code: def inference(
      output_dir: str,
      maxlenratio: float,
      minlenratio: float,
      batch_size: int,
      dtype: str,
      beam_size: int,
      ngpu: int,
      seed: int,
      ctc_weight: float,
      lm_weight: float,
      penalty: float,
      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],
      st_train_config: str,
      st_model_file: str,
      lm_train_config: Optional[str],
      lm_file: Optional[str],
      word_lm_train_config: Optional[str],
      word_lm_file: Optional[str],
      token_type: Optional[str],
      bpemodel: Optional[str],
      allow_variable_data_keys: bool,
      sim_chunk_length: int,
      disable_repetition_detection: bool,
      encoded_feat_length_limit: int,
      decoder_text_length_limit: int,
      time_sync: bool,
      incremental_decode: bool,
      blank_penalty: float,
      hold_n: int,
      transducer_conf: Optional[dict],
      hugging_face_decoder: bool,
  ):
      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 speech2text
      speech2text = Speech2TextStreaming(
          st_train_config=st_train_config,
          st_model_file=st_model_file,
          lm_train_config=lm_train_config,
          lm_file=lm_file,
          token_type=token_type,
          bpemodel=bpemodel,
          device=device,
          maxlenratio=maxlenratio,
          minlenratio=minlenratio,
          dtype=dtype,
          beam_size=beam_size,
          ctc_weight=ctc_weight,
          lm_weight=lm_weight,
          penalty=penalty,
          nbest=nbest,
          normalize_length=normalize_length,
          disable_repetition_detection=disable_repetition_detection,
          decoder_text_length_limit=decoder_text_length_limit,
          encoded_feat_length_limit=encoded_feat_length_limit,
          time_sync=time_sync,
          incremental_decode=incremental_decode,
          blank_penalty=blank_penalty,
          hold_n=hold_n,
          transducer_conf=transducer_conf,
          hugging_face_decoder=hugging_face_decoder,
      )
  
      # 3. Build data-iterator
      loader = STTask.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=STTask.build_preprocess_fn(
              speech2text.st_train_args, False
          ),
          collate_fn=STTask.build_collate_fn(
              speech2text.st_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")
              }
              assert len(batch.keys()) == 1
  
              try:
                  if sim_chunk_length == 0:
                      # N-best list of (text, token, token_int, hyp_object)
                      results = speech2text(**batch)
                  else:
                      speech = batch["speech"]
                      if (len(speech) // sim_chunk_length) > 1:
                          # recompute with incrementally longer input
                          if isinstance(
                              speech2text.st_model.frontend,
                              S3prlFrontend,
                          ):
                              for i in range(
                                  len(speech) // sim_chunk_length
                              ):
                                  speech2text(
                                      speech=speech[
                                          : (i + 1) * sim_chunk_length
                                      ],
                                      is_final=False,
                                  )
                              results = speech2text(
                                  speech[: len(speech)],
                                  is_final=True,
                              )
                          # non recompute
                          else:
                              for i in range(
                                  len(speech) // sim_chunk_length
                              ):
                                  speech2text(
                                      speech=speech[
                                          i
                                          * sim_chunk_length : (i + 1)
                                          * sim_chunk_length
                                      ],
                                      is_final=False,
                                  )
                              results = speech2text(
                                  speech[
                                      (i + 1)
                                      * sim_chunk_length : len(speech)
                                  ],
                                  is_final=True,
                              )
                      else:
                          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 math
  import sys
  from pathlib import Path
  from typing import List, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  from typeguard import check_argument_types, check_return_type
  from espnet2.asr.encoder.contextual_block_conformer_encoder import ContextualBlockConformerEncoder
  from espnet2.asr.encoder.contextual_block_transformer_encoder import ContextualBlockTransformerEncoder
  from espnet2.asr.frontend.s3prl import S3prlFrontend
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.tasks.lm import LMTask
  from espnet2.tasks.st import STTask
  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.batch_beam_search_online import BatchBeamSearchOnline
  from espnet.nets.beam_search import Hypothesis
  from espnet.nets.pytorch_backend.transformer.subsampling import TooShortUttError
  from espnet.nets.scorer_interface import BatchScorerInterface
  from espnet.nets.scorers.ctc import CTCPrefixScorer
  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, maxlenratio, minlenratio, batch_size, dtype, beam_size, ngpu, seed, ctc_weight, lm_weight, penalty, nbest, normalize_length, num_workers, log_level, data_path_and_name_and_type, key_file, st_train_config, st_model_file, lm_train_config, lm_file, word_lm_train_config, word_lm_file, token_type, bpemodel, allow_variable_data_keys, sim_chunk_length, disable_repetition_detection, encoded_feat_length_limit, decoder_text_length_limit, time_sync, incremental_decode, blank_penalty, hold_n, transducer_conf, hugging_face_decoder) without return types