code: def test_observe_deterministic():
      y_censored_obs = np.array(
          [0.9, 0.5, 0.3, 1, 1], dtype=config.floatX
      )
  
      with pm.Model() as m_old:
          x = pm.Normal("x")
          y = pm.Normal.dist(x, shape=(5,))
          y_censored = pm.Deterministic(
              "y_censored", pm.math.clip(y, -1, 1)
          )
  
      m_new = observe(m_old, {y_censored: y_censored_obs})
  
      with pm.Model() as m_ref:
          x = pm.Normal("x")
          pm.Censored(
              "y_censored",
              pm.Normal.dist(x),
              lower=-1,
              upper=1,
              observed=y_censored_obs,
          )
  
imports: import arviz
  import numpy
  import pytest
  from pytensor import config
  import pymc
  from pymc.distributions.transforms import logodds
  from pymc.model.transform.conditioning import change_value_transforms, do, observe, remove_value_transforms
  from pymc.variational.minibatch_rv import create_minibatch_rv
label_desc: Use image node_id 3 for calling a global function with example usage: test_observe_deterministic() without return types