Does eval and decode after training every eval_freq_in_steps.

def train_eval_and_decode(self):
    """Does eval and decode after training every eval_freq_in_steps."""
    eval_steps = self._hparams.eval_freq_in_steps
    packed_dataset = "_packed" in self._hparams.problem.name
    mlperf_log.transformer_print(key=mlperf_log.TRAIN_LOOP)
    for i in range(0, self._train_spec.max_steps, eval_steps):
      mlperf_log.transformer_print(
          key=mlperf_log.TRAIN_EPOCH, value=i // eval_steps)
      if packed_dataset and i > 0:
        problem = registry.problem(self._hparams.problem.name + "_packed")
        p_hparams = problem.get_hparams(self._hparams)
        self._hparams.problem = problem
        self._hparams.problem_hparams = p_hparams
      self._estimator.train(
          self._train_spec.input_fn,
          steps=eval_steps,
          hooks=self._train_spec.hooks)
      self._set_eval_dir_name("eval")
      self._estimator.evaluate(
          self._eval_spec.input_fn,
          steps=self._eval_spec.steps,
          hooks=self._eval_spec.hooks,
          name="eval")
      if packed_dataset:
        problem = registry.problem(
            self._hparams.problem.name.replace("_packed", ""))
        p_hparams = problem.get_hparams(self._hparams)
        self._hparams.problem = problem
        self._hparams.problem_hparams = p_hparams
      mlperf_log.transformer_print(key=mlperf_log.EVAL_START)
      if self._hparams.mlperf_mode:
        self._decode_hparams.mlperf_decode_step = i + eval_steps
      self.decode(dataset_split=tf.estimator.ModeKeys.EVAL)
      d_hparams = self._decode_hparams
      if self._hparams.mlperf_mode and d_hparams.mlperf_success:
        mlperf_log.transformer_print(
            key=mlperf_log.RUN_STOP, value={"success": "true"})
        break

    d_hparams = self._decode_hparams
    if self._hparams.mlperf_mode and not d_hparams.mlperf_success:
      mlperf_log.transformer_print(
          key=mlperf_log.RUN_STOP, value={"success": "false"})