code: def train(self, config, epoch, data_loader):
      self.model.train()
      loss_list = []
      pred_result = []
      label_result = []
  
      for i, data_batch in tqdm(
          enumerate(data_loader), desc="Training"
      ):
          data_batch = [data.cuda() for data in data_batch[:-1]]
  
          (
              bert_inputs,
              grid_labels,
              grid_mask2d,
              pieces2word,
              dist_inputs,
              sent_length,
          ) = data_batch
  
          outputs = self.model(
              bert_inputs,
              grid_mask2d,
              dist_inputs,
              pieces2word,
              sent_length,
          )
  
          grid_mask2d = grid_mask2d.clone()
          loss = self.criterion(
              outputs[grid_mask2d], grid_labels[grid_mask2d]
          )
  
          loss.backward()
          torch.nn.utils.clip_grad_norm_(
              self.model.parameters(), config.clip_grad_norm
          )
          self.optimizer.step()
          self.optimizer.zero_grad()
  
          loss_list.append(loss.cpu().item())
  
          outputs = torch.argmax(outputs, -1)
          grid_labels = grid_labels[grid_mask2d].contiguous().view(-1)
          outputs = outputs[grid_mask2d].contiguous().view(-1)
  
          label_result.append(grid_labels.cpu())
          pred_result.append(outputs.cpu())
  
          self.scheduler.step()
  
      label_result = torch.cat(label_result)
      pred_result = torch.cat(pred_result)
  
      p, r, f1, _ = precision_recall_fscore_support(
          label_result.numpy(), pred_result.numpy(), average="macro"
      )
      # table = pt.PrettyTable(["Train {}".format(epoch), "Loss"])
      # table.add_row(["Label", "{:.4f}".format(np.mean(loss_list))])
  
      table = pt.PrettyTable(
          [
              "Train {}".format(epoch),
              "Loss",
              "F1",
              "Precision",
              "Recall",
          ]
      )
      table.add_row(
          ["Label", "{:.4f}".format(np.mean(loss_list))]
          + ["{:3.4f}".format(x) for x in [f1, p, r]]
      )
      logger.info("\n{}".format(table))
      return f1
  
imports: import argparse
  import json
  import numpy
  import prettytable
  import torch
  import torch.autograd
  import torch.nn
  import transformers
  from sklearn.metrics import precision_recall_fscore_support, f1_score, classification_report
  from torch.utils.data import DataLoader
  import hydra
  from deepke.name_entity_re.standard.w2ner import *
  from deepke.name_entity_re.standard.w2ner.utils import *
  from deepke.name_entity_re.standard.tools import *
label_desc: Use image node_id 2 for calling the Trainer obj's underlying member method code with example usage: obj.train(config, epoch, data_loader) and returns: f1