code: def parse_detection_result(self, input_dict, fetch_dict):
      bboxes = fetch_dict[GLOBAL_VAR["fetch_vars"][0]]
      bboxes_num = fetch_dict[GLOBAL_VAR["fetch_vars"][1]]
      if GLOBAL_VAR["model_config"].mask:
          masks = fetch_dict[GLOBAL_VAR["fetch_vars"][2]]
      idx = 0
      results = {}
      for img_name, num in zip(input_dict.keys(), bboxes_num):
          if num == 0:
              results[img_name] = "No object detected!"
          else:
              result = []
              bbox = bboxes[idx : idx + num]
              for line in bbox:
                  if (
                      line[0] > -1
                      and line[1]
                      > GLOBAL_VAR["model_config"].draw_threshold
                  ):
                      result.append(
                          f"{int(line[0])} {line[1]} "
                          f"{line[2]} {line[3]} {line[4]} {line[5]}"
                      )
              if len(result) == 0:
                  result = "No object detected!"
              results[img_name] = result
          idx += num
      return results
  
imports: import copy
  from paddle_serving_server.web_service import WebService, Op
  from paddle_serving_server.proto import general_model_config_pb2
  import google.protobuf.text_format
  import os
  import numpy
  import base64
  from PIL import Image
  import io
  from preprocess_ops import Compose
  from postprocess_ops import HRNetPostProcess
  from argparse import ArgumentParser, RawDescriptionHelpFormatter
  import yaml
label_desc: Use image node_id 5 for calling the DetectorOp obj's underlying member method code with example usage: obj.parse_detection_result(input_dict, fetch_dict) and returns: results