docstr: Allows to statically evaluate any symbolic expression over the trace.
  
  Parameters
  ----------
  node: PyTensor Variables (or PyTensor expressions)
  
  Returns
  -------
  evaluated node(s) over the posterior trace contained in the empirical approximation
code: def evaluate_over_trace(self, node):
      R"""
      Allows to statically evaluate any symbolic expression over the trace.
  
      Parameters
      ----------
      node: PyTensor Variables (or PyTensor expressions)
  
      Returns
      -------
      evaluated node(s) over the posterior trace contained in the empirical approximation
      """
      node = self.to_flat_input(node)
  
      def sample(post, *_):
          return graph_replace(node, {self.input: post}, strict=False)
  
      nodes, _ = pytensor.scan(
          sample,
          self.histogram,
          non_sequences=_known_scan_ignored_inputs(makeiter(node)),
      )
      return nodes
  
imports: from typing import Optional
  import numpy
  import pytensor
  from arviz import InferenceData
  from pytensor import tensor
  from pytensor.graph.basic import Variable
  from pytensor.graph.replace import graph_replace
  from pytensor.tensor.variable import TensorVariable
  import pymc
  from pymc.blocking import DictToArrayBijection
  from pymc.distributions.dist_math import rho2sigma
  from pymc.util import makeiter
  from pymc.variational import opvi
  from pymc.variational.opvi import Approximation, Group, NotImplementedInference, _known_scan_ignored_inputs, node_property
label_desc: Use image node_id 2 for calling the Empirical obj's underlying member method code with example usage: obj.evaluate_over_trace(node) and returns: nodes, graph_replace