code: def train_epoch(self, epoch, split="train"):
      assert split in ["train"]
      self.logger.info(
          "Start training in split {}, Epoch: {}".format(split, epoch)
      )
      loss_collect = []
      dataloader = self.train_dataloader
      step_all_train = len(dataloader)
      for step, data in enumerate(dataloader):
          graph, tgt, gt_str = (
              data["graph_data"],
              data["tgt_seq"],
              data["output_str"],
          )
          graph = graph.to(self.device)
          tgt = tgt.to(self.device)
          oov_dict = None
          if self.use_copy:
              oov_dict, tgt = prepare_ext_vocab(
                  graph, self.vocab, gt_str=gt_str, device=self.device
              )
  
          prob, enc_attn_weights, coverage_vectors = self.model(
              graph, tgt, oov_dict=oov_dict
          )
          loss = self.loss(
              logits=prob,
              label=tgt,
              enc_attn_weights=enc_attn_weights,
              coverage_vectors=coverage_vectors,
          )
          loss_collect.append(loss.item())
          if step % self.opt["loss_display_step"] == 0 and step != 0:
              self.logger.info(
                  "Epoch {}: [{} / {}] loss: {:.3f}".format(
                      epoch, step, step_all_train, np.mean(loss_collect)
                  )
              )
              loss_collect = []
          self.optimizer.zero_grad()
          loss.backward()
          self.optimizer.step()
  
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 12 for calling the Jobs obj's underlying member method code with example usage: obj.train_epoch(epoch, split) without return types