to handle worker preemption/maintenance and cluster-wise consistent
        error reporting for `tf.distribute.MultiWorkerMirroredStrategy`.
        Specifically, for the type of interruption with advance notice, it
        automatically saves a checkpoint, exits the program without raising an
        unrecoverable error, and restores the progress when training restarts.

*   `tf.math`:

    TF_FloatTensor:$product
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_RngReadAndSkipOp : TF_Op<"RngReadAndSkip", []> {
  let summary = "Advance the counter of a counter-based RNG.";

  let description = [{
The state of the RNG after
`rng_read_and_skip(n)` will be the same as that after `uniform([n])`