How to use the tfx.components.example_gen.csv_example_gen.component.CsvExampleGen function in tfx

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_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'])

  return pipeline.Pipeline(
      pipeline_name='chicago_taxi_simple',
      pipeline_root=_pipeline_root,
      components=[

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():
  """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'])

  # 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='chicago_taxi_simple',
      pipeline_root=_pipeline_root,
      components=[

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.csv_input(csv_input_location)

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

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_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.
  transform = Transform(
      input_data=example_gen.outputs.examples,
      schema=infer_schema.outputs.output,
      module_file=_taxi_module_file)

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.
  # pylint: disable=line-too-long
  # statistics_gen = StatisticsGen(examples=example_gen.outputs['examples']) # Step 3
  # pylint: enable=line-too-long

  # Generates schema based on statistics files.
  # infer_schema = SchemaGen(
  #     statistics=statistics_gen.outputs['statistics']) # Step 3

  # Performs anomaly detection based on statistics and data schema.
  # validate_stats = ExampleValidator( # Step 3
  #     statistics=statistics_gen.outputs['statistics'], # Step 3
  #           schema=infer_schema.outputs['schema']) # Step 3

  # Performs transformations and feature engineering in training and serving.

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'])

  # Performs transformations and feature engineering in training and serving.
  transform = Transform(

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'])

  # Performs transformations and feature engineering in training and serving.
  transform = Transform(

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.
  transform = Transform(
      input_data=example_gen.outputs['examples'],
      schema=infer_schema.outputs['output'],