code: def eval_model(self, eval_loader):
      self.eval()
      pred_score = []
      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 = (
                  self.parallel_model.module.logit_to_score(logits)
                  .cpu()
                  .numpy()
              )
              # Save result
              pred_score.append(score)
              # Log
      pred_score = np.concatenate(pred_score, 0)
      result = eval_loader.dataset.eval(pred_score)
      return result
  
file_path: OpenNRE/opennre/framework/multi_label_sentence_re.py
imports: import os, logging, json
  from tqdm import tqdm
  import torch
  from torch import nn, optim
  from .data_loader import MultiLabelSentenceRELoader
  from .utils import AverageMeter
  import numpy
label_desc: Use image node_id 3 for calling the MultiLabelSentenceRE obj's underlying member method code with example usage: obj.eval_model(eval_loader) and returns: result