code: def empty_results(num_classes, num_images):
      """Return empty results lists for boxes, masks, and keypoints.
      Box detections are collected into:
        all_boxes[cls][image] = N x 5 array with columns (x1, y1, x2, y2, score)
      Instance mask predictions are collected into:
        all_segms[cls][image] = [...] list of COCO RLE encoded masks that are in
        1:1 correspondence with the boxes in all_boxes[cls][image]
      Keypoint predictions are collected into:
        all_keyps[cls][image] = [...] list of keypoints results, each encoded as
        a 3D array (#rois, 4, #keypoints) with the 4 rows corresponding to
        [x, y, logit, prob] (See: utils.keypoints.heatmaps_to_keypoints).
        Keypoints are recorded for person (cls = 1); they are in 1:1
        correspondence with the boxes in all_boxes[cls][image].
      Body uv predictions are collected into:
        TODO
      """
      # Note: do not be tempted to use [[] * N], which gives N references to the
      # *same* empty list.
      all_boxes = [
          [[] for _ in range(num_images)] for _ in range(num_classes)
      ]
      all_segms = [
          [[] for _ in range(num_images)] for _ in range(num_classes)
      ]
      all_keyps = [
          [[] for _ in range(num_images)] for _ in range(num_classes)
      ]
      all_bodys = [
          [[] for _ in range(num_images)] for _ in range(num_classes)
      ]
      return all_boxes, all_segms, all_keyps, all_bodys
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from collections import defaultdict
  import cv2
  import datetime
  import logging
  import numpy
  import os
  import yaml
  from caffe2.python import workspace
  from detectron.core.config import cfg
  from detectron.core.config import get_output_dir
  from detectron.core.rpn_generator import generate_rpn_on_dataset
  from detectron.core.rpn_generator import generate_rpn_on_range
  from detectron.core.test import im_detect_all
  from detectron.datasets import task_evaluation
  from detectron.datasets.json_dataset import JsonDataset
  from detectron.modeling import model_builder
  from detectron.utils.io import save_object
  from detectron.utils.timer import Timer
  import detectron.utils.c2
  import detectron.utils.env
  import detectron.utils.net
  import detectron.utils.subprocess
  import detectron.utils.vis
label_desc: Use image node_id 9 for calling a global function with example usage: empty_results(num_classes, num_images) and returns: all_boxes,  all_segms,  all_keyps,  all_bodys