code: def __init__(self, cfg):
      super(RewardModel, self).__init__()
  
      AutoConfig.register(
          "gpt_neox_reward_model", GPTNeoXRewardModelConfig
      )
      AutoModelForSequenceClassification.register(
          GPTNeoXRewardModelConfig, GPTNeoXRewardModel
      )
  
      self.cfg = cfg
      self.model_name = cfg.reward_model
      self.device = cfg.environment._device
      self.model = AutoModelForSequenceClassification.from_pretrained(
          self.model_name,
          torch_dtype=torch.float16
          if (
              torch.cuda.is_available()
              and len(cfg.environment.gpus) > 0
          )
          else torch.float32,
      ).to(self.device)
      self.tokenizer = AutoTokenizer.from_pretrained(
          self.model_name, max_model_input_sizes=2048
      )
  
imports: from dataclasses import dataclass
  from typing import Literal, Optional
  import torch
  import torch.nn
  from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer
  from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXConfig, GPTNeoXModel, GPTNeoXPreTrainedModel
  from transformers.utils import ModelOutput
  from llm_studio.src.datasets.text_utils import TEXT_SEPARATOR
label_desc: Use image node_id 1 to create a new RewardModel object from inherited base classes: nn with example: obj = RewardModel(cfg)