docstr: inps: a torch tensor of shape [batch, sequence]
  the size of sequence may vary from call to call
  
  returns: a torch tensor of shape [batch, sequence, vocab] with the
  logits returned from the model
docstr_tok: ["inps",":","a","torch","tensor","of","shape","[","batch",",","sequence","]","the","size","of","sequence","may","vary","from","call","to","call","returns",":","a","torch","tensor","of","shape","[","batch",",","sequence",",","vocab","]","with","the","logits","returned","from","the","model"]
code: def _model_call(self, inps):
      """
      inps: a torch tensor of shape [batch, sequence]
      the size of sequence may vary from call to call
  
      returns: a torch tensor of shape [batch, sequence, vocab] with the
      logits returned from the model
      """
      with torch.no_grad():
          return self.gpt2(inps)[0]
  
file_path: lm-evaluation-harness/lm_eval/models/gpt2.py
imports: import torch
  import transformers
  from typing import Optional, Union
  from lm_eval.base import BaseLM
label_desc: Use image node_id 9 for calling the HFLM obj's underlying member method code with example usage: obj._model_call(inps) and returns: self