class_docstr: Defines a config class from which an upper confidence bound bandit can be built.
  
  Example:
      >>> from ray.rllib.algorithms.bandit import BanditLinUCBConfig# doctest: +SKIP
      >>> from ray.rllib.examples.env.bandit_envs_discrete import WheelBanditEnv
      >>> config = BanditLinUCBConfig()  # doctest: +SKIP
      >>> config = config.rollouts(num_rollout_workers=4) # doctest: +SKIP
      >>> print(config.to_dict())  # doctest: +SKIP
      >>> # Build a Algorithm object from the config and run 1 training iteration.
      >>> algo = config.build(env=WheelBanditEnv)  # doctest: +SKIP
      >>> algo.train()  # doctest: +SKIP
imports: import logging
  from typing import Optional, Type, Union
  from rllib_bandit.bandit.bandit_tf_policy import BanditTFPolicy
  from rllib_bandit.bandit.bandit_torch_policy import BanditTorchPolicy
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.policy.policy import Policy
  from ray.rllib.utils.annotations import override
label_desc: Use image node_id 1 to create a new BanditLinUCBConfig object from inherited base classes: BanditConfig with example: obj = BanditLinUCBConfig()