code: def configure_optimizers_and_lr_scheduler(
      self, module: torch.nn.Module
  ) -> Tuple[torch.optim.Optimizer, TLRScheduler]:
      my_optimizer = torch.optim.SGD(module.parameters(), lr=0.01)
      my_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
          my_optimizer, gamma=0.9
      )
      return my_optimizer, my_lr_scheduler
  
imports: from typing import Iterable, Iterator, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from torch.utils.data import DataLoader, Dataset, IterableDataset, TensorDataset
  from torchtnt.framework.auto_unit import AutoUnit
  from torchtnt.framework.state import EntryPoint, PhaseState, State
  from torchtnt.framework.unit import EvalUnit, PredictUnit, TrainUnit
  from torchtnt.utils.lr_scheduler import TLRScheduler
label_desc: Use image node_id 2 for calling the DummyAutoUnit obj's underlying member method code with example usage: obj.configure_optimizers_and_lr_scheduler(module) and returns: my_optimizer,  my_lr_scheduler