class_docstr: Args:
      use_gpu (bool): whether use gpu
      threshold (float): threshold to reserve the result for output.
class_docstr_tok: ["Args",":","use_gpu","(","bool",")",":","whether","use","gpu","threshold","(","float",")",":","threshold","to","reserve","the","result","for","output","."]
docstr: Run as a service.
docstr_tok: ["Run","as","a","service","."]
code: def serving_method(self, images: list, **kwargs):
      """
      Run as a service.
      """
      images_decode = D.base64_to_cv2(images[0])
      results = self.predict(image=images_decode, **kwargs)
      final = {}
      final["segm"] = base64.b64encode(results["segm"]).decode("utf8")
      final["label"] = base64.b64encode(results["label"]).decode("utf8")
      final["score"] = base64.b64encode(results["score"]).decode("utf8")
      return final
  
imports: import base64
  import os
  import time
  from functools import reduce
  from typing import Union
  import numpy
  import solov2.data_feed
  import solov2.processor
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import serving
label_desc: Use image node_id 3 for calling the DetectorSOLOv2 obj's underlying member method code with example usage: obj.serving_method(images) and returns: final