code: def _build_actor_net(self, name_):
      actor_hidden_activation = self.model_config[
          "actor_hidden_activation"
      ]
      actor_hiddens = self.model_config["actor_hiddens"]
  
      # Build the policy network.
      actor_net = nn.Sequential()
  
      activation = get_activation_fn(
          actor_hidden_activation, framework="torch"
      )
      ins = self.obs_ins
      for i, n in enumerate(actor_hiddens):
          actor_net.add_module(
              f"{name_}_hidden_{i}",
              SlimFC(
                  ins,
                  n,
                  initializer=torch.nn.init.xavier_uniform_,
                  activation_fn=activation,
              ),
          )
          ins = n
  
      # also includes log_std in continuous case
      n_act_out = (
          self.action_space.n
          if self._is_action_discrete
          else 2 * self.action_dim
      )
      actor_net.add_module(
          f"{name_}_out",
          SlimFC(
              ins,
              n_act_out,
              initializer=torch.nn.init.xavier_uniform_,
              activation_fn=None,
          ),
      )
  
      return actor_net
  
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 2 for calling the CRRModel obj's underlying member method code with example usage: obj._build_actor_net(name_) and returns: actor_net