class_docstr: The default RNN model for QMIX.
code: def forward(self, input_dict, hidden_state, seq_lens):
      x = nn.functional.relu(self.fc1(input_dict["obs_flat"].float()))
      h_in = hidden_state[0].reshape(-1, self.rnn_hidden_dim)
      h = self.rnn(x, h_in)
      q = self.fc2(h)
      return q, [h]
  
file_path: ray/rllib_contrib/qmix/src/rllib_qmix/qmix/model.py
imports: from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.preprocessors import get_preprocessor
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
label_desc: Use image node_id 3 for calling the RNNModel obj's underlying member method code with example usage: obj.forward(input_dict, hidden_state, seq_lens) and returns: q,  list