def METHOD_NAME(config="../../config.yaml", param="./linr_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]
    if isinstance(param, str):
        param = JobConfig.load_from_file(param)
    guest_train_data = {"name": "motor_hetero_guest", "namespace": f"experiment{namespace}"}
    host_train_data = {"name": "motor_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)
    # 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",
                                                          label_name=param["label_name"], label_type="float")
    # 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 component
    intersection_0 = Intersection(name="intersection_0")
    param = {
        "penalty": param["penalty"],
        "max_iter": param["max_iter"],
        "optimizer": param["optimizer"],
        "learning_rate": param["learning_rate"],
        "init_param": param["init_param"],
        "batch_size": param["batch_size"],
        "alpha": param["alpha"]
    }
    hetero_linr_0 = HeteroLinR(name='hetero_linr_0', **param)
    hetero_linr_1 = HeteroLinR(name='hetero_linr_1')
    evaluation_0 = Evaluation(name='evaluation_0', eval_type="regression",
                              metrics=["r2_score",
                                       "mean_squared_error",
                                       "root_mean_squared_error",
                                       "explained_variance"])
    # 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))
    pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data))
    pipeline.add_component(hetero_linr_0, data=Data(train_data=intersection_0.output.data))
    pipeline.add_component(hetero_linr_1, data=Data(test_data=intersection_0.output.data),
                           model=Model(hetero_linr_0.output.model))
    pipeline.add_component(evaluation_0, data=Data(data=hetero_linr_0.output.data))
    # compile pipeline once finished adding modules, this step will form conf and dsl files for running job
    pipeline.compile()
    # fit model
    pipeline.fit()
    metric_summary = parse_summary_result(pipeline.get_component("evaluation_0").get_summary())
    data_linr_0 = extract_data(pipeline.get_component("hetero_linr_0").get_output_data(), "predict_result")
    data_linr_1 = extract_data(pipeline.get_component("hetero_linr_1").get_output_data(), "predict_result")
    desc_linr_0 = regression_metric.Describe().compute(data_linr_0)
    desc_linr_1 = regression_metric.Describe().compute(data_linr_1)
    metric_summary["script_metrics"] = {"linr_train": desc_linr_0,
                                        "linr_validate": desc_linr_1}
    data_summary = {"train": {"guest": guest_train_data["name"], "host": host_train_data["name"]},
                    "test": {"guest": guest_train_data["name"], "host": host_train_data["name"]}
                    }
    return data_summary, metric_summary