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]
  
      epochs = 10
      batch_size = 64
      in_feat = 18
      lr = 0.01
      class_num = 4
  
      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="vehicle_scale_homo_guest",
      )
      reader_0.hosts[0].task_parameters(
          namespace=f"experiment{namespace}",
          name="vehicle_scale_homo_host",
      )
  
      conf = get_config_of_default_runner(
          algo="fedavg",
          model=ModelLoader(
              "multi_model", "Multi", feat=in_feat, class_num=class_num
          ),
          loss=nn.CrossEntropyLoss(),
          optimizer=optim.Adam(lr=lr),
          training_args=TrainingArguments(
              num_train_epochs=epochs,
              per_device_train_batch_size=batch_size,
              seed=114514,
          ),
          fed_args=FedAVGArguments(),
          task_type="multi",
      )
  
      homo_nn_0 = HomoNN(
          "nn_0",
          runner_conf=conf,
          train_data=reader_0.outputs["output_data"],
      )
  
      evaluation_0 = Evaluation(
          "eval_0",
          runtime_parties=dict(guest=guest),
          default_eval_setting="multi",
          input_data=[homo_nn_0.outputs["train_data_output"]],
      )
  
      pipeline.add_tasks([reader_0, homo_nn_0, evaluation_0])
      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}")
  
file_path: FATE/examples/pipeline/homo_nn/test_nn_multi.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.evaluation import Evaluation
  from fate_client.pipeline.components.fate.reader import Reader
  from fate_client.pipeline import FateFlowPipeline
  from fate_client.pipeline.components.fate.nn.torch import nn, optim
  from fate_client.pipeline.components.fate.nn.loader import ModelLoader
  from fate_client.pipeline.components.fate.homo_nn import HomoNN, get_config_of_default_runner
  from fate_client.pipeline.components.fate.nn.algo_params import TrainingArguments, FedAVGArguments
label_desc: Use image node_id 1 for calling a global function with example usage: main(config, namespace) without return types