code: def evaluate(self, epoch):
      self.model.eval()
      self.logger.info("***** Running evaluate *****")
      self.logger.info(
          "  Num instance = %d", len(self.dev_data) * self.batch_size
      )
      self.logger.info("  Batch size = %d", self.batch_size)
      with torch.no_grad():
          with tqdm(
              total=len(self.dev_data), leave=False, dynamic_ncols=True
          ) as pbar:
              pbar.set_description_str(desc="Dev")
              for batch in self.dev_data:
                  batch = (
                      tup.to(self.device)
                      if isinstance(tup, torch.Tensor)
                      else tup
                      for tup in batch
                  )  # to cpu/cuda device
                  self._step(batch, mode="dev")
                  pbar.update()
              # evaluate done
              eva_result = self.metrics.get_metric()
              pbar.close()
              self.logger.info(
                  "Epoch {}/{}, best f1: {}, current f1 score: {:.2f}, recall: {:.2f}, precision: {:.2f}.".format(
                      epoch,
                      self.num_epochs,
                      self.best_metric,
                      eva_result["f"],
                      eva_result["rec"],
                      eva_result["pre"],
                  )
              )
              # self.writer.add_scalars('evaluate', {'f1': eva_result['f'],
              #                                     'recall': eva_result['rec'],
              #                                     'precision': eva_result['pre']}, epoch)
              self.writer.log(
                  {
                      "f1": eva_result["f"],
                      "recall": eva_result["rec"],
                      "precision": eva_result["pre"],
                  }
              )
  
              if (
                  eva_result["f"] >= self.best_metric
              ):  # this epoch get best performance
                  self.logger.info(
                      "Get better performance at epoch {}".format(epoch)
                  )
                  self.best_dev_epoch = epoch
                  self.best_metric = eva_result[
                      "f"
                  ]  # update best metric(f1 score)
                  if self.save_path is not None:  # need to save model
                      torch.save(
                          self.model.state_dict(),
                          self.save_path + "/best_model.pth",
                      )
                      self.logger.info(
                          "Save best model at {}".format(self.save_path)
                      )
  
      self.model.train()
  
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 3 for calling the Trainer obj's underlying member method code with example usage: obj.evaluate(epoch) without return types