code: def postprocess(
      data_out,
      org_im,
      org_im_path,
      image_info,
      output_dir,
      visualization,
      palette,
  ):
      """
      Postprocess output of network. one image at a time.
  
      Args:
          data_out (numpy.ndarray): output of neural network.
          org_im (numpy.ndarray): orginal image.
          org_im_path (str): path of original image.
          image_info (dict): info about the preprocessed image.
          output_dir (str): output directory to store image.
          visualization (bool): whether to save image or not.
          palette (list): The palette to draw.
  
      Returns:
          res (list[dict]): keys contain 'path', 'data', the corresponding value is:
              path (str): The path of original image.
              data (numpy.ndarray): The postprocessed image data, only the alpha channel.
      """
      result = dict()
      result["path"] = org_im_path
  
      image_center = image_info["image_center"]
      image_scale = image_info["image_scale"]
      image_width = image_info["image_width"]
      image_height = image_info["image_height"]
      scale = image_info["scale"]
  
      data_out = np.squeeze(data_out)
      data_out = np.transpose(data_out, [1, 2, 0])
      logits_result = transform_logits(
          data_out,
          image_center,
          image_scale,
          image_width,
          image_height,
          scale,
      )
      parsing = np.argmax(logits_result, axis=2)
      parsing_im = np.asarray(parsing, dtype=np.uint8)
      result["data"] = parsing_im
  
      if visualization:
          check_dir(output_dir)
          save_im_path = get_save_image_name(
              org_im, org_im_path, output_dir
          )
          parsing_im = Image.fromarray(parsing_im)
          parsing_im.putpalette(palette)
          parsing_im.save(save_im_path)
  
      return result
  
file_path: PaddleHub/modules/image/semantic_segmentation/ace2p/processor.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import time
  import base64
  import cv2
  import numpy
  from PIL import Image
label_desc: Use image node_id 10 for calling a global function with example usage: postprocess(data_out, org_im, org_im_path, image_info, output_dir, visualization, palette) and returns: result