code: def configure_optimizers(self):
      parameters = list(self.parameters())
      trainable_parameters = list(
          filter(lambda p: p.requires_grad, parameters)
      )
      rank_zero_info(
          f"The model will start training with only {len(trainable_parameters)} "
          f"trainable parameters out of {len(parameters)}."
      )
      optimizer = optim.Adam(trainable_parameters, lr=self.lr)
      scheduler = MultiStepLR(
          optimizer,
          milestones=self.milestones,
          gamma=self.lr_scheduler_gamma,
      )
      return [optimizer], [scheduler]
  
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 7 for calling the TransferLearningModel obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list