code: def training_step(self, batch, batch_idx):
      (
          input_ids,
          attn_masks,
          labels,
          candidate_input_ids,
          candidate_attn_masks,
          template_length,
          utterance_length,
          correct_candidate,
      ) = batch
      # construct training samples as generating " Answer: yes/no" after "<utterance> <label_type>: <candidate_label>"
      if self.eval_mode == "binary_score":
          new_input_ids = []
          new_attn_masks = []
          for i in range(candidate_input_ids.size(0)):
              # in some datasets like assistant, there might be 60+ possible intents with 1 correct intent
              # therefore we might not want to use all possible intents as negative samples
              # instead use {binary_score_subsample_ratio} negative samples for every positive sample
              if self.cfg.dataset.binary_score_subsample:
                  new_input_ids.append(
                      candidate_input_ids[
                          i, 2 * correct_candidate[i].item(), :
                      ]
                  )
                  new_attn_masks.append(
                      candidate_attn_masks[
                          i, 2 * correct_candidate[i].item(), :
                      ]
                  )
                  possible_negatives = []
                  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
                      if j != 2 * correct_candidate[i].item():
                          possible_negatives.append(j)
                  negative_samples = random.choices(
                      possible_negatives,
                      k=int(
                          self.cfg.dataset.binary_score_subsample_ratio
                      ),
                  )
                  for negative_sample in negative_samples:
                      new_input_ids.append(
                          candidate_input_ids[i, negative_sample, :]
                      )
                      new_attn_masks.append(
                          candidate_attn_masks[i, negative_sample, :]
                      )
  
              else:
                  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
                      new_input_ids.append(candidate_input_ids[i, j, :])
                      new_attn_masks.append(
                          candidate_attn_masks[i, j, :]
                      )
          input_ids = torch.stack(new_input_ids)
          attn_masks = torch.stack(new_attn_masks)
          labels = self.get_binary_score_labels(input_ids)
  
      loss, _ = self(input_ids, attn_masks, labels, inference=False)
      self.log(
          "train_loss",
          loss,
          on_step=True,
          on_epoch=True,
          prog_bar=True,
          logger=True,
      )
      return {"loss": loss}
  
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 3 for calling the DialogueGPTClassificationModel obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: dict