code: def rank_candidates(
      self,
      candidate_input_ids,
      candidate_attn_masks,
      utterance_length,
      labels,
      template_length,
      minus_prior=True,
      inference=False,
  ):
      best_candidate_input_ids = []
  
      for i in range(candidate_input_ids.size(0)):
          # candidates are padded with first candidate to ensure equal number of candidates in batch
          # run for loop to strip redundant candidates
          last_j = candidate_input_ids.size(1)
          for j in range(1, candidate_input_ids.size(1)):
              if torch.equal(
                  candidate_input_ids[i, j, :],
                  candidate_input_ids[i, 0, :],
              ):
                  last_j = j
                  break
  
          utterance_end = utterance_length[i].item()
          # this might cause GPU memory pressure there are many candidates
          # if OOM, re-write to do this in a for loop with as many as train_ds.batch_size
          _, loss_per_sample = self(
              candidate_input_ids[i, :last_j, :],
              candidate_attn_masks[i, :last_j, :],
              candidate_input_ids[i, :last_j, :],
              inference=inference,
          )
  
          if minus_prior:
              _, utterance_free_cand_loss_per_sample = self(
                  candidate_input_ids[i, :last_j, utterance_end:],
                  candidate_attn_masks[i, :last_j, utterance_end:],
                  candidate_input_ids[i, :last_j, utterance_end:],
                  inference=inference,
              )
              considered_loss = (
                  loss_per_sample - utterance_free_cand_loss_per_sample
              )
          else:
              considered_loss = loss_per_sample
          best_j = torch.argmin(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 19 for calling the DialogueGPTClassificationModel obj's underlying member method code with example usage: obj.rank_candidates(candidate_input_ids, candidate_attn_masks, utterance_length, labels, template_length, minus_prior, inference) and returns: generated_field,  ground_truth_field