text: import torch
  import torch.nn as nn
  from torch.autograd import Variable
  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
  
  class coRNN(nn.Module):
      def __init__(self, n_inp, n_hid, dt, gamma, epsilon):
          super(coRNN, self).__init__()
          self.n_hid = n_hid
          self.dt = dt
          self.gamma = gamma
          self.epsilon = epsilon
          self.i2h = nn.Linear(n_inp, n_hid)
          self.h2h = nn.Linear(n_hid+n_hid, n_hid, bias=False)
  
      def forward(self, x):
          hy = Variable(torch.zeros(x.size(1),self.n_hid)).to(device)
          hz = Variable(torch.zeros(x.size(1),self.n_hid)).to(device)
          inputs = self.i2h(x)
          for t in range(x.size(0)):
              hz = hz + self.dt * (torch.tanh(self.h2h(torch.cat((hz,hy),dim=1)) + inputs[t])
                                            - self.gamma * hy - self.epsilon * hz)
              hy = hy + self.dt * hz
  
          return hy
  
  class RNNModel(nn.Module):
      def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, pad_idx, dt, gamma, epsilon):
          super().__init__()
          self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx = pad_idx)
          self.rnn = coRNN(embedding_dim, hidden_dim,dt,gamma,epsilon).to(device)
          self.readout = nn.Linear(hidden_dim, output_dim)
  
      def forward(self, text, text_lengths):
          embedded = self.embedding(text)
          hidden = self.rnn(embedded)
          return self.readout(hidden)
  