code: def predict(self):
      self.model.eval()
      self.logger.info("***** Running testing *****")
      self.logger.info(
          "  Num instance = %d", len(self.test_data) * self.batch_size
      )
      self.logger.info("  Batch size = %d", self.batch_size)
      if self.load_path is not None:  # load model from load_path
          self.logger.info(
              "Loading model from {}".format(self.load_path)
          )
          self.model.load_state_dict(torch.load(self.load_path))
          self.logger.info("Load model successful!")
          self.model.to(self.device)
  
      with torch.no_grad():
          with tqdm(
              total=len(self.test_data), leave=False, dynamic_ncols=True
          ) as pbar:
              pbar.set_description_str(desc="Test")
              texts = []
              labels = []
              for batch in self.test_data:
                  batch = (
                      tup.to(self.device)
                      if isinstance(tup, torch.Tensor)
                      else tup
                      for tup in batch
                  )  # to cpu/cuda device
                  src_tokens, src_seq_len, first, raw_words = batch
                  preds = self._step(
                      (src_tokens, src_seq_len, first), mode="test"
                  )
                  outputs = convert_preds_to_outputs(
                      preds,
                      raw_words,
                      self.process.mapping,
                      self.process.tokenizer,
                  )
                  texts.extend(raw_words)
                  labels.extend(outputs)
                  pbar.update()
  
      self.logger.info("***** Predict example *****")
      idx = random.randint(0, len(texts))
      print(len(texts), len(labels))
      self.logger.info("Raw texts: " + " ".join(texts[idx]))
      self.logger.info("Prediction: " + " ".join(labels[idx]))
      if self.args.write_path is not None:  # write predict
          write_predictions(self.args.write_path, texts, labels)
          self.logger.info(
              "Write into {}!".format(self.args.write_path)
          )
  
file_path: DeepKE/src/deepke/name_entity_re/few_shot/module/train.py
imports: import torch
  from torch import optim
  from tqdm import tqdm
  from ..utils import convert_preds_to_outputs, write_predictions
  import random
label_desc: Use image node_id 4 for calling the Trainer obj's underlying member method code with example usage: obj.predict() without return types