BatchNorm3d
class torch.nn.BatchNorm3d(num_features, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True, device=None, dtype=None)
Applies Batch 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 .
  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 \gamma and \beta are learnable parameter
   vectors of size C (where C is the input size). By default, the
   elements of \gamma are set to 1 and the elements of \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