code: def forward(self, inputs, hidden_state):
      x = F.relu(self.fc1(inputs))
      h_in = hidden_state.reshape(-1, self.rnn_hidden_dim)
  
      h = self.rnn(x, h_in)
      q = self.fc2(h)  # (batch_size, n_actions)
      return q, h
  
file_path: PARL/benchmark/torch/qmix/rnn_model.py
imports: import torch.nn
  import torch.nn.functional
  import parl
label_desc: Use image node_id 3 for calling the RNNModel obj's underlying member method code with example usage: obj.forward(inputs, hidden_state) and returns: q,  h