 num_features: :math:`C` from an expected input of size
            :math:`(N, C, +)`
        eps: a value added to the denominator for numerical stability.
            Default: ``1e-5``
        momentum: the value used for the running_mean and running_var
            computation. Can be set to ``None`` for cumulative moving average
            (i.e. simple average). Default: 0.1
        affine: a boolean value that when set to ``True``, this module has
            learnable affine parameters. Default: ``True``
        track_running_stats: a boolean value that when set to ``True``, this
            module tracks the running mean and variance, and when set to ``False``,
            this module does not track such statistics, and initializes statistics
            buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
            When these buffers are ``None``, this module always uses batch statistics.
            in both training and eval modes. Default: ``True``
        process_group: synchronization of stats happen within each process group
            individually. Default behavior is synchronization across the whole
            world

    Shape:
        - Input: :math:`(N, C, +)`
        - Output: :math:`(N, C, +)` (same shape as input)

    .. note::
        Synchronization of batchnorm statistics occurs only while training, i.e.
        synchronization is disabled when ``model.eval()`` is set or if
        ``self.training`` is otherwise ``False``.

    Examples::

        >>> # xdoctest: +SKIP
        >>> # With Learnable Parameters
        >>> m = nn.SyncBatchNorm(100)
        >>> # 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.
        >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
        >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
        >>> # Without Learnable Parameters
        >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group)
        >>> input = torch.randn(20, 100, 35, 45, 10)
        >>> output = m(input)

        >>> # network is nn.BatchNorm layer
        >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group)
        >>> # only single gpu per process is currently supported
        >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel(
        >>>                         sync_bn_network,
        >>>                         device_ids=[args.local_rank],
        >>>                         output_device=args.local_rank)
    r