code: def loss(
      self,
      model: ModelV2,
      dist_class: Type[TorchDistributionWrapper],
      train_batch: SampleBatch,
  ) -> Union[TensorType, List[TensorType]]:
      # update the actor
      # compute the weights assigned to every transition
      # (s_t, a_t) and log(pi(a_t|s_t))
      self._compute_action_weights_and_logps(
          model, dist_class, train_batch
      )
  
      # compute actor loss
      actor_loss = self._compute_actor_loss(
          model, dist_class, train_batch
      )
  
      # update the critic
      # standard critic update with pessimistic Q-learning (e.g. DQN)
      critic_loss = self._compute_critic_loss(
          model, dist_class, train_batch
      )
  
      self.log("loss_actor", actor_loss)
      self.log("loss_critic", critic_loss)
  
      return actor_loss, critic_loss
  
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 5 for calling the CRRTorchPolicy obj's underlying member method code with example usage: obj.loss(model, dist_class, train_batch) and returns: actor_loss,  critic_loss