code: def setup_late_mixins(
      policy: Policy,
      obs_space: gym.spaces.Space,
      action_space: gym.spaces.Space,
      config: AlgorithmConfigDict,
  ) -> None:
      """Call mixin classes' constructors after Policy initialization.
  
      - Moves the target model(s) to the GPU, if necessary.
      - Adds the `compute_td_error` method to the given policy.
      Calling `compute_td_error` with batch data will re-calculate the loss
      on that batch AND return the per-batch-item TD-error for prioritized
      replay buffer record weight updating (in case a prioritized replay buffer
      is used).
      - Also adds the `update_target` method to the given policy.
      Calling `update_target` updates all target Q-networks' weights from their
      respective "main" Q-metworks, based on tau (smooth, partial updating).
  
      Args:
          policy: The Policy object.
          obs_space (gym.spaces.Space): The Policy's observation space.
          action_space (gym.spaces.Space): The Policy's action space.
          config: The Policy's config.
      """
      ComputeTDErrorMixin.__init__(policy)
      TargetNetworkMixin.__init__(policy)
  
imports: import gymnasium
  from gymnasium.spaces import Box, Discrete
  import logging
  import tree
  from typing import Dict, List, Optional, Tuple, Type, Union
  import ray
  import ray.experimental.tf_utils
  from ray.rllib.algorithms.sac.sac_tf_policy import build_sac_model, postprocess_trajectory, validate_spaces
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS
  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, TorchDirichlet, TorchSquashedGaussian, TorchDiagGaussian, TorchBeta
  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.utils.framework import try_import_torch
  from ray.rllib.utils.spaces.simplex import Simplex
  from ray.rllib.policy.torch_mixins import TargetNetworkMixin
  from ray.rllib.utils.torch_utils import apply_grad_clipping, concat_multi_gpu_td_errors, huber_loss
  from ray.rllib.utils.typing import LocalOptimizer, ModelInputDict, TensorType, AlgorithmConfigDict
label_desc: Use image node_id 7 for calling a global function with example usage: setup_late_mixins(policy, obs_space, action_space, config) without return types