How to use the streamz.core.Stream.register_api function in streamz

file = open(filename, mode=mode)

    def write(text):
        file.write(prefix + text + suffix)
        if flush:
            file.flush()

    upstream.sink(write)
    return file


class Source(Stream):
    _graphviz_shape = 'doubleoctagon'


@Stream.register_api(staticmethod)
class from_textfile(Source):
    """ Stream data from a text file

    Parameters
    ----------
    f: file or string
    poll_interval: Number
        Interval to poll file for new data in seconds
    delimiter: str ("\n")
        Character(s) to use to split the data into parts
    start: bool (False)
        Whether to start running immediately; otherwise call stream.start()
        explicitly.

    Example
    -------

stream_name = kwargs.pop("stream_name", None)
        self.kwargs = kwargs
        self.args = args

        Stream.__init__(self, upstream, stream_name=stream_name)
        _global_sinks.add(self)

    def update(self, x, who=None):
        result = self.func(x, *self.args, **self.kwargs)
        if gen.isawaitable(result):
            return result
        else:
            return []


@Stream.register_api()
class map(Stream):
    """ Apply a function to every element in the stream

    Parameters
    ----------
    func: callable
    *args :
        The arguments to pass to the function.
    **kwargs:
        Keyword arguments to pass to func

    Examples
    --------
    >>> source = Stream()
    >>> source.map(lambda x: 2*x).sink(print)
    >>> for i in range(5):

self.server = EmitServer(**self.server_kwargs)
        self.server.listen(self.port)

    def start(self):
        if self.stopped:
            self.loop.add_callback(self._start_server)
            self.stopped = False

    def stop(self):
        if not self.stopped:
            self.server.stop()
            self.server = None
            self.stopped = True


@Stream.register_api(staticmethod)
class from_http_server(Source):
    """Listen for HTTP POSTs on given port

    Each connection will emit one event, containing the body data of
    the request

    Parameters
    ----------
    port : int
        The port to listen on
    path : str
        Specific path to listen on. Can be regex, but content is not used.
    start : bool
        Whether to immediately startup the server. Usually you want to connect
        downstream nodes first, and then call ``.start()``.
    server_kwargs : dict or None

class Source(Stream):
    _graphviz_shape = 'doubleoctagon'

    def __init__(self, **kwargs):
        self.stopped = True
        super(Source, self).__init__(**kwargs)

    def stop(self):  # pragma: no cover
        # fallback stop method - for poll functions with while not self.stopped
        if not self.stopped:
            self.stopped = True


@Stream.register_api(staticmethod)
class from_textfile(Source):
    """ Stream data from a text file

    Parameters
    ----------
    f: file or string
        Source of the data. If string, will be opened.
    poll_interval: Number
        Interval to poll file for new data in seconds
    delimiter: str
        Character(s) to use to split the data into parts
    start: bool
        Whether to start running immediately; otherwise call stream.start()
        explicitly.
    from_end: bool
        Whether to begin streaming from the end of the file (i.e., only emit

@Stream.register_api(staticmethod)
def from_kafka_batched(topic, consumer_params, poll_interval='1s',
                       npartitions=1, start=False, dask=False,
                       max_batch_size=10000, keys=False,
                       engine=None, **kwargs):
    """ Get messages and keys (optional) from Kafka in batches

    Uses the confluent-kafka library,
    https://docs.confluent.io/current/clients/confluent-kafka-python/

    This source will emit lists of messages for each partition of a single given
    topic per time interval, if there is new data. If using dask, one future
    will be produced per partition per time-step, if there is data.

    Checkpointing is achieved through the use of reference counting. A reference
    counter is emitted downstream for each batch of data. A callback is
    triggered when the reference count reaches zero and the offsets are

@Stream.register_api(staticmethod)
def from_kafka_batched(topic, consumer_params, poll_interval='1s',
                       npartitions=1, start=False, dask=False, **kwargs):
    """ Get messages from Kafka in batches

    Uses the confluent-kafka library,
    https://docs.confluent.io/current/clients/confluent-kafka-python/

    This source will emit lists of messages for each partition of a single given
    topic per time interval, if there is new data. If using dask, one future
    will be produced per partition per time-step, if there is data.

    Parameters
    ----------
    topic: str
        Kafka topic to consume from
    consumer_params: dict