// 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.

// In-process TensorFlow server.
typedef struct TF_Server TF_Server;

The sequence of execution before FastAPI 0.106.0 was like this diagram:

                # except HTTPException inside a dependency with yield.
                # This needs to happen after user middlewares because those create a
                # new contextvars context copy by using a new AnyIO task group.
                # This AsyncExitStack preserves the context for contextvars, not
                # strictly necessary for closing files but it was one of the original
                # intentions.

        `tf.distribute.InputOptions` for specific options when used with
        distribution.
    *   `tf.keras.experimental.SidecarEvaluator` is now available for a program
        intended to be run on an evaluator task, which is commonly used to
        supplement a training cluster running with
        `tf.distribute.experimental.ParameterServerStrategy` (see
        `https://www.tensorflow.org/tutorials/distribute/parameter_server_training).