applies scalar scale and bias for each entire channel/plane with
     the "affine" option, Layer Normalization applies per-element
     scale and bias with "elementwise_affine".
This layer uses statistics computed from input data in both
   training and evaluation modes.
Parameters:
      * normalized_shape (int or list or torch.Size) --
    input shape from an expected input of size

       [* \times \text{normalized\_shape}[0] \times
       \text{normalized\_shape}[1]     \times \ldots \times
       \text{normalized\_shape}[-1]]

    If a single integer is used, it is treated as a singleton
    list, and this module will normalize over the last dimension
    which is expected to be of that specific size.

  * **eps** (*float*) -- a value added to the denominator for
    numerical stability. Default: 1e-5

  * **elementwise_affine** (*bool*) -- a boolean value that when
    set to "True", this module has learnable per-element affine
