How to use the tfx.orchestration.metadata.sqlite_metadata_connection_config function in tfx

statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])

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

  # Performs anomaly detection based on statistics and data schema.
  validate_stats = ExampleValidator(
      statistics=statistics_gen.outputs['statistics'],
      schema=infer_schema.outputs['schema'])

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

model_export=trainer.outputs['output'],
      model_blessing=model_validator.outputs['blessing'],
      # Empty data_spec.example_splits will result in using all splits.
      data_spec=bulk_inferrer_pb2.DataSpec(),
      model_spec=bulk_inferrer_pb2.ModelSpec())

  return pipeline.Pipeline(
      pipeline_name=pipeline_name,
      pipeline_root=pipeline_root,
      components=[
          training_example_gen, inference_example_gen, statistics_gen,
          infer_schema, validate_stats, transform, trainer, model_analyzer,
          model_validator, bulk_inferrer
      ],
      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])

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))

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, evaluator, model_validator, pusher
      ],
      enable_cache=True,
      metadata_connection_config=metadata.sqlite_metadata_connection_config(
          metadata_path),
  )

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])

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),
  )

"""

    if not pipeline_name:
      pipeline_name = ('interactive-%s' %
                       datetime.datetime.now().isoformat().replace(':', '_'))
    if not pipeline_root:
      pipeline_root = tempfile.mkdtemp(prefix='tfx-%s-' % pipeline_name)
      absl.logging.warning(
          'InteractiveContext pipeline_root argument not provided: using '
          'temporary directory %s as root for pipeline outputs.', pipeline_root)
    if not metadata_connection_config:
      # TODO(ccy): consider reconciling similar logic here with other instances
      # in tfx/orchestration/...
      metadata_sqlite_path = os.path.join(pipeline_root,
                                          self._DEFAULT_SQLITE_FILENAME)
      metadata_connection_config = metadata.sqlite_metadata_connection_config(
          metadata_sqlite_path)
      absl.logging.warning(
          'InteractiveContext metadata_connection_config not provided: using '
          'SQLite ML Metadata database at %s.', metadata_sqlite_path)
    self.pipeline_name = pipeline_name
    self.pipeline_root = pipeline_root
    self.metadata_connection_config = metadata_connection_config

    # Register IPython formatters.
    notebook_formatters.register_formatters()

    # Register artifact visualizations.
    standard_visualizations.register_standard_visualizations()

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])

"""

    if not pipeline_name:
      pipeline_name = ('interactive-%s' %
                       datetime.datetime.now().isoformat().replace(':', '_'))
    if not pipeline_root:
      pipeline_root = tempfile.mkdtemp(prefix='tfx-%s-' % pipeline_name)
      absl.logging.warning(
          'InteractiveContext pipeline_root argument not provided: using '
          'temporary directory %s as root for pipeline outputs.', pipeline_root)
    if not metadata_connection_config:
      # TODO(ccy): consider reconciling similar logic here with other instances
      # in tfx/orchestration/...
      metadata_sqlite_path = os.path.join(pipeline_root,
                                          self._DEFAULT_SQLITE_FILENAME)
      metadata_connection_config = metadata.sqlite_metadata_connection_config(
          metadata_sqlite_path)
      absl.logging.warning(
          'InteractiveContext metadata_connection_config not provided: using '
          'SQLite ML Metadata database at %s.', metadata_sqlite_path)
    self.pipeline_name = pipeline_name
    self.pipeline_root = pipeline_root
    self.metadata_connection_config = metadata_connection_config

    # Register IPython formatters.
    notebook_formatters.register_formatters()

    # Register artifact visualizations.
    standard_visualizations.register_standard_visualizations()