class_docstr: The ValueHead class implements a head for GPT2 that returns a scalar for each output token.
class_docstr_tok: ["The","ValueHead","class","implements","a","head","for","GPT2","that","returns","a","scalar","for","each","output","token","."]
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
  
imports: import torch
  import torch.nn
  from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM
  from .modeling_base import PreTrainedModelWrapper
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