'Return the kernel k(X, Y) and optionally its gradient.
Parameters
X : array, shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y)
Y : array, shape (n_samples_Y, n_features), (optional, default=None)
Right argument of the returned kernel k(X, Y). If None, k(X, X)
if evaluated instead.
eval_gradient : bool (optional, default=False)
Determines whether the gradient with respect to the kernel
hyperparameter is determined.
Returns
K : array, shape (n_samples_X, n_samples_Y)
Kernel k(X, Y)
K_gradient : array (opt.), shape (n_samples_X, n_samples_X, n_dims)
The gradient of the kernel k(X, X) with respect to the
hyperparameter of the kernel. Only returned when eval_gradient
is True.'

def __call__(self, X, Y=None, eval_gradient=False):

    if eval_gradient:
        (K1, K1_gradient) = self.k1(X, Y, eval_gradient=True)
        (K2, K2_gradient) = self.k2(X, Y, eval_gradient=True)
        return ((K1 + K2), np.dstack((K1_gradient, K2_gradient)))
    else:
        return (self.k1(X, Y) + self.k2(X, Y))
