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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 run_parallel( workflow, *, n_threads, registry, db_file, echo_log=True, always_cache=False):
 """Run a workflow in parallel threads, storing results in a Sqlite3 database. :param workflow: Workflow or PromisedObject to evaluate. :param n_threads: number of threads to use (in addition to the scheduler). :param registry: serialization Registry function. :param db_file: filename of Sqlite3 database, give `':memory:'` to keep the database in memory only. :param echo_log: set log-level high enough :param always_cache: enable caching by schedule hint. :return: Evaluated result. """

    if echo_log:
        logging.getLogger('noodles').setLevel(logging.DEBUG)
        logging.debug("--- start log ---")

    with JobDB(db_file, registry) as db:
        job_queue = Queue()
        result_queue = Queue()

        job_logger = make_logger("worker", push_map, db)
        result_logger = make_logger("worker", pull_map, db)

        worker_pool = job_queue.source \
            >> pass_job(db, result_queue, always_cache) \
            >> thread_pool(*repeat(worker, n_threads), results=result_queue)
        job_front_end = job_logger >> job_queue.sink
        result_front_end = worker_pool \
            >> pass_result(db, always_cache) \
            >> result_logger

        scheduler = Scheduler(job_keeper=db)
        parallel_sqlite_worker = Connection(result_front_end, job_front_end)

        result = scheduler.run(parallel_sqlite_worker, get_workflow(workflow))

    return registry().dereference(result)