class_docstr: Augment the observation with the current time step in the episode.
  
  The observation space of the wrapped environment is assumed to be a flat :class:`Box`.
  In particular, pixel observations are not supported. This wrapper will append the current timestep within the current episode to the observation.
  
  Example:
      >>> import gym
      >>> env = gym.make('CartPole-v1')
      >>> env = TimeAwareObservation(env)
      >>> env.reset()
      array([ 0.03810719,  0.03522411,  0.02231044, -0.01088205,  0.        ])
      >>> env.step(env.action_space.sample())[0]
      array([ 0.03881167, -0.16021058,  0.0220928 ,  0.28875574,  1.        ])
docstr: Initialize :class:`TimeAwareObservation` that requires an environment with a flat :class:`Box` observation space.
  
  Args:
      env: The environment to apply the wrapper
code: def __init__(self, env: gym.Env):
      """Initialize :class:`TimeAwareObservation` that requires an environment with a flat :class:`Box` observation space.
  
      Args:
          env: The environment to apply the wrapper
      """
      super().__init__(env)
      assert isinstance(env.observation_space, Box)
      assert env.observation_space.dtype == np.float32
      low = np.append(self.observation_space.low, 0.0)
      high = np.append(self.observation_space.high, np.inf)
      self.observation_space = Box(low, high, dtype=np.float32)
      self.is_vector_env = getattr(env, "is_vector_env", False)
  
file_path: gym/gym/wrappers/time_aware_observation.py
imports: import numpy
  import gym
  from gym.spaces import Box
label_desc: Use image node_id 1 to create a new TimeAwareObservation object from inherited base classes: gym with example: obj = TimeAwareObservation(env)