def METHOD_NAME(config="../../config.yaml", namespace=""):
    # obtain config
    if isinstance(config, str):
        config = load_job_config(config)
    parties = config.parties
    guest = parties.guest[0]
    host = parties.host[0]
    arbiter = parties.arbiter[0]
    guest_train_data = {"name": "vehicle_scale_homo_guest", "namespace": f"experiment{namespace}"}
    guest_validate_data = {"name": "vehicle_scale_homo_test", "namespace": f"experiment{namespace}"}
    host_train_data = {"name": "vehicle_scale_homo_host", "namespace": f"experiment{namespace}"}
    host_validate_data = {"name": "vehicle_scale_homo_test", "namespace": f"experiment{namespace}"}
    pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host, arbiter=arbiter)
    data_transform_0, data_transform_1 = DataTransform(name="data_transform_0"), DataTransform(name='data_transform_1')
    reader_0, reader_1 = Reader(name="reader_0"), Reader(name='reader_1')
    reader_0.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data)
    reader_0.get_party_instance(role='host', party_id=host).component_param(table=host_train_data)
    data_transform_0.get_party_instance(
        role='guest', party_id=guest).component_param(
        with_label=True, output_format="dense")
    data_transform_0.get_party_instance(
        role='host', party_id=host).component_param(
        with_label=True, output_format="dense")
    reader_1.get_party_instance(role='guest', party_id=guest).component_param(table=guest_validate_data)
    reader_1.get_party_instance(role='host', party_id=host).component_param(table=host_validate_data)
    data_transform_1.get_party_instance(
        role='guest', party_id=guest).component_param(
        with_label=True, output_format="dense")
    data_transform_1.get_party_instance(
        role='host', party_id=host).component_param(
        with_label=True, output_format="dense")
    homo_secureboost_0 = HomoSecureBoost(name="homo_secureboost_0",
                                         num_trees=3,
                                         task_type='classification',
                                         objective_param={"objective": "cross_entropy"},
                                         tree_param={
                                             "max_depth": 3
                                         },
                                         validation_freqs=1
                                         )
    evaluation_0 = Evaluation(name='evaluation_0', eval_type='multi')
    pipeline.add_component(reader_0)
    pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
    pipeline.add_component(reader_1)
    pipeline.add_component(
        data_transform_1, data=Data(
            data=reader_1.output.data), model=Model(
            data_transform_0.output.model))
    pipeline.add_component(homo_secureboost_0, data=Data(train_data=data_transform_0.output.data,
                                                         validate_data=data_transform_1.output.data
                                                         ))
    pipeline.add_component(evaluation_0, data=Data(homo_secureboost_0.output.data))
    pipeline.compile()
    pipeline.fit()