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

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)))

  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_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)))

  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])

def _make_local_temp_destination(self) -> Text:
    """Make a temp destination to push the model."""
    temp_dir = tempfile.mkdtemp()
    push_destination = pusher_pb2.PushDestination(
        filesystem=pusher_pb2.PushDestination.Filesystem(
            base_directory=temp_dir))
    return json_format.MessageToJson(push_destination)

#   * To reject the model, users need to reply the thread sent out by the bot
  #     started by SlackComponent with 'decline' or 'reject'.
  slack_validator = SlackComponent(
      model=trainer.outputs['model'],
      model_blessing=model_validator.outputs['blessing'],
      slack_token=_slack_token,
      slack_channel_id=_slack_channel_id,
      timeout_sec=3600,
  )

  # 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=slack_validator.outputs['slack_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, slack_validator, pusher
      ],
      enable_cache=True,
      metadata_connection_config=metadata.sqlite_metadata_connection_config(
          _metadata_db_root),
  )

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)))

  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
      ],
      # TODO(b/141578059): The multi-processing API might change.
      beam_pipeline_args=['--direct_num_workers=%s' % direct_num_workers],
      additional_pipeline_args={},
  )

model=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=hello.outputs['output_data'], 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, hello, 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))