code: def uwmgi_rle_decode(mask_rle, image_shape):
      s = mask_rle.split()
      starts, lengths = [
          np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])
      ]
      starts -= 1
      ends = starts + lengths
      img = np.zeros(image_shape[0] * image_shape[1], dtype="uint8")
      for low, high in zip(starts, ends):
          img[low:high] = 1
      return img.reshape(image_shape)
  
file_path: PaddleSeg/tools/data/convert_multilabel.py
imports: import argparse
  import os
  import random
  import zipfile
  import cv2
  import numpy
  import pandas
  from PIL import Image
  from pycocotools.coco import COCO
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling a global function with example usage: uwmgi_rle_decode(mask_rle, image_shape) and returns: img