code: def _hammer_function_precompute(self,x0, L, Min, model):
          
          if x0 is None: return None, None
          if len(x0.shape)==1: x0 = x0[None,:]
          m = model.predict(x0)[0]
          pred = model.predict(x0)[1].copy()
          pred[pred<1e-16] = 1e-16
          s = np.sqrt(pred)
          r_x0 = (m-Min)/L
          s_x0 = s/L
          r_x0 = r_x0.flatten()
          s_x0 = s_x0.flatten()
          return r_x0, s_x0
docs: Pre-computes the parameters of a penalizer centered at x0.