code: def ap_loguniform_sampler(
      obs, prior_weight, low, high, size=(), rng=None
  ):
      prior_mu = 0.5 * (high + low)
      prior_sigma = 1.0 * (high - low)
      weights, mus, sigmas = scope.adaptive_parzen_normal(
          scope.log(obs), prior_weight, prior_mu, prior_sigma
      )
      rval = scope.LGMM1(
          weights, mus, sigmas, low=low, high=high, size=size, rng=rng
      )
      return rval
  
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 18 for calling a global function with example usage: ap_loguniform_sampler(obs, prior_weight, low, high, size, rng) and returns: rval