code: def ap_qloguniform_sampler(
      obs, prior_weight, low, high, q, size=(), rng=None
  ):
      prior_mu = 0.5 * (high + low)
      prior_sigma = 1.0 * (high - low)
      weights, mus, sigmas = scope.adaptive_parzen_normal(
          scope.log(
              # -- map observations that were quantized to be below exp(low)
              #    (particularly 0) back up to exp(low) where they will
              #    interact in a reasonable way with the AdaptiveParzen
              #    thing.
              scope.maximum(
                  obs,
                  scope.maximum(  # -- protect against exp(low) underflow
                      EPS, scope.exp(low)
                  ),
              )
          ),
          prior_weight,
          prior_mu,
          prior_sigma,
      )
      return scope.LGMM1(
          weights, mus, sigmas, low, high, q=q, size=size, rng=rng
      )
  
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 18 for calling a global function with example usage: ap_qloguniform_sampler(obs, prior_weight, low, high, q, size, rng) and returns: scope