code: def optimizer(
      self,
  ) -> Union[List["torch.optim.Optimizer"], "torch.optim.Optimizer"]:
      # Set epsilons to match tf.keras.optimizers.Adam's epsilon default.
      actor_optimizer = torch.optim.Adam(
          params=self.model.policy_variables(),
          lr=self.config["actor_lr"],
          betas=(0.9, 0.999),
          eps=1e-8,
      )
  
      critic_optimizer = torch.optim.Adam(
          params=self.model.q_variables(),
          lr=self.config["critic_lr"],
          betas=(0.9, 0.999),
          eps=1e-8,
      )
  
      # Return them in the same order as the respective loss terms are returned.
      return actor_optimizer, critic_optimizer
  
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 4 for calling the CRRTorchPolicy obj's underlying member method code with example usage: obj.optimizer() and returns: actor_optimizer,  critic_optimizer