code: def truncated_default_transform(op, rv):
      # Don't transform discrete truncated distributions
      if op.base_rv_op.dtype.startswith("int"):
          return None
      # Lower and Upper are the arguments -3 and -2
      return bounded_cont_transform(op, rv, bound_args_indices=(-3, -2))
  
imports: from functools import singledispatch
  import numpy
  import pytensor
  import pytensor.tensor
  from pytensor import scan
  from pytensor.graph import Op
  from pytensor.graph.basic import Node
  from pytensor.raise_op import CheckAndRaise
  from pytensor.scan import until
  from pytensor.tensor import TensorConstant, TensorVariable
  from pytensor.tensor.random.basic import NormalRV
  from pytensor.tensor.random.op import RandomVariable
  from pymc.distributions.continuous import TruncatedNormal, bounded_cont_transform
  from pymc.distributions.dist_math import check_parameters
  from pymc.distributions.distribution import Distribution, SymbolicRandomVariable, _moment, moment
  from pymc.distributions.shape_utils import _change_dist_size, change_dist_size, to_tuple
  from pymc.distributions.transforms import _default_transform
  from pymc.exceptions import TruncationError
  from pymc.logprob.abstract import _logcdf, _logprob
  from pymc.logprob.basic import icdf, logcdf
  from pymc.math import logdiffexp
  from pymc.util import check_dist_not_registered
label_desc: Use image node_id 4 for calling a global function with example usage: truncated_default_transform(op, rv) and returns: bounded_cont_transform, None