minor update

        saver = VariableSaver(get_variables_as_dict(model_vs), save_dir)

        saver.restore()

If you need more advanced outputs from the model, you may derive the outputs

by using `model.vae` directly, for example:

.. code-block:: python

    from donut import iterative_masked_reconstruct

    # Obtain the reconstructed `x`, with MCMC missing data imputation.

    # See also:

    #   :meth:`donut.Donut.get_score`

    #   :func:`donut.iterative_masked_reconstruct`

    #   :meth:`tfsnippet.modules.VAE.reconstruct`

    input_x = ...  # 2-D `float32` :class:`tf.Tensor`, input `x` windows

    input_y = ...  # 2-D `int32` :class:`tf.Tensor`, missing point indicators

                   # for the `x` windows

    x_r = model.vae.reconstruct(

        iterative_masked_reconstruct(

            reconstruct=model.vae.reconstruct,

            x=input_x,

            mask=input_y,

            iter_count=mcmc_iteration,

            back_prop=False

        )

    )

    # `x_r` is a :class:`tfsnippet.stochastic.StochasticTensor`, from which

    # you may derive many useful outputs, for example:

    x_r.tensor  # the `x` samples

    x_r.log_prob(group_ndims=0)  # element-wise log p(x|z)

    x_r.distribution.mean, x_r.distribution.std  # mean and std of p(x|z)

                (default :obj:`True`)

        Returns:

            tf.Tensor: The reconstruction probability.

            tf.Tensor: The reconstruction probability, with the shape

                ``(len(x) - self.x_dims + 1,)`` if `last_point_only` is

                :obj:`True`, or ``(len(x) - self.x_dims + 1, self.x_dims)``

                if `last_point_only` is :obj:`False`.  This is because the

                first ``self.x_dims - 1`` points are not the last point of

                any window.

        """

        with tf.name_scope('Donut.get_score'):

            # MCMC missing data imputation