code: def gaussian_kernel(n, std):
      gaussian_1d = signal.gaussian(n, std)
      gaussian_2d = np.outer(gaussian_1d, gaussian_1d)
      gaussian_3d = np.outer(gaussian_2d, gaussian_1d)
      gaussian_3d = gaussian_3d.reshape(n, n, n)
      gaussian_3d = np.cbrt(gaussian_3d)
      gaussian_3d /= gaussian_3d.max()
      return gaussian_3d
  
file_path: tinygrad/extra/datasets/kits19.py
imports: import random
  import functools
  from pathlib import Path
  import numpy
  import nibabel
  from scipy import signal
  import torch
  import torch.nn.functional
  from tinygrad.tensor import Tensor
  from tinygrad.helpers import fetch
label_desc: Use image node_id 8 for calling a global function with example usage: gaussian_kernel(n, std) and returns: gaussian_3d