https://github.com/apache/airflow/blob/main/airflow/cli/commands/task_command.py#L145
if has_event_filters:
                count_query = count_query.join(DatasetEvent, DatasetEvent.dataset_id == DatasetModel.id)
            filters = [~DatasetModel.is_orphaned]
            if uri_pattern:
                filters.append(DatasetModel.uri.ilike(f"%{uri_pattern}%"))
            if updated_after:
                filters.append(DatasetEvent.timestamp >= updated_after)
            if updated_before:
                filters.append(DatasetEvent.timestamp <= updated_before)
            query = query.where(*filters).offset(offset).limit(limit)
            count_query = count_query.where(*filters)
            query = session.execute(query)
            datasets = [dict(dataset) for dataset in query]
            data = {"datasets": datasets, "total_entries": session.scalar(count_query)}
            return (
                htmlsafe_json_dumps(data, separators=(",", ":"), cls=utils_json.WebEncoder),
                {"Content-Type": "application/json; charset=utf-8"},
            )
    @expose("/robots.txt")
    @action_logging
    def robots(self):
        """
        Return a robots.txt file for blocking certain search engine crawlers.
        This mitigates some of the risk associated with exposing Airflow to the public
        internet, however it does not address the real security risks associated with
        such a deployment.
        """
        return send_from_directory(get_airflow_app().static_folder, "robots.txt")
    @expose("/audit_log")
    def legacy_audit_log(self):