code: def test_grad():
      prior = SoftTopHatPrior(
          lower_bound=np.exp(-5),
          upper_bound=np.exp(5),
          exponent=2,
          seed=1,
      )
  
      # Legal values
      assert prior.get_gradient(-5) == 0
      assert prior.get_gradient(0) == 0
      assert prior.get_gradient(5) == 0
  
      for theta in [-10, -7, -6, -5.1, 5.1, 6, 7, 10]:
          # Gradient approximation becomes unstable when going closer to zero
          theta += 1e-2
          grad = prior.get_gradient(theta)
          grad_vector = prior.get_gradient(theta)
          assert grad == grad_vector
  
          def prob(x):
              return prior.get_log_probability(x[0])
  
          def grad(x):
              return prior.get_gradient(x[0])
  
          error = scipy.optimize.check_grad(
              prob, grad, np.array([theta]), epsilon=1e-5
          )
          assert np.round(error) == 0
  
imports: from functools import partial
  import numpy
  import pytest
  import scipy.optimize
  from smac.constants import VERY_SMALL_NUMBER
  from smac.model.gaussian_process.priors import GammaPrior, HorseshoePrior, LogNormalPrior, SoftTopHatPrior, TophatPrior
label_desc: Use image node_id 16 for calling a global function with example usage: test_grad() and returns: prior, prior