code: def train(self):
      dur = []
      for epoch in range(self.config["epochs"]):
          self.model.train()
          train_loss = []
          t0 = time.time()
  
          for i, data in enumerate(self.train_dataloader):
              data = all_to_cuda(data, self.config["device"])
              to_cuda(data["graph_data"], self.config["device"])
  
              oov_dict = None
              if self.use_copy:
                  oov_dict, tgt = prepare_ext_vocab(
                      data["graph_data"],
                      self.vocab,
                      gt_str=data["output_str"],
                      device=self.config["device"],
                  )
                  data["tgt_seq"] = tgt
  
              logits, loss = self.model(
                  data, oov_dict=oov_dict, require_loss=True
              )
              self.optimizer.zero_grad()
              loss.backward()
              if self.config.get("grad_clipping", None) not in (
                  None,
                  0,
              ):
                  # Clip gradients
                  parameters = [
                      p
                      for p in self.model.parameters()
                      if p.requires_grad
                  ]
  
                  torch.nn.utils.clip_grad_norm_(
                      parameters, self.config["grad_clipping"]
                  )
  
              self.optimizer.step()
              train_loss.append(loss.item())
              print(
                  "Epoch = {}, Step = {}, Loss = {:.3f}".format(
                      epoch, i, loss.item()
                  )
              )
  
              dur.append(time.time() - t0)
  
          val_scores = self.evaluate(self.val_dataloader)
          self.scheduler.step(
              val_scores[self.config["early_stop_metric"]]
          )
          format_str = "Epoch: [{} / {}] | Time: {:.2f}s | Loss: {:.4f} | Val scores:".format(
              epoch + 1,
              self.config["epochs"],
              np.mean(dur),
              np.mean(train_loss),
          )
          format_str += self.metric_to_str(val_scores)
          print(format_str)
          self.logger.write(format_str)
  
          if self.stopper.step(
              val_scores[self.config["early_stop_metric"]], self.model
          ):
              break
  
      return self.stopper.best_score
  
imports: import argparse
  import os
  import time
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.nn
  import torch.optim
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.datasets.cnn import CNNDataset
  from graph4nlp.pytorch.models.graph2seq import Graph2Seq
  from graph4nlp.pytorch.models.graph2seq_loss import Graph2SeqLoss
  from graph4nlp.pytorch.modules.config import get_basic_args
  from graph4nlp.pytorch.modules.evaluation.rouge import ROUGE
  from graph4nlp.pytorch.modules.graph_embedding_initialization.embedding_construction import WordEmbedding
  from graph4nlp.pytorch.modules.utils import constants
  from graph4nlp.pytorch.modules.utils.config_utils import get_yaml_config, update_values
  from graph4nlp.pytorch.modules.utils.copy_utils import prepare_ext_vocab
  from graph4nlp.pytorch.modules.utils.generic_utils import EarlyStopping, to_cuda
  from graph4nlp.pytorch.modules.utils.logger import Logger
  from graph4nlp.pytorch.modules.utils.summarization_utils import wordid2str
label_desc: Use image node_id 6 for calling the ModelHandler obj's underlying member method code with example usage: obj.train() and returns: self