code: def test_lnprob_and_grad_scalar():
      prior = GammaPrior(a=0.5, scale=1 / 2, loc=0, seed=1)
  
      # Legal scalar
      x = -1
      assert (
          pytest.approx(prior.get_log_probability(x)) == -0.46155023
      )  # 7
      assert prior.get_gradient(x) == -1.2357588823428847
  
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 11 for calling a global function with example usage: test_lnprob_and_grad_scalar() without return types