def f_tf(x):
    x = tf.reshape(x, ((- 1), input_dim))
    mean_f = (utils.compute_mean_f(x, input_dim, 1, X, (Y - gp_hyper['meanf']), gp_hyper['lengthscale'].reshape(1, input_dim), gp_hyper['signal_var'].reshape(1, 1), gp_hyper['noise_var'].reshape(1, 1), NKInvs, dtype=dtype, kernel_type='se') + gp_hyper['meanf'])
    return (- mean_f)
def f_tf(x):
    x = tf.reshape(x, ((- 1), input_dim))
    mean_f = (utils.compute_mean_f(x, input_dim, 1, X, (Y - gp_hyper['meanf']), gp_hyper['lengthscale'].reshape(1, input_dim), gp_hyper['signal_var'].reshape(1, 1), gp_hyper['noise_var'].reshape(1, 1), NKInvs, dtype=dtype, kernel_type='se') + gp_hyper['meanf'])
    return (- mean_f)