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": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
    host_train_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"}
    guest_eval_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
    host_eval_data = {"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=host, 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=host).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=host).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))
    reader_1 = Reader(name="reader_1")
    reader_1.get_party_instance(role='guest', party_id=guest).component_param(table=guest_eval_data)
    reader_1.get_party_instance(role='host', party_id=host).component_param(table=host_eval_data)
    pipeline.add_component(reader_1)
    data_transform_1 = DataTransform(name="data_transform_1", output_format='dense')
    pipeline.add_component(data_transform_1,
                           data=Data(data=reader_1.output.data),
                           model=Model(data_transform_0.output.model))
    # define Intersection components
    intersection_1 = Intersection(name="intersection_1")
    pipeline.add_component(intersection_1, data=Data(data=data_transform_1.output.data))
    lr_param = {
        "penalty": "L2",
        "optimizer": "rmsprop",
        "tol": 0.0001,
        "alpha": 0.01,
        "max_iter": 30,
        "early_stop": "diff",
        "batch_size": -1,
        "learning_rate": 0.15,
        "callback_param": {
            "callbacks": ["ModelCheckpoint", "EarlyStopping"],
            "validation_freqs": 1,
            "early_stopping_rounds": 1,
            "metrics": None,
            "use_first_metric_only": False,
            "save_freq": 1
        },
        "init_param": {
            "init_method": "zeros"
        },
        "sqn_param": {
            "update_interval_L": 3,
            "memory_M": 5,
            "sample_size": 5000,
            "random_seed": None
        },
        "cv_param": {
            "n_splits": 5,
            "shuffle": False,
            "random_seed": 103,
            "need_cv": False
        }
    }
    hetero_lr_0 = HeteroLR(name="hetero_lr_0", **lr_param)
    pipeline.add_component(hetero_lr_0, data=Data(train_data=intersection_0.output.data,
                                                  validate_data=intersection_1.output.data))
    hetero_lr_1 = HeteroLR(name='hetero_lr_1')
    pipeline.add_component(hetero_lr_1, data=Data(test_data=intersection_1.output.data),
                           model=Model(hetero_lr_0.output.model))
    evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
    pipeline.add_component(evaluation_0, data=Data(data=[hetero_lr_0.output.data,
                                                         hetero_lr_1.output.data]))
    pipeline.compile()
    pipeline.fit()