code: def ap_quniform_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(
          obs, prior_weight, prior_mu, prior_sigma
      )
      return scope.GMM1(
          weights,
          mus,
          sigmas,
          low=low,
          high=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 16 for calling a global function with example usage: ap_quniform_sampler(obs, prior_weight, low, high, q, size, rng) and returns: scope