How to use the tfx.proto.evaluator_pb2.SingleSlicingSpec function in tfx

transform = Transform(
      input_data=example_gen.outputs['examples'],
      schema=infer_schema.outputs['output'],
      module_file=taxi_module_file)
  trainer = Trainer(
      module_file=taxi_module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['output'],
      transform_output=transform.outputs['transform_output'],
      train_args=trainer_pb2.TrainArgs(num_steps=10),
      eval_args=trainer_pb2.EvalArgs(num_steps=5))
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['output'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
          evaluator_pb2.SingleSlicingSpec(
              column_for_slicing=['trip_start_hour'])
      ]))
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['output'])
  pusher = Pusher(
      model_export=trainer.outputs['output'],
      model_blessing=model_validator.outputs['blessing'],
      push_destination=pusher_pb2.PushDestination(
          filesystem=pusher_pb2.PushDestination.Filesystem(
              base_directory=os.path.join(pipeline_root, 'model_serving'))))

  return [
      example_gen, statistics_gen, infer_schema, validate_stats, transform,
      trainer, model_analyzer, model_validator, pusher
  ]

transform = Transform(
      examples=example_gen.outputs['examples'],
      schema=infer_schema.outputs['schema'],
      module_file=taxi_module_file)
  trainer = Trainer(
      module_file=taxi_module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10),
      eval_args=trainer_pb2.EvalArgs(num_steps=5))
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
          evaluator_pb2.SingleSlicingSpec(
              column_for_slicing=['trip_start_hour'])
      ]))
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['model'])
  pusher = Pusher(
      model=trainer.outputs['model'],
      model_blessing=model_validator.outputs['blessing'],
      push_destination=pusher_pb2.PushDestination(
          filesystem=pusher_pb2.PushDestination.Filesystem(
              base_directory=os.path.join(pipeline_root, 'model_serving'))))

  return [
      example_gen, statistics_gen, infer_schema, validate_stats, transform,
      trainer, model_analyzer, model_validator, pusher
  ]

# Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(
      module_file=module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['output'],
      transform_output=transform.outputs['transform_output'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000))

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=training_example_gen.outputs['examples'],
      model_exports=trainer.outputs['output'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
          evaluator_pb2.SingleSlicingSpec(
              column_for_slicing=['trip_start_hour'])
      ]))

  # Performs quality validation of a candidate model (compared to a baseline).
  model_validator = ModelValidator(
      examples=training_example_gen.outputs['examples'],
      model=trainer.outputs['output'])

  inference_examples = external_input(inference_data_root)

  # Brings inference data into the pipeline.
  inference_example_gen = CsvExampleGen(
      input_base=inference_examples,
      output_config=example_gen_pb2.Output(
          split_config=example_gen_pb2.SplitConfig(
              splits=[example_gen_pb2.SplitConfig.Split(

# Train using a deprecated flag.
    trainer = Trainer(
        module_file=_taxi_utils,
        transformed_examples=transform.outputs['transformed_examples'],
        schema=infer_schema.outputs['output'],
        transform_output=transform.outputs['transform_output'],
        train_args=trainer_pb2.TrainArgs(num_steps=10000),
        eval_args=trainer_pb2.EvalArgs(num_steps=5000),
        custom_config={'cmle_training_args': _ai_platform_training_args})

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['output'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
          evaluator_pb2.SingleSlicingSpec(
              column_for_slicing=['trip_start_hour'])
      ]))

  # Performs quality validation of a candidate model (compared to a baseline).
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['output'])

  # Checks whether the model passed the validation steps and pushes the model
  # to a destination if check passed.
  try:
    from tfx.extensions.google_cloud_ai_platform.pusher import executor as ai_platform_pusher_executor  # pylint: disable=g-import-not-at-top
    # Deploy the model on Google Cloud AI Platform. This requires TFX >=0.14.
    pusher = Pusher(
        executor_class=ai_platform_pusher_executor.Executor,
        model_export=trainer.outputs['output'],
        model_blessing=model_validator.outputs['blessing'],

# to train a model on Google Cloud AI Platform.
  trainer = Trainer(
      module_file=module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000),
  )

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
          evaluator_pb2.SingleSlicingSpec(
              column_for_slicing=['trip_start_hour'])
      ]))

  # Performs quality validation of a candidate model (compared to a baseline).
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['model'])

  # Checks whether the model passed the validation steps and pushes the model
  # to  Google Cloud AI Platform if check passed.
  pusher = Pusher(
      model=trainer.outputs['model'],
      model_blessing=model_validator.outputs['blessing'],
      push_destination=pusher_pb2.PushDestination(
          filesystem=pusher_pb2.PushDestination.Filesystem(
              base_directory=serving_model_dir)))

# Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(
      module_file=module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000))

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
          evaluator_pb2.SingleSlicingSpec(
              column_for_slicing=['trip_start_hour'])
      ]))

  # Performs quality validation of a candidate model (compared to a baseline).
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['model'])

  # Checks whether the model passed the validation steps and pushes the model
  # to a file destination if check passed.
  pusher = Pusher(
      model=trainer.outputs['model'],
      model_blessing=model_validator.outputs['blessing'],
      push_destination=pusher_pb2.PushDestination(
          filesystem=pusher_pb2.PushDestination.Filesystem(
              base_directory=serving_model_dir)))

# Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(
      module_file=module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=user_schema_importer.outputs['result'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000))

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
          evaluator_pb2.SingleSlicingSpec(
              column_for_slicing=['trip_start_hour'])
      ]))

  # Performs quality validation of a candidate model (compared to a baseline).
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['model'])

  # Checks whether the model passed the validation steps and pushes the model
  # to a file destination if check passed.
  pusher = Pusher(
      model=trainer.outputs['model'],
      model_blessing=model_validator.outputs['blessing'],
      push_destination=pusher_pb2.PushDestination(
          filesystem=pusher_pb2.PushDestination.Filesystem(
              base_directory=serving_model_dir)))

module_file=taxi_module_file,
  )
  trainer = Trainer(
      module_file=taxi_module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_output=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10),
      eval_args=trainer_pb2.EvalArgs(num_steps=5),
  )
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(
          specs=[
              evaluator_pb2.SingleSlicingSpec(
                  column_for_slicing=['trip_start_hour']
              )
          ]
      ),
  )
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['model']
  )

  # Hack: ensuring push_destination can be correctly parameterized and interpreted.
  # pipeline root will be specified as a dsl.PipelineParam with the name
  # pipeline-root, see:
  # https://github.com/tensorflow/tfx/blob/1c670e92143c7856f67a866f721b8a9368ede385/tfx/orchestration/kubeflow/kubeflow_dag_runner.py#L226
  _pipeline_root_param = dsl.PipelineParam(name='pipeline-root')
  pusher = Pusher(
      model_export=trainer.outputs['model'],

# to train a model on Google Cloud AI Platform.
  trainer = Trainer(
      module_file=module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000),
  )

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
          evaluator_pb2.SingleSlicingSpec(
              column_for_slicing=['trip_start_hour'])
      ]))

  # Performs quality validation of a candidate model (compared to a baseline).
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'], model=trainer.outputs['model'])

  # Checks whether the model passed the validation steps and pushes the model
  # to  Google Cloud AI Platform if check passed.
  pusher = Pusher(
      model=trainer.outputs['model'],
      model_blessing=model_validator.outputs['blessing'],
      push_destination=pusher_pb2.PushDestination(
          filesystem=pusher_pb2.PushDestination.Filesystem(
              base_directory=serving_model_dir)))