def gradients_X(self,dL_dK,X,X2,target):
        """derivative of the covariance matrix with respect to X."""
        AX = np.dot(X,self.transform)
        if X2 is None:
            X2 = X
            ZX2 = AX
        else:
            AX2 = np.dot(X2, self.transform)
        self.k.gradients_X(dL_dK, X, X2, target)
        self.k.gradients_X(dL_dK, AX, X2, target)
        self.k.gradients_X(dL_dK, X, AX2, target)
        self.k.gradients_X(dL_dK, AX ,AX2, target)
dataset/ETHPy150Open SheffieldML/GPy/GPy/kern/src/todo/symmetric.py/Symmetric.gradients_X/original