code: def train_epoch(self, epoch):
      loss_to_print = 0
      num_batch = len(self.train_data_loader)
      for _, data in tqdm(
          enumerate(self.train_data_loader),
          desc=f"Epoch {epoch:02d}",
          total=len(self.train_data_loader),
      ):
          batch_graph, batch_tree_list, batch_original_tree_list = (
              data["graph_data"],
              data["dec_tree_batch"],
              data["original_dec_tree_batch"],
          )
          batch_graph = batch_graph.to(self.device)
          self.optimizer.zero_grad()
          oov_dict = (
              self.prepare_ext_vocab(batch_graph, self.src_vocab)
              if self.use_copy
              else None
          )
  
          if self.use_copy:
              batch_tree_list_refined = []
              for item in batch_original_tree_list:
                  tgt_list = oov_dict.get_symbol_idx_for_list(
                      item.strip().split()
                  )
                  tgt_tree = Tree.convert_to_tree(
                      tgt_list, 0, len(tgt_list), oov_dict
                  )
                  batch_tree_list_refined.append(tgt_tree)
          loss = self.model(
              batch_graph,
              batch_tree_list_refined
              if self.use_copy
              else batch_tree_list,
              oov_dict=oov_dict,
          )
          loss.backward()
          torch.nn.utils.clip_grad_value_(
              self.model.parameters(), self.opt["grad_clip"]
          )
          self.optimizer.step()
          loss_to_print += loss
      return loss_to_print / num_batch
  
imports: import copy
  import random
  import time
  import warnings
  import numpy
  import torch
  import torch.optim
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from graph4nlp.pytorch.datasets.mathqa import MathQADatasetForTree
  from graph4nlp.pytorch.models.graph2tree import Graph2Tree
  from graph4nlp.pytorch.modules.utils.tree_utils import Tree
label_desc: Use image node_id 6 for calling the MathQA obj's underlying member method code with example usage: obj.train_epoch(epoch) and returns: unknown