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": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
    host_train_data = {"name": "breast_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')
    # 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)
    # 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))
    lr_param = {
        "name": "hetero_sshe_lr_0",
        "penalty": "L2",
        "optimizer": "rmsprop",
        "tol": 0.0001,
        "alpha": 0.01,
        "max_iter": 30,
        "early_stop": "weight_diff",
        "batch_size": -1,
        "learning_rate": 0.15,
        "init_param": {
            "init_method": "zeros",
            "fit_intercept": False
        },
        "encrypt_param": {
            "key_length": 1024
        },
        "reveal_every_iter": True,
        "reveal_strategy": "respectively"
    }
    hetero_sshe_lr_0 = HeteroSSHELR(**lr_param)
    pipeline.add_component(hetero_sshe_lr_0, data=Data(train_data=intersection_0.output.data))
    evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
    pipeline.add_component(evaluation_0, data=Data(data=hetero_sshe_lr_0.output.data))
    pipeline.compile()
    # fit model
    pipeline.fit()
    # query component summary
    prettify(pipeline.get_component("hetero_sshe_lr_0").get_summary())
    prettify(pipeline.get_component("evaluation_0").get_summary())
    pipeline.deploy_component([data_transform_0, intersection_0, hetero_sshe_lr_0])
    predict_pipeline = PipeLine()
    # add data reader onto predict pipeline
    predict_pipeline.add_component(reader_0)
    # add selected components from train pipeline onto predict pipeline
    # specify data source
    predict_pipeline.add_component(
        pipeline, data=Data(
            predict_input={
                pipeline.data_transform_0.input.data: reader_0.output.data}))
    # run predict model
    predict_pipeline.predict()
    return pipeline