code: def test_xor_training(self, xor_trial_controller: Callable) -> None:
      def make_workloads() -> workload.Stream:
          trainer = utils.TrainAndValidate()
  
          yield from trainer.send(
              steps=10, validation_freq=1, scheduling_unit=100
          )
          training_metrics, validation_metrics = trainer.result()
  
          # We expect the validation error and training loss to be
          # monotonically decreasing.
  
          # TODO(DET-1597): actually use a model and optimizer where the losses
          # are monotonically decreasing.
          for older, newer in zip(
              training_metrics[::100], training_metrics[::100][1:]
          ):
              assert newer["loss"] <= older["loss"]
  
          for older, newer in zip(
              validation_metrics, validation_metrics[1:]
          ):
              assert (
                  newer["val_categorical_error"]
                  <= older["val_categorical_error"]
              )
  
          epsilon = 0.0001
          assert (
              abs(validation_metrics[-1]["val_categorical_error"])
              < epsilon
          )
  
      controller = xor_trial_controller(
          self.hparams,
          make_workloads(),
          scheduling_unit=100,
          trial_seed=self.trial_seed,
      )
      controller.run()
  
imports: import os
  from pathlib import Path
  from typing import Any, Callable, Dict, List, Optional
  import pytest
  import tensorflow
  from packaging import version
  import determined
  from determined import keras, workload
  from tests.experiment import utils
  from tests.experiment.fixtures import tf_keras_xor_model
label_desc: Use image node_id 6 for calling the TestKerasTrial obj's underlying member method code with example usage: obj.test_xor_training(xor_trial_controller) without return types