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 "momentum" of 0.1.
Note:
 This "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 \hat{x}_\text{new}
 = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times
 x_t, where \hat{x} is the estimated statistic and x_t is the new
 observed value.

Note:
 "InstanceNorm2d" and "LayerNorm" are very similar, but have some
 subtle differences. "InstanceNorm2d" is applied on each channel
 of channeled data like RGB images, but "LayerNorm" is usually
 applied on entire sample and often in NLP tasks. Additionally,
 "LayerNorm" applies elementwise affine transform, while
 "InstanceNorm2d" usually don't apply affine transform.

Parameters: