Central finite distance gradient simplified

I'm asked to compute central finite difference scheme (f(i+1)-f(i-1)) on an image. My attempt is something like:

def gradient_x_diff(img):
    img = img.astype(float)
    return np.fabs(np.roll(imgf,1, axis = 0) - imgf(np.roll(imgf,1, axis = 0))

However, it's hinted that the solution is straightforward. It should be something like this:

def gradient_x_diff(img):
  img = img.astype(float)
  return img[*]-img[**]

What should I parse instead of the * and ** ?