code: def plot_image_with_boxes(img, boxes, pred_cls):
      text_size = 5
      text_th = 5
      rect_th = 6
  
      for i in range(len(boxes)):
          # Draw Rectangle with the coordinates
  
          cv2.rectangle(
              img,
              (int(boxes[i][0][0]), int(boxes[i][0][1])),
              (int(boxes[i][1][0]), int(boxes[i][1][1])),
              color=(0, 255, 0),
              thickness=rect_th,
          )
          # Write the prediction class
          cv2.putText(
              img,
              pred_cls[i],
              (int(boxes[i][0][0]), int(boxes[i][0][1])),
              cv2.FONT_HERSHEY_SIMPLEX,
              text_size,
              (0, 255, 0),
              thickness=text_th,
          )
      plt.axis("off")
      plt.imshow(img.astype(np.uint8), interpolation="nearest")
      plt.show()
  
file_path: adversarial-robustness-toolbox/examples/application_object_detection.py
imports: import cv2
  import numpy
  import matplotlib.pyplot
  from art.estimators.object_detection import PyTorchFasterRCNN
  from art.attacks.evasion import ProjectedGradientDescent
label_desc: Use image node_id 2 for calling a global function with example usage: plot_image_with_boxes(img, boxes, pred_cls) without return types