code: def training_step(self, batch, batch_idx):
      input_ids, input_attn_mask, _, _, labels = batch
      loss, _ = self(input_ids, input_attn_mask, labels)
      lr = self._optimizer.param_groups[0]["lr"]
  
      self.log("lr", lr, prog_bar=True)
      self.log(
          "train_loss",
          loss,
          on_step=True,
          on_epoch=True,
          prog_bar=True,
          logger=True,
      )
  
      return {"loss": loss}
  
imports: import collections
  from typing import List, Optional
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from transformers import AutoModelForCausalLM
  from nemo.collections.nlp.data.question_answering.data_processor.qa_processing import QAProcessor
  from nemo.collections.nlp.data.question_answering.dataset.qa_gpt_dataset import GPTQADataset
  from nemo.collections.nlp.metrics.qa_metrics import QAMetrics
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
  from nemo.collections.nlp.models.question_answering.qa_base_model import BaseQAModel
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling the GPTQAModel obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: dict