code: def training_step(self, batch, batch_idx) -> Dict:
      loss_tensors = self._step(batch)
  
      logs = {
          name: loss.detach()
          for name, loss in zip(self.loss_names, loss_tensors)
      }
      # tokens per batch
      tgt_pad_token_id = (
          self.tokenizer.generator.pad_token_id
          if isinstance(self.tokenizer, RagTokenizer)
          else self.tokenizer.pad_token_id
      )
      src_pad_token_id = (
          self.tokenizer.question_encoder.pad_token_id
          if isinstance(self.tokenizer, RagTokenizer)
          else self.tokenizer.pad_token_id
      )
      logs["tpb"] = (
          batch["input_ids"].ne(src_pad_token_id).sum()
          + batch["decoder_input_ids"].ne(tgt_pad_token_id).sum()
      )
  
      return {"loss": loss_tensors[0], "log": logs}
  
imports: import argparse
  import logging
  import os
  import sys
  import time
  from collections import defaultdict
  from pathlib import Path
  from typing import Any, Dict, List, Tuple
  import numpy
  import pytorch_lightning
  import torch
  import torch.distributed
  import torch.distributed
  from pytorch_lightning.plugins.training_type import DDPPlugin
  from torch.utils.data import DataLoader
  from transformers import AutoConfig, AutoTokenizer, BartForConditionalGeneration, BatchEncoding, RagConfig, RagSequenceForGeneration, RagTokenForGeneration, RagTokenizer, T5ForConditionalGeneration
  from transformers import logging
  from transformers.integrations import is_ray_available
  from callbacks_rag import get_checkpoint_callback, get_early_stopping_callback, Seq2SeqLoggingCallback
  from distributed_pytorch_retriever import RagPyTorchDistributedRetriever
  from utils_rag import calculate_exact_match, flatten_list, get_git_info, is_rag_model, lmap, pickle_save, save_git_info, save_json, set_extra_model_params, Seq2SeqDataset
  from lightning_base import BaseTransformer, add_generic_args, generic_train
label_desc: Use image node_id 6 for calling the GenerativeQAModule obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: dict