code: def test_truncated_transform_logp():
      with Model() as m:
          base_dist = rejection_normal(0, 1)
          x = Truncated(
              "x", base_dist, lower=0, upper=None, transform=None
          )
          y = Truncated("y", base_dist, lower=0, upper=None)
          logp_eval = m.compile_logp(sum=False)(
              {"x": -1, "y_interval__": -1}
          )
      assert logp_eval[0] == -np.inf
      assert np.isfinite(logp_eval[1])
  
imports: import numpy
  import pytensor
  import pytensor.tensor
  import pytest
  import scipy
  from pytensor.tensor.random.basic import GeometricRV, NormalRV
  from pymc import Censored, Model, draw, find_MAP
  from pymc.distributions.continuous import Exponential, Gamma, TruncatedNormal, TruncatedNormalRV
  from pymc.distributions.shape_utils import change_dist_size
  from pymc.distributions.transforms import _default_transform
  from pymc.distributions.truncated import Truncated, TruncatedRV, _truncated
  from pymc.exceptions import TruncationError
  from pymc.logprob.abstract import _icdf
  from pymc.logprob.basic import logcdf, logp
  from pymc.logprob.transforms import IntervalTransform
  from pymc.logprob.utils import ParameterValueError
  from pymc.testing import assert_moment_is_expected
label_desc: Use image node_id 14 for calling a global function with example usage: test_truncated_transform_logp() without return types