code: def __init__(self, argv=sys.argv[1:]):
      inputdir = None
      outputfile = None
      subset_list = None
      batch_size = 1
      opts, args = getopt.getopt(
          argv,
          "i:o:b:s",
          ["inputdir=", "outfile=", "batch_size=", "subset_list="],
      )
      for opt, arg in opts:
          if opt in ("-i", "--inputdir"):
              inputdir = arg
          elif opt in ("-o", "--outfile"):
              outputfile = arg
          elif opt in ("-b", "--batch_size"):
              batch_size = int(arg)
          elif opt in ("-s", "--subset_list"):
              subset_list = arg
  
      assert (
          inputdir is not None
      )  # and os.path.isdir(inputdir), f"{inputdir}"
      assert outputfile is not None and not os.path.isfile(
          outputfile
      ), f"{outputfile}"
      if subset_list is not None:
          with open(os.path.realpath(subset_list)) as f:
              self.subset_list = set(
                  map(lambda x: self._vqa_file_split()[0], tryload(f))
              )
      else:
          self.subset_list = None
  
      self.config = CONFIG
      if torch.cuda.is_available():
          self.config.model.device = "cuda"
      self.inputdir = os.path.realpath(inputdir)
      self.outputfile = os.path.realpath(outputfile)
      self.preprocess = Preprocess(self.config)
      self.model = GeneralizedRCNN.from_pretrained(
          "unc-nlp/frcnn-vg-finetuned", config=self.config
      )
      self.batch = batch_size if batch_size != 0 else 1
      self.schema = DEFAULT_SCHEMA
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/lxmert/extracting_data.py
imports: import getopt
  import json
  import os
  import sys
  from collections import OrderedDict
  import datasets
  import numpy
  import torch
  from modeling_frcnn import GeneralizedRCNN
  from processing_image import Preprocess
  from utils import Config
label_desc: Use image node_id 1 to create a new Extract object  with example: obj = Extract(argv)