code: def configure_optimizers_and_lr_scheduler(
      self, module: torch.nn.Module
  ) -> Tuple[torch.optim.Optimizer, Optional[TLRScheduler]]:
      optimizer = Adadelta(module.parameters(), lr=self.lr)
      lr_scheduler = StepLR(optimizer, step_size=1, gamma=self.gamma)
      return optimizer, lr_scheduler
  
imports: import argparse
  import sys
  import tempfile
  from argparse import Namespace
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.optim import Adadelta
  from torch.optim.lr_scheduler import StepLR
  from torch.utils.data import DataLoader
  from torcheval.metrics import MulticlassAccuracy
  from torchtnt.framework.auto_unit import AutoUnit, TrainStepResults
  from torchtnt.framework.fit import fit
  from torchtnt.framework.state import State
  from torchtnt.utils import init_from_env, seed, TLRScheduler
  from torchtnt.utils.loggers import TensorBoardLogger
  from torchvision import datasets, transforms
label_desc: Use image node_id 2 for calling the MyUnit obj's underlying member method code with example usage: obj.configure_optimizers_and_lr_scheduler(module) and returns: optimizer,  lr_scheduler