minor update

    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(

    x = model.vae.reconstruct(

        iterative_masked_reconstruct(

            reconstruct=model.vae.reconstruct,

            x=input_x,

            back_prop=False

        )

    )

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

    # `x` 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)

    x.tensor  # the `x` samples

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

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