code: def test_invalid_broadcasted_transform_rv_fails():
      loc = pt.vector("loc")
      y_rv = loc + pt.random.normal(0, 1, size=1, name="base_rv")
      y_rv.name = "y"
      y_vv = y_rv.clone()
  
      # This logp derivation should fail or count only once the values that are broadcasted
      logprob = logp(y_rv, y_vv)
      assert (
          logprob.eval({y_vv: [0, 0, 0, 0], loc: [0, 0, 0, 0]}).shape
          == ()
      )
  
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 13 for calling a global function with example usage: test_invalid_broadcasted_transform_rv_fails() without return types