How to use the tfx.orchestration.pipeline.Pipeline function in tfx

def _create_pipeline():
  pipeline_name = _PIPELINE_NAME
  test_output_dir = 'gs://{}/test_output'.format(test_utils.BUCKET_NAME)
  pipeline_root = os.path.join(test_output_dir, pipeline_name)
  components = test_utils.create_e2e_components(pipeline_root,
                                                test_utils.DATA_ROOT,
                                                test_utils.TAXI_MODULE_FILE)
  return tfx_pipeline.Pipeline(
      pipeline_name=pipeline_name,
      pipeline_root=pipeline_root,
      metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
      components=components,
      log_root='/var/tmp/tfx/logs',
      additional_pipeline_args={
          'WORKFLOW_ID': pipeline_name,
      },

def _create_pipeline():
  pipeline_name = _PIPELINE_NAME
  test_output_dir = 'gs://{}/test_output'.format(test_utils.BUCKET_NAME)
  pipeline_root = os.path.join(test_output_dir, pipeline_name)
  components = test_utils.create_e2e_components(pipeline_root,
                                                test_utils.DATA_ROOT,
                                                test_utils.TAXI_MODULE_FILE)
  return tfx_pipeline.Pipeline(
      pipeline_name=pipeline_name,
      pipeline_root=pipeline_root,
      metadata_connection_config=metadata_store_pb2.ConnectionConfig(),
      components=components[:4],
      log_root='/var/tmp/tfx/logs',
      additional_pipeline_args={
          'WORKFLOW_ID': pipeline_name,
      },

def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
                     metadata_path: Text) -> pipeline.Pipeline:
  """Implements the chicago taxi pipeline with TFX."""
  examples = external_input(data_root)

  # Brings data into the pipeline or otherwise joins/converts training data.
  example_gen = CsvExampleGen(input=examples)

  # Computes statistics over data for visualization and example validation.
  statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])

  # Generates schema based on statistics files.
  infer_schema = SchemaGen(statistics=statistics_gen.outputs['statistics'])

  return pipeline.Pipeline(
      pipeline_name=pipeline_name,
      pipeline_root=pipeline_root,
      components=[example_gen, statistics_gen, infer_schema],
      enable_cache=True,
      metadata_connection_config=metadata.sqlite_metadata_connection_config(
          metadata_path),
      additional_pipeline_args={},
  )

def _create_pipeline():
  """Implements the chicago taxi pipeline with TFX."""
  examples = csv_input(_data_root)

  # Brings data into the pipeline or otherwise joins/converts training data.
  example_gen = CsvExampleGen(input=examples)

  # Computes statistics over data for visualization and example validation.
  statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])

  # Generates schema based on statistics files.
  infer_schema = SchemaGen(statistics=statistics_gen.outputs['statistics'])

  return pipeline.Pipeline(
      pipeline_name='chicago_taxi_simple',
      pipeline_root=_pipeline_root,
      components=[
          example_gen, statistics_gen, infer_schema
      ],
      enable_cache=True,
      metadata_db_root=_metadata_db_root,
  )

]))

  # 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},
  )

]))

  # 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=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_connection_config)

]))

  # 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=%d' % direct_num_workers],
  )

]))

  # 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, user_schema_importer, 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])

]))

  # 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(
      custom_executor_spec=executor_spec.ExecutorClassSpec(
          ai_platform_pusher_executor.Executor),
      model=trainer.outputs['model'],
      model_blessing=model_validator.outputs['blessing'],
      custom_config={'ai_platform_serving_args': ai_platform_serving_args})

  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
      ],
      additional_pipeline_args={
          'beam_pipeline_args': beam_pipeline_args,
      },