code: def __init__(
      self,
      env: EnvWrapper,
      agent: Agent,
      replay_buffer: ReplayBuffer,
      batch_size: int,
      training_frequency: int = 1,
      num_episodes: Optional[int] = None,
      max_steps: Optional[int] = None,
      post_episode_callback: Optional[Callable] = None,
      trainer_preprocessor=None,
      replay_buffer_inserter=None,
  ):
      super().__init__()
      self._env = env
      self._agent = agent
      self._replay_buffer = replay_buffer
      self._batch_size = batch_size
      self._training_frequency = training_frequency
      self._num_episodes = num_episodes
      self._max_steps = max_steps
      self._post_episode_callback = post_episode_callback
      self._trainer_preprocessor = trainer_preprocessor
      assert replay_buffer_inserter is not None
      self._replay_buffer_inserter = replay_buffer_inserter
  
file_path: ReAgent/reagent/gym/datasets/replay_buffer_dataset.py
imports: import logging
  from typing import Callable, Optional
  import torch
  from reagent.gym.agents.agent import Agent
  from reagent.gym.envs import EnvWrapper
  from reagent.gym.preprocessors import make_replay_buffer_inserter, make_replay_buffer_trainer_preprocessor
  from reagent.gym.types import Trajectory, Transition
  from reagent.replay_memory.circular_replay_buffer import ReplayBuffer
label_desc: Use image node_id 1 to create a new ReplayBufferDataset object from inherited base classes: torch.utils.data with example: obj = ReplayBufferDataset(env, agent, replay_buffer, batch_size, training_frequency, num_episodes, max_steps, post_episode_callback, trainer_preprocessor, replay_buffer_inserter)