code: def __init__(
      self,
      pooling: Literal["mean", "last"] = "last",
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.pooling = pooling or "last"
  
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 GPTNeoXRewardModelConfig object from inherited base classes: GPTNeoXConfig with example: obj = GPTNeoXRewardModelConfig(pooling)