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 step(self, arm):
      arm_reward = self._actual_rewards[arm]
      reward = self.np_random.normal(loc=arm_reward, scale=1)
  
      observation = self._best_arm
      info = {}
      terminated = False
      truncated = False
      return observation, reward, terminated, truncated, info
  
file_path: river/river/bandit/envs/testbed.py
imports: from __future__ import annotations
  import math
  import gym
label_desc: Use image node_id 3 for calling the KArmedTestbed obj's underlying member method code with example usage: obj.step(arm) and returns: observation,  reward,  terminated,  truncated,  info