code: def get_detectron_features(self, image_paths):
      img_tensor, im_scales, im_infos = [], [], []
  
      for image_path in image_paths:
          im, im_scale, im_info = self._image_transform(image_path)
          img_tensor.append(im)
          im_scales.append(im_scale)
          im_infos.append(im_info)
  
      # Image dimensions should be divisible by 32, to allow convolutions
      # in detector to work
      current_img_list = to_image_list(img_tensor, size_divisible=32)
      current_img_list = current_img_list.to("cuda")
  
      with torch.no_grad():
          output = self.detection_model(current_img_list)
  
      feat_list = self._process_feature_extraction(
          output,
          im_scales,
          im_infos,
          self.args.feature_name,
          self.args.confidence_threshold,
      )
  
      return feat_list
  
imports: import argparse
  import os
  import cv2
  import numpy
  import torch
  from maskrcnn_benchmark.config import cfg
  from maskrcnn_benchmark.layers import nms
  from maskrcnn_benchmark.modeling.detector import build_detection_model
  from maskrcnn_benchmark.structures.image_list import to_image_list
  from maskrcnn_benchmark.utils.model_serialization import load_state_dict
  from mmf.utils.download import download
  from PIL import Image
  from tools.scripts.features.extraction_utils import chunks, get_image_files
label_desc: Use image node_id 7 for calling the FeatureExtractor obj's underlying member method code with example usage: obj.get_detectron_features(image_paths) and returns: feat_list