How to use the tfx.components.statistics_gen.component.StatisticsGen 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=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,
  )

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=[
          example_gen, statistics_gen, infer_schema, validate_stats
      ],
      enable_cache=True,

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_base=examples)

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

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

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={},
  )

pickup_census_tract,
            dropoff_census_tract,
            payment_type,
            company,
            trip_seconds,
            dropoff_community_area,
            tips
          FROM `bigquery-public-data.chicago_taxi_trips.taxi_trips`
          ORDER BY trip_start_timestamp
          LIMIT 100000000"""  # 100 Million.

  # Brings data into the pipeline or otherwise joins/converts training data.
  example_gen = BigQueryExampleGen(query=query)

  # 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_utils)

  # Uses user-provided Python function that implements a model using TF-Learn.

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(
      examples=example_gen.outputs['examples'],
      schema=infer_schema.outputs['schema'],
      module_file=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'])

  # Performs transformations and feature engineering in training and serving.
  transform = Transform(
      examples=example_gen.outputs['examples'],
      schema=infer_schema.outputs['schema'],
      module_file=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_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)

  # Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(

def _create_pipeline():
  """Implements the chicago taxi pipeline with TFX and Kubeflow Pipelines."""

  # Brings data into the pipeline or otherwise joins/converts training data.
  example_gen = BigQueryExampleGen(query=_query)

  # 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_utils)

  # Uses user-provided Python function that implements a model using TF-Learn