class_docstr: Layer norm at the the end of all encoder layers if
  args.encoder_enormalize_before = True
class_docstr_tok: ["Layer","norm","at","the","the","end","of","all","encoder","layers","if","args.encoder_enormalize_before","=","True"]
code: def forward(self, input):
      x = input[0]
      encoder_padding_mask = input[1]
      prev_output_tokens = input[2]
      if self.layer_norm:
          x = self.layer_norm(x)
      # keeping track of the incremental_state is not supported yet
      return (x, encoder_padding_mask, prev_output_tokens)
  
imports: import math
  from collections import namedtuple
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import options, utils
  from fairseq.modules import AdaptiveSoftmax, LayerNorm, MultiheadAttention, PositionalEmbedding
label_desc: Use image node_id 2 for calling the TransformerEncoderLayerNorm obj's underlying member method code with example usage: obj.forward(input) and returns: x,  encoder_padding_mask,  prev_output_tokens