class_docstr: Dataset for the pretraining of the actor model
  read a json file with the following format:
  [
      {
          "user_input": "..."
          "completion": "..."
      },
      ...
  ]
  Where:
      user_input: the input of the user
      completion: the output of the user
code: def __init__(
      self,
      path: str,
  ) -> None:
      self.path = path
      with open(path, "r") as f:
          data = json.load(f)
      self.data = [d["user_input"] + d["completion"] for d in data]
  
imports: import json
  import yaml
  import os
  import shutil
  import deepspeed
  import torch
  from accelerate import Accelerator
  from beartype import beartype
  from beartype.typing import Tuple
  from einops import rearrange
  from peft import get_peft_model, LoraConfig, TaskType
  from torch.utils.data import DataLoader, Dataset
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from chatllama.rlhf.config import ConfigActor
  from chatllama.rlhf.model_list import hf_models_causal_lm, llama_models, hf_models
  from chatllama.rlhf.model_loader import ModelLoader
  from chatllama.rlhf.utils import TrainingStats
label_desc: Use image node_id 1 to create a new ActorDataset object from inherited base classes: Dataset with example: obj = ActorDataset(path)