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