code: def _step(self, batch: dict) -> Tuple:
      source_ids, source_mask, target_ids = (
          batch["input_ids"],
          batch["attention_mask"],
          batch["decoder_input_ids"],
      )
  
      rag_kwargs = {}
      if isinstance(self.model, T5ForConditionalGeneration):
          decoder_input_ids = self.model._shift_right(target_ids)
          lm_labels = target_ids
      elif isinstance(self.model, BartForConditionalGeneration):
          decoder_input_ids = target_ids[:, :-1].contiguous()
          lm_labels = target_ids[:, 1:].clone()
      else:
          assert self.is_rag_model
          generator = self.model.rag.generator
          if isinstance(generator, T5ForConditionalGeneration):
              decoder_start_token_id = (
                  generator.config.decoder_start_token_id
              )
              decoder_input_ids = (
                  torch.cat(
                      [
                          torch.tensor(
                              [[decoder_start_token_id]]
                              * target_ids.shape[0]
                          ).to(target_ids),
                          target_ids,
                      ],
                      dim=1,
                  )
                  if target_ids.shape[0] < self.target_lens["train"]
                  else generator._shift_right(target_ids)
              )
          elif isinstance(generator, BartForConditionalGeneration):
              decoder_input_ids = target_ids
          lm_labels = decoder_input_ids
          rag_kwargs["reduce_loss"] = True
  
      assert decoder_input_ids is not None
  
      outputs = self(
          source_ids,
          attention_mask=source_mask,
          decoder_input_ids=decoder_input_ids,
          use_cache=False,
          labels=lm_labels,
          **rag_kwargs,
      )
  
      loss = outputs["loss"]
      return (loss,)
  
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 4 for calling the GenerativeQAModule obj's underlying member method code with example usage: obj._step(batch) and returns: loss