code: def get_args() -> Namespace:
      """Parse command line arguments"""
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--seed", type=int, default=0, help="random seed"
      )
      parser.add_argument(
          "--truncate", type=float, default=0.05, help="truncate data"
      )
      parser.add_argument(
          "--train-split",
          type=float,
          default=0.9,
          help="training split",
      )
      parser.add_argument(
          "--block-size", type=int, default=128, help="block size"
      )
      parser.add_argument(
          "--batch-size", type=int, default=216, help="batch size"
      )
      parser.add_argument(
          "--max-steps", type=int, help="training steps"
      )
      parser.add_argument(
          "--max-steps-epoch", type=int, help="training steps per epoch"
      )
      parser.add_argument(
          "--max-epochs", type=int, default=10, help="training epochs"
      )
      parser.add_argument(
          "--n-layer",
          type=int,
          default=8,
          help="number of block layers",
      )
      parser.add_argument(
          "--n-head", type=int, default=8, help="number of heads"
      )
      parser.add_argument(
          "--n-embd", type=int, default=512, help="embedding dimension"
      )
      parser.add_argument(
          "--lr", type=float, default=0.0003, help="learning rate"
      )
      parser.add_argument(
          "--weight-decay", type=float, default=0.1, help="weight decay"
      )
      parser.add_argument(
          "--clip-grad-norm",
          type=float,
          default=1.0,
          help="clip gradient norm",
      )
      parser.add_argument(
          "--log-every-n-steps",
          type=int,
          default=10,
          help="log every n steps",
      )
  
      return parser.parse_args()
  
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 4 for calling a global function with example usage: get_args() and returns: parser