code: def build_posterior_wrapper(domain, prior_weight, gamma):
      """
      Calls build_posterior
      Args:
          domain (hyperopt.base.Domain): contains info about the obj function and the hp
              space passed to fmin
          prior_weight (float): smoothing factor for counts, to avoid having 0 prob
          # TODO: consider renaming or improving documentation for suggest
          gamma (float): the threshold to split between l(x) and g(x), see eq. 2 in
              https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf
  
      Returns:
  
      """
  
      # -- these dummy values will be replaced in build_posterior() and never used
      observed = {"idxs": pyll.Literal(), "vals": pyll.Literal()}
      observed_loss = {"idxs": pyll.Literal(), "vals": pyll.Literal()}
  
      posterior = 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),
          pyll.Literal(float(prior_weight)),
      )
  
      return observed, observed_loss, posterior
  
imports: 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: build_posterior_wrapper(domain, prior_weight, gamma) and returns: observed,  observed_loss,  posterior