'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:
        (K, K_gradient) = self.kernel(X, Y, eval_gradient=True)
        K_gradient *= (self.exponent * (K[:, :, np.newaxis] ** (self.exponent - 1)))
        return ((K ** self.exponent), K_gradient)
    else:
        K = self.kernel(X, Y, eval_gradient=False)
        return (K ** self.exponent)
