old_contents: # python3
  # Copyright 2020 Google Inc. 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.
  """Demo for TF AI Pipeline."""
  from ai_pipeline.models import TFModel
  
  
  def main():
      config = "examples/tf/config.yaml"
      model = TFModel(config)
  
      job_id = model.train()
      model.serve(job_id=job_id)
  
  
  if __name__ == "__main__":
      main()
  
new_contents: # python3
  # Copyright 2020 Google Inc. 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.
  """Demo for TF AI Pipeline."""
  from ai_pipeline.models import TFModel
  
  
  def main():
      config = "examples/tf/config.yaml"
      pred_input = [{"age": 25,
                    "workclass": " Private",
                    "education": " 11th",
                    "education_num": 7,
                    "marital_status":" Never-married",
                    "occupation": " Machine-op-inspct",
                    "relationship": " Own-child",
                    "race": " Black",
                    "gender": " Male",
                    "capital_gain": 0,
                    "capital_loss": 0,
                    "hours_per_week": 40,
                    "native_country": " United-States"}]
  
      model = TFModel(config)
      job_id = model.train()
      version = model.serve(job_id=job_id)
      preds = model.online_predict(pred_input, version=version)
  
      print("Features: {}".format(pred_input))
      print("Predictions: {}".format(preds))
  
  
  if __name__ == "__main__":
      main()
  
subject: Add online prediction example for TF