human
I wrote the below code

    def unpack_kernel_params(params):
        mean = params[0]
        cov_params = params[2:]
        noise_scale = (np.exp(params[1]) + 0.0001)
        return (mean, cov_params, noise_scale)
    def predict(params, x, y, xstar):
        'Returns    the    predictive    mean    and    covariance    at    locations    xstar,\n                                            of    the    latent    function    value    f    (without    observation    noise).'
        (mean, cov_params, noise_scale) = unpack_kernel_params(params)
        cov_f_f = cov_func(cov_params, xstar, xstar)
        cov_y_f = cov_func(cov_params, x, xstar)
        cov_y_y = (cov_func(cov_params, x, x) + (noise_scale * np.eye(len(y))))
        pred_mean = (mean + np.dot(solve(cov_y_y, cov_y_f).T, (y - mean)))
        pred_cov = (cov_f_f - np.dot(solve(cov_y_y, cov_y_f).T, cov_y_f))
        return (pred_mean, pred_cov)
    def log_marginal_likelihood(params, x, y):
        (mean, cov_params, noise_scale) = unpack_kernel_params(params)
        cov_y_y = (cov_func(cov_params, x, x) + (noise_scale * np.eye(len(y))))
        prior_mean = (mean * np.ones(len(y)))
        return mvn.logpdf(y, prior_mean, cov_y_y)
    return ((num_cov_params + 2), predict, log_marginal_likelihood)

What's a good function header?
assistant
#Here's a good function header.

def make_gp_funs(cov_func, num_cov_params):