code: def add_model_specific_args(parent_parser):
      parser = ArgumentParser(parents=[parent_parser], add_help=False)
      parser.add_argument(
          "--batch_size",
          type=int,
          default=64,
          metavar="N",
          help="input batch size for training (default: 64)",
      )
      parser.add_argument(
          "--num_workers",
          type=int,
          default=3,
          metavar="N",
          help="number of workers (default: 3)",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=0.001,
          metavar="LR",
          help="learning rate (default: 0.001)",
      )
      return parser
  
imports: import logging
  import os
  from argparse import ArgumentParser
  import mlflow
  import mlflow.pytorch
  import pytorch_lightning
  import torch
  from pytorch_lightning.callbacks import LearningRateMonitor
  from pytorch_lightning.callbacks import ModelCheckpoint
  from pytorch_lightning.callbacks.early_stopping import EarlyStopping
  from torch.nn import functional
  from torch.utils.data import DataLoader
  from torch.utils.data import random_split
  from torchvision import datasets
  from torchvision import transforms
label_desc: Use image node_id 2 for calling the LightningMNISTClassifier obj's underlying member method code with example usage: obj.add_model_specific_args(parent_parser) and returns: parser