class_docstr: Detection metric for COCO bbox task.
  
  Parameters
  ----------
  dataset : instance of gluoncv.data.COCODetection
      The validation dataset.
  save_prefix : str
      Prefix for the saved JSON results.
  use_time : bool
      Append unique datetime string to created JSON file name if ``True``.
  cleanup : bool
      Remove created JSON file if ``True``.
  score_thresh : float
      Detection results with confident scores smaller than ``score_thresh`` will
      be discarded before saving to results.
  data_shape : tuple of int, default is None
      If `data_shape` is provided as (height, width), we will rescale bounding boxes when
      saving the predictions.
      This is helpful when SSD/YOLO box predictions cannot be rescaled conveniently. Note that
      the data_shape must be fixed for all validation images.
  post_affine : a callable function with input signature (orig_w, orig_h, out_w, out_h)
      If not None, the bounding boxes will be affine transformed rather than simply scaled.
code: def __del__(self):
      if self._cleanup:
          try:
              os.remove(self._filename)
          except IOError as err:
              warnings.warn(str(err))
  
file_path: gluon-cv/gluoncv/utils/metrics/coco_detection.py
imports: from __future__ import absolute_import
  import sys
  import os
  from os import path
  import warnings
  import numpy
  import mxnet
label_desc: Use image node_id 2 for calling the COCODetectionMetric obj's underlying member method code with example usage: obj.__del__() without return types