code: def inference(
      output_dir: str,
      batch_size: int,
      dtype: str,
      beam_size: int,
      ngpu: int,
      seed: int,
      lm_weight: float,
      nbest: int,
      num_workers: int,
      log_level: Union[int, str],
      data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
      asr_train_config: Optional[str],
      asr_model_file: Optional[str],
      beam_search_config: Optional[dict],
      lm_train_config: Optional[str],
      lm_file: Optional[str],
      model_tag: Optional[str],
      token_type: Optional[str],
      bpemodel: Optional[str],
      key_file: Optional[str],
      allow_variable_data_keys: bool,
      quantize_asr_model: Optional[bool],
      quantize_modules: Optional[List[str]],
      quantize_dtype: Optional[str],
      streaming: bool,
      decoding_window: int,
      left_context: int,
      display_hypotheses: bool,
  ) -> None:
      """Transducer model inference.
  
      Args:
          output_dir: Output directory path.
          batch_size: Batch decoding size.
          dtype: Data type.
          beam_size: Beam size.
          ngpu: Number of GPUs.
          seed: Random number generator seed.
          lm_weight: Weight of language model.
          nbest: Number of final hypothesis.
          num_workers: Number of workers.
          log_level: Level of verbose for logs.
          data_path_and_name_and_type:
          asr_train_config: ASR model training config path.
          asr_model_file: ASR model path.
          beam_search_config: Beam search config path.
          lm_train_config: Language Model training config path.
          lm_file: Language Model path.
          model_tag: Model tag.
          token_type: Type of token units.
          bpemodel: BPE model path.
          key_file: File key.
          allow_variable_data_keys: Whether to allow variable data keys.
          quantize_asr_model: Whether to apply dynamic quantization to ASR model.
          quantize_modules: List of module names to apply dynamic quantization on.
          quantize_dtype: Dynamic quantization data type.
          streaming: Whether to perform chunk-by-chunk inference.
          decoding_window: Audio length (in milliseconds) to process during decoding.
          left_context: Number of previous frames the attention module can see
                        in current chunk (used by Conformer and Branchformer block).
          display_hypotheses: Whether to display (partial and full) hypotheses.
  
      """
      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,
          beam_search_config=beam_search_config,
          lm_train_config=lm_train_config,
          lm_file=lm_file,
          token_type=token_type,
          bpemodel=bpemodel,
          device=device,
          dtype=dtype,
          beam_size=beam_size,
          lm_weight=lm_weight,
          nbest=nbest,
          quantize_asr_model=quantize_asr_model,
          quantize_modules=quantize_modules,
          quantize_dtype=quantize_dtype,
          streaming=streaming,
          decoding_window=decoding_window,
          left_context=left_context,
      )
      speech2text = Speech2Text.from_pretrained(
          model_tag=model_tag,
          **speech2text_kwargs,
      )
  
      if speech2text.streaming:
          decoding_samples = (
              speech2text.audio_processor.decoding_samples
          )
  
      # 3. Build data-iterator
      loader = ASRTransducerTask.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=ASRTransducerTask.build_preprocess_fn(
              speech2text.asr_train_args, False
          ),
          collate_fn=ASRTransducerTask.build_collate_fn(
              speech2text.asr_train_args, False
          ),
          allow_variable_data_keys=allow_variable_data_keys,
          inference=True,
      )
  
      # 4 .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")
              }
              assert len(batch.keys()) == 1
  
              try:
                  if speech2text.streaming:
                      speech = batch["speech"]
  
                      decoding_steps = len(speech) // decoding_samples
  
                      for i in range(0, decoding_steps + 1, 1):
                          _start = i * decoding_samples
  
                          if i == decoding_steps:
                              final_hyps = speech2text.streaming_decode(
                                  speech[
                                      i * decoding_samples : len(speech)
                                  ],
                                  is_final=True,
                              )
                          else:
                              part_hyps = speech2text.streaming_decode(
                                  speech[
                                      (i * decoding_samples) : _start
                                      + decoding_samples
                                  ],
                                  is_final=False,
                              )
  
                              if display_hypotheses:
                                  _result = (
                                      speech2text.hypotheses_to_results(
                                          part_hyps
                                      )
                                  )
                                  _length = (i + 1) * decoding_window
  
                                  logging.info(
                                      f"Current best hypothesis (0-{_length}ms): "
                                      f"{keys}: {_result[0][0]}"
                                  )
                  else:
                      final_hyps = speech2text(**batch)
  
                  results = speech2text.hypotheses_to_results(
                      final_hyps
                  )
  
                  if display_hypotheses:
                      logging.info(
                          f"Final best hypothesis: {keys}: {results[0][0]}"
                      )
              except TooShortUttError as e:
                  logging.warning(f"Utterance {keys} {e}")
                  hyp = Hypothesis(score=0.0, yseq=[], dec_state=None)
                  results = [[" ", ["<space>"], [2], hyp]] * nbest
  
              key = keys[0]
              for n, (text, token, token_int, hyp) in zip(
                  range(1, nbest + 1), results
              ):
                  ibest_writer = writer[f"{n}best_recog"]
  
                  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: from __future__ import annotations
  import argparse
  import logging
  import sys
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  from packaging.version import parse
  from typeguard import check_argument_types, check_return_type
  from espnet2.asr_transducer.beam_search_transducer import BeamSearchTransducer, Hypothesis
  from espnet2.asr_transducer.frontend.online_audio_processor import OnlineAudioProcessor
  from espnet2.asr_transducer.utils import TooShortUttError
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.tasks.asr_transducer import ASRTransducerTask
  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.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 1 for calling a global function with example usage: inference(output_dir, batch_size, dtype, beam_size, ngpu, seed, lm_weight, nbest, num_workers, log_level, data_path_and_name_and_type, asr_train_config, asr_model_file, beam_search_config, lm_train_config, lm_file, model_tag, token_type, bpemodel, key_file, allow_variable_data_keys, quantize_asr_model, quantize_modules, quantize_dtype, streaming, decoding_window, left_context, display_hypotheses) without return types