def METHOD_NAME() -> None:
    """
    A parameter with an Exp transform, and a uniform prior on its unconstrained, should have a
    prior in the constrained space that scales as 1/value.
    """
    initial_value = 1.0
    scale_factor = 10.0
    uniform_prior = Uniform(low=np.float64(0), high=np.float64(100))
    param = gpflow.Parameter(
        initial_value,
        transform=Exp(),
        prior=uniform_prior,
        prior_on=PriorOn.UNCONSTRAINED,
    )
    low_value = param.log_prior_density().numpy()
    param.assign(scale_factor * initial_value)
    high_value = param.log_prior_density().numpy()
    assert np.isclose(low_value, high_value + np.log(scale_factor))