code: def _param_grad_helper(self, dL_dK, X, X2, target):
          
          self._K_computations(X, X2)
          base = self.variance*self.degree*self._K_poly_arg**(self.degree-1)
          base_cov_grad = base*dL_dK
  
  
              
          target[0] += np.sum(self._K_dvar*dL_dK)
          target[1] += (self._K_inner_prod*base_cov_grad).sum()
          target[2] += base_cov_grad.sum()
docs: Derivative of the covariance with respect to the parameters.