code: def test_long_episode(model_class):
      """
      Check that the model does not break when the replay buffer is still empty
      after the first rollout (because the episode is not over).
      """
      # n_bits > nb_rollout_steps
      n_bits = 10
      env = BitFlippingEnv(
          n_bits,
          continuous=model_class in [DDPG, SAC, TD3],
          max_steps=n_bits,
      )
      kwargs = {}
      if model_class == DDPG:
          kwargs["nb_rollout_steps"] = 9  # < n_bits
      elif model_class in [DQN, SAC, TD3]:
          kwargs["batch_size"] = 8  # < n_bits
          kwargs["learning_starts"] = 0
  
      model = HER(
          "MlpPolicy",
          env,
          model_class,
          n_sampled_goal=4,
          goal_selection_strategy="future",
          verbose=0,
          **kwargs,
      )
      model.learn(100)
  
file_path: stable-baselines/tests/test_her.py
imports: import os
  import pytest
  from stable_baselines import HER, DQN, SAC, DDPG, TD3
  from stable_baselines.her import GoalSelectionStrategy, HERGoalEnvWrapper
  from stable_baselines.her.replay_buffer import KEY_TO_GOAL_STRATEGY
  from stable_baselines.common.bit_flipping_env import BitFlippingEnv
  from stable_baselines.common.vec_env import DummyVecEnv, VecNormalize
label_desc: Use image node_id 3 for calling a global function with example usage: test_long_episode(model_class) without return types