code: def add_model_specific_args(parser, root_dir):
      BaseTransformer.add_model_specific_args(parser, root_dir)
      add_generic_args(parser, root_dir)
      parser.add_argument(
          "--max_source_length",
          default=128,
          type=int,
          help=(
              "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          ),
      )
      parser.add_argument(
          "--max_target_length",
          default=25,
          type=int,
          help=(
              "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          ),
      )
      parser.add_argument(
          "--val_max_target_length",
          default=25,
          type=int,
          help=(
              "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          ),
      )
      parser.add_argument(
          "--test_max_target_length",
          default=25,
          type=int,
          help=(
              "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          ),
      )
      parser.add_argument(
          "--logger_name",
          type=str,
          choices=["default", "wandb", "wandb_shared"],
          default="default",
      )
      parser.add_argument(
          "--n_train",
          type=int,
          default=-1,
          required=False,
          help="# examples. -1 means use all.",
      )
      parser.add_argument(
          "--n_val",
          type=int,
          default=-1,
          required=False,
          help="# examples. -1 means use all.",
      )
      parser.add_argument(
          "--n_test",
          type=int,
          default=-1,
          required=False,
          help="# examples. -1 means use all.",
      )
      parser.add_argument(
          "--label_smoothing", type=float, default=0.0, required=False
      )
      parser.add_argument(
          "--prefix",
          type=str,
          default=None,
          help="Prefix added at the beginning of each text, typically used with T5-based models.",
      )
      parser.add_argument(
          "--early_stopping_patience",
          type=int,
          default=-1,
          required=False,
          help=(
              "-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
              " val_check_interval will effect it."
          ),
      )
      parser.add_argument(
          "--distributed-port",
          type=int,
          default=-1,
          required=False,
          help="Port number for distributed training.",
      )
      parser.add_argument(
          "--model_type",
          choices=["rag_sequence", "rag_token", "bart", "t5"],
          type=str,
          help=(
              "RAG model type: sequence or token, if none specified, the type is inferred from the"
              " model_name_or_path"
          ),
      )
      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 20 for calling the GenerativeQAModule obj's underlying member method code with example usage: obj.add_model_specific_args(parser, root_dir) and returns: parser