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(
          "--input-dim", type=int, default=32, help="input dimension"
      )
      parser.add_argument(
          "--max-epochs", type=int, default=2, help="training epochs"
      )
      parser.add_argument(
          "--num-batches-per-epoch",
          type=int,
          default=1024,
          help="number of batches per epoch",
      )
      parser.add_argument(
          "--batch-size", type=int, default=32, help="batch size"
      )
      parser.add_argument(
          "--lr", type=float, default=0.1, help="learning rate"
      )
      parser.add_argument(
          "--log-every-n-steps",
          type=int,
          default=10,
          help="log every n steps",
      )
      parser.add_argument(
          "--precision",
          type=str,
          default=None,
          help="fp16, bf16, or fp32",
      )
      return parser.parse_args()
  
imports: import argparse
  import logging
  import tempfile
  import uuid
  from argparse import Namespace
  from typing import Literal, Optional, Tuple, Union
  import torch
  import torch.nn
  from torch.distributed import launcher
  from torch.utils.data.dataset import Dataset, TensorDataset
  from torcheval.metrics import BinaryAccuracy
  from torchtnt.framework.auto_unit import AutoUnit, Strategy, SWAParams, TrainStepResults
  from torchtnt.framework.fit import fit
  from torchtnt.framework.state import EntryPoint, State
  from torchtnt.utils import init_from_env, seed, TLRScheduler
  from torchtnt.utils.loggers import TensorBoardLogger
  from torchtnt.utils.prepare_module import ActivationCheckpointParams, TorchCompileParams
label_desc: Use image node_id 5 for calling a global function with example usage: get_args() and returns: parser