code: def __init__(
      self,
      input_size,
      hidden_size,
      num_layers,
      dropout_rate=0,
      dropout_output=False,
      rnn_type=nn.LSTM,
      concat_layers=False,
      padding=False,
  ):
      super(StackedBRNN, self).__init__()
      self.padding = padding
      self.dropout_output = dropout_output
      self.dropout_rate = dropout_rate
      self.num_layers = num_layers
      self.concat_layers = concat_layers
      self.rnns = nn.ModuleList()
      for i in range(num_layers):
          input_size = input_size if i == 0 else 2 * hidden_size
          # self.rnns.append(rnn_type(input_size, hidden_size,
          #                          num_layers=1,
          #                          bidirectional=True))
          self.rnns.append(
              sru.SRUCell(
                  input_size,
                  hidden_size,
                  dropout=dropout_rate,
                  rnn_dropout=dropout_rate,
                  use_tanh=0,
                  bidirectional=True,
              )
          )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
  import sru
label_desc: Use image node_id 1 to create a new StackedBRNN object from inherited base classes: nn with example: obj = StackedBRNN(input_size, hidden_size, num_layers, dropout_rate, dropout_output, rnn_type, concat_layers, padding)