code: def printlike_multiscale(
      image, blur=1.0, blotches=5e-5, inverted=None
  ):
      if inverted:
          selector = image
      elif inverted is None:
          selector = autoinvert(image)
      else:
          selector = 1 - image
  
      selector = random_blotches(selector, 3 * blotches, blotches)
      paper = make_multiscale_noise_uniform(
          image.shape[:2], span=(0.8, 1.0)
      )
      ink = make_multiscale_noise_uniform(
          image.shape[:2], span=(0.0, 0.2)
      )
      blurred = (
          cv.GaussianBlur(
              selector,
              (0, 0),
              sigmaX=blur,
              borderType=cv.BORDER_REFLECT,
          )
          + selector
      ) / 2
      if blurred.ndim == 3:
          ink = np.repeat(ink[:, :, None], 3, 2)
          paper = np.repeat(paper[:, :, None], 3, 2)
  
      printed = blurred * ink + (1 - blurred) * paper
      if inverted:
          return 1 - printed
      else:
          return printed
  
imports: import random
  from random import randint
  import numpy
  import cv2
label_desc: Use image node_id 17 for calling a global function with example usage: printlike_multiscale(image, blur, blotches, inverted) and returns: unknown, printed