code: def __init__(self, hidden_size, output_keep_prob):
      super(RNN, self).__init__()
      self.hidden_size = hidden_size
      self.bid_rnn = nn.GRU(
          hidden_size, hidden_size, batch_first=True, bidirectional=True
      )
      self.output_keep_prob = output_keep_prob
  
      self.out_dropout = nn.Dropout(p=(1 - self.output_keep_prob))
  
imports: import torch
  import torch.nn.functional
  from torch import nn
  from utils import get_length, INF
label_desc: Use image node_id 1 to create a new RNN object from inherited base classes: nn with example: obj = RNN(hidden_size, output_keep_prob)