class_docstr: An `Op` constructed from an PyTensor graph
  that represents a truncated univariate random variable.
class_docstr_tok: ["An","`","Op","`","constructed","from","an","PyTensor","graph","that","represents","a","truncated","univariate","random","variable","."]
docstr: Return the update mapping for the noise RV.
docstr_tok: ["Return","the","update","mapping","for","the","noise","RV","."]
code: def update(self, node: Node):
      """Return the update mapping for the noise RV."""
      # Since RNG is a shared variable it shows up as the last node input
      return {node.inputs[-1]: node.outputs[0]}
  
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 2 for calling the TruncatedRV obj's underlying member method code with example usage: obj.update(node) and returns: dict