code: def test_log_prior_on_unconstrained() -> 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))
  
imports: from typing import Any, Tuple, Type
  import numpy
  import pytest
  import tensorflow
  import tensorflow_probability
  from tensorflow_probability.python.bijectors import Exp
  from tensorflow_probability.python.distributions import Uniform
  import gpflow
  from gpflow.base import AnyNDArray, PriorOn
  from gpflow.config import set_default_float
  from gpflow.utilities import to_default_float
label_desc: Use image node_id 4 for calling a global function with example usage: test_log_prior_on_unconstrained() without return types