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 __init__(self, k: int = 10):
      super().__init__()
      self.k = k
      self.action_space = gym.spaces.Discrete(k)
      self.observation_space = gym.spaces.Discrete(k)
      self.reward_range = (-math.inf, math.inf)
  
file_path: river/river/bandit/envs/testbed.py
imports: from __future__ import annotations
  import math
  import gym
label_desc: Use image node_id 1 to create a new KArmedTestbed object from inherited base classes: gym with example: obj = KArmedTestbed(k)