code: def extract_predictions(predictions_, conf_thresh):
      # Get the predicted class
      predictions_class = [
          COCO_INSTANCE_CATEGORY_NAMES[i]
          for i in list(predictions_["labels"])
      ]
      #  print("\npredicted classes:", predictions_class)
      if len(predictions_class) < 1:
          return [], [], []
      # Get the predicted bounding boxes
      predictions_boxes = [
          [(i[0], i[1]), (i[2], i[3])]
          for i in list(predictions_["boxes"])
      ]
  
      # Get the predicted prediction score
      predictions_score = list(predictions_["scores"])
      # print("predicted score:", predictions_score)
  
      # Get a list of index with score greater than threshold
      threshold = conf_thresh
      predictions_t = [
          predictions_score.index(x)
          for x in predictions_score
          if x > threshold
      ]
      if len(predictions_t) == 0:
          return [], [], []
  
      # predictions in score order
      predictions_boxes = [predictions_boxes[i] for i in predictions_t]
      predictions_class = [predictions_class[i] for i in predictions_t]
      predictions_scores = [predictions_score[i] for i in predictions_t]
      return predictions_class, predictions_boxes, predictions_scores
  
file_path: adversarial-robustness-toolbox/examples/get_started_yolo.py
imports: import requests
  import numpy
  from PIL import Image
  from io import BytesIO
  import torch
  from art.estimators.object_detection.pytorch_yolo import PyTorchYolo
  from art.attacks.evasion import ProjectedGradientDescent
  import cv2
  import matplotlib
  import matplotlib.pyplot
label_desc: Use image node_id 1 for calling a global function with example usage: extract_predictions(predictions_, conf_thresh) and returns: predictions_class,  predictions_boxes,  predictions_scores, list,  list,  list, list,  list,  list