'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. Only supported when Y is None.
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):

    X = np.atleast_2d(X)
    if (Y is None):
        K = (np.inner(X, X) + (self.sigma_0 ** 2))
    else:
        if eval_gradient:
            raise ValueError('Gradient    can    only    be    evaluated    when    Y    is    None.')
        K = (np.inner(X, Y) + (self.sigma_0 ** 2))
    if eval_gradient:
        if (not self.hyperparameter_sigma_0.fixed):
            K_gradient = np.empty((K.shape[0], K.shape[1], 1))
            K_gradient[..., 0] = (2 * (self.sigma_0 ** 2))
            return (K, K_gradient)
        else:
            return (K, np.empty((X.shape[0], X.shape[0], 0)))
    else:
        return K
