def METHOD_NAME(config="../../config.yaml", namespace=""):
    if isinstance(config, str):
        config = load_job_config(config)
    parties = config.parties
    guest = parties.guest[0]
    hosts = parties.host
    arbiter = parties.arbiter[0]
    guest_train_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
    host_train_data = [{"name": "breast_hetero_host", "namespace": f"experiment{namespace}"},
                       {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"}]
    # initialize pipeline
    pipeline = PipeLine()
    # set job initiator
    pipeline.set_initiator(role='guest', party_id=guest)
    # set participants information
    pipeline.set_roles(guest=guest, host=hosts, arbiter=arbiter)
    # define Reader components to read in data
    reader_0 = Reader(name="reader_0")
    # configure Reader for guest
    reader_0.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data)
    # configure Reader for host
    reader_0.get_party_instance(role='host', party_id=hosts[0]).component_param(table=host_train_data[0])
    reader_0.get_party_instance(role='host', party_id=hosts[1]).component_param(table=host_train_data[1])
    # define DataTransform components
    data_transform_0 = DataTransform(name="data_transform_0")  # start component numbering at 0
    # get DataTransform party instance of guest
    data_transform_0_guest_party_instance = data_transform_0.get_party_instance(role='guest', party_id=guest)
    # configure DataTransform for guest
    data_transform_0_guest_party_instance.component_param(with_label=True, output_format="dense")
    # get and configure DataTransform party instance of host
    data_transform_0.get_party_instance(role='host', party_id=hosts[0]).component_param(with_label=False)
    data_transform_0.get_party_instance(role='host', party_id=hosts[1]).component_param(with_label=False)
    # define Intersection components
    intersection_0 = Intersection(name="intersection_0")
    param = {
        "k": 3,
        "max_iter": 10
    }
    hetero_kmeans_0 = HeteroKmeans(name='hetero_kmeans_0', **param)
    evaluation_0 = Evaluation(name='evaluation_0', eval_type='clustering')
    # add components to pipeline, in order of task execution
    pipeline.add_component(reader_0)
    pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
    # set data input sources of intersection components
    pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data))
    pipeline.add_component(hetero_kmeans_0,
                           data=Data(train_data=intersection_0.output.data))
    # print(f"data: {hetero_kmeans_0.output.data.data[0]}")
    pipeline.add_component(evaluation_0, data=Data(data=hetero_kmeans_0.output.data.data[0]))
    # compile pipeline once finished adding modules, this step will form conf and dsl files for running job
    pipeline.compile()
    # fit model
    pipeline.fit()
    # query component summary
    print(pipeline.get_component("hetero_kmeans_0").get_summary())