text: import torch as th
  import torch.nn as nn
  import torch.nn.functional as F
  
  class RNNAgent(nn.Module):
      def __init__(self, input_shape, args):
          super(RNNAgent, self).__init__()
          self.args = args
  
          self.fc1 = nn.Linear(input_shape, args.rnn_hidden_dim)
          self.rnn = nn.GRUCell(args.rnn_hidden_dim, args.rnn_hidden_dim)
          self.fc2 = nn.Linear(args.rnn_hidden_dim, args.n_actions)
  
      def init_hidden(self):
          # make hidden states on same device as model
          return self.fc1.weight.new(1, self.args.rnn_hidden_dim).zero_()
  
      def forward(self, inputs, hidden_state):
          x = F.relu(self.fc1(inputs))
          h_in = hidden_state.reshape(-1, self.args.rnn_hidden_dim)
          h = self.rnn(x, h_in)
          q = self.fc2(h)
          return q, h
  