code: def stats(self):
      self.mean_tree_accept = (
          np.exp(self.log_accept_sum) / self.n_proposals
      )
      return {
          "depth": self.depth,
          "mean_tree_accept": self.mean_tree_accept,
          "energy_error": self.proposal.energy - self.start.energy,
          "energy": self.proposal.energy,
          "tree_size": self.n_proposals,
          "max_energy_error": self.max_energy_change,
          "model_logp": self.proposal.logp,
          "index_in_trajectory": self.proposal.index_in_trajectory,
      }
  
imports: from __future__ import annotations
  from collections import namedtuple
  import numpy
  from pymc.math import logbern
  from pymc.pytensorf import floatX
  from pymc.stats.convergence import SamplerWarning
  from pymc.step_methods.compound import Competence
  from pymc.step_methods.hmc import integration
  from pymc.step_methods.hmc.base_hmc import BaseHMC, DivergenceInfo, HMCStepData
  from pymc.step_methods.hmc.integration import IntegrationError, State
  from pymc.vartypes import continuous_types
label_desc: Use image node_id 5 for calling the _Tree obj's underlying member method code with example usage: obj.stats() and returns: dict