code: def test_base_exponent_non_measurable():
      # test dual sources of measuravility fails
      base_rv = pt.random.normal([2])
      x_raw_rv = pt.random.normal()
      x_rv = pt.power(base_rv, x_raw_rv)
      x_rv.name = "x"
  
      x_vv = x_rv.clone()
  
      with pytest.raises(
          RuntimeError,
          match="The logprob terms of the following value variables could not be derived: {x}",
      ):
          conditional_logp({x_rv: x_vv})
  
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 9 for calling a global function with example usage: test_base_exponent_non_measurable() without return types