code: def _norm_box(self, bbox, w, h):
      bbox_aug = torch.zeros(bbox.size(0), 6)
      bbox_aug[:, :4] = bbox
      bbox_aug[:, 0] /= w
      bbox_aug[:, 1] /= h
      bbox_aug[:, 2] /= w
      bbox_aug[:, 3] /= h
      bbox_aug[:, 4] = bbox_aug[:, 2] - bbox_aug[:, 0]
      bbox_aug[:, 5] = bbox_aug[:, 3] - bbox_aug[:, 1]
      return bbox_aug
  
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 6 for calling the FeatureExtractor obj's underlying member method code with example usage: obj._norm_box(bbox, w, h) and returns: bbox_aug