code: def main(config="../../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]
      arbiter = parties.arbiter[0]
  
      pipeline = FateFlowPipeline().set_parties(
          guest=guest, host=host, arbiter=arbiter
      )
  
      reader_0 = Reader(
          "reader_0", runtime_parties=dict(guest=guest, host=host)
      )
      reader_0.guest.task_parameters(
          namespace=f"experiment{namespace}", name="breast_hetero_guest"
      )
      reader_0.hosts[0].task_parameters(
          namespace=f"experiment{namespace}", name="breast_hetero_host"
      )
      psi_0 = PSI("psi_0", input_data=reader_0.outputs["output_data"])
  
      training_args = TrainingArguments(
          num_train_epochs=1,
          per_device_train_batch_size=16,
          logging_strategy="epoch",
      )
  
      guest_conf = get_config_of_default_runner(
          bottom_model=nn.Linear(10, 10),
          top_model=Sequential(nn.Linear(10, 1), nn.Sigmoid()),
          training_args=training_args,
          optimizer=optim.Adam(lr=0.01),
          loss=nn.BCELoss(),
      )
  
      host_conf = get_config_of_default_runner(
          bottom_model=nn.Linear(20, 20),
          optimizer=optim.Adam(lr=0.01),
          training_args=training_args,
          agglayer_arg=FedPassArgument(
              layer_type="linear",
              in_channels_or_features=20,
              hidden_features=20,
              out_channels_or_features=10,
              passport_mode="single",
              passport_distribute="gaussian",
          ),
      )
  
      hetero_nn_0 = HeteroNN(
          "hetero_nn_0", train_data=psi_0.outputs["output_data"]
      )
  
      hetero_nn_0.guest.task_parameters(runner_conf=guest_conf)
      hetero_nn_0.hosts[0].task_parameters(runner_conf=host_conf)
  
      hetero_nn_1 = HeteroNN(
          "hetero_nn_1",
          test_data=psi_0.outputs["output_data"],
          predict_model_input=hetero_nn_0.outputs["train_model_output"],
      )
  
      evaluation_0 = Evaluation(
          "eval_0",
          runtime_parties=dict(guest=guest),
          metrics=["auc"],
          input_data=[
              hetero_nn_1.outputs["predict_data_output"],
              hetero_nn_0.outputs["train_data_output"],
          ],
      )
  
      pipeline.add_tasks(
          [reader_0, psi_0, hetero_nn_0, hetero_nn_1, evaluation_0]
      )
      pipeline.compile()
      pipeline.fit()
  
      result_summary = pipeline.get_task_info(
          "eval_0"
      ).get_output_metric()[0]["data"]
      print(f"result_summary: {result_summary}")
  
file_path: FATE/examples/pipeline/hetero_nn/test_nn_binary_fedpass.py
imports: import argparse
  from fate_client.pipeline.utils import test_utils
  from fate_client.pipeline import FateFlowPipeline
  from fate_client.pipeline.components.fate.nn.torch import nn, optim
  from fate_client.pipeline.components.fate.nn.torch.base import Sequential
  from fate_client.pipeline.components.fate.hetero_nn import HeteroNN, get_config_of_default_runner
  from fate_client.pipeline.components.fate.psi import PSI
  from fate_client.pipeline.components.fate.reader import Reader
  from fate_client.pipeline.components.fate.nn.algo_params import TrainingArguments
  from fate_client.pipeline.components.fate import Evaluation
  from fate_client.pipeline.components.fate.nn.algo_params import FedPassArgument
label_desc: Use image node_id 1 for calling a global function with example usage: main(config, namespace) without return types