code: def train_eager(self):
      self.train_module = ddp(self.train_module)
      for epoch in range(self.start_epoch, self.cfg.num_epoch):
          self.train_module.train()
  
          one_epoch_steps = len(self.train_data_loader)
          for steps in range(one_epoch_steps):
              self.global_step += 1
              image, label = self.train_data_loader()
              image = image.to("cuda")
              label = label.to("cuda")
              features_fc7 = self.train_module(image, label)
              features_fc7 = (
                  self.margin_softmax(features_fc7, label) * 64
              )
              loss = self.of_cross_entropy(features_fc7, label)
              loss.backward()
              self.optimizer.step()
              self.optimizer.zero_grad()
  
              loss = loss.numpy()
              self.losses.update(loss, 1)
              self.callback_logging(
                  self.global_step,
                  self.losses,
                  epoch,
                  False,
                  self.scheduler.get_last_lr()[0],
              )
              self.callback_verification(
                  self.global_step, self.backbone
              )
              self.scheduler.step()
          self.callback_checkpoint(
              self.global_step, epoch, self.train_module
          )
  
imports: import oneflow
  from oneflow.nn.parallel import DistributedDataParallel
  from utils.ofrecord_data_utils import OFRecordDataLoader, SyntheticDataLoader
  from utils.utils_logging import AverageMeter
  from utils.utils_callbacks import CallBackVerification, CallBackLogging, CallBackModelCheckpoint
  from backbones import get_model
  from graph import TrainGraph, EvalGraph
  from utils.losses import CrossEntropyLoss_sbp
  import logging
label_desc: Use image node_id 6 for calling the Trainer obj's underlying member method code with example usage: obj.train_eager() without return types