code: def train(self):
      max_score = -1
      self._best_epoch = -1
      for epoch in range(200):
          self.model.train()
          self.train_epoch(epoch, split="train")
          self._adjust_lr(epoch)
          if epoch >= 0:
              score = self.evaluate(split="test")
              if score >= max_score:
                  self.logger.info(
                      "Best model saved, epoch {}".format(epoch)
                  )
                  self.model.save_checkpoint(
                      self.opt["checkpoint_save_path"], "best.pt"
                  )
                  self._best_epoch = epoch
              max_score = max(max_score, score)
          if epoch >= 30 and self._stop_condition(epoch):
              break
      return max_score
  
imports: import numpy
  import torch
  import torch.optim
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.datasets.jobs import JobsDataset
  from graph4nlp.pytorch.models.graph2seq_loss import Graph2SeqLoss
  from graph4nlp.pytorch.modules.utils.copy_utils import prepare_ext_vocab
  from args import get_args
  from build_model import get_model
  from evaluation import ExpressionAccuracy
  from utils import get_log, wordid2str
label_desc: Use image node_id 9 for calling the Jobs obj's underlying member method code with example usage: obj.train() and returns: max_score