code: def greedy_until(
      self, requests: List[Tuple[str, Union[List[str], str]]]
  ) -> List[str]:
      def _collate(x):
          tokens = self.tok_encode(x[0])
          return len(tokens), x[0]
  
      results = []
      reorder = utils.Reorderer(requests, _collate)
      for chunk in utils.chunks(
          tqdm(reorder.get_reordered(), disable=False), self.batch_size
      ):
          context = [c[0] for c in chunk]
          request_args = chunk[0][1]
          stop_sequences = (
              request_args
              if isinstance(request_args, list)
              else [request_args]
          )  # request_args["stop_sequences"]
          max_generation_length = (
              self._max_gen_toks
          )  # request_args["max_generation_length"]
  
          assert (
              isinstance(max_generation_length, int)
              or max_generation_length is None
          )
          assert (
              isinstance(stop_sequences, list) or stop_sequences is None
          )
  
          # TODO: Find a better way to handle stop sequences for 0-shot.
          if stop_sequences is None:
              until = [self.eot_token]
          else:
              until = stop_sequences + [self.eot_token]
  
          if max_generation_length is None:
              max_tokens = self.max_gen_toks
          else:
              max_tokens = max_generation_length
  
          token_context = self.tok_encode_batch(context)
  
          responses = self._model_generate(
              inputs=token_context,
              max_tokens=max_tokens,
              stop=until,
          )
          responses = self.tok_decode(responses.tolist())
  
          for response in responses:
              # Ensure the generated responses do not contain the stop sequences.
              for term in until:
                  response = response.split(term)[0]
              # partial caching
              self.cache_hook.add_partial(
                  "greedy_until", (context, until), response
              )
              results.append(response)
      return reorder.get_original(results)
  
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 15 for calling the HuggingFaceAutoLM obj's underlying member method code with example usage: obj.greedy_until(requests) and returns: reorder, len,  x