code: def add_arguments_to_parser(self, parser):
      parser.add_lightning_class_args(
          MilestonesFinetuning, "finetuning"
      )
      parser.link_arguments("data.batch_size", "model.batch_size")
      parser.link_arguments("finetuning.milestones", "model.milestones")
      parser.link_arguments("finetuning.train_bn", "model.train_bn")
      parser.set_defaults(
          {
              "trainer.max_epochs": 15,
              "trainer.enable_model_summary": False,
              "trainer.num_sanity_val_steps": 0,
          }
      )
  
imports: import logging
  from pathlib import Path
  from typing import Union
  import torch
  import torch.nn.functional
  from lightning.pytorch import LightningDataModule, LightningModule, cli_lightning_logo
  from lightning.pytorch.callbacks.finetuning import BaseFinetuning
  from lightning.pytorch.cli import LightningCLI
  from lightning.pytorch.utilities import rank_zero_info
  from lightning.pytorch.utilities.model_helpers import get_torchvision_model
  from torch import nn, optim
  from torch.optim.lr_scheduler import MultiStepLR
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  from torchmetrics import Accuracy
  from torchvision import transforms
  from torchvision.datasets import ImageFolder
  from torchvision.datasets.utils import download_and_extract_archive
label_desc: Use image node_id 1 for calling the MyLightningCLI obj's underlying member method code with example usage: obj.add_arguments_to_parser(parser) without return types