code: def rnn(name, input, state, kernel, bias, new_state, number_of_gates = 2):
      ''' - Ht = f(Xt*Wi + Ht_1*Ri + Wbi + Rbi)
      '''
  
      nn = Build(name)
      nn.tanh(
          nn.mad(kernel=kernel, bias=bias,
              x=nn.concat(input, state)),
          out=new_state);
      return nn.layersf rnn(name, input, state, kernel, bias, new_state, number_of_gates = 2):
      ''' - Ht = f(Xt*Wi + Ht_1*Ri + Wbi + Rbi)
      '''
  
      nn = Build(name)
      nn.tanh(
          nn.mad(kernel=kernel, bias=bias,
              x=nn.concat(input, state)),
          out=new_state);
      return nn.layers;
docs: - Ht = f(Xt*Wi + Ht_1*Ri + Wbi + Rbi)