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="Iterations in 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(
          "--opts",
          help="Update the key-value pairs of all options.",
          default=None,
          nargs="+",
      )
      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 the model to resume.",
          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="Number of workers for data loader.",
          type=int,
          default=0,
      )
      parser.add_argument(
          "--do_eval",
          dest="do_eval",
          help="Whether to do evaluation 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(
          "--seed",
          dest="seed",
          help="Set the random seed during training.",
          default=None,
          type=int,
      )
      parser.add_argument(
          "--precision",
          default="fp32",
          type=str,
          choices=["fp32", "fp16"],
          help="Use AMP (Auto mixed precision) if precision='fp16'. If precision='fp32', the training is normal.",
      )
      parser.add_argument(
          "--amp_level",
          default="O1",
          type=str,
          choices=["O1", "O2"],
          help="Auto mixed precision level. Accepted values are “O1” and “O2”: O1 represent mixed precision, the input \
                  data type of each operator will be casted by white_list and black_list; O2 represent Pure fp16, all operators \
                  parameters and input data will be casted to fp16, except operators in black_list, don’t support fp16 kernel \
                  and batchnorm. Default is O1(amp).",
      )
      parser.add_argument(
          "--data_format",
          dest="data_format",
          help='Data format that specifies the layout of input. It can be "NCHW" or "NHWC". Default: "NCHW".',
          type=str,
          default="NCHW",
      )
      parser.add_argument(
          "--profiler_options",
          type=str,
          default=None,
          help="The option of train profiler. If profiler_options is not None, the train "
          "profiler is enabled. Refer to the paddleseg/utils/train_profiler.py for details.",
      )
      parser.add_argument(
          "--device",
          dest="device",
          help="Device place to be set, which can be gpu, xpu, npu, mlu or cpu.",
          default="gpu",
          choices=["cpu", "gpu", "xpu", "npu", "mlu"],
          type=str,
      )
      parser.add_argument(
          "--repeats",
          type=int,
          default=1,
          help="Repeat the samples in the dataset for `repeats` times in each epoch.",
      )
  
      return parser.parse_args()
  
file_path: PaddleSeg/contrib/RSSegBenchmark/train.py
imports: import argparse
  import random
  import paddle
  import numpy
  import cv2
  import backbones
  from paddleseg.cvlibs import manager, Config
  from paddleseg.utils import get_sys_env, logger
  from paddleseg.core import train
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: parser