code: def make_parser():
      parser = ArgumentParser(
          description="Train Single Shot MultiBox Detector on COCO"
      )
      parser.add_argument(
          "--data",
          "-d",
          type=str,
          default="/coco",
          required=True,
          help="path to test and training data files",
      )
      parser.add_argument(
          "--epochs",
          "-e",
          type=int,
          default=65,
          help="number of epochs for training",
      )
      parser.add_argument(
          "--batch-size",
          "--bs",
          type=int,
          default=32,
          help="number of examples for each iteration",
      )
      parser.add_argument(
          "--eval-batch-size",
          "--ebs",
          type=int,
          default=32,
          help="number of examples for each evaluation iteration",
      )
      parser.add_argument(
          "--seed",
          "-s",
          type=int,
          default=0,
          help="manually set random seed for torch",
      )
      parser.add_argument(
          "--evaluation",
          nargs="*",
          type=int,
          default=[3, 21, 31, 37, 42, 48, 53, 59, 64],
          help="epochs at which to evaluate",
      )
      parser.add_argument(
          "--multistep",
          nargs="*",
          type=int,
          default=[43, 54],
          help="epochs at which to decay learning rate",
      )
      parser.add_argument(
          "--target",
          type=float,
          default=None,
          help="target mAP to assert against at the end",
      )
  
      # Hyperparameters
      parser.add_argument(
          "--learning-rate",
          "--lr",
          type=float,
          default=2.6e-3,
          help="learning rate",
      )
      parser.add_argument(
          "--momentum",
          "-m",
          type=float,
          default=0.9,
          help="momentum argument for SGD optimizer",
      )
      parser.add_argument(
          "--weight-decay",
          "--wd",
          type=float,
          default=0.0005,
          help="momentum argument for SGD optimizer",
      )
      parser.add_argument("--warmup", type=int, default=None)
      parser.add_argument(
          "--backbone",
          type=str,
          default="resnet50",
          choices=[
              "resnet18",
              "resnet34",
              "resnet50",
              "resnet101",
              "resnet152",
          ],
      )
      parser.add_argument("--num-workers", type=int, default=4)
      parser.add_argument(
          "--fp16-mode",
          default=True,
          action="store_true",
          help="Enable half precision mode",
      )
      # Pipeline control
      parser.add_argument(
          "--data_pipeline",
          type=str,
          default="dali",
          choices=["dali", "no_dali"],
          help="data preprocessing pipeline to use",
      )
  
      return parser
  
imports: import os
  import sys
  import time
  from argparse import ArgumentParser
  import math
  import numpy
  import time
  import torch
  from torch.optim.lr_scheduler import MultiStepLR
  import torch.utils.data.distributed
  from src.model import model, Loss
  from src.utils import dboxes300_coco, Encoder
  from src.evaluate import evaluate
  from src.train import train_loop, tencent_trick
  from src.data import *
label_desc: Use image node_id 1 for calling a global function with example usage: make_parser() and returns: parser