class_docstr: Seq2Seq language modeling.
  You can find a set of supported models in the following documentation:
  https://huggingface.co/docs/transformers/main/model_doc/auto#transformers.AutoModelForSeq2SeqLM
class_docstr_tok: ["Seq2Seq","language","modeling",".","You","can","find","a","set","of","supported","models","in","the","following","documentation",":","https",":","\/\/huggingface.co\/docs\/transformers\/main\/model_doc\/auto","#","transformers.AutoModelForSeq2SeqLM"]
code: def _model_call(
      self,
      inputs: TokenSequence,
      labels: Optional[TokenSequence] = None,
  ) -> TokenSequence:
      return self.model(**inputs, labels=labels["input_ids"])
  
imports: import math
  import torch
  import torch.nn.functional
  import transformers
  import peft
  from pathlib import Path
  from typing import List, Mapping, NewType, Optional, Tuple, Union
  from tqdm import tqdm
  from transformers import BatchEncoding
  from lm_eval import utils
  from lm_eval.base import BaseLM
label_desc: Use image node_id 5 for calling the AutoSeq2SeqLM obj's underlying member method code with example usage: obj._model_call(inputs, labels) and returns: self