code: def run_length_decoding(mask_rle, 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(shape[1] * shape[0], dtype=np.uint8)
      for lo, hi in zip(starts, ends):
          img[lo:hi] = 255
      return img.reshape((shape[1], shape[0])).T
  
imports: import os
  import json
  import sys
  import time
  import numpy
  import pandas
  from PIL import Image
  from tqdm import tqdm
  from pycocotools import mask
  from sklearn.model_selection import KFold
  import settings
label_desc: Use image node_id 5 for calling a global function with example usage: run_length_decoding(mask_rle, shape) and returns: img