How to use the tfx.proto.trainer_pb2 function in tfx

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(
      module_file=_taxi_module_file,
      transformed_examples=transform.outputs.transformed_examples,
      schema=infer_schema.outputs.output,
      transform_output=transform.outputs.transform_output,
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000))

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=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=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.

schema=infer_schema.outputs['output'])

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

  # Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(
      module_file=module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['output'],
      transform_output=transform.outputs['transform_output'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000))

  # 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)

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(
      module_file=_taxi_module_file,
      transformed_examples=transform.outputs.transformed_examples,
      schema=infer_schema.outputs.output,
      transform_output=transform.outputs.transform_output,
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000))

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=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=example_gen.outputs.examples, model=trainer.outputs.output)

  # Checks whether the model passed the validation steps and pushes the model

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)

  # Uses user-provided Python function that implements a model using TF-Learn
  # to train a model on Google Cloud AI Platform.
  trainer = Trainer(
      module_file=module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000),
  )

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      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=example_gen.outputs['examples'], model=trainer.outputs['model'])

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)

  # Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(
      module_file=module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000))

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      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=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.

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)

  # Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(
      module_file=module_file,
      examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=1000),
      eval_args=trainer_pb2.EvalArgs(num_steps=500))

  # Uses TFMA to compute a evaluation statistics over features of a model.
  evaluator = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(
          specs=[evaluator_pb2.SingleSlicingSpec()]))

  # 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(

}
  # TODO(b/139281215): these input / output names will be renamed in the future.
  # These compatibility aliases are provided for forwards compatibility.
  _INPUT_COMPATIBILITY_ALIASES = {
      'examples': 'input_data',
  }
  _OUTPUT_COMPATIBILITY_ALIASES = {
      'statistics': 'output',
  }


class TrainerSpec(ComponentSpec):
  """Trainer component spec."""

  PARAMETERS = {
      'train_args': ExecutionParameter(type=trainer_pb2.TrainArgs),
      'eval_args': ExecutionParameter(type=trainer_pb2.EvalArgs),
      'module_file': ExecutionParameter(type=(str, Text), optional=True),
      'trainer_fn': ExecutionParameter(type=(str, Text), optional=True),
      'custom_config': ExecutionParameter(type=Dict[Text, Any], optional=True),
  }
  INPUTS = {
      'examples':
          ChannelParameter(type=standard_artifacts.Examples),
      # TODO(b/139281215): this will be renamed to 'transform_graph' in the
      # future.
      'transform_output':
          ChannelParameter(
              type=standard_artifacts.TransformGraph, optional=True),
      'schema':
          ChannelParameter(type=standard_artifacts.Schema),
      'base_model':

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.
  trainer = Trainer(
      module_file=_taxi_utils,
      transformed_examples=transform.outputs.transformed_examples,
      schema=infer_schema.outputs.output,
      transform_output=transform.outputs.transform_output,
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000),
      custom_config={'cmle_training_args': _cmle_training_args})

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(  # pylint: disable=unused-variable
      examples=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=example_gen.outputs.examples, model=trainer.outputs.output)

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

  # Uses user-provided Python function that implements a model using TF-Learn
  # to train a model on Google Cloud AI Platform.
  trainer = Trainer(
      custom_executor_spec=executor_spec.ExecutorClassSpec(
          ai_platform_trainer_executor.Executor),
      module_file=module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=infer_schema.outputs['schema'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000),
      custom_config={'ai_platform_training_args': ai_platform_training_args})

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      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=example_gen.outputs['examples'], model=trainer.outputs['model'])

  # Checks whether the model passed the validation steps and pushes the model

schema=user_schema_importer.outputs['result'])

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

  # Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(
      module_file=module_file,
      transformed_examples=transform.outputs['transformed_examples'],
      schema=user_schema_importer.outputs['result'],
      transform_graph=transform.outputs['transform_graph'],
      train_args=trainer_pb2.TrainArgs(num_steps=10000),
      eval_args=trainer_pb2.EvalArgs(num_steps=5000))

  # Uses TFMA to compute a evaluation statistics over features of a model.
  model_analyzer = Evaluator(
      examples=example_gen.outputs['examples'],
      model_exports=trainer.outputs['model'],
      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=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.