How to use the bigdl.nn.layer.Layer function in bigdl

class StatelessMetric(JavaValue):
    def __init__(self, metric_name, idx):
        self.name = metric_name
        self.idx = idx
        JavaValue.__init__(self, None, "float", metric_name, idx)


class BigDLMetric(object):
    def __init__(self, val_method, outputs, labels):
        self.val_method = val_method
        self.outputs = outputs
        self.labels = labels


class TFTrainingHelper(Layer):
    def __init__(self, path, config_proto, saver, meta, sess):
        self.saver = saver
        self.meta = meta
        self.export_dir = path
        self.sess = sess

        if config_proto is not None:
            import tensorflow as tf
            assert isinstance(config_proto, tf.ConfigProto), \
                "session_config should be a tf.ConfigProto"
            config_proto.use_per_session_threads = True
            byte_arr = bytearray(config_proto.SerializeToString())
        else:
            byte_arr = None

        super(TFTrainingHelper, self).__init__(None, "float", path, byte_arr)

def _do_load(jmodel, bigdl_type="float"):
        model = Layer(jvalue=jmodel, bigdl_type=bigdl_type)
        model.value = jmodel
        return model

Return a list of shape tuples if there are multiple outputs.
        Return one shape tuple otherwise.
        """
        output = callBigDlFunc(self.bigdl_type, "getOutputShape",
                               self.value)
        return self.__process_shape(output)


class KerasCreator(JavaValue):
    def jvm_class_constructor(self):
        name = "createKeras" + self.__class__.__name__
        print("creating: " + name)
        return name


class KerasLayer(Layer, InferShape, KerasCreator):
    def __init__(self, jvalue, *args, **kwargs):
        allowed_kwargs = {"name", "bigdl_type"}
        for kwarg in kwargs.keys():
            if kwarg not in allowed_kwargs:
                raise TypeError("Wrong argument for the layer:", kwarg)
        bigdl_type = kwargs.get("bigdl_type")
        if not bigdl_type:
            bigdl_type = "float"
        super(KerasCreator, self).__init__(jvalue, bigdl_type, *args)
        name = kwargs.get("name")
        if name:
            self.set_name(name)


class Input(Node, KerasCreator):
    """

            return results.map(lambda result: Layer.convert_output(result))
        else:

            return results.map(lambda result: Layer.convert_output(result))
        else:

            return results.map(lambda result: Layer.convert_output(result))
        else: