code: def _build_detection_model(self):
      if self.args.config_file:
          frcnn_cfg = Config.from_pretrained(self.args.config_file)
      else:
          frcnn_cfg = Config.from_pretrained(
              self.CONFIG_URL.get(
                  self.args.config_name, self.args.config_name
              )
          )
      if self.args.model_file:
          frcnn = GeneralizedRCNN.from_pretrained(
              self.args.model_file, config=frcnn_cfg
          )
      else:
          frcnn = GeneralizedRCNN.from_pretrained(
              self.MODEL_URL.get(
                  self.args.model_name, self.args.model_name
              ),
              config=frcnn_cfg,
          )
  
      return frcnn, frcnn_cfg
  
file_path: mmf/tools/scripts/features/frcnn/extract_features_frcnn.py
imports: import argparse
  import copy
  import logging
  import os
  import numpy
  import torch
  from tools.scripts.features.extraction_utils import chunks, get_image_files
  from tools.scripts.features.frcnn.frcnn_utils import Config
  from tools.scripts.features.frcnn.modeling_frcnn import GeneralizedRCNN
  from tools.scripts.features.frcnn.processing_image import Preprocess
label_desc: Use image node_id 3 for calling the FeatureExtractor obj's underlying member method code with example usage: obj._build_detection_model() and returns: frcnn,  frcnn_cfg