human
I wrote the below code

    def _mapfn(iter):
        for i in iter:
            worker_num = i
        job_name = 'default'
        task_index = (-1)
        cluster_id = cluster_meta['id']
        cluster_template = cluster_meta['cluster_template']
        for jobtype in cluster_template:
            nodes = cluster_template[jobtype]
            if (worker_num in nodes):
                job_name = jobtype
                task_index = nodes.index(worker_num)
                break
        host = util.get_ip_address()
        ppid = os.getppid()
        port = 0
        if ((TFSparkNode.mgr is not None) and (str(TFSparkNode.mgr.get('state')) != "'stopped'")):
            if (TFSparkNode.cluster_id == cluster_id):
                raise Exception('TFManager    already    started    on    {0},    ppid={1},    state={2}'.format(host, ppid, str(TFSparkNode.mgr.get('state'))))
            else:
                logging.warn('Ignoring    old    TFManager    with    cluster_id    {0},    requested    cluster_id    {1}'.format(TFSparkNode.cluster_id, cluster_id))
        authkey = uuid.uuid4().bytes
        addr = None
        if (job_name == 'ps'):
            TFSparkNode.mgr = TFManager.start(authkey, ['control'], 'remote')
            addr = (host, TFSparkNode.mgr.address[1])
        else:
            TFSparkNode.mgr = TFManager.start(authkey, queues)
            addr = TFSparkNode.mgr.address
        TFSparkNode.mgr.set('state', 'running')
        TFSparkNode.cluster_id = cluster_id
        tb_pid = 0
        tb_port = 0
        if (tensorboard and (job_name == 'worker') and (task_index == 0)):
            tb_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
            tb_sock.bind(('', 0))
            tb_port = tb_sock.getsockname()[1]
            tb_sock.close()
            logdir = ('tensorboard_%d' % worker_num)
            if ('PYSPARK_PYTHON' in os.environ):
                pypath = os.environ['PYSPARK_PYTHON']
                logging.info('PYSPARK_PYTHON:    {0}'.format(pypath))
                pydir =...

What's a good function header?
assistant
#Here's a good function header.

def run(fn, tf_args, cluster_meta, tensorboard, queues, background):