code: def get_score(
      self,
      prompts=None,
      answers=None,
  ):
      scores = []
      for prompt, answer in zip(prompts, answers):
          if "deberta-v3" in self.model_name:
              inputs = self.tokenizer(
                  " ".join(prompt.split(TEXT_SEPARATOR)),
                  answer,
                  return_tensors="pt",
                  max_length=2048,
              ).to(self.device)
          elif self.model_name in [
              "OpenAssistant/oasst-rm-2.1-pythia-1.4b-epoch-2.5",
              "OpenAssistant/oasst-rm-2-pythia-6.9b-epoch-1",
          ]:
              prompt = prompt.split(TEXT_SEPARATOR)
  
              input_text = ""
  
              for i, prompt_part in enumerate(prompt[::-1]):
                  if i % 2 == 0:
                      prefix = "<|prompter|>"
                  else:
                      prefix = "<|assistant|>"
                  input_text = (
                      f"{prefix}{prompt_part}<|endoftext|>" + input_text
                  )
  
              input_text = (
                  input_text + f"<|assistant|>{answer}<|endoftext|>"
              )
  
              inputs = self.tokenizer(
                  input_text, return_tensors="pt", max_length=2048
              ).to(self.device)
          else:
              raise ValueError(
                  f"Reward model {self.model_name} not supported for scoring."
              )
  
          scores.append(
              self.model(**inputs).logits[0].cpu().detach().item()
          )
          del inputs
      return scores
  
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 2 for calling the RewardModel obj's underlying member method code with example usage: obj.get_score(prompts, answers) and returns: scores