code: def parse_keypoint_result(self, input_dict, fetch_dict):
      heatmap = fetch_dict["conv2d_441.tmp_1"]
      keypoint_postprocess = HRNetPostProcess()
      im_shape = []
      for key, data in input_dict.items():
          data = base64.b64decode(data.encode("utf8"))
          byte_stream = io.BytesIO(data)
          img = Image.open(byte_stream).convert("RGB")
          im_shape.append([img.width, img.height])
      im_shape = np.array(im_shape)
      center = np.round(im_shape / 2.0)
      scale = im_shape / 200.0
      kpts, scores = keypoint_postprocess(heatmap, center, scale)
      results = {"keypoint": kpts, "scores": scores}
      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 6 for calling the DetectorOp obj's underlying member method code with example usage: obj.parse_keypoint_result(input_dict, fetch_dict) and returns: results