code: def postprocess(self, data):
      # Format output following the example ObjectDetectionHandler format
      output = []
      for data_sample in data:
          pred_instances = data_sample.pred_instances
          bboxes = (
              pred_instances.bboxes.cpu()
              .numpy()
              .astype(np.float32)
              .tolist()
          )
          labels = (
              pred_instances.labels.cpu()
              .numpy()
              .astype(np.int32)
              .tolist()
          )
          scores = (
              pred_instances.scores.cpu()
              .numpy()
              .astype(np.float32)
              .tolist()
          )
          preds = []
          for idx in range(len(labels)):
              cls_score, bbox, cls_label = (
                  scores[idx],
                  bboxes[idx],
                  labels[idx],
              )
              if cls_score >= self.threshold:
                  class_name = self.model.dataset_meta["classes"][
                      cls_label
                  ]
                  result = dict(
                      class_label=cls_label,
                      class_name=class_name,
                      bbox=bbox,
                      score=cls_score,
                  )
                  preds.append(result)
          output.append(preds)
      return output
  
file_path: mmdetection/tools/deployment/mmdet_handler.py
imports: import base64
  import os
  import mmcv
  import numpy
  import torch
  from ts.torch_handler.base_handler import BaseHandler
  from mmdet.apis import inference_detector, init_detector
label_desc: Use image node_id 4 for calling the MMdetHandler obj's underlying member method code with example usage: obj.postprocess(data) and returns: output