class_docstr: k-armed testbed.
  
  This is a simple environment that can be used to test bandit algorithms. It is based on the
  10 armed testbed described in the book "Reinforcement Learning: An Introduction" by Sutton and
  Barto.
  
  Parameters
  ----------
  k
      Number of arms.
code: def reset(self, seed=None, options=None):
      super().reset(seed=seed)
      self._actual_rewards = self.np_random.normal(
          loc=0, scale=1, size=self.k
      ).tolist()
      self._best_arm = max(
          enumerate(self._actual_rewards), key=lambda x: x[1]
      )[0]
      observation = self._best_arm
      info = {}
      return observation, info
  
file_path: river/river/bandit/envs/testbed.py
imports: from __future__ import annotations
  import math
  import gym
label_desc: Use image node_id 2 for calling the KArmedTestbed obj's underlying member method code with example usage: obj.reset(seed, options) and returns: observation,  info