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 seed(self, seed):
      super().seed(seed)
      self._np_random.seed(seed)
  
imports: import gym
  import numpy
label_desc: Use image node_id 3 for calling the RandomizeAction obj's underlying member method code with example usage: obj.seed(seed) without return types