class_docstr: Implements an exploration strategy for Policies.
  
  An Exploration takes model outputs, a distribution, and a timestep from
  the agent and computes an action to apply to the environment using an
  implemented exploration schema.
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docstr: May add optimizer(s) to the Policy's own `optimizers`.
  
  The number of optimizers (Policy's plus Exploration's optimizers) must
  match the number of loss terms produced by the Policy's loss function
  and the Exploration component's loss terms.
  
  Args:
      optimizers: The list of the Policy's local optimizers.
  
  Returns:
      The updated list of local optimizers to use on the different
      loss terms.
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code: def get_exploration_optimizer(
      self, optimizers: List[LocalOptimizer]
  ) -> List[LocalOptimizer]:
      """May add optimizer(s) to the Policy's own `optimizers`.
  
      The number of optimizers (Policy's plus Exploration's optimizers) must
      match the number of loss terms produced by the Policy's loss function
      and the Exploration component's loss terms.
  
      Args:
          optimizers: The list of the Policy's local optimizers.
  
      Returns:
          The updated list of local optimizers to use on the different
          loss terms.
      """
      return optimizers
  
file_path: ray/python/ray/rllib/utils/exploration/exploration.py
imports: from gymnasium.spaces import Space
  from typing import Dict, List, Optional, Union, TYPE_CHECKING
  from ray.rllib.env.base_env import BaseEnv
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import DeveloperAPI, PublicAPI
  from ray.rllib.utils.framework import try_import_torch, TensorType
  from ray.rllib.utils.typing import LocalOptimizer, AlgorithmConfigDict
label_desc: Use image node_id 7 for calling the Exploration obj's underlying member method code with example usage: obj.get_exploration_optimizer(optimizers) and returns: optimizers