Calculate fast approximate intra-alignment pairwise distances and variances using
        ML (requires ML models to have been set up using `calc_trees`).
        :return: None (all side effects)

def calc_distances(self, indices=None, task_interface=None, jobhandler=default_jobhandler, batchsize=1,
                       show_progress=True):
        """
        Calculate fast approximate intra-alignment pairwise distances and variances using
        ML (requires ML models to have been set up using `calc_trees`).
        :return: None (all side effects)
        """
        if indices is None:
            indices = list(range(len(self)))

        if task_interface is None:
            task_interface = tasks.MLDistanceTaskInterface()

        records = [self[i] for i in indices]

        # Assemble argument lists
        args, to_delete = task_interface.scrape_args(records)

        # Dispatch
        msg = '{} estimation'.format(task_interface.name) if show_progress else ''
        map_result = jobhandler(task_interface.get_task(), args, msg, batchsize)

        # Process results
        with fileIO.TempFileList(to_delete):
            # pbar = setup_progressbar('Processing results', len(map_result))
            # j = 0
            # pbar.start()
            for rec, result in zip(records, map_result):
                rec.parameters.partitions.distances = result['partitions'][0]['distances']
                rec.parameters.partitions.variances = result['partitions'][0]['variances']
                rec.parameters.nj_tree = result['nj_tree']