code: def get_parser(self):
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--model_name",
          default="X-152-C4",
          type=str,
          help="Model to use for detection",
      )
      parser.add_argument(
          "--model_file",
          default=None,
          type=str,
          help="Detectron model file. This overrides the model_name param.",
      )
      parser.add_argument(
          "--config_file",
          default=None,
          type=str,
          help="Detectron config file",
      )
      parser.add_argument(
          "--start_index",
          default=0,
          type=int,
          help="Index to start from ",
      )
      parser.add_argument(
          "--end_index", default=None, type=int, help=""
      )
      parser.add_argument(
          "--batch_size", type=int, default=2, help="Batch size"
      )
      parser.add_argument(
          "--num_features",
          type=int,
          default=100,
          help="Number of features to extract.",
      )
      parser.add_argument(
          "--output_folder",
          type=str,
          default="./output",
          help="Output folder",
      )
      parser.add_argument(
          "--image_dir", type=str, help="Image directory or file"
      )
      parser.add_argument(
          "--exclude_list",
          type=str,
          help="List of images to be excluded from feature conversion. "
          + "Each image on a new line",
          default="./list",
      )
      parser.add_argument(
          "--background",
          action="store_true",
          help="The model will output predictions for the background class when set",
      )
      return parser
  
imports: import argparse
  import os
  import cv2
  import numpy
  import torch
  from maskrcnn_benchmark.config import cfg
  from maskrcnn_benchmark.data.transforms import build_transforms
  from maskrcnn_benchmark.structures.image_list import to_image_list
  from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer
  from mmf.utils.download import download
  from PIL import Image
  from scene_graph_benchmark.AttrRCNN import AttrRCNN
  from scene_graph_benchmark.config import sg_cfg
  from tools.scripts.features.extraction_utils import chunks, get_image_files
label_desc: Use image node_id 3 for calling the FeatureExtractor obj's underlying member method code with example usage: obj.get_parser() and returns: parser