code: def __init__(self: TorchPolicyV2):
      def compute_td_error(
          obs_t,
          act_t,
          rew_t,
          obs_tp1,
          terminateds_mask,
          importance_weights,
      ):
          input_dict = self._lazy_tensor_dict(
              SampleBatch(
                  {
                      SampleBatch.CUR_OBS: obs_t,
                      SampleBatch.ACTIONS: act_t,
                      SampleBatch.REWARDS: rew_t,
                      SampleBatch.NEXT_OBS: obs_tp1,
                      SampleBatch.TERMINATEDS: terminateds_mask,
                      PRIO_WEIGHTS: importance_weights,
                  }
              )
          )
          # Do forward pass on loss to update td errors attribute
          # (one TD-error value per item in batch to update PR weights).
          self.loss(self.model, None, input_dict)
  
          # `self.model.td_error` is set within actor_critic_loss call.
          return self.model.tower_stats["td_error"]
  
      self.compute_td_error = compute_td_error
  
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 1 to create a new ComputeTDErrorMixin object  with example: obj = ComputeTDErrorMixin()