1F9A5..1F9AA  ; valid                  ;      ; NV8    # 12.0 SLOTH..OYSTER
1F9AB..1F9AD  ; valid                  ;      ; NV8    # 13.0 BEAVER..SEAL
1F9AE..1F9AF  ; valid                  ;      ; NV8    # 12.0 GUIDE DOG..PROBING CANE
1F9B0..1F9B9  ; valid                  ;      ; NV8    # 11.0 EMOJI COMPONENT RED HAIR..SUPERVILLAIN
1F9BA..1F9BF  ; valid                  ;      ; NV8    # 12.0 SAFETY VEST..MECHANICAL LEG

}

def TF_TPURoundRobinOp : TF_Op<"TPURoundRobin", []> {
  let summary = "Round-robin load balancing on TPU cores.";

  let description = [{
A load balancing op that round-robins among TPU cores.

This op round-robins between the integers in [0, NumTPUCoresVisiblePerHost]. It
is useful for interfacing with TensorFlow ops that take as input a TPU core on

## Major Features and Improvements

*   Support for Python3.9 has been added.
*   `tf.data`:
    *   `tf.data` service now supports strict round-robin reads, which is useful
        for synchronous training workloads where example sizes vary. With strict
        round robin reads, users can guarantee that consumers get similar-sized
        examples in the same step.