class_docstr: Assign the `compute_td_error` method to the R2D2TorchPolicy
  
  This allows us to prioritize on the worker side.
class_docstr_tok: ["Assign","the","`","compute_td_error","`","method","to","the","R2D2TorchPolicy","This","allows","us","to","prioritize","on","the","worker","side","."]
code: def __init__(self):
      def compute_td_error(
          obs_t,
          act_t,
          rew_t,
          obs_tp1,
          terminateds_mask,
          importance_weights,
      ):
          input_dict = self._lazy_tensor_dict(
              {SampleBatch.CUR_OBS: obs_t}
          )
          input_dict[SampleBatch.ACTIONS] = act_t
          input_dict[SampleBatch.REWARDS] = rew_t
          input_dict[SampleBatch.NEXT_OBS] = obs_tp1
          input_dict[SampleBatch.TERMINATEDS] = terminateds_mask
          input_dict[PRIO_WEIGHTS] = importance_weights
  
          # Do forward pass on loss to update td error attribute
          r2d2_loss(self, self.model, None, input_dict)
  
          return self.model.tower_stats["td_error"]
  
      self.compute_td_error = compute_td_error
  
imports: from typing import Dict, Tuple
  import gymnasium
  import ray
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS, postprocess_nstep_and_prio
  from ray.rllib.algorithms.dqn.dqn_torch_policy import adam_optimizer, build_q_model_and_distribution, compute_q_values
  from ray.rllib.algorithms.r2d2.r2d2_tf_policy import get_distribution_inputs_and_class
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.policy_template import build_policy_class
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import LearningRateSchedule, TargetNetworkMixin
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.torch_utils import FLOAT_MIN, apply_grad_clipping, concat_multi_gpu_td_errors, huber_loss, sequence_mask
  from ray.rllib.utils.typing import AlgorithmConfigDict, TensorType
label_desc: Use image node_id 1 to create a new ComputeTDErrorMixin object  with example: obj = ComputeTDErrorMixin()