def METHOD_NAME() -> None:
    """
    Verifies the objective for a set of priors which are defined on the unconstrained space.
    """
    # Set up priors on the model parameters such that we can readily compute their expected values.
    expected_log_prior = 0.0
    prior_width = 200.0
    data = build_data()
    model = build_model_with_uniform_prior_no_transforms(data, PriorOn.UNCONSTRAINED, prior_width)
    hmc_helper = gpflow.optimizers.SamplingHelper(
        model.log_posterior_density, model.trainable_parameters
    )
    for _ in model.trainable_parameters:
        prior_density = 1 / prior_width
        expected_log_prior += np.log(prior_density)
    target_log_prob_fn = hmc_helper.target_log_prob_fn
    expected_log_prob = model.log_marginal_likelihood().numpy() + expected_log_prior
    np.testing.assert_allclose(target_log_prob_fn(), expected_log_prob)