code: def eval_model(self, eval_loader):
      self.eval()
      avg_acc = AverageMeter()
      pred_result = []
      with torch.no_grad():
          t = tqdm(eval_loader)
          for iter, data in enumerate(t):
              if torch.cuda.is_available():
                  for i in range(len(data)):
                      try:
                          data[i] = data[i].cuda()
                      except:
                          pass
              label = data[0]
              args = data[1:]
              logits = self.parallel_model(*args)
              score, pred = logits.max(-1)  # (B)
              # Save result
              for i in range(pred.size(0)):
                  pred_result.append(pred[i].item())
              # Log
              acc = float((pred == label).long().sum()) / label.size(0)
              avg_acc.update(acc, pred.size(0))
              t.set_postfix(acc=avg_acc.avg)
      result = eval_loader.dataset.eval(pred_result)
      return result
  
file_path: OpenNRE/opennre/framework/sentence_re.py
imports: import os, logging, json
  from tqdm import tqdm
  import torch
  from torch import nn, optim
  from .data_loader import SentenceRELoader
  from .utils import AverageMeter
label_desc: Use image node_id 3 for calling the SentenceRE obj's underlying member method code with example usage: obj.eval_model(eval_loader) and returns: result