code: def _compute_critic_loss(
      self,
      model: ModelV2,
      dist_class: Type[TorchDistributionWrapper],
      train_batch: SampleBatch,
  ):
      discount = self.config["gamma"]
  
      # Compute bellman targets to regress on
      # target, use target model to compute the target
      target_model = cast(CRRModel, self.target_models[model])
      target_out_next, _ = target_model(
          {SampleBatch.OBS: train_batch[SampleBatch.NEXT_OBS]}
      )
  
      # compute target values with no gradient
      with torch.no_grad():
          # get the action of the current policy evaluated at the next state
          pi_s_next = dist_class(
              target_model.get_policy_output(target_out_next),
              target_model,
          )
          target_a_next = pi_s_next.sample()
          if not self._is_action_discrete:
              target_a_next = target_a_next.clamp(
                  torch.from_numpy(self.action_space.low).to(
                      target_a_next
                  ),
                  torch.from_numpy(self.action_space.high).to(
                      target_a_next
                  ),
              )
  
          # q1_target = target_model.get_q_values(target_out_next, target_a_next)
          # q2_target = target_model.get_twin_q_values(target_out_next, target_a_next)
          # target_q_next = torch.minimum(q1_target, q2_target).squeeze(-1)
          target_q_next = self._get_q_value(
              target_model, target_out_next, target_a_next
          ).squeeze(-1)
  
          target = (
              train_batch[SampleBatch.REWARDS]
              + discount
              * (1.0 - train_batch[SampleBatch.TERMINATEDS].float())
              * target_q_next
          )
  
      # compute the predicted output
      model = cast(CRRModel, model)
      model_out_t, _ = model(
          {SampleBatch.OBS: train_batch[SampleBatch.OBS]}
      )
      q1 = model.get_q_values(
          model_out_t, train_batch[SampleBatch.ACTIONS]
      ).squeeze(-1)
      q2 = model.get_twin_q_values(
          model_out_t, train_batch[SampleBatch.ACTIONS]
      ).squeeze(-1)
  
      # compute the MSE loss for all q-functions
      td_error_q1 = q1 - target
      td_error_q2 = q2 - target
      loss_fn = (
          l2_loss
          if self.config["td_error_loss_fn"] == "mse"
          else huber_loss
      )
      loss = torch.mean(
          loss_fn(torch.cat((td_error_q1, td_error_q2), dim=0))
      )
  
      # logging
      self.log("td_error_q1", (td_error_q1**2).mean())
      self.log("td_error_q2", (td_error_q2**2).mean())
      self.log("td_error", loss)
      self.log("targets_avg", target.mean())
      self.log("targets_max", target.max())
      self.log("targets_min", target.min())
  
      return 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 12 for calling the CRRTorchPolicy obj's underlying member method code with example usage: obj._compute_critic_loss(model, dist_class, train_batch) and returns: loss