class_docstr: Whole utterance augmented memory transformer.
  
  This is not pyspeech nn layer. It is used as a module in a master layer where
  multiple transformers is used.
class_docstr_tok: ["Whole","utterance","augmented","memory","transformer",".","This","is","not","pyspeech","nn","layer",".","It","is","used","as","a","module","in","a","master","layer","where","multiple","transformers","is","used","."]
code: def after_attention_ops(self, attention_output, residual_input):
      output = self.dropout(attention_output)
      output = output + residual_input
      output = self.pos_ff(output)
      output = self.layer_norm(output)
      return output
  
imports: import math
  import re
  from functools import partial
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from torch import Tensor
  from torch import device
  from fairseq.models import FairseqEncoder
  from fairseq.models.speech_to_text.utils import NoOp, attention_suppression, layer_norm_backward_hook, lengths_to_padding_mask, segments_to_sequence
label_desc: Use image node_id 5 for calling the NoSegAugmentedMemoryTransformer obj's underlying member method code with example usage: obj.after_attention_ops(attention_output, residual_input) and returns: output