code: def parse_args():
      parser = argparse.ArgumentParser(description="Model training")
      # params of training
      parser.add_argument(
          "--config",
          dest="cfg",
          help="The config file.",
          default=None,
          type=str,
      )
      parser.add_argument(
          "--iters",
          dest="iters",
          help="iters for training",
          type=int,
          default=None,
      )
      parser.add_argument(
          "--batch_size",
          dest="batch_size",
          help="Mini batch size of one gpu or cpu",
          type=int,
          default=None,
      )
      parser.add_argument(
          "--learning_rate",
          dest="learning_rate",
          help="Learning rate",
          type=float,
          default=None,
      )
      parser.add_argument(
          "--save_interval",
          dest="save_interval",
          help="How many iters to save a model snapshot once during training.",
          type=int,
          default=1000,
      )
      parser.add_argument(
          "--resume_model",
          dest="resume_model",
          help="The path of resume model",
          type=str,
          default=None,
      )
      parser.add_argument(
          "--save_dir",
          dest="save_dir",
          help="The directory for saving the model snapshot",
          type=str,
          default="./output",
      )
      parser.add_argument(
          "--keep_checkpoint_max",
          dest="keep_checkpoint_max",
          help="Maximum number of checkpoints to save",
          type=int,
          default=5,
      )
      parser.add_argument(
          "--num_workers",
          dest="num_workers",
          help="Num workers for data loader",
          type=int,
          default=0,
      )
      parser.add_argument(
          "--do_eval",
          dest="do_eval",
          help="Eval while training",
          action="store_true",
      )
      parser.add_argument(
          "--log_iters",
          dest="log_iters",
          help="Display logging information at every log_iters",
          default=10,
          type=int,
      )
      parser.add_argument(
          "--use_vdl",
          dest="use_vdl",
          help="Whether to record the data to VisualDL during training",
          action="store_true",
      )
      parser.add_argument(
          "--threshold",
          dest="threshold",
          help="Threshold applied to center heatmap score",
          type=float,
          default=0.1,
      )
      parser.add_argument(
          "--nms_kernel",
          dest="nms_kernel",
          help="NMS max pooling kernel size",
          type=int,
          default=7,
      )
      parser.add_argument(
          "--top_k",
          dest="top_k",
          help="Top k centers to keep",
          type=int,
          default=200,
      )
  
      return parser.parse_args()
  
file_path: PaddleSeg/contrib/PanopticDeepLab/train.py
imports: import argparse
  import paddle
  from paddleseg.cvlibs import manager, Config
  from paddleseg.utils import get_sys_env, logger
  from core import train
  from datasets import CityscapesPanoptic
  from models import PanopticDeepLab
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: parser