code: def binary_score_candidates(
      self,
      candidate_input_ids,
      candidate_attn_masks,
      utterance_length,
      labels,
      template_length,
      correct_candidate,
      minus_negative=True,
      inference=False,
  ):
      best_candidate_input_ids = []
  
      for i in range(candidate_input_ids.size(0)):
          best_j = 0
  
          lowest_loss = float("inf")
  
          for j in range(0, candidate_input_ids.size(1), 2):
              if j > 0 and torch.equal(
                  candidate_input_ids[i, j, :],
                  candidate_input_ids[i, 0, :],
              ):
                  break
  
              start_yes = (
                  j if j // 2 == correct_candidate[i].item() else j + 1
              )
  
              cand_loss = self(
                  candidate_input_ids[i, start_yes : start_yes + 1, :],
                  candidate_attn_masks[i, start_yes : start_yes + 1, :],
                  self.get_binary_score_labels(
                      candidate_input_ids[
                          i, start_yes : start_yes + 1, :
                      ]
                  ),
                  inference=inference,
              )
  
              considered_loss = cand_loss.item()
  
              if minus_negative:
                  start_no = (
                      j + 1
                      if j // 2 == correct_candidate[i].item()
                      else j
                  )
  
                  negative_cand_loss = self(
                      candidate_input_ids[
                          i, start_no : start_no + 1, :
                      ],
                      candidate_attn_masks[
                          i, start_no : start_no + 1, :
                      ],
                      self.get_binary_score_labels(
                          candidate_input_ids[
                              i, start_no : start_no + 1, :
                          ]
                      ),
                      inference=inference,
                  )
                  considered_loss -= negative_cand_loss.item()
  
              if considered_loss < lowest_loss:
                  best_j = start_yes
                  lowest_loss = considered_loss
  
          best_candidate_input_ids.append(
              candidate_input_ids[i, best_j, :]
          )
  
      candidate_tokens = torch.stack(best_candidate_input_ids)
      (
          generated_field,
          ground_truth_field,
      ) = self.process_into_structured_fields(
          candidate_tokens, labels, template_length=template_length
      )
      return generated_field, ground_truth_field
  
imports: import collections
  import copy
  import os
  import random
  from typing import Dict, Optional, Union
  import numpy
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from torch.utils.data import DataLoader
  from transformers import AutoModelWithLMHead
  from nemo.collections.nlp.data.dialogue import DialogueGPTClassificationDataset, DialogueSGDDataProcessor
  from nemo.collections.nlp.data.dialogue.data_processor.assistant_data_processor import DialogueAssistantDataProcessor
  from nemo.collections.nlp.data.dialogue.data_processor.design_data_processor import DialogueDesignDataProcessor
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport
  from nemo.collections.nlp.metrics.dialogue_metrics import DialogueClassificationMetrics
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_prompt_learning_model import MegatronGPTPromptLearningModel
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.collections.nlp.modules.common import VirtualPromptSource, VirtualPromptStyle
  from nemo.collections.nlp.modules.common.text_generation_utils import get_default_sampling_params, megatron_gpt_generate
  from nemo.collections.nlp.modules.common.transformer.text_generation import LengthParam
  from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 17 for calling the DialogueGPTClassificationModel obj's underlying member method code with example usage: obj.binary_score_candidates(candidate_input_ids, candidate_attn_masks, utterance_length, labels, template_length, correct_candidate, minus_negative, inference) and returns: generated_field,  ground_truth_field