code: def optimizer_fn(
      policy: Policy, config: AlgorithmConfigDict
  ) -> Tuple["torch.optim.Optimizer"]:
      optimizer_choice = torch.optim.Adam(
          policy.model.choice_model.parameters(),
          lr=config["lr_choice_model"],
      )
      optimizer_q_value = torch.optim.RMSprop(
          policy.model.q_model.parameters(),
          lr=config["lr"],
          eps=config["rmsprop_epsilon"],
          momentum=0.0,
          weight_decay=0.95,
          centered=True,
      )
      return optimizer_choice, optimizer_q_value
  
imports: import logging
  from typing import Dict, Tuple, Type
  import gymnasium
  import numpy
  from rllib_slate_q.slate_q.slateq_torch_model import SlateQTorchModel
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical, 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 TargetNetworkMixin
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.torch_utils import apply_grad_clipping, concat_multi_gpu_td_errors, convert_to_torch_tensor, huber_loss
  from ray.rllib.utils.typing import AlgorithmConfigDict, TensorType
label_desc: Use image node_id 8 for calling a global function with example usage: optimizer_fn(policy, config) and returns: optimizer_choice,  optimizer_q_value