class_docstr: Transformer Decoder layer block in Pre-Norm style.
  Pre-Norm style is an improvement w.r.t. Original paper's Post-Norm style,
  providing better converge speed and performance. This is also the actual
  implementation in tensor2tensor and also avalable in fairseq.
  See https://tunz.kr/post/4 and :cite:`DeeperTransformer`.
class_docstr_tok: ["Transformer","Decoder","layer","block","in","Pre-Norm","style",".","Pre-Norm","style","is","an","improvement","w.r.t",".","Original","paper","'s","Post-Norm","style",",","providing","better","converge","speed","and","performance",".","This","is","also","the","actual","implementation","in","tensor2tensor","and","also","avalable","in","fairseq",".","See","https",":","\/\/tunz.kr\/post\/4","and",":","cite",":","`","DeeperTransformer","`","."]
code: def update_dropout(self, dropout, attention_dropout):
      super(TransformerDecoderLayer, self).update_dropout(
          dropout, attention_dropout
      )
      self.context_attn.update_dropout(attention_dropout)
  
imports: import torch
  import torch.nn
  from onmt.decoders.decoder import DecoderBase
  from onmt.modules import MultiHeadedAttention, AverageAttention
  from onmt.modules.position_ffn import PositionwiseFeedForward
  from onmt.modules.position_ffn import ActivationFunction
  from onmt.modules.moe import MoE
  from onmt.utils.misc import sequence_mask
  from onmt.modules.rmsnorm import RMSNorm
label_desc: Use image node_id 2 for calling the TransformerDecoderLayer obj's underlying member method code with example usage: obj.update_dropout(dropout, attention_dropout) without return types