loader, device) and returns: acc