code: def make_multiscale_noise(
      shape, scales, weights=None, span=(0.0, 1.0)
  ):
      if weights is None:
          weights = [1.0] * len(scales)
      result = make_noise_at_scale(shape, scales[0]) * weights[0]
      for s, w in zip(scales, weights):
          result += make_noise_at_scale(shape, s) * w
      lo, hi = span
      result -= np.amin(result)
      result /= np.amax(result)
      result *= hi - lo
      result += lo
      return result
  
imports: import random
  from random import randint
  import numpy
  import cv2
label_desc: Use image node_id 11 for calling a global function with example usage: make_multiscale_noise(shape, scales, weights, span) and returns: result