code: def inference(
      output_dir: str,
      maxlen: int,
      minlen: int,
      batch_size: int,
      dtype: str,
      beam_size: int,
      ngpu: int,
      seed: int,
      ngram_weight: float,
      penalty: float,
      nbest: int,
      num_workers: int,
      log_level: Union[int, str],
      data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
      key_file: Optional[str],
      lm_train_config: Optional[str],
      lm_file: Optional[str],
      word_lm_train_config: Optional[str],
      word_lm_file: Optional[str],
      ngram_file: Optional[str],
      model_tag: Optional[str],
      token_type: Optional[str],
      bpemodel: Optional[str],
      allow_variable_data_keys: bool,
      quantize_lm: bool,
      quantize_modules: List[str],
      quantize_dtype: str,
  ):
      assert check_argument_types()
      if batch_size > 1:
          raise NotImplementedError("batch decoding is not implemented")
      if word_lm_train_config is not None:
          raise NotImplementedError("Word LM 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 generatetext
      generatetext_kwargs = dict(
          lm_train_config=lm_train_config,
          lm_file=lm_file,
          ngram_file=ngram_file,
          token_type=token_type,
          bpemodel=bpemodel,
          device=device,
          maxlen=maxlen,
          minlen=minlen,
          dtype=dtype,
          beam_size=beam_size,
          ngram_weight=ngram_weight,
          penalty=penalty,
          nbest=nbest,
          quantize_lm=quantize_lm,
          quantize_modules=quantize_modules,
          quantize_dtype=quantize_dtype,
      )
      generatetext = GenerateText.from_pretrained(
          model_tag=model_tag,
          **generatetext_kwargs,
      )
  
      # 3. Build data iterator
      loader = LMTask.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=LMTask.build_preprocess_fn(
              generatetext.lm_train_args, False
          ),
          collate_fn=LMTask.build_collate_fn(
              generatetext.lm_train_args, False
          ),
          allow_variable_data_keys=allow_variable_data_keys,
          inference=True,
      )
  
      # 4. 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")
              }
  
              # N-best list of (text, token, token_int, hyp_object)
              results = generatetext(**batch)
  
              # 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.quantization
  from typeguard import check_argument_types, check_return_type
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.tasks.lm import LMTask
  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.scorer_interface import BatchScorerInterface
  from espnet.nets.scorers.length_bonus import LengthBonus
  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, maxlen, minlen, batch_size, dtype, beam_size, ngpu, seed, ngram_weight, penalty, nbest, num_workers, log_level, data_path_and_name_and_type, key_file, lm_train_config, lm_file, word_lm_train_config, word_lm_file, ngram_file, model_tag, token_type, bpemodel, allow_variable_data_keys, quantize_lm, quantize_modules, quantize_dtype) without return types