code: def parse_args():
      parser = argparse.ArgumentParser(
          description="PyTorch CIFAR10 DP Training"
      )
      parser.add_argument(
          "--grad_sample_mode", type=str, default="hooks"
      )
      parser.add_argument(
          "-j",
          "--workers",
          default=2,
          type=int,
          metavar="N",
          help="number of data loading workers (default: 2)",
      )
      parser.add_argument(
          "--epochs",
          default=90,
          type=int,
          metavar="N",
          help="number of total epochs to run",
      )
      parser.add_argument(
          "--start-epoch",
          default=1,
          type=int,
          metavar="N",
          help="manual epoch number (useful on restarts)",
      )
      parser.add_argument(
          "-b",
          "--batch-size-test",
          default=256,
          type=int,
          metavar="N",
          help="mini-batch size for test dataset (default: 256), this is the total "
          "batch size of all GPUs on the current node when "
          "using Data Parallel or Distributed Data Parallel",
      )
      parser.add_argument(
          "--batch-size",
          default=2000,
          type=int,
          metavar="N",
          help="approximate bacth size",
      )
      parser.add_argument(
          "--lr",
          "--learning-rate",
          default=0.1,
          type=float,
          metavar="LR",
          help="initial learning rate",
          dest="lr",
      )
      parser.add_argument(
          "--momentum",
          default=0.9,
          type=float,
          metavar="M",
          help="SGD momentum",
      )
      parser.add_argument(
          "--wd",
          "--weight-decay",
          default=0,
          type=float,
          metavar="W",
          help="SGD weight decay",
          dest="weight_decay",
      )
      parser.add_argument(
          "-p",
          "--print-freq",
          default=10,
          type=int,
          metavar="N",
          help="print frequency (default: 10)",
      )
      parser.add_argument(
          "--resume",
          default="",
          type=str,
          metavar="PATH",
          help="path to latest checkpoint (default: none)",
      )
      parser.add_argument(
          "-e",
          "--evaluate",
          dest="evaluate",
          action="store_true",
          help="evaluate model on validation set",
      )
      parser.add_argument(
          "--seed",
          default=None,
          type=int,
          help="seed for initializing training. ",
      )
  
      parser.add_argument(
          "--sigma",
          type=float,
          default=1.5,
          metavar="S",
          help="Noise multiplier (default 1.0)",
      )
      parser.add_argument(
          "-c",
          "--max-per-sample-grad_norm",
          type=float,
          default=10.0,
          metavar="C",
          help="Clip per-sample gradients to this norm (default 1.0)",
      )
      parser.add_argument(
          "--disable-dp",
          action="store_true",
          default=False,
          help="Disable privacy training and just train with vanilla SGD",
      )
      parser.add_argument(
          "--secure-rng",
          action="store_true",
          default=False,
          help="Enable Secure RNG to have trustworthy privacy guarantees."
          "Comes at a performance cost. Opacus will emit a warning if secure rng is off,"
          "indicating that for production use it's recommender to turn it on.",
      )
      parser.add_argument(
          "--delta",
          type=float,
          default=1e-5,
          metavar="D",
          help="Target delta (default: 1e-5)",
      )
  
      parser.add_argument(
          "--checkpoint-file",
          type=str,
          default="checkpoint",
          help="path to save check points",
      )
      parser.add_argument(
          "--data-root",
          type=str,
          default="../cifar10",
          help="Where CIFAR10 is/will be stored",
      )
      parser.add_argument(
          "--log-dir",
          type=str,
          default="/tmp/stat/tensorboard",
          help="Where Tensorboard log will be stored",
      )
      parser.add_argument(
          "--optim",
          type=str,
          default="SGD",
          help="Optimizer to use (Adam, RMSprop, SGD)",
      )
      parser.add_argument(
          "--lr-schedule",
          type=str,
          choices=["constant", "cos"],
          default="cos",
      )
  
      parser.add_argument(
          "--device",
          type=str,
          default="cpu",
          help="Device on which to run the code.",
      )
      parser.add_argument(
          "--local_rank",
          type=int,
          default=-1,
          help="Local rank if multi-GPU training, -1 for single GPU training. Will be overriden by the environment variables if running on a Slurm cluster.",
      )
  
      parser.add_argument(
          "--dist_backend",
          type=str,
          default="gloo",
          help="Choose the backend for torch distributed from: gloo, nccl, mpi",
      )
  
      parser.add_argument(
          "--clip_per_layer",
          action="store_true",
          default=False,
          help="Use static per-layer clipping with the same clipping threshold for each layer. Necessary for DDP. If `False` (default), uses flat clipping.",
      )
      parser.add_argument(
          "--debug",
          type=int,
          default=0,
          help="debug level (default: 0)",
      )
  
      return parser.parse_args()
  
imports: import argparse
  import logging
  import os
  import shutil
  import sys
  from datetime import datetime, timedelta
  import numpy
  import torch
  import torch.nn
  import torch.optim
  import torch.utils.data
  import torch.utils.data.distributed
  import torchvision.transforms
  from opacus import PrivacyEngine
  from opacus.distributed import DifferentiallyPrivateDistributedDataParallel
  from opacus.grad_sample.functorch import make_functional
  from torch.func import grad, grad_and_value, vmap
  from torch.nn.parallel import DistributedDataParallel
  from torchvision.datasets import CIFAR10
  from tqdm import tqdm
label_desc: Use image node_id 9 for calling a global function with example usage: parse_args() and returns: parser