code: def train(self):
      self.before_train()  # something should do before training
      self.step = 0
      self.model.train()
      self.logger.info("***** Running training *****")
      self.logger.info(
          "  Num instance = %d", len(self.train_data) * self.batch_size
      )
      self.logger.info("  Num epoch = %d", self.num_epochs)
      self.logger.info("  Batch size = %d", self.batch_size)
      self.logger.info("  Learning rate = {}".format(self.lr))
      self.logger.info("  Evaluate begin = %d", self.eval_begin_epoch)
  
      if self.load_path is not None:  # load model from load_path
          self.logger.info(
              "Loading model from {}".format(self.load_path)
          )
          load_model_dict = torch.load(self.args.load_path)
          model_dict = self.model.state_dict()
          for name in load_model_dict:
              if name in model_dict:
                  if (
                      model_dict[name].shape
                      == load_model_dict[name].shape
                  ):
                      model_dict[name] = load_model_dict[name]
                  else:
                      self.logger.info(
                          f"Skip loading parameter: {name}, "
                          f"required shape: {model_dict[name].shape}, "
                          f"loaded shape: {load_model_dict[name].shape}"
                      )
              else:
                  self.logger.info(
                      f"Not Found! Skip loading parameter: {name}."
                  )
          self.model.load_state_dict(model_dict)
          self.logger.info("Load model successful!")
  
      with tqdm(
          total=self.train_num_steps,
          postfix="loss:{0:<6.5f}",
          leave=False,
          dynamic_ncols=True,
          initial=self.step,
      ) as pbar:
          self.pbar = pbar
          avg_loss = 0
          for epoch in range(self.num_epochs):
              pbar.set_description_str(
                  desc="Epoch {}/{}".format(epoch, self.num_epochs)
              )
              for batch in self.train_data:
                  self.step += 1
                  batch = (
                      tup.to(self.device)
                      if isinstance(tup, torch.Tensor)
                      else tup
                      for tup in batch
                  )
                  loss = self._step(batch, mode="train")
                  avg_loss += loss.item()
  
                  self.optimizer.zero_grad()
                  loss.backward()
                  self.optimizer.step()
  
                  if self.step % self.refresh_step == 0:
                      avg_loss = float(avg_loss) / self.refresh_step
                      print_output = "loss:{:<6.5f}".format(avg_loss)
                      pbar.update(1)
                      pbar.set_postfix_str(print_output)
                      # self.writer.add_scalar(tag='loss', scalar_value=avg_loss, global_step=self.step)    # tensorbordx
                      self.writer.log({"avg_loss": avg_loss})
                      avg_loss = 0
  
              if epoch >= self.eval_begin_epoch:
                  self.evaluate(epoch)  # generator to dev.
          pbar.close()
          self.pbar = None
          self.logger.info(
              "Get best performance at epoch {}, best f1 score is {:.2f}".format(
                  self.best_dev_epoch, self.best_metric
              )
          )
  
file_path: DeepKE/src/deepke/name_entity_re/few_shot/module/train.py
imports: import torch
  from torch import optim
  from tqdm import tqdm
  from ..utils import convert_preds_to_outputs, write_predictions
  import random
label_desc: Use image node_id 2 for calling the Trainer obj's underlying member method code with example usage: obj.train() without return types