code: def add_retriever_specific_args(parser):
      parser.add_argument(
          "--index_name",
          type=str,
          default=None,
          help=(
              "Name of the index to use: 'hf' for a canonical dataset from the datasets library (default), 'custom'"
              " for a local index, or 'legacy' for the orignal one)"
          ),
      )
      parser.add_argument(
          "--passages_path",
          type=str,
          default=None,
          help=(
              "Path to the dataset of passages for custom index. More info about custom indexes in the RagRetriever"
              " documentation as well as in `examples/rag/use_own_knowledge_dataset.py`"
          ),
      )
      parser.add_argument(
          "--index_path",
          type=str,
          default=None,
          help=(
              "Path to the faiss index for custom index. More info about custom indexes in the RagRetriever"
              " documentation as well as in `examples/rag/use_own_knowledge_dataset.py`"
          ),
      )
      parser.add_argument(
          "--distributed_retriever",
          choices=["ray", "pytorch"],
          type=str,
          default="pytorch",
          help=(
              "What implementation to use for distributed retriever? If "
              "pytorch is selected, the index is loaded on training "
              "worker 0, and torch.distributed is used to handle "
              "communication between training worker 0, and the other "
              "training workers. If ray is selected, the Ray library is "
              "used to create load the index on separate processes, "
              "and Ray handles the communication between the training "
              "workers and the retrieval actors."
          ),
      )
      parser.add_argument(
          "--use_dummy_dataset",
          type=bool,
          default=False,
          help=(
              "Whether to use the dummy version of the dataset index. More info about custom indexes in the"
              " RagRetriever documentation as well as in `examples/rag/use_own_knowledge_dataset.py`"
          ),
      )
      return parser
  
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 21 for calling the GenerativeQAModule obj's underlying member method code with example usage: obj.add_retriever_specific_args(parser) and returns: parser