code: def predict(self):
      self.model.eval()
      self.logger.info("\n***** Running predicting *****")
      self.logger.info(
          "  Num instance = %d",
          len(self.test_data) * self.args.batch_size,
      )
      self.logger.info("  Batch size = %d", self.args.batch_size)
  
      if self.args.load_path is not None:  # load model from load_path
          self.logger.info(
              "Loading model from {}".format(self.args.load_path)
          )
          self.model.load_state_dict(torch.load(self.args.load_path))
          self.logger.info("Load model successful!")
      true_labels, pred_labels = [], []
      with torch.no_grad():
          with tqdm(
              total=len(self.test_data), leave=False, dynamic_ncols=True
          ) as pbar:
              pbar.set_description_str(desc="Predicting")
              total_loss = 0
              for batch in self.test_data:
                  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)
              # save predict results
              import os
  
              with open(
                  os.path.join(self.args.cwd, "data/txt/result.txt"),
                  "w",
                  encoding="utf-8",
              ) as wf:
                  wf.write(sk_result)
                  print("Successful write!!")
  
      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 5 for calling the Trainer obj's underlying member method code with example usage: obj.predict() without return types