code: def __init__(
      self,
      obs_space: gym.spaces.Space,
      action_space: gym.spaces.Space,
      num_outputs: int,
      model_config: ModelConfigDict,
      name: str,
  ):
      TorchModelV2.__init__(
          self, obs_space, action_space, num_outputs, model_config, name
      )
      nn.Module.__init__(self)
  
      self._is_action_discrete = isinstance(
          action_space, gym.spaces.Discrete
      )
  
      # TODO: I don't know why this is true yet? (in = num_outputs)
      self.obs_ins = num_outputs
      self.action_dim = np.product(self.action_space.shape)
      self.actor_model = self._build_actor_net("actor")
      twin_q = self.model_config["twin_q"]
      self.q_model = self._build_q_net("q")
      if twin_q:
          self.twin_q_model = self._build_q_net("twin_q")
      else:
          self.twin_q_model = None
  
imports: from typing import Dict, List, Union
  import gymnasium
  import numpy
  from ray.rllib.models.torch.misc import SlimFC
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.models.utils import get_activation_fn
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.typing import ModelConfigDict, TensorType
label_desc: Use image node_id 1 to create a new CRRModel object from inherited base classes: TorchModelV2, nn with example: obj = CRRModel(obs_space, action_space, num_outputs, model_config, name)