code: def main(
      config="../../config.yaml",
      param="./sbt_breast_config.yaml",
      namespace="",
  ):
      # obtain config
      if isinstance(config, str):
          config = test_utils.load_job_config(config)
      parties = config.parties
      guest = parties.guest[0]
      host = parties.host[0]
  
      if isinstance(param, str):
          param = test_utils.JobConfig.load_from_file(param)
  
      assert isinstance(param, dict)
  
      guest_data_table = param.get("data_guest")
      host_data_table = param.get("data_host")
  
      guest_train_data = {
          "name": guest_data_table,
          "namespace": f"experiment{namespace}",
      }
      host_train_data = {
          "name": host_data_table,
          "namespace": f"experiment{namespace}",
      }
      pipeline = FateFlowPipeline().set_parties(guest=guest, host=host)
  
      reader_0 = Reader("reader_0")
      reader_0.guest.task_parameters(
          namespace=guest_train_data["namespace"],
          name=guest_train_data["name"],
      )
      reader_0.hosts[0].task_parameters(
          namespace=host_train_data["namespace"],
          name=guest_train_data["name"],
      )
  
      psi_0 = PSI("psi_0", input_data=reader_0.outputs["output_data"])
  
      config_param = {
          "num_trees": param["num_trees"],
          "max_depth": param["max_depth"],
          "max_bin": param["max_bin"],
          "objective": param.get("objective", "binary:bce"),
      }
      hetero_sbt_0 = HeteroSecureBoost(
          "sbt_0",
          train_data=psi_0.outputs["output_data"],
          num_trees=config_param["num_trees"],
          max_bin=config_param["max_bin"],
          max_depth=config_param["max_depth"],
          he_param={"kind": "paillier", "key_length": 1024},
          objective=config_param["objective"],
      )
  
      if config_param["objective"] == "regression:l2":
          evaluation_0 = Evaluation(
              "eval_0",
              runtime_parties=dict(guest=guest),
              input_data=[hetero_sbt_0.outputs["train_data_output"]],
              default_eval_setting="regression",
          )
  
      else:
          evaluation_0 = Evaluation(
              "eval_0",
              runtime_parties=dict(guest=guest),
              metrics=["auc"],
              input_data=[hetero_sbt_0.outputs["train_data_output"]],
          )
  
      pipeline.add_tasks([reader_0, psi_0, hetero_sbt_0, evaluation_0])
      if config.task_cores:
          pipeline.conf.set("task_cores", config.task_cores)
      if config.timeout:
          pipeline.conf.set("timeout", config.timeout)
      pipeline.compile()
      pipeline.fit()
  
      result_summary = parse_summary_result(
          pipeline.get_task_info("eval_0").get_output_metric()[0][
              "data"
          ]
      )
      print(f"result_summary: {result_summary}")
  
      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, result_summary
  
file_path: FATE/examples/benchmark_quality/hetero_secureboost/pipeline_hetero_sbt.py
imports: import argparse
  from fate_test.utils import parse_summary_result
  from fate_client.pipeline.utils import test_utils
  from fate_client.pipeline.components.fate import HeteroSecureBoost
  from fate_client.pipeline.components.fate import PSI, Reader
  from fate_client.pipeline.components.fate.evaluation import Evaluation
  from fate_client.pipeline import FateFlowPipeline
label_desc: Use image node_id 1 for calling a global function with example usage: main(config, param, namespace) and returns: data_summary,  result_summary