code: #
  #  Copyright 2019 The FATE Authors. All Rights Reserved.
  #
  #  Licensed under the Apache License, Version 2.0 (the "License");
  #  you may not use this file except in compliance with the License.
  #  You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  #  Unless required by applicable law or agreed to in writing, software
  #  distributed under the License is distributed on an "AS IS" BASIS,
  #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  #  See the License for the specific language governing permissions and
  #  limitations under the License.
  #
  
  import argparse
  
  from pipeline.backend.pipeline import PipeLine
  from pipeline.component import DataTransform
  from pipeline.component.hetero_ftl import HeteroFTL
  from pipeline.component.reader import Reader
  from pipeline.interface.data import Data
  from tensorflow.keras import optimizers
  from tensorflow.keras.layers import Dense
  from tensorflow.keras import initializers
  from pipeline.component.evaluation import Evaluation
  
  from pipeline.utils.tools import load_job_config
  
  
  def main(config="../../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]
  
      guest_train_data = {"name": "nus_wide_guest", "namespace": f"experiment{namespace}"}
      host_train_data = {"name": "nus_wide_host", "namespace": f"experiment{namespace}"}
      pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host)
  
      reader_0 = Reader(name="reader_0")
      reader_0.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data)
      reader_0.get_party_instance(role='host', party_id=host).component_param(table=host_train_data)
  
      data_transform_0 = DataTransform(name="data_transform_0")
      data_transform_0.get_party_instance(
          role='guest', party_id=guest).component_param(
          with_label=True, output_format="dense")
      data_transform_0.get_party_instance(role='host', party_id=host).component_param(with_label=False)
  
      hetero_ftl_0 = HeteroFTL(name='hetero_ftl_0',
                               epochs=10, alpha=1, batch_size=-1, mode='plain')
  
      hetero_ftl_0.add_nn_layer(Dense(units=32, activation='sigmoid',
                                      kernel_initializer=initializers.RandomNormal(stddev=1.0),
                                      bias_initializer=initializers.Zeros()))
  
      hetero_ftl_0.compile(optimizer=optimizers.Adam(lr=0.01))
      evaluation_0 = Evaluation(name='evaluation_0', eval_type="binary")
  
      pipeline.add_component(reader_0)
      pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
      pipeline.add_component(hetero_ftl_0, data=Data(train_data=data_transform_0.output.data))
      pipeline.add_component(evaluation_0, data=Data(data=hetero_ftl_0.output.data))
  
      pipeline.compile()
  
      pipeline.fit()
  
      # predict
      # deploy required components
      pipeline.deploy_component([data_transform_0, hetero_ftl_0])
  
      predict_pipeline = PipeLine()
      # add data reader onto predict pipeline
      predict_pipeline.add_component(reader_0)
      # add selected components from train pipeline onto predict pipeline
      # specify data source
      predict_pipeline.add_component(
          pipeline, data=Data(
              predict_input={
                  pipeline.data_transform_0.input.data: reader_0.output.data}))
      # run predict model
      predict_pipeline.predict()
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser("PIPELINE DEMO")
      parser.add_argument("-config", type=str,
                          help="config file")
      args = parser.parse_args()
      if args.config is not None:
          main(args.config)
      else:
          main()
  
apis: tensorflow.keras.initializers.Zeros tensorflow.keras.optimizers.Adam tensorflow.keras.initializers.RandomNormal