code: def train_one_epoch(
      model,
      optimizer,
      data_loader,
      device,
      epoch,
      use_amp=False,
      warmup=True,
  ):
      model.train()
      loss_function = torch.nn.CrossEntropyLoss()
      accu_loss = torch.zeros(1).to(device)  # 累计损失
      accu_num = torch.zeros(1).to(device)  # 累计预测正确的样本数
      optimizer.zero_grad()
  
      lr_scheduler = None
      if (
          epoch == 0 and warmup is True
      ):  # 当训练第一轮（epoch=0）时，启用warmup训练方式，可理解为热身训练
          warmup_factor = 1.0 / 1000
          warmup_iters = min(1000, len(data_loader) - 1)
  
          lr_scheduler = warmup_lr_scheduler(
              optimizer, warmup_iters, warmup_factor
          )
  
      # 在进程0中打印训练进度
      if is_main_process():
          data_loader = tqdm(data_loader, file=sys.stdout)
  
      enable_amp = use_amp and "cuda" in device.type
      scaler = torch.cuda.amp.GradScaler(enabled=enable_amp)
  
      sample_num = 0
      for step, data in enumerate(data_loader):
          images, labels = data
          sample_num += images.shape[0]
  
          with torch.cuda.amp.autocast(enabled=enable_amp):
              pred = model(images.to(device))
              loss = loss_function(pred, labels.to(device))
  
              pred_classes = torch.max(pred, dim=1)[1]
              accu_num += torch.eq(
                  pred_classes, labels.to(device)
              ).sum()
  
          scaler.scale(loss).backward()
          scaler.step(optimizer)
          scaler.update()
          optimizer.zero_grad()
  
          loss = reduce_value(loss, average=True)
          accu_loss += loss.detach()
  
          # 在进程0中打印平均loss
          if is_main_process():
              info = "[epoch {}] loss: {:.3f}, train_acc: {:.3f}, lr: {:.5f}".format(
                  epoch,
                  accu_loss.item() / (step + 1),
                  accu_num.item() / sample_num,
                  optimizer.param_groups[0]["lr"],
              )
              data_loader.desc = info
  
          if not torch.isfinite(loss):
              print("WARNING: non-finite loss, ending training ", loss)
              sys.exit(1)
  
          if lr_scheduler is not None:  # 如果使用warmup训练，逐渐调整学习率
              lr_scheduler.step()
  
      # 等待所有进程计算完毕
      if device != torch.device("cpu"):
          torch.cuda.synchronize(device)
  
      return accu_loss.item() / (step + 1)
  
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 1 for calling a global function with example usage: train_one_epoch(model, optimizer, data_loader, device, epoch, use_amp, warmup) and returns: unknown