was the previous way of updating variables in this situation.
*   `tf.distribute.experimental.MultiWorkerMirroredStrategy` adds support for
    partial batches. Workers running out of data now continue to participate in
    the training with empty inputs, instead of raising an error. Learn more
    about
    [partial batches here](https://www.tensorflow.org/tutorials/distribute/input#partial_batches).

// In-process TensorFlow server functionality, for use in distributed training.
// A Server instance encapsulates a set of devices and a Session target that
// can participate in distributed training. A server belongs to a cluster
// (specified by a ClusterSpec), and corresponds to a particular task in a
// named job. The server can communicate with any other server in the same
// cluster.