class_docstr: Implementation of "Training RNNs as Fast as CNNs"
  :cite:`DBLP:journals/corr/abs-1709-02755`
  
  TODO: turn to pytorch's implementation when it is available.
  
  This implementation is adpoted from the author of the paper:
  https://github.com/taolei87/sru/blob/master/cuda_functional.py.
  
  Args:
    input_size (int): input to model
    hidden_size (int): hidden dimension
    num_layers (int): number of layers
    dropout (float): dropout to use (stacked)
    rnn_dropout (float): dropout to use (recurrent)
    bidirectional (bool): bidirectional
    use_tanh (bool): activation
    use_relu (bool): activation
class_docstr_tok: ["Implementation","of","``","Training","RNNs","as","Fast","as","CNNs","''",":","cite",":","`","DBLP",":","journals\/corr\/abs-1709-02755","`","TODO",":","turn","to","pytorch","'s","implementation","when","it","is","available",".","This","implementation","is","adpoted","from","the","author","of","the","paper",":","https",":","\/\/github.com\/taolei87\/sru\/blob\/master\/cuda_functional.py",".","Args",":","input_size","(","int",")",":","input","to","model","hidden_size","(","int",")",":","hidden","dimension","num_layers","(","int",")",":","number","of","layers","dropout","(","float",")",":","dropout","to","use","(","stacked",")","rnn_dropout","(","float",")",":","dropout","to","use","(","recurrent",")","bidirectional","(","bool",")",":","bidirectional","use_tanh","(","bool",")",":","activation","use_relu","(","bool",")",":","activation"]
code: def set_bias(self, bias_val=0):
      for l in self.rnn_lst:
          l.set_bias(bias_val)
  
imports: import subprocess
  import platform
  import os
  import re
  import configargparse
  import torch
  import torch.nn
  from torch.autograd import Function
  from torch.cuda.amp import custom_fwd, custom_bwd
  from collections import namedtuple
label_desc: Use image node_id 2 for calling the SRU obj's underlying member method code with example usage: obj.set_bias(bias_val) without return types