code: def evaluate(self, epoch):
      self.model.eval()
      self.logger.info("***** Running evaluate *****")
      self.logger.info(
          "  Num instance = %d",
          len(self.dev_data) * self.args.batch_size,
      )
      self.logger.info("  Batch size = %d", self.args.batch_size)
      step = 0
      true_labels, pred_labels = [], []
      with torch.no_grad():
          with tqdm(
              total=len(self.dev_data), leave=False, dynamic_ncols=True
          ) as pbar:
              pbar.set_description_str(desc="Dev")
              total_loss = 0
              for batch in self.dev_data:
                  step += 1
                  batch = (
                      tup.to(self.args.device)
                      if isinstance(tup, torch.Tensor)
                      else tup
                      for tup in batch
                  )  # to cpu/cuda device
                  (loss, logits), labels = self._step(
                      batch, mode="dev"
                  )  # logits: batch, 3
                  total_loss += loss.detach().cpu().item()
  
                  preds = logits.argmax(-1)
                  true_labels.extend(
                      labels.view(-1).detach().cpu().tolist()
                  )
                  pred_labels.extend(
                      preds.view(-1).detach().cpu().tolist()
                  )
                  pbar.update()
              # evaluate done
              pbar.close()
              sk_result = classification_report(
                  y_true=true_labels,
                  y_pred=pred_labels,
                  labels=list(self.re_dict.values())[1:],
                  target_names=list(self.re_dict.keys())[1:],
                  digits=4,
              )
              self.logger.info("%s\n", sk_result)
              result = eval_result(
                  true_labels, pred_labels, self.re_dict, self.logger
              )
              acc, micro_f1 = round(result["acc"] * 100, 4), round(
                  result["micro_f1"] * 100, 4
              )
  
              self.logger.info(
                  "Epoch {}/{}, best dev f1: {}, best epoch: {}, current dev f1 score: {}, acc: {}.".format(
                      epoch,
                      self.args.num_epochs,
                      self.best_dev_metric,
                      self.best_dev_epoch,
                      micro_f1,
                      acc,
                  )
              )
              self.writer.log({"eva_f1": micro_f1, "eva_accuracy": acc})
              if (
                  micro_f1 >= self.best_dev_metric
              ):  # this epoch get best performance
                  self.logger.info(
                      "Get better performance at epoch {}".format(epoch)
                  )
                  self.best_dev_epoch = epoch
                  self.best_dev_metric = (
                      micro_f1  # update best metric(f1 score)
                  )
                  if self.args.save_path is not None:
                      torch.save(
                          self.model.state_dict(),
                          self.args.save_path + "/best_model.pth",
                      )
                      self.logger.info(
                          "Save best model at {}".format(
                              self.args.save_path
                          )
                      )
  
      self.model.train()
  
file_path: DeepKE/src/deepke/relation_extraction/multimodal/modules/train.py
imports: import torch
  from torch import optim
  from tqdm import tqdm
  from sklearn.metrics import classification_report
  from transformers.optimization import get_linear_schedule_with_warmup
  from .metrics import eval_result
label_desc: Use image node_id 3 for calling the Trainer obj's underlying member method code with example usage: obj.evaluate(epoch) without return types