LayerNorm
class torch.nn.LayerNorm(normalized_shape, eps=1e-05, elementwise_affine=True, device=None, dtype=None)
Applies Layer Normalization over a mini-batch of inputs as
   described in the paper Layer Normalization
  y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}}
  * \gamma + \beta

The mean and standard-deviation are calculated over the last D
   dimensions, where D is the dimension of "normalized_shape". For
   example, if "normalized_shape" is "(3, 5)" (a 2-dimensional shape),
   the mean and standard-deviation are computed over the last 2
   dimensions of the input (i.e. "input.mean((-2, -1))"). \gamma and
   \beta are learnable affine transform parameters of
   "normalized_shape" if "elementwise_affine" is "True". The standard-
   deviation is calculated via the biased estimator, equivalent to
   torch.var(input, unbiased=False).
Note:
 Unlike Batch Normalization and Instance Normalization, which
