code: def gaussian_blur(
      x: np.ndarray,
      sigma: float,
      channel_axis: int = -1,
      xrange: tuple = None,
  ) -> np.ndarray:
      """
      Apply Gaussian blur.
  
      Parameters
      ----------
      x
          Instance to be perturbed.
      sigma
          Standard deviation determining the strength of the blur.
      channel_axis
          Denotes the axis of the colour channel. If `None` the image is assumed to be grayscale.
      xrange
          Tuple with min and max data range.
  
      Returns
      -------
      Perturbed instance.
      """
      x, scale_back = scale_minmax(x, xrange)
      x_gb = gaussian(x, sigma=sigma, channel_axis=channel_axis)
      if scale_back:
          x_gb = x_gb * (xrange[1] - xrange[0]) + xrange[0]
      if isinstance(xrange, tuple):
          return np.clip(x_gb, xrange[0], xrange[1])
      else:
          return x_gb
  
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 10 for calling a global function with example usage: gaussian_blur(x, sigma, channel_axis, xrange) and returns: np, x_gb