code: def tpe_transform(domain, prior_weight, gamma):
      s_prior_weight = pyll.Literal(float(prior_weight))
  
      # -- these dummy values will be replaced in suggest1() and never used
      observed = dict(idxs=pyll.Literal(), vals=pyll.Literal())
      observed_loss = dict(idxs=pyll.Literal(), vals=pyll.Literal())
  
      specs, idxs, vals = build_posterior(
          # -- vectorized clone of bandit template
          domain.vh.v_expr,
          # -- this dict and next represent prior dists
          domain.vh.idxs_by_label(),
          domain.vh.vals_by_label(),
          observed["idxs"],
          observed["vals"],
          observed_loss["idxs"],
          observed_loss["vals"],
          pyll.Literal(gamma),
          s_prior_weight,
      )
  
      return (
          s_prior_weight,
          observed,
          observed_loss,
          specs,
          idxs,
          vals,
      )
  
imports: from __future__ import print_function
  from __future__ import absolute_import
  from __future__ import division
  from builtins import str
  from builtins import map
  from builtins import zip
  from builtins import range
  from past.utils import old_div
  import logging
  import time
  import numpy
  from scipy.special import erf
  from .None import pyll
  from .pyll import scope
  from .pyll.stochastic import implicit_stochastic
  from .base import miscs_to_idxs_vals
  from .base import miscs_update_idxs_vals
  from .None import rand
label_desc: Use image node_id 30 for calling a global function with example usage: tpe_transform(domain, prior_weight, gamma) and returns: s_prior_weight,  observed,  observed_loss,  specs,  idxs,  vals