code: def gaussian_noise(x, severity=1):
    
    c = [.08, .12, 0.18, 0.26, 0.38][severity - 1]
    x = np.array(x) / 255.
    x_clip = np.clip(x + np.random.normal(size=x.shape, scale=c), 0, 1) * 255
    return around_and_astype(x_clip)
docs: Gaussian noise corruption to images.
  
    Args:
      x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255].
      severity: integer, severity of corruption.
  
    Returns:
      numpy array, image with uint8 pixels in [0,255]. Added Gaussian noise.