code: def __init__(self, config):
      if isinstance(config, GPTNeoXConfig):
          # When a normal GPTNeoX was loaded it will be converted into a reward model.
          # The direct `type(config) == GPTNeoXConfig` comparison is used (instead of
          # `isinstance()`) since the configuration class of the reward model is also
          # derived form `GPTNeoXConfig`.
          config = GPTNeoXRewardModelConfig.from_dict(config.to_dict())
      super().__init__(config)
  
      self.gpt_neox = GPTNeoXModel(config)
      self.out_proj = nn.Linear(config.hidden_size, 1)
      self.pooling = config.pooling
  
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 GPTNeoXRewardModel object from inherited base classes: GPTNeoXPreTrainedModel with example: obj = GPTNeoXRewardModel(config)