code: def randomize(self, rand_gen=None, *args, **kwargs):
      
      if rand_gen is None:
          rand_gen = np.random.normal
      # first take care of all parameters (from N(0,1))
      x = rand_gen(size=self._size_transformed(), *args, **kwargs)
      updates = self.update_model()
      self.update_model(False) # Switch off the updates
      self.optimizer_array = x  # makes sure all of the tied parameters get the same init (since there's only one prior object...)
      # now draw from prior where possible
      x = self.param_array.copy()
      [np.put(x, ind, p.rvs(ind.size)) for p, ind in self.priors.items() if not p is None]
      unfixlist = np.ones((self.size,),dtype=np.bool)
      from paramz.transformations import __fixed__
      unfixlist[self.constraints[__fixed__]] = False
      self.param_array.flat[unfixlist] = x.view(np.ndarray).ravel()[unfixlist]
      self.update_model(updates)
docs: Randomize the model.
      Make this draw from the prior if one exists, else draw from given random generator
  
      :param rand_gen: np random number generator which takes args and kwargs
      :param flaot loc: loc parameter for random number generator
      :param float scale: scale parameter for random number generator
      :param args, kwargs: will be passed through to random number generator