code: def adaptive_parzen_normal_orig(
      mus, prior_weight, prior_mu, prior_sigma
  ):
      """
      A heuristic estimator for the mu and sigma values of a GMM
      TODO: try to find this heuristic in the literature, and cite it - Yoshua
      mentioned the term 'elastic' I think?
  
      mus - matrix (N, M) of M, N-dimensional component centers
      """
      mus_orig = np.array(mus)
      mus = np.array(mus)
      assert str(mus.dtype) != "object"
  
      if mus.ndim != 1:
          raise TypeError("mus must be vector", mus)
      if len(mus) == 0:
          mus = np.asarray([prior_mu])
          sigma = np.asarray([prior_sigma])
      elif len(mus) == 1:
          mus = np.asarray([prior_mu] + [mus[0]])
          sigma = np.asarray([prior_sigma, prior_sigma * 0.5])
      elif len(mus) >= 2:
          order = np.argsort(mus)
          mus = mus[order]
          sigma = np.zeros_like(mus)
          sigma[1:-1] = np.maximum(
              mus[1:-1] - mus[0:-2], mus[2:] - mus[1:-1]
          )
          if len(mus) > 2:
              lsigma = mus[2] - mus[0]
              usigma = mus[-1] - mus[-3]
          else:
              lsigma = mus[1] - mus[0]
              usigma = mus[-1] - mus[-2]
  
          sigma[0] = lsigma
          sigma[-1] = usigma
  
          # XXX: is sorting them necessary anymore?
          # un-sort the mus and sigma
          mus[order] = mus.copy()
          sigma[order] = sigma.copy()
  
          if not np.all(mus_orig == mus):
              print("orig", mus_orig)
              print("mus", mus)
          assert np.all(mus_orig == mus)
  
          # put the prior back in
          mus = np.asarray([prior_mu] + list(mus))
          sigma = np.asarray([prior_sigma] + list(sigma))
  
      maxsigma = prior_sigma
      # -- magic formula:
      minsigma = old_div(prior_sigma, np.sqrt(1 + len(mus)))
  
      sigma = np.clip(sigma, minsigma, maxsigma)
  
      weights = np.ones(len(mus), dtype=mus.dtype)
      weights[0] = prior_weight
  
      weights = old_div(weights, weights.sum())
      if 0:
          print("WEIGHTS", weights)
          print("MUS", mus)
          print("SIGMA", sigma)
  
      return weights, mus, sigma
  
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 12 for calling a global function with example usage: adaptive_parzen_normal_orig(mus, prior_weight, prior_mu, prior_sigma) and returns: weights,  mus,  sigma