code: def __init__(
      self, obs_space, action_space, num_outputs, model_config, name
  ):
      ActorCriticModel.__init__(
          self, obs_space, action_space, num_outputs, model_config, name
      )
      in_shape = 84
      self.shared_layers = nn.Sequential(
          Flatten(),
          nn.Linear(in_features=in_shape, out_features=2048),
          nn.ReLU(),
          nn.Linear(in_features=2048, out_features=2048),
          nn.ReLU(),
      )
      self.actor_layers = nn.Sequential(
          nn.Linear(in_features=2048, out_features=action_space.n)
      )
      self.critic_layers = nn.Sequential(
          nn.Linear(in_features=2048, out_features=1), nn.Tanh()
      )
      self._value_out = None
  
file_path: ray/rllib_contrib/leela_chess_zero/examples/custom_model.py
imports: import torch.nn
  from ray.rllib.algorithms.alpha_zero.models.custom_torch_models import ActorCriticModel, Flatten
label_desc: Use image node_id 1 to create a new DenseModel object from inherited base classes: ActorCriticModel with example: obj = DenseModel(obs_space, action_space, num_outputs, model_config, name)