code: def argsparser():
      parser = argparse.ArgumentParser("PaDiM")
      parser.add_argument(
          "--config",
          type=str,
          default=None,
          help="Path of config",
          required=True,
      )
      parser.add_argument("--device", type=str, default=None)
      parser.add_argument("--batch_size", type=int, default=None)
      parser.add_argument("--num_workers", type=int, default=None)
      parser.add_argument("--seed", type=int, default=None)
      parser.add_argument("--data_path", type=str, default=None)
      parser.add_argument("--save_path", type=str, default=None)
      parser.add_argument("--category", type=str, default=None)
      parser.add_argument("--resize", type=list or tuple, default=None)
      parser.add_argument(
          "--crop_size", type=list or tuple, default=None
      )
      parser.add_argument(
          "--backbone",
          type=str,
          default=None,
          help="backbone model arch, one of [resnet18, resnet50, wide_resnet50_2]",
      )
      parser.add_argument("--do_eval", type=bool, default=None)
      parser.add_argument("--save_pic", type=bool, default=None)
  
      parser.add_argument("--save_model", type=bool, default=True)
      parser.add_argument("--print_freq", type=int, default=20)
      return parser.parse_args()
  
file_path: PaddleSeg/contrib/QualityInspector/tools/uad/padim/train.py
imports: import os
  import sys
  import time
  import random
  import argparse
  import datetime
  from random import sample
  from collections import OrderedDict
  import numpy
  import pandas
  import paddle
  import paddle.nn.functional
  from paddle.io import DataLoader
  import qinspector.uad.datasets.mvtec
  from qinspector.uad.models.padim import ResNet_PaDiM
  from qinspector.cvlib.uad_configs import ConfigParser
  from val import val
label_desc: Use image node_id 1 for calling a global function with example usage: argsparser() and returns: parser