How to use the omegaml.store.qops.SORT function in omegaml

def _amend_pipeline(self, pipeline):
        """ amend pipeline with default ops on coll.aggregate() calls """
        if self.sort_order:
            sort = qops.SORT(**dict(qops.make_sortkey(self.sort_order)))
            pipeline.append(sort)
        return pipeline

def _amend_pipeline(self, pipeline):
        """ amend pipeline with default ops on coll.aggregate() calls """
        if self.should_sort:
            sort = qops.SORT(**dict(qops.make_sortkey('_id')))
            pipeline.append(sort)
        return pipeline

def _count(self):
        count_columns = self._non_group_columns()
        if len(count_columns) == 0:
            count_columns.append('_'.join(self.columns) + '_count')
        groupby = {
            "$group": {
                "_id": {k: "$%s" % k for k in self.columns},
            }
        }
        for k in count_columns:
            groupby['$group']['%s' % k] = {"$sum": 1}
        pipeline = self._amend_pipeline([groupby])
        if self.should_sort:
            sort = qops.SORT(**dict(qops.make_sortkey('_id')))
            pipeline.append(sort)
        return list(self.collection.aggregate(pipeline))

# have it from left
                continue
            if right_col in self.columns:
                left_col = '%s%s' % (right_col, suffixes[1])
            else:
                left_col = '%s' % right_col
            project[left_col] = '$%s.%s' % (target_field, right_col)
        expected_columns = list(project.keys())
        project = {"$project": project}
        # store merged documents and return an MDataFrame to it
        out = qops.OUT(target_name)
        pipeline = [lookup, unwind, project]
        if sort:
            sort_cols = make_list(on or [left_on, right_on])
            sort_key = qops.make_sortkey(sort_cols)
            sort = qops.SORT(**dict(sort_key))
            pipeline.append(sort)
        pipeline.append(out)
        if inspect:
            result = pipeline
        else:
            result = self.collection.aggregate(pipeline)
            result = MDataFrame(self.collection.database[target_name],
                                force_columns=expected_columns)
        return result