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[0]
    guest_train_data = {"name": "ionosphere_scale_hetero_guest", "namespace": f"experiment{namespace}"}
    host_train_data = {"name": "ionosphere_scale_hetero_host", "namespace": f"experiment{namespace}"}
    # guest_train_data = {"name": "default_credit_hetero_guest", "namespace": f"experiment{namespace}"}
    # host_train_data = {"name": "default_credit_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)
    # 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).component_param(table=host_train_data)
    data_transform_0 = DataTransform(name="data_transform_0", output_format='dense', missing_fill=False)
    # 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, label_name="label")
    # get and configure DataTransform party instance of host
    data_transform_0.get_party_instance(role='host', party_id=hosts).component_param(with_label=False)
    # define Intersection components
    intersection_0 = Intersection(name="intersection_0")
    pipeline.add_component(reader_0)
    pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
    pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data))
    statistic_param = {
        "name": "statistic_0",
        "statistics": ["95%", "coefficient_of_variance", "stddev"],
        "column_indexes": [1, 2],
        "column_names": ["x3"]
    }
    statistic_0 = DataStatistics(**statistic_param)
    pipeline.add_component(statistic_0, data=Data(data=intersection_0.output.data))
    pipeline.compile()
    # fit model
    pipeline.fit()
    # query component summary
    prettify(pipeline.get_component("statistic_0").get_summary())