code: def loss(
      self,
      model: ModelV2,
      dist_class: Type[TorchDistributionWrapper],
      train_batch: SampleBatch,
  ) -> List[TensorType]:
      target_model = self.target_models[model]
  
      twin_q = self.config["twin_q"]
      gamma = self.config["gamma"]
      n_step = self.config["n_step"]
      use_huber = self.config["use_huber"]
      huber_threshold = self.config["huber_threshold"]
      l2_reg = self.config["l2_reg"]
  
      input_dict = SampleBatch(
          obs=train_batch[SampleBatch.CUR_OBS], _is_training=True
      )
      input_dict_next = SampleBatch(
          obs=train_batch[SampleBatch.NEXT_OBS], _is_training=True
      )
  
      model_out_t, _ = model(input_dict, [], None)
      model_out_tp1, _ = model(input_dict_next, [], None)
      target_model_out_tp1, _ = target_model(input_dict_next, [], None)
  
      # Policy network evaluation.
      # prev_update_ops = set(tf1.get_collection(tf.GraphKeys.UPDATE_OPS))
      policy_t = model.get_policy_output(model_out_t)
      # policy_batchnorm_update_ops = list(
      #    set(tf1.get_collection(tf.GraphKeys.UPDATE_OPS)) - prev_update_ops)
  
      policy_tp1 = target_model.get_policy_output(target_model_out_tp1)
  
      # Action outputs.
      if self.config["smooth_target_policy"]:
          target_noise_clip = self.config["target_noise_clip"]
          clipped_normal_sample = torch.clamp(
              torch.normal(
                  mean=torch.zeros(policy_tp1.size()),
                  std=self.config["target_noise"],
              ).to(policy_tp1.device),
              -target_noise_clip,
              target_noise_clip,
          )
  
          policy_tp1_smoothed = torch.min(
              torch.max(
                  policy_tp1 + clipped_normal_sample,
                  torch.tensor(
                      self.action_space.low,
                      dtype=torch.float32,
                      device=policy_tp1.device,
                  ),
              ),
              torch.tensor(
                  self.action_space.high,
                  dtype=torch.float32,
                  device=policy_tp1.device,
              ),
          )
      else:
          # No smoothing, just use deterministic actions.
          policy_tp1_smoothed = policy_tp1
  
      # Q-net(s) evaluation.
      # prev_update_ops = set(tf1.get_collection(tf.GraphKeys.UPDATE_OPS))
      # Q-values for given actions & observations in given current
      q_t = model.get_q_values(
          model_out_t, train_batch[SampleBatch.ACTIONS]
      )
  
      # Q-values for current policy (no noise) in given current state
      q_t_det_policy = model.get_q_values(model_out_t, policy_t)
  
      actor_loss = -torch.mean(q_t_det_policy)
  
      if twin_q:
          twin_q_t = model.get_twin_q_values(
              model_out_t, train_batch[SampleBatch.ACTIONS]
          )
      # q_batchnorm_update_ops = list(
      #     set(tf1.get_collection(tf.GraphKeys.UPDATE_OPS)) - prev_update_ops)
  
      # Target q-net(s) evaluation.
      q_tp1 = target_model.get_q_values(
          target_model_out_tp1, policy_tp1_smoothed
      )
  
      if twin_q:
          twin_q_tp1 = target_model.get_twin_q_values(
              target_model_out_tp1, policy_tp1_smoothed
          )
  
      q_t_selected = torch.squeeze(q_t, axis=len(q_t.shape) - 1)
      if twin_q:
          twin_q_t_selected = torch.squeeze(
              twin_q_t, axis=len(q_t.shape) - 1
          )
          q_tp1 = torch.min(q_tp1, twin_q_tp1)
  
      q_tp1_best = torch.squeeze(input=q_tp1, axis=len(q_tp1.shape) - 1)
      q_tp1_best_masked = (
          1.0 - train_batch[SampleBatch.TERMINATEDS].float()
      ) * q_tp1_best
  
      # Compute RHS of bellman equation.
      q_t_selected_target = (
          train_batch[SampleBatch.REWARDS]
          + gamma**n_step * q_tp1_best_masked
      ).detach()
  
      # Compute the error (potentially clipped).
      if twin_q:
          td_error = q_t_selected - q_t_selected_target
          twin_td_error = twin_q_t_selected - q_t_selected_target
          if use_huber:
              errors = huber_loss(
                  td_error, huber_threshold
              ) + huber_loss(twin_td_error, huber_threshold)
          else:
              errors = 0.5 * (
                  torch.pow(td_error, 2.0)
                  + torch.pow(twin_td_error, 2.0)
              )
      else:
          td_error = q_t_selected - q_t_selected_target
          if use_huber:
              errors = huber_loss(td_error, huber_threshold)
          else:
              errors = 0.5 * torch.pow(td_error, 2.0)
  
      critic_loss = torch.mean(train_batch[PRIO_WEIGHTS] * errors)
  
      # Add l2-regularization if required.
      if l2_reg is not None:
          for name, var in model.policy_variables(as_dict=True).items():
              if "bias" not in name:
                  actor_loss += l2_reg * l2_loss(var)
          for name, var in model.q_variables(as_dict=True).items():
              if "bias" not in name:
                  critic_loss += l2_reg * l2_loss(var)
  
      # Model self-supervised losses.
      if self.config["use_state_preprocessor"]:
          # Expand input_dict in case custom_loss' need them.
          input_dict[SampleBatch.ACTIONS] = train_batch[
              SampleBatch.ACTIONS
          ]
          input_dict[SampleBatch.REWARDS] = train_batch[
              SampleBatch.REWARDS
          ]
          input_dict[SampleBatch.TERMINATEDS] = train_batch[
              SampleBatch.TERMINATEDS
          ]
          input_dict[SampleBatch.NEXT_OBS] = train_batch[
              SampleBatch.NEXT_OBS
          ]
          [actor_loss, critic_loss] = model.custom_loss(
              [actor_loss, critic_loss], input_dict
          )
  
      # Store values for stats function in model (tower), such that for
      # multi-GPU, we do not override them during the parallel loss phase.
      model.tower_stats["q_t"] = q_t
      model.tower_stats["actor_loss"] = actor_loss
      model.tower_stats["critic_loss"] = critic_loss
      # TD-error tensor in final stats
      # will be concatenated and retrieved for each individual batch item.
      model.tower_stats["td_error"] = td_error
  
      # Return two loss terms (corresponding to the two optimizers, we create).
      return [actor_loss, critic_loss]
  
imports: import logging
  from typing import Any, Dict, List, Optional, Tuple, Type
  import gymnasium
  from rllib_ddpg.ddpg.utils import make_ddpg_models, validate_spaces
  import ray
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS, postprocess_nstep_and_prio
  from ray.rllib.evaluation import Episode
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchDeterministic, TorchDirichlet, TorchDistributionWrapper
  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.metrics.learner_info import LEARNER_STATS_KEY
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.spaces.simplex import Simplex
  from ray.rllib.utils.torch_utils import apply_grad_clipping, concat_multi_gpu_td_errors, huber_loss, l2_loss
  from ray.rllib.utils.typing import AlgorithmConfigDict, ModelGradients, TensorType
label_desc: Use image node_id 7 for calling the DDPGTorchPolicy obj's underlying member method code with example usage: obj.loss(model, dist_class, train_batch) and returns: list