code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Deploy for matting model"
      )
      parser.add_argument(
          "--config",
          dest="cfg",
          help="The config file.",
          default=None,
          type=str,
          required=True,
      )
      parser.add_argument(
          "--image_path",
          dest="image_path",
          help="The directory or path or file list of the images to be predicted.",
          type=str,
          default=None,
      )
      parser.add_argument(
          "--trimap_path",
          dest="trimap_path",
          help="The directory or path or file list of the triamp to help predicted.",
          type=str,
          default=None,
      )
      parser.add_argument(
          "--batch_size",
          dest="batch_size",
          help="Mini batch size of one gpu or cpu. When video inference, it is invalid.",
          type=int,
          default=1,
      )
      parser.add_argument(
          "--video_path",
          dest="video_path",
          help="The path of the video to be predicted.",
          type=str,
          default=None,
      )
      parser.add_argument(
          "--save_dir",
          dest="save_dir",
          help="The directory for saving the predict result.",
          type=str,
          default="./output",
      )
      parser.add_argument(
          "--device",
          choices=["cpu", "gpu"],
          default="gpu",
          help="Select which device to inference, defaults to gpu.",
      )
      parser.add_argument(
          "--fg_estimate",
          default=True,
          type=eval,
          choices=[True, False],
          help="Whether to estimate foreground when predicting.",
      )
  
      parser.add_argument(
          "--cpu_threads",
          default=10,
          type=int,
          help="Number of threads to predict when using cpu.",
      )
      parser.add_argument(
          "--enable_mkldnn",
          default=False,
          type=eval,
          choices=[True, False],
          help="Enable to use mkldnn to speed up when using cpu.",
      )
      parser.add_argument(
          "--use_trt",
          default=False,
          type=eval,
          choices=[True, False],
          help="Whether to use Nvidia TensorRT to accelerate prediction.",
      )
      parser.add_argument(
          "--precision",
          default="fp32",
          type=str,
          choices=["fp32", "fp16", "int8"],
          help="The tensorrt precision.",
      )
      parser.add_argument(
          "--enable_auto_tune",
          default=False,
          type=eval,
          choices=[True, False],
          help="Whether to enable tuned dynamic shape. We uses some images to collect "
          "the dynamic shape for trt sub graph, which avoids setting dynamic shape manually.",
      )
      parser.add_argument(
          "--auto_tuned_shape_file",
          type=str,
          default="auto_tune_tmp.pbtxt",
          help="The temp file to save tuned dynamic shape.",
      )
  
      parser.add_argument(
          "--benchmark",
          type=eval,
          default=False,
          help="Whether to log some information about environment, model, configuration and performance.",
      )
      parser.add_argument(
          "--model_name",
          default="",
          type=str,
          help="When `--benchmark` is True, the specified model name is displayed.",
      )
      parser.add_argument(
          "--print_detail",
          default=True,
          type=eval,
          choices=[True, False],
          help="Print GLOG information of Paddle Inference.",
      )
  
      return parser.parse_args()
  
imports: import argparse
  import codecs
  import os
  import sys
  import cv2
  import tqdm
  import yaml
  import numpy
  import paddle
  from paddle.inference import create_predictor, PrecisionType
  from paddle.inference import Config
  from paddleseg.cvlibs import manager
  from paddleseg.utils import get_sys_env, logger
  import ppmatting.transforms
  from ppmatting.utils import get_image_list, mkdir, estimate_foreground_ml, VideoReader, VideoWriter
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: parser