def METHOD_NAME() -> None:
    num_classes = 3
    model = gpflow.models.SVGP(
        gpflow.kernels.SquaredExponential(),
        gpflow.likelihoods.MultiClass(num_classes=num_classes),
        inducing_variable=Datum.X.copy(),
        num_latent_gps=num_classes,
    )
    gpflow.set_trainable(model.inducing_variable, False)
    # lambda because: https://github.com/GPflow/GPflow/issues/1929
    elbo_lambda = lambda data: model.elbo(data)
    # test with explicitly unknown shapes:
    tensor_spec = tf.TensorSpec(shape=None, dtype=default_float())
    elbo = tf.function(
        elbo_lambda,
        input_signature=[(tensor_spec, tensor_spec)],
    )
    @tf.function
    def model_closure() -> tf.Tensor:
        return -elbo(Datum.cdata)
    model_closure()  # Trigger compilation.
    opt = gpflow.optimizers.Scipy()
    # simply test whether it runs without erroring...:
    opt.minimize(
        model_closure,
        variables=model.trainable_variables,
        options=dict(maxiter=3),
        compile=True,
    )