class_docstr: RNN wrapper with optional batch norm.
  Instantiates an RNN. If it is an LSTM it initialises the forget gate
  bias =`lstm_gate_bias`. Optionally applies a batch normalisation layer to
  the input with the statistics computed over all time steps.  If dropout > 0
  then it is applied to all layer outputs except the last.
class_docstr_tok: ["RNN","wrapper","with","optional","batch","norm",".","Instantiates","an","RNN",".","If","it","is","an","LSTM","it","initialises","the","forget","gate","bias","=","`","lstm_gate_bias","`",".","Optionally","applies","a","batch","normalisation","layer","to","the","input","with","the","statistics","computed","over","all","time","steps",".","If","dropout",">","0","then","it","is","applied","to","all","layer","outputs","except","the","last","."]
code: def _flatten_parameters(self):
      for layer in self.layers:
          if isinstance(
              layer, (torch.nn.LSTM, torch.nn.GRU, torch.nn.RNN)
          ):
              layer._flatten_parameters()
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the BNRNNSum obj's underlying member method code with example usage: obj._flatten_parameters() without return types