code: def scale_minmax(
      x: np.ndarray, xrange: tuple = None
  ) -> Tuple[np.ndarray, bool]:
      """
      Minmax scaling to [0,1].
  
      Parameters
      ----------
      x
          Numpy array to be scaled.
      xrange
          Tuple with min and max data range.
  
      Returns
      -------
      Scaled array and boolean whether the array is actually scaled.
      """
      scale_back = False
      if isinstance(xrange, tuple):
          scale_back = True
          x = (x - xrange[0]) / (xrange[1] - xrange[0])
      return x, scale_back
  
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 5 for calling a global function with example usage: scale_minmax(x, xrange) and returns: x,  scale_back