Converter flag for allowing network to receive input from outside

[studywolf]

The scenario is that I want to convert a Keras network to a subnetwork in a larger Nengo network. Currently this doesn't work because I can't pass input into the converted network. I can hack this in by changing

@Converter.register(tf.keras.layers.InputLayer)
class ConvertInput(LayerConverter):
    """Convert ``tf.keras.layers.InputLayer`` to Nengo objects."""
    def convert(self, node_id):
        try:
            # if this input layer has an input obj, that means it is a passthrough
            # (so we just return the input)
            output = self.get_input_obj(node_id)
        except KeyError:
            # not a passthrough input, so create input node
            shape = self.output_shape(node_id)
            if any(x is None for x in shape):
                raise ValueError(
                    "Input shapes must be fully specified; got %s" % (shape,)
                )
            # output = nengo.Node(np.zeros(np.prod(shape)), label=self.layer.name)
            output = nengo.Node(size_in=np.prod(shape), label=self.layer.name)
...

and then i can

with nengo.Network() as net:
    converter = nengo_dl.Convert(model)
    vision = converter.net
    ens = nengo.Ensemble(...)
    nengo.Connection(ens, converter.inputs[model.input])
    nengo.Connection(converter.layers[model.output_layer], ens)

which isn't great but works. But now it no longer works when just running it in with the nengo dl sim.predict. Since i'm unfamiliar with the code i'm not sure what setting it to handle both cases would look like, but ideally there would be another function to export to a subnetwork. So i could

with nengo.Network() as net:
    converter = nengo_dl.Convert(model, make_subnet=True)
    visionnet = converter.ExportSubNet()
    ens = nengo.Ensemble(...)
    nengo.Connection(ens, visionnet.input)
    nengo.Connection(visionnet.outpt, ens)

or somesuch!