InstanceNorm1d
class torch.nn.InstanceNorm1d(num_features, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False, device=None, dtype=None)
Applies Instance Normalization over a 2D (unbatched) or 3D
   (batched) input as described in the paper Instance Normalization:
   The Missing Ingredient for Fast Stylization.
  y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}}
  * \gamma + \beta

The mean and standard-deviation are calculated per-dimension
   separately for each object in a mini-batch. \gamma and \beta are
   learnable parameter vectors of size C (where C is the number of
   features or channels of the input) if "affine" is "True". The
   standard-deviation is calculated via the biased estimator,
   equivalent to torch.var(input, unbiased=False).
By default, this layer uses instance statistics computed from input
   data in both training and evaluation modes.
If "track_running_stats" is set to "True", during training this