does it use the same dropout mask for every timestep?  if not how to make it work that way while still maintaining the same performance as using nn.RNN?
This type of dropout is better according to the following paper, and it is also the dropout used in keras.

  
      arxiv.org
  
  
    
1512.05287.pdf 26

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Thanks.