code: def make_model(self) -> ModelV2:
      # copying ddpg build model to here to be explicit
      model_config = self.config["model"]
      model_config.update(
          dict(
              actor_hidden_activation=self.config[
                  "actor_hidden_activation"
              ],
              actor_hiddens=self.config["actor_hiddens"],
              critic_hidden_activation=self.config[
                  "critic_hidden_activation"
              ],
              critic_hiddens=self.config["critic_hiddens"],
              twin_q=self.config["twin_q"],
          )
      )
      num_outputs = int(np.product(self.observation_space.shape))
  
      # TODO: why do we even have to go through this get_model_v2 function?
      self.model = ModelCatalog.get_model_v2(
          obs_space=self.observation_space,
          action_space=self.action_space,
          num_outputs=num_outputs,
          model_config=model_config,
          framework=self.config["framework"],
          # use this model for interface (get_q, get_q_twin, .etc)
          model_interface=CRRModel,
          default_model=TorchNoopModel,
          name="model",
      )
  
      # TODO: this is a bad python pattern to assign attributes that do not exist in
      #  the constructor
      self.target_model = ModelCatalog.get_model_v2(
          obs_space=self.observation_space,
          action_space=self.action_space,
          num_outputs=num_outputs,
          model_config=model_config,
          framework=self.config["framework"],
          # use this model for interface (get_q, get_q_twin, .etc)
          model_interface=CRRModel,
          default_model=TorchNoopModel,
          name="target_model",
      )
  
      return self.model
  
imports: from typing import Dict, List, Tuple, Type, Union, cast
  import gymnasium
  import numpy
  from rllib_crr.crr.crr_torch_model import CRRModel
  from ray.rllib.algorithms import AlgorithmConfig
  from ray.rllib.algorithms.ddpg.noop_model import TorchNoopModel
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical, TorchDistributionWrapper, get_torch_categorical_class_with_temperature
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import TargetNetworkMixin
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.torch_utils import huber_loss, l2_loss
  from ray.rllib.utils.typing import TensorType, TrainerConfigDict
label_desc: Use image node_id 3 for calling the CRRTorchPolicy obj's underlying member method code with example usage: obj.make_model() and returns: self