class_docstr: Reranks n-best hypotheses based on extra models and parameters
docstr: run models and compute scores based on p(y), p(x|y) etc.
docstr_tok: ["run","models","and","compute","scores","based","on","p","(","y",")",",","p","(","x|y",")","etc","."]
code: def score(self, src_tokens, hypos):
      """run models and compute scores based on p(y), p(x|y) etc."""
  
      scores = torch.zeros(
          (len(hypos), len(FeatureList)), dtype=torch.float
      )
  
      self.compute_l2r_model_scores(src_tokens, hypos, scores)
      self.compute_r2l_model_scores(src_tokens, hypos, scores)
      self.compute_reverse_model_scores(src_tokens, hypos, scores)
      self.compute_lm_scores(src_tokens, hypos, scores)
      self.compute_cloze_transformer_scores(src_tokens, hypos, scores)
  
      return scores
  
imports: import argparse
  import pickle
  from enum import Enum
  import torch
  from fairseq.scoring import bleu
  from pytorch_translate import hybrid_transformer_rnn, utils
  from pytorch_translate.rescoring.model_scorers import LMScorer, R2LModelScorer, ReverseModelScorer, SimpleModelScorer
  from pytorch_translate.research.rescore import cloze_transformer_model
  from pytorch_translate.tasks import pytorch_translate_task
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling the Rescorer obj's underlying member method code with example usage: obj.score(src_tokens, hypos) and returns: scores