class_docstr: Dataset class for the reward model
  read a json file with the following format:
  [
      {
          "user_input": "...",
          "completion": "...",
          "score": ...
      },
      ...
  ]
  Where:
      user_input: the initial input of the user
      completion: the completion generated by the model
      score: the score given by the user to the completion (or by the LLM)
class_docstr_tok: ["Dataset","class","for","the","reward","model","read","a","json","file","with","the","following","format",":","[","{","``","user_input","''",":","``","...","''",",","``","completion","''",":","``","...","''",",","``","score","''",":","...","}",",","...","]","Where",":","user_input",":","the","initial","input","of","the","user","completion",":","the","completion","generated","by","the","model","score",":","the","score","given","by","the","user","to","the","completion","(","or","by","the","LLM",")"]
code: def __getitem__(self, idx: int):
      user_input = self.data[idx]["user_input"]
      completion = self.data[idx]["completion"]
      if self.data[idx]["score"]:
          score = float(self.data[idx]["score"])
      else:
          score = 2.5
  
      item = (user_input + completion, score)
      return item
  
imports: import json
  import shutil
  import os
  import deepspeed
  import torch
  from accelerate import Accelerator
  from beartype import beartype
  from beartype.typing import Iterable, Tuple
  from einops.layers.torch import Rearrange
  from torch.utils.data import Dataset, DataLoader
  from transformers import AutoModel, AutoTokenizer
  from chatllama.rlhf.config import ConfigReward
  from chatllama.rlhf.model_list import hf_models
  from chatllama.rlhf.model_loader import ModelLoader
  from chatllama.rlhf.utils import TrainingStats
label_desc: Use image node_id 2 for calling the RewardDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: item