def _param_grad_helper(self, dL_dK, X, X2, target):
        self._K_computations(X, X2)
        self._dK_computations(dL_dK)
        if X2==None:
            gmapping = self.mapping.df_dtheta(2*self._dL_dl[:, None], X)
        else:
            gmapping = self.mapping.df_dtheta(self._dL_dl[:, None], X)
            gmapping += self.mapping.df_dtheta(self._dL_dl_two[:, None], X2)

        target+= np.hstack([(dL_dK*self._K_dvar).sum(), gmapping])
Derivative of the covariance with respect to the parameters.
dataset/ETHPy150Open SheffieldML/GPy/GPy/kern/src/todo/gibbs.py/Gibbs._param_grad_helper