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=str(
              Path(__file__).parent
              / "test_run"
              / "dummy-kb"
              / "my_knowledge_dataset"
          ),
          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=str(
              Path(__file__).parent
              / "test_run"
              / "dummy-kb"
              / "my_knowledge_dataset_hnsw_index.faiss"
          ),
          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="ray",
          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 copy
  import json
  import logging
  import multiprocessing
  import os
  import random
  import shutil
  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
  from datasets import concatenate_datasets, load_from_disk
  from torch.utils.data import DataLoader
  from transformers import AutoConfig, AutoTokenizer, BartForConditionalGeneration, BatchEncoding, DPRConfig, DPRContextEncoder, DPRContextEncoderTokenizerFast, RagConfig, RagSequenceForGeneration, RagTokenForGeneration, RagTokenizer, T5ForConditionalGeneration
  from transformers import logging
  from transformers.integrations import is_ray_available
  from glob import glob
  from callbacks_rag import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
  from kb_encode_utils import add_index, embed_update
  from lightning_base import BaseTransformer, add_generic_args, generic_train
  from pynvml import nvmlDeviceGetCount, nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo, nvmlInit
  from utils_rag import Seq2SeqDataset, calculate_exact_match, get_git_info, is_rag_model, lmap, pickle_save, save_git_info, save_json, set_extra_model_params
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