code: def test_logcdf_transformed_argument():
      with pm.Model() as m:
          sigma = pm.HalfFlat("sigma")
          x = pm.Normal("x", 0, sigma)
          pm.Potential("norm_term", -logcdf(x, 1.0))
  
      sigma_value_log = -1.0
      sigma_value = np.exp(sigma_value_log)
      x_value = 0.5
  
      observed = m.compile_logp(jacobian=False)(
          {"sigma_log__": sigma_value_log, "x": x_value}
      )
      expected = pm.logp(
          pm.TruncatedNormal.dist(
              0, sigma_value, lower=None, upper=1.0
          ),
          x_value,
      ).eval()
      assert np.isclose(observed, expected)
  
file_path: pymc/tests/logprob/test_abstract.py
imports: import re
  import numpy
  import pytensor.tensor
  import pytest
  import scipy.stats.distributions
  from pytensor.scalar import Exp, exp
  import pymc
  from pymc.logprob.abstract import MeasurableElemwise, MeasurableVariable, _logcdf_helper
  from pymc.logprob.basic import logcdf
label_desc: Use image node_id 4 for calling a global function with example usage: test_logcdf_transformed_argument() without return types