'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 not None) and eval_gradient):
        raise ValueError('Gradient    can    only    be    evaluated    when    Y    is    None.')
    if (Y is None):
        K = (self.noise_level * np.eye(X.shape[0]))
        if eval_gradient:
            if (not self.hyperparameter_noise_level.fixed):
                return (K, (self.noise_level * np.eye(X.shape[0])[:, :, np.newaxis]))
            else:
                return (K, np.empty((X.shape[0], X.shape[0], 0)))
        else:
            return K
    else:
        return np.zeros((X.shape[0], Y.shape[0]))
