code: def predict(
      self,
      images=None,
      paths=None,
      data=None,
      batch_size=1,
      use_gpu=False,
      **kwargs,
  ):
      all_data = images if images is not None else read_images(paths)
      total_num = len(all_data)
      loop_num = int(np.ceil(total_num / batch_size))
  
      res = []
      for iter_id in range(loop_num):
          batch_data = list()
          handle_id = iter_id * batch_size
          for image_id in range(batch_size):
              try:
                  batch_data.append(all_data[handle_id + image_id])
              except IndexError:
                  break
          out = self.model.batch_predict(batch_data, **kwargs)
          res.extend(out)
      return res
  
imports: from __future__ import absolute_import
  from __future__ import division
  import os
  import cv2
  import argparse
  import base64
  import paddlex
  from math import *
  import time, math, re
  import numpy
  import paddlehub
  from paddlehub.module.module import moduleinfo, runnable, serving
label_desc: Use image node_id 2 for calling the MODULE obj's underlying member method code with example usage: obj.predict(images, paths, data, batch_size, use_gpu) and returns: res