class_docstr: ObjectDetector handler class. This handler takes an image
  and returns list of detected classes and bounding boxes respectively
code: def postprocess(self, data):
      result = []
  
      box_filters = [row["scores"] >= self.threshold for row in data]
      filtered_boxes, filtered_classes, filtered_scores = [
          [
              row[key][box_filter].tolist()
              for row, box_filter in zip(data, box_filters)
          ]
          for key in ["boxes", "labels", "scores"]
      ]
  
      for classes, boxes, scores in zip(
          filtered_classes, filtered_boxes, filtered_scores
      ):
          retval = []
          for _class, _box, _score in zip(classes, boxes, scores):
              _retval = map_class_to_label(
                  [[_box]], self.mapping, [[_class]]
              )[0]
              _retval["score"] = _score
              retval.append(_retval)
          result.append(retval)
  
      return result
  
file_path: serve/ts/torch_handler/object_detector.py
imports: import torch
  from torchvision import transforms
  from torchvision import __version__
  from packaging import version
  from .vision_handler import VisionHandler
  from ..utils.util import map_class_to_label
label_desc: Use image node_id 2 for calling the ObjectDetector obj's underlying member method code with example usage: obj.postprocess(data) and returns: result