code: def evaluate(model, data_loader, device):
      model.eval()
  
      # 验证集样本个数
      num_samples = len(data_loader.dataset)
  
      # 用于存储预测正确的样本个数
      sum_num = torch.zeros(1).to(device)
  
      # 在进程0中打印验证进度
      if is_main_process():
          data_loader = tqdm(data_loader, 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()
  
      # 等待所有进程计算完毕
      if device != torch.device("cpu"):
          torch.cuda.synchronize(device)
  
      sum_num = reduce_value(sum_num, average=False)
      acc = sum_num.item() / num_samples
  
      return acc
  
imports: import sys
  from tqdm import tqdm
  import torch
  from .distributed_utils import reduce_value, is_main_process, warmup_lr_scheduler
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, data_loader, device) and returns: acc