How to use the tfx.proto.pusher_pb2 function in tfx

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
  ]

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 file destination if check passed.
  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=_serving_model_dir)))

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

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)))

  return pipeline.Pipeline(
      pipeline_name=pipeline_name,
      pipeline_root=pipeline_root,
      components=[
          example_gen, statistics_gen, infer_schema, validate_stats, transform,
          trainer, model_analyzer, model_validator, pusher
      ],
      enable_cache=True,
      metadata_connection_config=metadata.sqlite_metadata_connection_config(
          metadata_path),
  )

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)))

  return pipeline.Pipeline(
      pipeline_name=pipeline_name,
      pipeline_root=pipeline_root,
      components=[
          example_gen, statistics_gen, infer_schema, validate_stats, transform,
          trainer, model_analyzer, model_validator, pusher
      ],
      enable_cache=True,
      metadata_connection_config=metadata.sqlite_metadata_connection_config(
          metadata_path),
      # TODO(b/141578059): The multi-processing API might change.
      beam_pipeline_args=['--direct_num_workers=%d' % direct_num_workers])

),
  )
  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'],
      model_blessing=model_validator.outputs['blessing'],
      push_destination=pusher_pb2.PushDestination(
          filesystem=pusher_pb2.PushDestination.Filesystem(
              base_directory=os.path.
              join(str(_pipeline_root_param), 'model_serving')
          )
      ),
  )

  return pipeline.Pipeline(
      pipeline_name='parameterized_tfx_oss',
      pipeline_root=pipeline_root,
      components=[
          example_gen, statistics_gen, infer_schema, validate_stats, transform,
          trainer, model_analyzer, model_validator, pusher
      ],
      enable_cache=enable_cache,
  )

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'],
        custom_config={'ai_platform_serving_args': _ai_platform_serving_args})
  except ImportError:
    # Deploy the model on Google Cloud AI Platform, using a deprecated flag.
    pusher = Pusher(
        model_export=trainer.outputs['output'],
        model_blessing=model_validator.outputs['blessing'],
        custom_config={'cmle_serving_args': _ai_platform_serving_args},
        push_destination=pusher_pb2.PushDestination(
            filesystem=pusher_pb2.PushDestination.Filesystem(
                base_directory=_serving_model_dir)))

  return pipeline.Pipeline(
      pipeline_name='chicago_taxi_pipeline_kubeflow',
      pipeline_root=_pipeline_root,
      components=[
          example_gen, statistics_gen, infer_schema, validate_stats,
          transform, trainer, model_analyzer, model_validator, pusher
      ],
      additional_pipeline_args={
          'beam_pipeline_args': [
              '--runner=DataflowRunner',
              '--experiments=shuffle_mode=auto',
              '--project=' + _project_id,
              '--temp_location=' + os.path.join(_output_bucket, 'tmp'),
              '--region=' + _gcp_region,

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)))

  return pipeline.Pipeline(
      pipeline_name=pipeline_name,
      pipeline_root=pipeline_root,
      components=[
          example_gen, statistics_gen, infer_schema, validate_stats, transform,
          latest_model_resolver, trainer, model_analyzer, model_validator,
          pusher
      ],
      enable_cache=True,
      metadata_connection_config=metadata.sqlite_metadata_connection_config(
          metadata_path),
      # TODO(b/141578059): The multi-processing API might change.
      beam_pipeline_args=['--direct_num_workers=%d' % direct_num_workers])

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 file destination if check passed.
  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=_serving_model_dir)))

  return pipeline.Pipeline(
      pipeline_name='taxi',
      pipeline_root=_pipeline_root,
      components=[
          example_gen, statistics_gen, infer_schema, validate_stats, transform,
          trainer, model_analyzer, model_validator, pusher
      ],
      enable_cache=True,
      metadata_db_root=_metadata_db_root,
      additional_pipeline_args={'logger_args': logger_overrides},
  )

# 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'],
        custom_config={'ai_platform_serving_args': _ai_platform_serving_args})
  except ImportError:
    # Deploy the model on Google Cloud AI Platform, using a deprecated flag.
    pusher = Pusher(
        model_export=trainer.outputs['output'],
        model_blessing=model_validator.outputs['blessing'],
        custom_config={'cmle_serving_args': _ai_platform_serving_args},
        push_destination=pusher_pb2.PushDestination(
            filesystem=pusher_pb2.PushDestination.Filesystem(
                base_directory=_serving_model_dir)))

  return pipeline.Pipeline(
      pipeline_name='chicago_taxi_pipeline_kubeflow',
      pipeline_root=_pipeline_root,
      components=[
          example_gen, statistics_gen, infer_schema, validate_stats,
          transform, trainer, model_analyzer, model_validator, pusher
      ],
      additional_pipeline_args={
          'beam_pipeline_args': [
              '--runner=DataflowRunner',
              '--experiments=shuffle_mode=auto',
              '--project=' + _project_id,
              '--temp_location=' + os.path.join(_output_bucket, 'tmp'),