code: def postprocess(data_out, label_list, top_k):
      """
      Postprocess output of network, one image at a time.
  
      Args:
          data_out (numpy.ndarray): output data of network.
          label_list (list): list of label.
          top_k (int): Return top k results.
      """
      output = []
      for result in data_out:
          result_i = softmax(result)
          output_i = {}
          indexs = np.argsort(result_i)[::-1][0:top_k]
          for index in indexs:
              label = label_list[index]
              output_i[label] = float(result_i[index])
          output.append(output_i)
      return output
  
file_path: PaddleHub/modules/image/classification/resnet50_vd_wildanimals/processor.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import base64
  import cv2
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: postprocess(data_out, label_list, top_k) and returns: output