class_docstr: Dataset to train the actor-critic models
class_docstr_tok: ["Dataset","to","train","the","actor-critic","models"]
code: def __init__(
      self,
      memories: Deque[Memory],
      device: torch.device,
  ) -> None:
      super().__init__()
      self.data = list(memories)
  
imports: import json
  import os
  import random
  from collections import deque, namedtuple
  import deepspeed
  import torch
  import torch.distributed
  from accelerate import Accelerator
  from beartype import beartype
  from beartype.typing import Deque, List, Tuple, Union
  from deepspeed.runtime.engine import DeepSpeedEngine
  from torch.utils.data import DataLoader, Dataset
  from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts
  from chatllama.rlhf.actor import ActorModel
  from chatllama.rlhf.config import Config, ConfigActor, ConfigCritic, ConfigReward
  from chatllama.rlhf.model_list import hf_models
  from chatllama.rlhf.model_loader import ModelLoader
  from chatllama.rlhf.reward import RewardModel, CriticModel
  from chatllama.rlhf.utils import TrainingStats, ConversationLog
label_desc: Use image node_id 1 to create a new ExperienceDataset object from inherited base classes: Dataset with example: obj = ExperienceDataset(memories, device)