class_docstr: Simply sampling from the replay buffer
class_docstr_tok: ["Simply","sampling","from","the","replay","buffer"]
code: def __init__(
      self,
      env: EnvWrapper,
      replay_buffer: ReplayBuffer,
      batch_size: int,
      num_batches: int,
      trainer_preprocessor=None,
  ):
      super().__init__()
      self._env = env
      self._replay_buffer = replay_buffer
      self._batch_size = batch_size
      self._num_batches = num_batches
      self._trainer_preprocessor = trainer_preprocessor
  
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 OfflineReplayBufferDataset object from inherited base classes: torch.utils.data with example: obj = OfflineReplayBufferDataset(env, replay_buffer, batch_size, num_batches, trainer_preprocessor)