code: def eval_step_helper(self, batch, mode="val"):
      (
          input_ids,
          attn_masks,
          labels,
          candidate_input_ids,
          candidate_attn_masks,
          template_length,
          utterance_length,
          correct_candidate,
      ) = batch
  
      inference = mode == "test"
      loss, _ = self(input_ids, attn_masks, labels, inference=inference)
      self.log(
          "{}_loss".format(mode),
          loss,
          on_step=True,
          on_epoch=True,
          prog_bar=True,
          logger=True,
      )
  
      # ranking using perplexity of candidates following the "<utterance> <label_type>:"
      if self.eval_mode == "ranking":
          generated_field, ground_truth_field = self.rank_candidates(
              candidate_input_ids,
              candidate_attn_masks,
              utterance_length,
              labels,
              template_length,
              inference=inference,
          )
      # autoregressively generate candidates (possibly with constraint)
      elif self.eval_mode == "generation":
          (
              generated_field,
              ground_truth_field,
          ) = self.generate_candidates(
              labels, template_length, input_ids, attn_masks
          )
      # comparing likelihood based on the perplexity of generating " Answer: yes" after "<utterance> <label_type>: <candidate_label>"
      # (optionally, the difference of that with " Answer: no" using the flag minus_negative=True)
      elif self.eval_mode == "binary_score":
          (
              generated_field,
              ground_truth_field,
          ) = self.binary_score_candidates(
              candidate_input_ids,
              candidate_attn_masks,
              utterance_length,
              labels,
              template_length,
              correct_candidate,
              inference=inference,
          )
  
      else:
          raise ValueError(
              "{} is not among supported options (ranking, generation, binary_score)".format(
                  self.eval_mode
              )
          )
  
      return {
          "loss": loss,
          "input": self.tokenizer.tokenizer.batch_decode(
              input_ids, skip_special_tokens=True
          ),
          "generated_field": generated_field,
          "ground_truth_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 21 for calling the DialogueGPTClassificationModel obj's underlying member method code with example usage: obj.eval_step_helper(batch, mode) and returns: dict