def allreduce_hook(
    process_group: dist.ProcessGroup, bucket: dist.GradBucket
) -> torch.futures.Future[torch.Tensor]:
    """
    This DDP communication hook just calls ``allreduce`` using ``GradBucket``
    tensors. Once gradient tensors are aggregated across all workers, its ``then``
    callback takes the mean and returns the result. If user registers this hook,
    DDP results is expected to be same as the case where no hook was registered.
    Hence, this won't change behavior of DDP and user can use this as a reference
    or modify this hook to log useful information or any other purposes while
    unaffecting DDP behavior.

    Example::
        >>> ddp_model.register_comm_hook(process_group, allreduce_hook)
    """
    return _allreduce_fut(process_group, bucket.buffer())

    This DDP communication hook just calls ``allreduce`` using ``GradBucket``
    tensors. Once gradient tensors are aggregated across all workers, its ``then``
    callback takes the mean and returns the result. If user registers this hook,
    DDP results is expected to be same as the case where no hook was registered.
    Hence, this won't change behavior of DDP and user can use this as a reference
    or modify this hook to log useful information or any other purposes while
    unaffecting DDP behavior.

    Example::
        >>> ddp_model.register_comm_hook(process_group, allreduce_hook)
    
segmentation/mmcv_custom/ddp_hooks.py