code: def configure_optimizers_and_lr_scheduler(
      self,
      module: torch.nn.Module,
  ) -> Tuple[torch.optim.Optimizer, Optional[TLRScheduler]]:
      optimizer = create_optimizer(module, self.opt_cfg)
      return optimizer, None
  
imports: import argparse
  import logging
  import tempfile
  from argparse import Namespace
  from typing import Optional, Tuple
  import torch
  from libfb.py import parutil
  from torch.utils.data import random_split
  from torch.utils.data.dataset import Dataset, Subset
  from torchtnt.examples.mingpt.char_dataset import CharDataset, DataConfig
  from torchtnt.examples.mingpt.model import create_optimizer, GPT, GPTConfig, OptimizerConfig
  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
label_desc: Use image node_id 2 for calling the MinGPTUnit obj's underlying member method code with example usage: obj.configure_optimizers_and_lr_scheduler(module) and returns: optimizer,  None