code: def neg_log_likelihood(self, feats, mask, tags):
      # nonegative log likelihood
      forward_score, scores = self._calculate_PZ(feats, mask)
      # print('Forward', forward_score)
      gold_score = self._score_sentence(scores, mask, tags)
      # print('Gold', gold_score)
      # print "batch, f:", forward_score.data[0], " g:", gold_score.data[0], " dis:", forward_score.data[0] - gold_score.data[0]
      # exit(0)
      # if self.average_batch:
      #     return (forward_score - gold_score) / batch_size
      # else:
      return forward_score - gold_score
  
file_path: DeepKE/src/deepke/event_extraction/standard/bertcrf/crf.py
imports: import torch
  import torch.autograd
  import torch.nn
label_desc: Use image node_id 7 for calling the CRF obj's underlying member method code with example usage: obj.neg_log_likelihood(feats, mask, tags) and returns: unknown