code: def get_palette(num_cls):
      """
      Returns the color map for visualizing the segmentation mask.
  
      Args:
          num_cls: Number of classes
  
      Returns:
          The color map
      """
      n = num_cls
      palette = [0] * (n * 3)
      for j in range(0, n):
          lab = j
          palette[j * 3 + 0] = 0
          palette[j * 3 + 1] = 0
          palette[j * 3 + 2] = 0
          i = 0
          while lab:
              palette[j * 3 + 0] |= ((lab >> 0) & 1) << (7 - i)
              palette[j * 3 + 1] |= ((lab >> 1) & 1) << (7 - i)
              palette[j * 3 + 2] |= ((lab >> 2) & 1) << (7 - i)
              i += 1
              lab >>= 3
      return palette
  
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 9 for calling a global function with example usage: get_palette(num_cls) and returns: palette