code: def transform_back(rv, transformed, model) -> np.ndarray:
      return (
          model.rvs_to_transforms[rv]
          .backward(transformed, *rv.owner.inputs)
          .eval()
      )
  
imports: import cloudpickle
  import numpy
  import pytensor
  import pytensor.tensor
  import pytest
  from pytensor.tensor.random.op import RandomVariable
  import pymc
  from pymc.distributions.distribution import moment
  from pymc.initial_point import make_initial_point_fn, make_initial_point_fns_per_chain
label_desc: Use image node_id 2 for calling a global function with example usage: transform_back(rv, transformed, model) and returns: model