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

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=_taxi_module_file)

  # Uses user-provided Python function that implements a model using TF-Learn.
  trainer = Trainer(
      module_file=_taxi_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=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'])

  # This custom component serves as a bridge between pipeline and human 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'])

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.
    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})

    # Performs quality validation of a candidate model (compared to a baseline).
    model_validator = ModelValidator(
        examples=example_gen.outputs.examples, model=trainer.outputs.output)

    return [
        example_gen, statistics_gen, infer_schema, validate_stats, transform,
        trainer, model_validator
    ]

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,
      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

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,
      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

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,
      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

# 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(
      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'])