async def METHOD_NAME(self):
    self.required_plugins("dffml-config-yaml", "dffml-model-scratch")
    # Load get_single and model_predict
    get_single = Operation.load("get_single")
    model_predict = list(load("dffml.operation.model:model_predict"))[0]
    # Create new dataflow from operations
    dataflow = DataFlow.auto(get_single, model_predict)
    # Add the seed inputs
    dataflow.seed.append(
        Input(
            value=[
                definition.name
                for definition in model_predict.op.outputs.values()
            ],
            definition=get_single.inputs["spec"],
        )
    )
    # Write out the dataflow
    dataflow_yaml = pathlib.Path(self.mktempfile() + ".yaml")
    async with BaseConfigLoader.load("yaml").withconfig(
        {}
    ) as configloader:
        async with configloader() as loader:
            dataflow_yaml.write_bytes(
                await loader.dumpb(dataflow.export(linked=True))
            )
    # TODO Figure out how nested model config options will work
    # print(dataflow_yaml.read_text())
    return