code: def training_step(self, batch, batch_idx):
      input_ids, attn_masks, labels, _, _ = batch
  
      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 copy
  import os
  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.data_processor.mellon_qa_data_processor import DialogueMellonQADataProcessor
  from nemo.collections.nlp.data.dialogue.data_processor.ms_marco_data_processor import DialogueMSMarcoDataProcessor
  from nemo.collections.nlp.data.dialogue.dataset.dialogue_gpt_generation_dataset import DialogueGPTGenerationDataset
  from nemo.collections.nlp.metrics.dialogue_metrics import DialogueGenerationMetrics
  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.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling the DialogueGPTGenerationModel obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: dict