How to use the tfx.utils.dsl_utils.external_input function in tfx

def create_e2e_components(pipeline_root: Text, csv_input_location: Text,
                          taxi_module_file: Text) -> List[BaseComponent]:
  """Creates components for a simple Chicago Taxi TFX pipeline for testing.

  Args:
    pipeline_root: The root of the pipeline output.
    csv_input_location: The location of the input data directory.
    taxi_module_file: The location of the module file for Transform/Trainer.

  Returns:
    A list of TFX components that constitutes an end-to-end test pipeline.
  """
  examples = dsl_utils.external_input(csv_input_location)

  example_gen = CsvExampleGen(input_base=examples)
  statistics_gen = StatisticsGen(input_data=example_gen.outputs['examples'])
  infer_schema = SchemaGen(
      stats=statistics_gen.outputs['output'], infer_feature_shape=False)
  validate_stats = ExampleValidator(
      stats=statistics_gen.outputs['output'],
      schema=infer_schema.outputs['output'])
  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'],

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)

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

def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
                     module_file: Text, serving_model_dir: Text,
                     metadata_path: Text) -> pipeline.Pipeline:
  """Implements the cifar10 pipeline with TFX."""
  examples = external_input(data_root)
  input_split = example_gen_pb2.Input(splits=[
      example_gen_pb2.Input.Split(name='train', pattern='train.tfrecord'),
      example_gen_pb2.Input.Split(name='eval', pattern='test.tfrecord')
  ])
  example_gen = ImportExampleGen(input=examples, input_config=input_split)
  # 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'], infer_feature_shape=True)

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

# 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(
                  name='unlabelled', hash_buckets=100)])),
      instance_name='inference_example_gen')

  # Performs offline batch inference over inference examples.
  bulk_inferrer = BulkInferrer(
      examples=inference_example_gen.outputs['examples'],
      model_export=trainer.outputs['output'],
      model_blessing=model_validator.outputs['blessing'],
      # Empty data_spec.example_splits will result in using all splits.

def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
                     module_file: Text, serving_model_dir: 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'],
      infer_feature_shape=False)

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

def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
                     module_file: Text, serving_model_dir: Text,
                     metadata_path: Text,
                     direct_num_workers: int) -> 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'],
      infer_feature_shape=False)

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

def _create_pipeline(
    pipeline_name: Text, pipeline_root: Text, data_root: Text,
    module_file: Text, serving_model_dir: Text,
    metadata_connection_config: metadata_store_pb2.ConnectionConfig
) -> 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_base=examples)

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

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

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

  # Performs transformations and feature engineering in training and serving.

def _create_test_pipeline(
    pipeline_root: Text, csv_input_location: Text, taxi_module_file: Text,
    enable_cache: bool
):
  """Creates a simple Kubeflow-based Chicago Taxi TFX pipeline.

  Args:
    pipeline_root: The root of the pipeline output.
    csv_input_location: The location of the input data directory.
    taxi_module_file: The location of the module file for Transform/Trainer.
    enable_cache: Whether to enable cache or not.

  Returns:
    A logical TFX pipeline.Pipeline object.
  """
  examples = external_input(csv_input_location)

  example_gen = CsvExampleGen(input=examples)
  statistics_gen = StatisticsGen(input_data=example_gen.outputs['examples'])
  infer_schema = SchemaGen(
      stats=statistics_gen.outputs['statistics'],
      infer_feature_shape=False,
  )
  validate_stats = ExampleValidator(
      stats=statistics_gen.outputs['statistics'],
      schema=infer_schema.outputs['schema'],
  )
  transform = Transform(
      input_data=example_gen.outputs['examples'],
      schema=infer_schema.outputs['schema'],
      module_file=taxi_module_file,
  )

def _create_pipeline(pipeline_name: Text, pipeline_root: Text, data_root: Text,
                     module_file: Text, serving_model_dir: Text,
                     direct_num_workers: int) -> pipeline.Pipeline:
  """Implements the chicago taxi pipeline with TFX and Kubeflow Pipelines."""
  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'],
      infer_feature_shape=False)

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