class_docstr: Class that executes training, evaluation, prediction, export, and other
  actions of :tf_main:`Estimator <estimator/Estimator>`.
  
  Args:
      model: An instance of a subclass of
          :class:`~texar.tf.models.model_base.ModelBase`.
      data_hparams: A `dict` or an instance of :class:`~texar.tf.hparams.HParams`
          containing the hyperparameters of data. It must contain `train`
          and/or `eval` fields for relevant processes. For example, for
          :meth:`train_and_evaluate`, both fields are required.
      config: An instance of
          :tf_main:`tf.estimator.RunConfig <estimator/RunConfig>`, used as
          the :attr:`config` argument of
          :tf_main:`Estimator <estimator/Estimator#__init__>`.
      model_hparams (optional): A `dict` or an instance of
          :class:`~texar.tf.hparams.HParams` containing the hyperparameters of
          the model. If `None`, uses :attr:`model.hparams`. Used as
          the :attr:`params` argument of
          :tf_main:`Estimator <estimator/Estimator#__init__>`.
      train_hooks (optional): Iterable of :tf_main:`tf.train.SessionRunHook
          <train/SessionRunHook>` objects to run during training.
      eval_hooks (optional): Iterable of :tf_main:`tf.train.SessionRunHook
          <train/SessionRunHook>` objects to run during evaluation.
      session_config (optional): An instance of
          :tf_main:`tf.ConfigProto <ConfigProto>`, used as the :attr:`config`
          argument of :tf_main:`tf session <Session>`.
  
  Example:
  
      .. code-block:: python
  
          model = BasicSeq2seq(data_hparams, model_hparams)
          exor = Executor(
              model=model,
              data_hparams=data_hparams,
              config=run_config)
          exor.train_and_evaluate(
              max_train_steps=10000,
              eval_steps=100)
  
  See `bin/train.py` for the usage in detail.
docstr: Evaluates the model. See :tf_main:`tf.estimator.Estimator.evaluate
  <estimator/Estimator#evaluate>` for more details.
  
  Args:
      steps (int, optional): Number of steps for which to evaluate
          model. If `None`, evaluates until the eval data raises an
          OutOfRange exception.
      checkpoint_path (str, optional): Path of a specific checkpoint to
          evaluate. If `None`, the the latest checkpoint in
          :attr:`config.model_dir` is used. If there are no checkpoints
          in :attr:`model_dir`, evaluation is run with newly initialized
          variables instead of restored from checkpoint.
code: def evaluate(self, steps=None, checkpoint_path=None):
      """Evaluates the model. See :tf_main:`tf.estimator.Estimator.evaluate
      <estimator/Estimator#evaluate>` for more details.
  
      Args:
          steps (int, optional): Number of steps for which to evaluate
              model. If `None`, evaluates until the eval data raises an
              OutOfRange exception.
          checkpoint_path (str, optional): Path of a specific checkpoint to
              evaluate. If `None`, the the latest checkpoint in
              :attr:`config.model_dir` is used. If there are no checkpoints
              in :attr:`model_dir`, evaluation is run with newly initialized
              variables instead of restored from checkpoint.
      """
      eval_spec = self._get_eval_spec(steps=steps)
      self._estimator.evaluate(
          input_fn=eval_spec.input_fn,
          steps=eval_spec.steps,
          hooks=eval_spec.hooks,
          checkpoint_path=checkpoint_path,
      )
  
file_path: texar/texar/tf/run/executor.py
imports: import tensorflow
  from texar.tf.utils.dtypes import maybe_hparams_to_dict
label_desc: Use image node_id 5 for calling the Executor obj's underlying member method code with example usage: obj.evaluate(steps, checkpoint_path) without return types