class_docstr: A two-layer Feed-Forward-Network with residual layer norm.
  
  Args:
      d_model (int): the size of input for the first-layer of the FFN.
      d_ff (int): the hidden layer size of the second-layer
          of the FNN.
      dropout (float): dropout probability in :math:`[0, 1)`.
      activation_fn (ActivationFunction): activation function used.
      layer_norm (string): 'standard' or 'rms'
class_docstr_tok: ["A","two-layer","Feed-Forward-Network","with","residual","layer","norm",".","Args",":","d_model","(","int",")",":","the","size","of","input","for","the","first-layer","of","the","FFN",".","d_ff","(","int",")",":","the","hidden","layer","size","of","the","second-layer","of","the","FNN",".","dropout","(","float",")",":","dropout","probability","in",":","math",":","`","[","0",",","1",")","`",".","activation_fn","(","ActivationFunction",")",":","activation","function","used",".","layer_norm","(","string",")",":","'standard","'","or","'rms","'"]
code: def update_dropout(self, dropout):
      self.dropout_1.p = dropout
      self.dropout_2.p = dropout
  
file_path: OpenNMT-py/onmt/modules/position_ffn.py
imports: import torch.nn
  import torch.nn.functional
  from torch.utils.checkpoint import checkpoint
  from onmt.modules.rmsnorm import RMSNorm
  from torch.nn.utils import skip_init
  from torch.distributed import all_reduce
label_desc: Use image node_id 3 for calling the PositionwiseFeedForward obj's underlying member method code with example usage: obj.update_dropout(dropout) without return types