code: def printlike_fibrous(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(
          image.shape,
          [1.0, 5.0, 10.0, 50.0],
          weights=[1.0, 0.3, 0.5, 0.3],
          span=(0.7, 1.0),
      )
      paper -= make_fibrous_image(
          image.shape, 300, 500, 0.01, span=(0.0, 0.25), blur=0.5
      )
      ink = make_multiscale_noise(
          image.shape, [1.0, 5.0, 10.0, 50.0], span=(0.0, 0.5)
      )
      blurred = cv.GaussianBlur(
          selector, (0, 0), sigmaX=blur, borderType=cv.BORDER_REFLECT
      )
      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 18 for calling a global function with example usage: printlike_fibrous(image, blur, blotches, inverted) and returns: unknown, printed