code: def overlay_class_names(self, image, predictions):
          
          scores = predictions.get_field("scores").tolist()
          labels = predictions.get_field("labels").tolist()
          labels = [self.CATEGORIES[i] for i in labels]
          boxes = predictions.bbox
  
          template = "{}: {:.2f}"
          for box, score, label in zip(boxes, scores, labels):
              x, y = box[:2]
              s = template.format(label, score)
              cv2.putText(
                  image, s, (x, y), cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 255, 255), 1
              )
  
          return image
docs: Adds detected class names and scores in the positions defined by the
          top-left corner of the predicted bounding box
  
          Arguments:
              image (np.ndarray): an image as returned by OpenCV
              predictions (BoxList): the result of the computation by the model.
                  It should contain the field `scores` and `labels`.