code: def adam_optimizer(
      policy: Policy, config: AlgorithmConfigDict
  ) -> "torch.optim.Optimizer":
      # By this time, the models have been moved to the GPU - if any - and we
      # can define our optimizers using the correct CUDA variables.
      if not hasattr(policy, "q_func_vars"):
          policy.q_func_vars = policy.model.variables()
  
      return torch.optim.Adam(
          policy.q_func_vars,
          lr=policy.cur_lr,
          eps=config["adam_epsilon"],
      )
  
imports: from typing import Dict, List, Tuple
  import gymnasium
  import ray
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS, Q_SCOPE, Q_TARGET_SCOPE, postprocess_nstep_and_prio
  from ray.rllib.algorithms.dqn.dqn_torch_model import DQNTorchModel
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import get_torch_categorical_class_with_temperature, 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.error import UnsupportedSpaceException
  from ray.rllib.utils.exploration.parameter_noise import ParameterNoise
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.torch_utils import apply_grad_clipping, concat_multi_gpu_td_errors, FLOAT_MIN, huber_loss, l2_loss, reduce_mean_ignore_inf, softmax_cross_entropy_with_logits
  from ray.rllib.utils.typing import TensorType, AlgorithmConfigDict
label_desc: Use image node_id 4 for calling a global function with example usage: adam_optimizer(policy, config) and returns: torch