code: def get_args(argv: List[str]) -> Namespace:
      """Parse command line arguments"""
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--seed",
          type=int,
          default=1,
          metavar="S",
          help="random seed (default: 1)",
      )
      parser.add_argument(
          "--batch-size",
          type=int,
          default=64,
          metavar="N",
          help="input batch size for training (default: 64)",
      )
      parser.add_argument(
          "--test-batch-size",
          type=int,
          default=1000,
          metavar="N",
          help="input batch size for testing (default: 1000)",
      )
      parser.add_argument(
          "--max-epochs",
          type=int,
          default=14,
          metavar="N",
          help="number of epochs to train (default: 14)",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=1.0,
          metavar="LR",
          help="learning rate (default: 1.0)",
      )
      parser.add_argument(
          "--gamma",
          type=float,
          default=0.7,
          metavar="M",
          help="Learning rate step gamma (default: 0.7)",
      )
      parser.add_argument(
          "--save-model",
          action="store_true",
          default=False,
          help="For Saving the current Model",
      )
  
      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 or bf16",
          choices=["fp16", "bf16"],
      )
  
      parser.add_argument(
          "--max-train-steps-per-epoch",
          type=int,
          default=20,
          help="the max number of steps to run per epoch for training",
      )
  
      return parser.parse_args(argv)
  
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 a global function with example usage: get_args(argv) and returns: parser