'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):
        dists = squareform(pdist(X, metric='sqeuclidean'))
        tmp = (dists / ((2 * self.alpha) * (self.length_scale ** 2)))
        base = (1 + tmp)
        K = (base ** (- self.alpha))
        np.fill_diagonal(K, 1)
    else:
        if eval_gradient:
            raise ValueError('Gradient    can    only    be    evaluated    when    Y    is    None.')
        dists = cdist(X, Y, metric='sqeuclidean')
        K = ((1 + (dists / ((2 * self.alpha) * (self.length_scale ** 2)))) ** (- self.alpha))
    if eval_gradient:
        if (not self.hyperparameter_length_scale.fixed):
            length_scale_gradient = ((dists * K) / ((self.length_scale ** 2) * base))
            length_scale_gradient = length_scale_gradient[:, :, np.newaxis]
        else:
            length_scale_gradient = np.empty((K.shape[0], K.shape[1], 0))
        if (not self.hyperparameter_alpha.fixed):
            alpha_gradient = (K * (((- self.alpha) * np.log(base)) + (dists / ((2 * (self.length_scale ** 2)) * base))))
            alpha_gradient = alpha_gradient[:, :, np.newaxis]
        else:
            alpha_gradient = np.empty((K.shape[0], K.shape[1], 0))
        return (K, np.dstack((alpha_gradient, length_scale_gradient)))
    else:
        return K
