code: def _fill_queue(self, loglstar):
          
  
          # Add/zip arguments to submit to the queue.
          point_queue = []
          axes_queue = []
          while self.nqueue < self.queue_size:
              if self._beyond_unit_bound(loglstar):
                  # Propose points using the provided sampling/bounding options.
                  point, axes = self.propose_point()
                  evolve_point = self.evolve_point
              else:
                  # Propose/evaluate points directly from the unit cube.
                  point = self.rstate.rand(self.npdim)
                  axes = np.identity(self.npdim)
                  evolve_point = sample_unif
              point_queue.append(point)
              axes_queue.append(axes)
              self.nqueue += 1
          loglstars = [loglstar for i in range(self.queue_size)]
          scales = [self.scale for i in range(self.queue_size)]
          ptforms = [self.prior_transform for i in range(self.queue_size)]
          logls = [self.loglikelihood for i in range(self.queue_size)]
          kwargs = [self.kwargs for i in range(self.queue_size)]
          args = zip(point_queue, loglstars, axes_queue,
                     scales, ptforms, logls, kwargs)
  
          if self.use_pool_evolve:
              # Use the pool to propose ("evolve") a new live point.
              self.queue = list(self.M(evolve_point, args))
          else:
              # Propose ("evolve") a new live point using the default `map`
              # function.
              self.queue = list(map(evolve_point, args))
docs: Sequentially add new live point proposals to the queue.