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,
      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: Optional[str],
      asr_model_file: Optional[str],
      lm_train_config: Optional[str],
      lm_file: Optional[str],
      word_lm_train_config: Optional[str],
      word_lm_file: Optional[str],
      model_tag: Optional[str],
      token_type: Optional[str],
      bpemodel: Optional[str],
      allow_variable_data_keys: bool,
      streaming: bool,
      is_ctc_decoding: bool,
      use_nbest_rescoring: bool,
      num_paths: int,
      nbest_batch_size: int,
      nll_batch_size: int,
      k2_config: Optional[str],
  ):
      assert (
          is_ctc_decoding
      ), "Currently, only ctc_decoding graph is supported."
      assert check_argument_types()
      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)
      with open(k2_config) as k2_config_file:
          dict_k2_config = yaml.safe_load(k2_config_file)
  
      # 2. Build speech2text
      speech2text_kwargs = dict(
          asr_train_config=asr_train_config,
          asr_model_file=asr_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,
          streaming=streaming,
          is_ctc_decoding=is_ctc_decoding,
          use_nbest_rescoring=use_nbest_rescoring,
          num_paths=num_paths,
          nbest_batch_size=nbest_batch_size,
          nll_batch_size=nll_batch_size,
      )
  
      speech2text_kwargs = dict(**speech2text_kwargs, **dict_k2_config)
      speech2text = k2Speech2Text.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,
      )
  
      with DatadirWriter(output_dir) as writer:
          start_decoding_time = datetime.datetime.now()
          for batch_idx, (keys, batch) in enumerate(loader):
              if batch_idx % 10 == 0:
                  logging.info(f"Processing {batch_idx} batch")
              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}"
  
              # 1-best list of (text, token, token_int)
              results = speech2text(batch)
  
              for key_idx, (text, token, token_int, score) in enumerate(
                  results
              ):
                  key = keys[key_idx]
                  best_writer = writer["1best_recog"]
                  # Write the result to each file
                  best_writer["token"][key] = " ".join(token)
                  best_writer["token_int"][key] = " ".join(
                      map(str, token_int)
                  )
                  best_writer["score"][key] = str(score)
  
                  if text is not None:
                      best_writer["text"][key] = text
  
          end_decoding_time = datetime.datetime.now()
          decoding_duration = end_decoding_time - start_decoding_time
          logging.info(
              f"Decoding duration is {decoding_duration.seconds} seconds"
          )
  
imports: import argparse
  import datetime
  import logging
  import sys
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import k2
  import numpy
  import torch
  import yaml
  from typeguard import check_argument_types, check_return_type
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.fst.lm_rescore import nbest_am_lm_scores
  from espnet2.tasks.asr import ASRTask
  from espnet2.tasks.lm import LMTask
  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.utils.cli_utils import get_commandline_args
label_desc: Use image node_id 4 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, num_workers, log_level, data_path_and_name_and_type, key_file, asr_train_config, asr_model_file, lm_train_config, lm_file, word_lm_train_config, word_lm_file, model_tag, token_type, bpemodel, allow_variable_data_keys, streaming, is_ctc_decoding, use_nbest_rescoring, num_paths, nbest_batch_size, nll_batch_size, k2_config) without return types