code: def inference(
      output_dir: str,
      batch_size: int,
      dtype: str,
      ngpu: int,
      seed: int,
      nbest: int,
      normalize_length: bool,
      num_workers: int,
      log_level: Union[int, str],
      data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
      key_file: Optional[str],
      s2t_train_config: Optional[str],
      s2t_model_file: Optional[str],
      model_tag: Optional[str],
      token_type: Optional[str],
      bpemodel: Optional[str],
      allow_variable_data_keys: bool,
      quantize_s2t_model: bool,
      quantize_modules: List[str],
      quantize_dtype: str,
  ):
      assert check_argument_types()
      if batch_size > 1:
          raise NotImplementedError("batch decoding is not implemented")
      if ngpu > 1:
          raise NotImplementedError(
              "only single GPU decoding is supported"
          )
  
      logging.basicConfig(
          level=log_level,
          format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
      )
  
      if ngpu >= 1:
          device = "cuda"
      else:
          device = "cpu"
  
      # 1. Set random-seed
      set_all_random_seed(seed)
  
      # 2. Build speech2text
      speech2text_kwargs = dict(
          s2t_train_config=s2t_train_config,
          s2t_model_file=s2t_model_file,
          token_type=token_type,
          bpemodel=bpemodel,
          device=device,
          dtype=dtype,
          nbest=nbest,
          quantize_s2t_model=quantize_s2t_model,
          quantize_modules=quantize_modules,
          quantize_dtype=quantize_dtype,
      )
      speech2text = Speech2Text.from_pretrained(
          model_tag=model_tag,
          **speech2text_kwargs,
      )
  
      # 3. Build data-iterator
      loader = S2TTask.build_streaming_iterator(
          data_path_and_name_and_type,
          dtype=dtype,
          batch_size=batch_size,
          key_file=key_file,
          num_workers=num_workers,
          preprocess_fn=S2TTask.build_preprocess_fn(
              speech2text.s2t_train_args, False
          ),
          collate_fn=S2TTask.build_collate_fn(
              speech2text.s2t_train_args, False
          ),
          allow_variable_data_keys=allow_variable_data_keys,
          inference=True,
      )
  
      # 7 .Start for-loop
      # FIXME(kamo): The output format should be discussed about
      with DatadirWriter(output_dir) as writer:
          for keys, batch in loader:
              assert isinstance(batch, dict), type(batch)
              assert all(isinstance(s, str) for s in keys), keys
              _bs = len(next(iter(batch.values())))
              assert len(keys) == _bs, f"{len(keys)} != {_bs}"
              batch = {
                  k: v[0]
                  for k, v in batch.items()
                  if not k.endswith("_lengths")
              }
  
              logging.info(keys[0])
              # N-best list of (text, token, token_int, hyp_object)
              try:
                  results = speech2text(**batch)
              except TooShortUttError as e:
                  logging.warning(f"Utterance {keys} {e}")
                  hyp = Hypothesis(
                      score=0.0, scores={}, states={}, yseq=[]
                  )
                  results = [[" ", ["<space>"], [2], hyp]] * nbest
  
              # Only supporting batch_size==1
              key = keys[0]
              for n, (text, token, token_int, hyp) in zip(
                  range(1, nbest + 1), results
              ):
                  # Create a directory: outdir/{n}best_recog
                  ibest_writer = writer[f"{n}best_recog"]
  
                  # Write the result to each file
                  ibest_writer["token"][key] = " ".join(token)
                  ibest_writer["token_int"][key] = " ".join(
                      map(str, token_int)
                  )
                  ibest_writer["score"][key] = str(hyp.score)
  
                  if text is not None:
                      ibest_writer["text"][key] = text
  
imports: import argparse
  import logging
  import sys
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  import torch.quantization
  from typeguard import check_argument_types, check_return_type
  from espnet2.asr.decoder.s4_decoder import S4Decoder
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.tasks.s2t import S2TTask
  from espnet2.text.build_tokenizer import build_tokenizer
  from espnet2.text.token_id_converter import TokenIDConverter
  from espnet2.text.whisper_token_id_converter import OpenAIWhisperTokenIDConverter
  from espnet2.torch_utils.device_funcs import to_device
  from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
  from espnet2.utils import config_argparse
  from espnet2.utils.types import str2bool, str2triple_str, str_or_none
  from espnet.nets.batch_beam_search import BatchBeamSearch
  from espnet.nets.beam_search import BeamSearch, Hypothesis
  from espnet.nets.pytorch_backend.transformer.subsampling import TooShortUttError
  from espnet.nets.scorer_interface import BatchScorerInterface
  from espnet.utils.cli_utils import get_commandline_args
label_desc: Use image node_id 1 for calling a global function with example usage: inference(output_dir, batch_size, dtype, ngpu, seed, nbest, normalize_length, num_workers, log_level, data_path_and_name_and_type, key_file, s2t_train_config, s2t_model_file, model_tag, token_type, bpemodel, allow_variable_data_keys, quantize_s2t_model, quantize_modules, quantize_dtype) without return types