code: def fog(
      x: np.ndarray,
      fractal_mult: float,
      wibbledecay: float,
      xrange: tuple = None,
  ) -> np.ndarray:
      """
      Apply fog to instance.
  
      Parameters
      ----------
      x
          Instance to be perturbed.
      fractal_mult
          Strength applied to `plasma_fractal` output.
      wibbledecay
          Decay factor for size of noise that is applied.
      xrange
          Tuple with min and max data range.
  
      Returns
      -------
      Perturbed instance.
      """
      x, scale_back = scale_minmax(x, xrange)
      max_val = x.max()
      nrows, ncols = x.shape[:2]
      x_fo = (
          x
          + fractal_mult
          * plasma_fractal(wibbledecay=wibbledecay)[:nrows, :ncols][
              ..., np.newaxis
          ]
      )
      x_fo = x_fo * max_val / (max_val + fractal_mult)
      if scale_back:
          x_fo = x_fo * (xrange[1] - xrange[0]) + xrange[0]
      if isinstance(xrange, tuple):
          return np.clip(x_fo, xrange[0], xrange[1])
      else:
          return x_fo
  
imports: import random
  from io import BytesIO
  from typing import List, Tuple, Union
  import cv2
  import numpy
  import skimage
  from alibi_detect.utils.data import Bunch
  from alibi_detect.utils.discretizer import Discretizer
  from alibi_detect.utils.distance import abdm, multidim_scaling
  from alibi_detect.utils.mapping import ohe2ord
  from PIL import Image
  from scipy.ndimage import zoom
  from scipy.ndimage.interpolation import map_coordinates
  from skimage.filters import gaussian
label_desc: Use image node_id 17 for calling a global function with example usage: fog(x, fractal_mult, wibbledecay, xrange) and returns: np, x_fo