:`torch.nn.SyncBatchNorm` layers.

        Args:
            module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers
            process_group (optional): process group to scope synchronization,
                default is the whole world

        Returns:
            The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm`
            layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer,
            a new :class:`torch.nn.SyncBatchNorm` layer object will be returned
            instead.

        Example::

            >>> # Network with nn.BatchNorm layer
            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
            >>> module = torch.nn.Sequential(
            >>>            torch.nn.Linear(20, 100),
            >>>            torch.nn.BatchNorm1d(100),
            >>>          ).cuda()
            >>> # creating process group (optional)
            >>> # ranks is a list of int identifying rank ids.
            >>> ranks = list(range(8))
            >>> r1, r2 = ranks[:4], ranks[4:]
            >>> # Note: every rank calls into new_group for every
            >>> # process group created, even if that rank is not
            >>> # part of the group.
            >>> # xdoctest: +SKIP("distributed")
            >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
            >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
            >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group)

        N