"fp16_compress_wrapper(allreduce_hook)".
Example::
      >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, start_powerSGD_iter=10)
      >>> ddp_model.register_comm_hook(state, fp16_compress_wrapper(powerSGD_hook))
Return type:
      Callable[[Any, GradBucket], Future[Tensor]]
torch.distributed.algorithms.ddp_comm_hooks.default_hooks.bf16_compress_wrapper(hook)
Warning: This API is experimental, and it requires NCCL version
   later than 2.9.6.
This wrapper casts the input gradient tensor of a given DDP
   communication hook to half-precision Brain floating point format
   https://en.wikipedia.org/wiki/Bfloat16_floating-point_format _
   (``torch.bfloat16), and casts the resulting tensor of the given
   hook back to the input data type, such as "float32".
Therefore, "bf16_compress_hook" is equivalent to
   "bf16_compress_wrapper(allreduce_hook)".
Example::