code: def test_change_value_transforms():
      with pm.Model() as base_m:
          p = pm.Uniform("p", 0, 1, transform=None)
          w = pm.Binomial("w", n=9, p=p, observed=6)
          assert base_m.rvs_to_transforms[p] is None
          assert base_m.rvs_to_values[p].name == "p"
  
      with change_value_transforms(
          base_m, {"p": logodds}
      ) as transformed_p:
          new_p = transformed_p["p"]
          assert transformed_p.rvs_to_transforms[new_p] == logodds
          assert (
              transformed_p.rvs_to_values[new_p].name == "p_logodds__"
          )
          mean_q = pm.find_MAP(progressbar=False)
  
      with change_value_transforms(
          transformed_p, {"p": None}
      ) as untransformed_p:
          new_p = untransformed_p["p"]
          assert untransformed_p.rvs_to_transforms[new_p] is None
          assert untransformed_p.rvs_to_values[new_p].name == "p"
          std_q = ((1 / pm.find_hessian(mean_q, vars=[new_p])) ** 0.5)[
              0
          ]
  
      np.testing.assert_allclose(np.round(mean_q["p"], 2), 0.67)
      np.testing.assert_allclose(np.round(std_q[0], 2), 0.16)
  
imports: import arviz
  import numpy
  import pytest
  from pytensor import config
  import pymc
  from pymc.distributions.transforms import logodds
  from pymc.model.transform.conditioning import change_value_transforms, do, observe, remove_value_transforms
  from pymc.variational.minibatch_rv import create_minibatch_rv
label_desc: Use image node_id 12 for calling a global function with example usage: test_change_value_transforms() without return types