code: def score_one(self, original, generated):
      image = self.imager.build_image(original, generated)
  
      ent_scores = np.max(image[0], axis=0)
      co_scores = np.max(image[1], axis=0)
      if self.op1 == "mean":
          ent_scores = np.mean(image[0], axis=0)
          co_scores = np.mean(image[1], axis=0)
      elif self.op1 == "min":
          ent_scores = np.min(image[0], axis=0)
          co_scores = np.min(image[1], axis=0)
  
      if self.use_ent and self.use_con:
          scores = ent_scores - co_scores
      elif self.use_ent:
          scores = ent_scores
      elif self.use_con:
          scores = 1.0 - co_scores
  
      final_score = np.mean(scores)
      if self.op2 == "min":
          final_score = np.min(scores)
      elif self.op2 == "max":
          final_score = np.max(scores)
  
      return {"score": final_score, "image": image}
  
imports: from transformers import AutoTokenizer, AutoModelForSequenceClassification
  import nltk
  import numpy
  import torch
  import os
  import json
  from .None import utils_misc
label_desc: Use image node_id 3 for calling the SummaCZS obj's underlying member method code with example usage: obj.score_one(original, generated) and returns: dict