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]
    arbiter = parties.arbiter[0]
    guest_train_data = {"name": "vehicle_scale_homo_guest", "namespace": f"experiment{namespace}"}
    host_train_data = {"name": "vehicle_scale_homo_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).component_param(table=host_train_data)
    data_transform_0 = DataTransform(name="data_transform_0", output_format='dense', with_label=True)
    pipeline.add_component(reader_0)
    pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
    lr_param = {
        "penalty": "L2",
        "optimizer": "sgd",
        "tol": 1e-05,
        "alpha": 0.01,
        "early_stop": "diff",
        "batch_size": -1,
        "learning_rate": 0.15,
        "decay": 1,
        "decay_sqrt": True,
        "init_param": {
            "init_method": "zeros"
        },
        "cv_param": {
            "n_splits": 4,
            "shuffle": True,
            "random_seed": 33,
            "need_cv": False
        },
        "callback_param": {
            "callbacks": ["ModelCheckpoint", "EarlyStopping"]
        }
    }
    homo_lr_0 = HomoLR(name="homo_lr_0", max_iter=1, **lr_param)
    homo_lr_1 = HomoLR(name="homo_lr_1")
    pipeline.add_component(homo_lr_0, data=Data(train_data=data_transform_0.output.data))
    pipeline.add_component(homo_lr_1, data=Data(test_data=data_transform_0.output.data),
                           model=Model(model=homo_lr_0.output.model))
    evaluation_0 = Evaluation(name="evaluation_0", eval_type="multi")
    pipeline.add_component(evaluation_0, data=Data(data=[homo_lr_0.output.data,
                                                         homo_lr_1.output.data]))
    pipeline.compile()
    # fit model
    pipeline.fit()
    # query component summary
    prettify(pipeline.get_component("evaluation_0").get_summary())
    return pipeline