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

schema=infer_schema.outputs['schema'])

  # Performs transformations and feature engineering in training and serving.
  transform = Transform(
      examples=hello.outputs['output_data'],
      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=hello.outputs['output_data'],
      model=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=hello.outputs['output_data'], model=trainer.outputs['model'])

  # 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=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=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.

# 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'])

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

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)

  # 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.
  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.

schema=infer_schema.outputs['output'],
      module_file=_taxi_utils)

  # Uses user-provided Python function that implements a model using TF-Learn
  # to train a model on Google Cloud AI Platform.
  try:
    from tfx.extensions.google_cloud_ai_platform.trainer import executor as ai_platform_trainer_executor  # pylint: disable=g-import-not-at-top
    # Train using a custom executor. This requires TFX >= 0.14.
    trainer = Trainer(
        executor_class=ai_platform_trainer_executor.Executor,
        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={'ai_platform_training_args': _ai_platform_training_args})
  except ImportError:
    # Train using a deprecated flag.
    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': _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['output'],