class_docstr: Apply a random action instead of the one sent by the agent.
  
  This wrapper can be used to make a stochastic env. The common use is
  for evaluation in Atari environments, where actions are replaced with
  random ones with a low probability.
  
  Only gym.spaces.Discrete is supported as an action space.
  
  For exploration during training, use explorers like
  chainerrl.explorers.ConstantEpsilonGreedy instead of this wrapper.
  
  Args:
      env (gym.Env): Env to wrap.
      random_fraction (float): Fraction of actions that will be replaced
          with a random action. It must be in [0, 1].
code: def __init__(self, env, random_fraction):
      super().__init__(env)
      assert 0 <= random_fraction <= 1
      assert isinstance(
          env.action_space, gym.spaces.Discrete
      ), "RandomizeAction supports only gym.spaces.Discrete as an action space"  # NOQA
      self._random_fraction = random_fraction
      self._np_random = np.random.RandomState()
  
imports: import gym
  import numpy
label_desc: Use image node_id 1 to create a new RandomizeAction object from inherited base classes: gym with example: obj = RandomizeAction(env, random_fraction)