code: def eval_model(self, eval_loader):
      self.model.eval()
      with torch.no_grad():
          t = tqdm(eval_loader)
          pred_result = []
          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]
              bag_name = data[1]
              scope = data[2]
              args = data[3:]
              logits = self.model(
                  None,
                  scope,
                  *args,
                  train=False,
                  bag_size=self.bag_size,
              )  # results after softmax
              logits = logits.cpu().numpy()
              for i in range(len(logits)):
                  for relid in range(self.model.module.num_class):
                      if self.model.module.id2rel[relid] != "NA":
                          pred_result.append(
                              {
                                  "entpair": bag_name[i][:2],
                                  "relation": self.model.module.id2rel[
                                      relid
                                  ],
                                  "score": logits[i][relid],
                              }
                          )
          result = eval_loader.dataset.eval(pred_result)
      return result
  
file_path: OpenNRE/opennre/framework/bag_re.py
imports: import torch
  from torch import nn, optim
  import json
  from .data_loader import SentenceRELoader, BagRELoader
  from .utils import AverageMeter
  from tqdm import tqdm
  import os
label_desc: Use image node_id 3 for calling the BagRE obj's underlying member method code with example usage: obj.eval_model(eval_loader) and returns: result