code: def forward(
      self,
      input_ids,
      attention_mask: Optional[torch.FloatTensor] = None,
      inputs_embeds: Optional[torch.FloatTensor] = None,
      head_mask: Optional[torch.FloatTensor] = None,
      use_cache: Optional[bool] = None,
      return_dict: Optional[bool] = True,
  ) -> GPTNeoXRewardModelOutput:
      outputs = self.gpt_neox(
          input_ids,
          attention_mask=attention_mask,
          head_mask=head_mask,
          inputs_embeds=inputs_embeds,
          use_cache=use_cache,
          return_dict=return_dict,
      )
  
      hidden_states = outputs[0]
      if self.pooling == "mean":
          if attention_mask is None:
              pooled = hidden_states.mean(dim=1)
          else:
              pooled = (hidden_states * attention_mask).sum(
                  dim=1
              ) / attention_mask.sum(dim=1)
      elif self.pooling == "last":
          if attention_mask is None:
              pooled = hidden_states[:, -1]
          else:
              last_idx = attention_mask.cumsum(dim=1).argmax(dim=1)
              pooled = hidden_states.gather(
                  1,
                  last_idx.view(-1, 1, 1).expand(
                      -1, 1, hidden_states.size(-1)
                  ),
              ).squeeze(1)
      else:
          raise ValueError(f"Unknown pooling method: {self.pooling}")
  
      logits = self.out_proj(pooled)
  
      if not return_dict:
          return (logits,) + outputs[1:]
  
      return GPTNeoXRewardModelOutput(logits=logits)
  
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 GPTNeoXRewardModel obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, inputs_embeds, head_mask, use_cache, return_dict) and returns: GPTNeoXRewardModelOutput, unknown