parameters initialized to ones (for weights) and zeros (for
        biases). Default: "True".
Variables:
      * weight -- the learnable weights of the module of shape
        \text{normalized_shape} when "elementwise_affine" is set to
        "True". The values are initialized to 1.
  * **bias** -- the learnable bias of the module of shape
    \text{normalized\_shape} when "elementwise_affine" is set to
    "True". The values are initialized to 0.

Shape:
      * Input: (N, *)
  * Output: (N, *) (same shape as input)

Examples:
  >>> # NLP Example
  >>> batch, sentence_length, embedding_dim = 20, 5, 10
  >>> embedding = torch.randn(batch, sentence_length, embedding_dim)
  >>> layer_norm = nn.LayerNorm(embedding_dim)
  >>> # Activate module
  >>> layer_norm(embedding)
  >>>
  >>> # Image Example
  >>> N, C, H, W = 20, 5, 10, 10
  >>> input = torch.randn(N, C, H, W)
