code: def inference(
      output_dir: str,
      batch_size: int,
      dtype: str,
      ngpu: int,
      seed: int,
      num_workers: int,
      log_level: Union[int, str],
      data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
      key_file: Optional[str],
      asr_train_config: str,
      asr_model_file: str,
      model_tag: Optional[str],
      token_type: Optional[str],
      bpemodel: Optional[str],
      allow_variable_data_keys: bool,
      maskctc_n_iterations: int,
      maskctc_threshold_probability: float,
  ):
      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(
          asr_train_config=asr_train_config,
          asr_model_file=asr_model_file,
          token_type=token_type,
          bpemodel=bpemodel,
          device=device,
          batch_size=batch_size,
          dtype=dtype,
          maskctc_n_iterations=maskctc_n_iterations,
          maskctc_threshold_probability=maskctc_threshold_probability,
      )
      speech2text = Speech2Text.from_pretrained(
          model_tag=model_tag,
          **speech2text_kwargs,
      )
  
      # 3. Build data-iterator
      loader = ASRTask.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=ASRTask.build_preprocess_fn(
              speech2text.asr_train_args, False
          ),
          collate_fn=ASRTask.build_collate_fn(
              speech2text.asr_train_args, False
          ),
          allow_variable_data_keys=allow_variable_data_keys,
          inference=True,
      )
  
      # 7 .Start for-loop
      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")
              }
  
              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]]
  
              # Only supporting batch_size==1
              key = keys[0]
              (text, token, token_int, hyp) = results[0]
  
              # Create a directory: outdir/{n}best_recog
              ibest_writer = writer["1best_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, List, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  from typeguard import check_argument_types, check_return_type
  from espnet2.asr.maskctc_model import MaskCTCInference
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.tasks.asr import ASRTask
  from espnet2.text.build_tokenizer import build_tokenizer
  from espnet2.text.token_id_converter import TokenIDConverter
  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.beam_search import Hypothesis
  from espnet.nets.pytorch_backend.transformer.subsampling import TooShortUttError
  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, num_workers, log_level, data_path_and_name_and_type, key_file, asr_train_config, asr_model_file, model_tag, token_type, bpemodel, allow_variable_data_keys, maskctc_n_iterations, maskctc_threshold_probability) without return types