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 __init__(self, config):
      super().__init__()
      if not hasattr(config, "summary_dropout_prob"):
          summary_dropout_prob = 0.1
      else:
          summary_dropout_prob = config.summary_dropout_prob
  
      self.dropout = (
          nn.Dropout(summary_dropout_prob)
          if summary_dropout_prob
          else nn.Identity()
      )
  
      # some models such as OPT have a projection layer before the word embeddings
      # e.g. OPT-350m
      if hasattr(config, "word_embed_proj_dim"):
          hidden_size = config.word_embed_proj_dim
      else:
          hidden_size = config.hidden_size
  
      self.summary = nn.Linear(hidden_size, 1)
  
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 1 to create a new ValueHead object from inherited base classes: nn with example: obj = ValueHead(config)