code: def test_extra_bijective_rv_transforms(pt_transform, transform):
      base_rv = pt.random.normal(
          0.5, 1, name="base_rv"
      )  # Something not centered around 0 is usually better
      rv = pt_transform(base_rv)
  
      vv = rv.clone()
      rv_logp = logp(rv, vv)
  
      expected_logp = logp(
          base_rv, transform.backward(vv)
      ) + transform.log_jac_det(vv)
  
      vv_test = np.array(0.25)  # Arbitrary test value
      np.testing.assert_allclose(
          rv_logp.eval({vv: vv_test}),
          np.nan_to_num(expected_logp.eval({vv: vv_test}), nan=-np.inf),
      )
  
imports: import numpy
  import pytensor
  import pytensor.tensor
  import pytest
  import scipy
  import scipy.special
  from pytensor.graph.basic import equal_computations
  from pymc.distributions.continuous import Cauchy, ChiSquared
  from pymc.logprob.basic import conditional_logp, icdf, logcdf, logp
  from pymc.logprob.transforms import ArccoshTransform, ArcsinhTransform, ArctanhTransform, ChainedTransform, CoshTransform, ErfcTransform, ErfcxTransform, ErfTransform, ExpTransform, LocTransform, LogTransform, ScaleTransform, SinhTransform, TanhTransform, Transform
  from pymc.logprob.utils import ParameterValueError
  from pymc.testing import Rplusbig, Vector, assert_no_rvs
  from tests.distributions.test_transform import check_jacobian_det
label_desc: Use image node_id 4 for calling a global function with example usage: test_extra_bijective_rv_transforms(pt_transform, transform) without return types