code: def configure_optimizers(self):
      # return torch.optim.Adam(self.parameters(), lr=0.0002)
      opt = torch.optim.SGD(
          self.parameters(), lr=0.1, momentum=0.9, weight_decay=0.0005
      )
  
      def lr_step_func(epoch):
          return 0.1 ** len([m for m in [15, 25, 28] if m <= epoch])
  
      scheduler = torch.optim.lr_scheduler.LambdaLR(
          optimizer=opt, lr_lambda=lr_step_func
      )
      lr_scheduler = {
          "scheduler": scheduler,
          "name": "learning_rate",
          "interval": "epoch",
          "frequency": 1,
      }
      return [opt], [lr_scheduler]
  
imports: from argparse import ArgumentParser
  import os
  import os.path
  import torch
  import torch.nn
  from torch.nn import functional
  from torch.utils.data import DataLoader
  import pytorch_lightning
  from pytorch_lightning.callbacks import ModelCheckpoint
  from pytorch_lightning.callbacks import LearningRateMonitor
  from pytorch_lightning.loggers import TensorBoardLogger
  import timm
  from datasets.dataset_synthetics import FaceDataset, DataLoaderX
label_desc: Use image node_id 6 for calling the FaceSynthetics obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list, unknown