class_docstr: The default RNN model for QMIX.
code: def __init__(
      self, obs_space, action_space, num_outputs, model_config, name
  ):
      TorchModelV2.__init__(
          self, obs_space, action_space, num_outputs, model_config, name
      )
      nn.Module.__init__(self)
      self.obs_size = _get_size(obs_space)
      self.rnn_hidden_dim = model_config["lstm_cell_size"]
      self.fc1 = nn.Linear(self.obs_size, self.rnn_hidden_dim)
      self.rnn = nn.GRUCell(self.rnn_hidden_dim, self.rnn_hidden_dim)
      self.fc2 = nn.Linear(self.rnn_hidden_dim, num_outputs)
      self.n_agents = model_config["n_agents"]
  
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 1 to create a new RNNModel object from inherited base classes: TorchModelV2, nn with example: obj = RNNModel(obs_space, action_space, num_outputs, model_config, name)