Normalization over a 5D input (a mini-batch of 3D inputs
    with additional channel dimension) as described in the paper
    `Batch Normalization: Accelerating Deep Network Training by Reducing
    Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`__ .

    .. math::

        y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta

    The mean and standard-deviation are calculated per-dimension over
    the mini-batches and :math:`\gamma` and :math:`\beta` are learnable parameter vectors
    of size `C` (where `C` is the input size). By default, the elements of :math:`\gamma` are set
    to 1 and the elements of :math:`\beta` are set to 0. The standard-deviation is calculated
    via the biased estimator, equivalent to `torch.var(input, unbiased=False)`.

    Also by default, during training this layer keeps running estimates of its
    computed mean and variance, which are then used for normalization during
    evaluation. The running estimates are kept with a default :attr:`momentum`
    of 0.1.

    If :attr:`track_running_stats` is set to ``False``, this layer then does not
    keep running estimates, and batch statistics are instead used during
    evaluation time as well.

    .. note::
        This :attr:`momentum` argument is different from one used in optimizer
        classes and the conventional notion of momentum. Mathematically, the
        update rule for running statistics here is
        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
        where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
        new observed value.

    Because the Batch Normalization is done over the `C` dimension, computing statistics
    on `(N, D, H, W)` slices, it's common terminology to call this Volumetric Batch Normalization
    or Spatio-temporal Batch Normalization.

    Args:
        num_features: :math:`C` from an expected input of size
            :math:`(N, C, D, H, W)`
        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``

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

    Examples::

        >>> # With Learnable Parameters
        >>> m = nn.BatchNorm3d(100)
        >>> # Without Learnable Parameters
        >>> m = nn.BatchNorm3d(100, affine=False)
        >>> input = torch.randn(20, 100, 35, 45, 10)
        >>> output = m(input)
    c