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!")
          self.model.to(self.args.device)
      y_true, y_pred = [], []
  
      with torch.no_grad():
          with tqdm(
              total=len(self.test_data), leave=False, dynamic_ncols=True
          ) as pbar:
              pbar.set_description_str(desc="Predicting")
              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
                  attention_mask, labels, logits, loss = self._step(
                      batch, mode="dev"
                  )  # logits: batch, seq, num_labels
  
                  if isinstance(logits, torch.Tensor):  #
                      logits = (
                          logits.argmax(-1).detach().cpu().tolist()
                      )  # batch, seq, 1
                  label_ids = labels.detach().cpu().numpy()
                  input_mask = attention_mask.detach().cpu().numpy()
                  label_map = {
                      idx: label
                      for label, idx in self.label_map.items()
                  }
                  for i, mask in enumerate(input_mask):
                      temp_1 = []
                      temp_2 = []
                      for j, m in enumerate(mask):
                          if j == 0:
                              continue
                          if m:
                              if (
                                  label_map[label_ids[i][j]] != "X"
                                  and label_map[label_ids[i][j]]
                                  != "[SEP]"
                              ):
                                  temp_1.append(
                                      label_map[label_ids[i][j]]
                                  )
                                  temp_2.append(label_map[logits[i][j]])
                          else:
                              break
                      y_true.append(temp_1)
                      y_pred.append(temp_2)
  
                  pbar.update()
              # evaluate done
              pbar.close()
              results = classification_report(y_true, y_pred, digits=4)
              self.logger.info("***** Predict Eval results *****")
              self.logger.info("\n%s", results)
              f1_score = float(
                  results.split("\n")[-4]
                  .split("      ")[-2]
                  .split("    ")[-1]
              )
              if self.writer:
                  self.writer.log({"predict_f1": f1_score})
  
file_path: DeepKE/src/deepke/name_entity_re/multimodal/modules/train.py
imports: import torch
  from torch import optim
  from tqdm import tqdm
  from transformers.optimization import get_linear_schedule_with_warmup
  from seqeval.metrics import classification_report
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