class_docstr: The ValueHead class implements a head for GPT2 that returns a scalar for each
  output token.
  
  Based on the implementation of trl library:
  https://github.com/lvwerra/trl/blob/main/trl/models/modeling_value_head.py
class_docstr_tok: ["The","ValueHead","class","implements","a","head","for","GPT2","that","returns","a","scalar","for","each","output","token",".","Based","on","the","implementation","of","trl","library",":","https",":","\/\/github.com\/lvwerra\/trl\/blob\/main\/trl\/models\/modeling_value_head.py"]
code: def forward(self, hidden_states):
      output = self.dropout(hidden_states)
  
      # For now force upcast in fp32 if needed. Let's keep the
      # output in fp32 for numerical stability.
      if output.dtype != self.summary.weight.dtype:
          output = output.to(self.summary.weight.dtype)
  
      output = self.summary(output)
      return output
  
file_path: h2o-llmstudio/llm_studio/src/models/text_rlhf_language_modeling_model.py
imports: import logging
  from typing import Any, Dict
  import torch
  from torch import nn
  from transformers import AutoModelForCausalLM
  from llm_studio.src.metrics.text_causal_language_modeling_metrics import Perplexity
  from llm_studio.src.utils.data_utils import batch_padding
  from llm_studio.src.utils.modeling_utils import create_nlp_backbone, generate, prepare_lora
label_desc: Use image node_id 2 for calling the ValueHead obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: output