class_docstr: Hindsight Experience Replay (HER) https://arxiv.org/abs/1707.01495
  
  :param policy: (BasePolicy or str) The policy model to use (MlpPolicy, CnnPolicy, CnnLstmPolicy, ...)
  :param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
  :param model_class: (OffPolicyRLModel) The off policy RL model to apply Hindsight Experience Replay
      currently supported: DQN, DDPG, SAC
  :param n_sampled_goal: (int)
  :param goal_selection_strategy: (GoalSelectionStrategy or str)
code: def learn(
      self,
      total_timesteps,
      callback=None,
      log_interval=100,
      tb_log_name="HER",
      reset_num_timesteps=True,
  ):
      return self.model.learn(
          total_timesteps,
          callback=callback,
          log_interval=log_interval,
          tb_log_name=tb_log_name,
          reset_num_timesteps=reset_num_timesteps,
          replay_wrapper=self.replay_wrapper,
      )
  
imports: import functools
  from stable_baselines.common import BaseRLModel
  from stable_baselines.common import OffPolicyRLModel
  from stable_baselines.common.base_class import _UnvecWrapper
  from stable_baselines.common.vec_env import VecEnvWrapper
  from .replay_buffer import HindsightExperienceReplayWrapper, KEY_TO_GOAL_STRATEGY
  from .utils import HERGoalEnvWrapper
label_desc: Use image node_id 10 for calling the HER obj's underlying member method code with example usage: obj.learn(total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps) and returns: self