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
      )
  
      self.shared_layers = nn.Sequential(
          nn.Linear(
              in_features=obs_space.original_space["obs"].shape[0],
              out_features=256,
          ),
          nn.Linear(in_features=256, out_features=256),
      )
      self.actor_layers = nn.Sequential(
          nn.Linear(in_features=256, out_features=action_space.n)
      )
      self.critic_layers = nn.Sequential(
          nn.Linear(in_features=256, out_features=1)
      )
      self._value_out = None
  
imports: from abc import ABC
  import numpy
  from ray.rllib.models.modelv2 import restore_original_dimensions
  from ray.rllib.models.preprocessors import get_preprocessor
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.utils.framework import try_import_torch
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)