class_docstr: Transform the reward via an arbitrary function.
  
  Warning:
      If the base environment specifies a reward range which is not invariant under :attr:`f`, the :attr:`reward_range` of the wrapped environment will be incorrect.
  
  Example:
      >>> import gym
      >>> env = gym.make('CartPole-v1')
      >>> env = TransformReward(env, lambda r: 0.01*r)
      >>> env.reset()
      >>> observation, reward, terminated, truncated, info = env.step(env.action_space.sample())
      >>> reward
      0.01
docstr: Initialize the :class:`TransformReward` wrapper with an environment and reward transform function :param:`f`.
  
  Args:
      env: The environment to apply the wrapper
      f: A function that transforms the reward
docstr_tok: ["Initialize","the",":","class",":","`","TransformReward","`","wrapper","with","an","environment","and","reward","transform","function",":","param",":","`","f","`",".","Args",":","env",":","The","environment","to","apply","the","wrapper","f",":","A","function","that","transforms","the","reward"]
code: def __init__(self, env: gym.Env, f: Callable[[float], float]):
      """Initialize the :class:`TransformReward` wrapper with an environment and reward transform function :param:`f`.
  
      Args:
          env: The environment to apply the wrapper
          f: A function that transforms the reward
      """
      super().__init__(env)
      assert callable(f)
      self.f = f
  
imports: from typing import Callable
  import gym
  from gym import RewardWrapper
label_desc: Use image node_id 1 to create a new TransformReward object from inherited base classes: RewardWrapper with example: obj = TransformReward(env, f)