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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _fill_queue(self, loglstar):
 """Sequentially add new live point proposals to the queue."""


        # 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))