class_docstr: Block to perform pre/post processing on layer inputs.
  The processing steps are determined by the sequence argument, which can
  contain one of the three operations:
  n: layer normalization
  r: residual connection
  d: dropout
class_docstr_tok: ["Block","to","perform","pre\/post","processing","on","layer","inputs",".","The","processing","steps","are","determined","by","the","sequence","argument",",","which","can","contain","one","of","the","three","operations",":","n",":","layer","normalization","r",":","residual","connection","d",":","dropout"]
code: def __init__(self, sequence: str, dropout: float, **kwargs) -> None:
      super().__init__(**kwargs)
  
      self.sequence = sequence
      self.dropout = dropout
      self.layer_norm = None
      if "n" in sequence:
          self.layer_norm = LayerNormalization()
  
imports: from typing import Dict, Optional, Tuple
  import mxnet
  from mxnet.gluon import HybridBlock
  from gluonts.mx import Tensor
label_desc: Use image node_id 1 to create a new TransformerProcessBlock object from inherited base classes: HybridBlock with example: obj = TransformerProcessBlock(sequence, dropout)