code: def __init__(
      self,
      s2t_train_config: Union[Path, str] = None,
      s2t_model_file: Union[Path, str] = None,
      token_type: str = None,
      bpemodel: str = None,
      device: str = "cpu",
      batch_size: int = 1,
      dtype: str = "float32",
      nbest: int = 1,
      normalize_length: bool = False,
      quantize_s2t_model: bool = False,
      quantize_modules: List[str] = ["Linear"],
      quantize_dtype: str = "qint8",
  ):
      assert check_argument_types()
  
      quantize_modules = set(
          [getattr(torch.nn, q) for q in quantize_modules]
      )
      quantize_dtype = getattr(torch, quantize_dtype)
  
      # 1. Build S2T model
      s2t_model, s2t_train_args = S2TTask.build_model_from_file(
          s2t_train_config, s2t_model_file, device
      )
      s2t_model.to(dtype=getattr(torch, dtype)).eval()
  
      if quantize_s2t_model:
          logging.info("Use quantized s2t model for decoding.")
  
          s2t_model = torch.quantization.quantize_dynamic(
              s2t_model,
              qconfig_spec=quantize_modules,
              dtype=quantize_dtype,
          )
  
      decoder = s2t_model.decoder
      token_list = s2t_model.token_list
      scorers = dict(
          decoder=decoder,
      )
  
      # 4. Build BeamSearch object
      weights = dict(
          decoder=1.0,
      )
      beam_search = BeamSearch(
          beam_size=nbest,
          weights=weights,
          scorers=scorers,
          sos=s2t_model.sos,
          eos=s2t_model.eos,
          vocab_size=len(token_list),
          token_list=token_list,
          pre_beam_score_key="full",
          normalize_length=normalize_length,
      )
  
      # TODO(karita): make all scorers batchfied
      if batch_size == 1:
          non_batch = [
              k
              for k, v in beam_search.full_scorers.items()
              if not isinstance(v, BatchScorerInterface)
          ]
          if len(non_batch) == 0:
              beam_search.__class__ = BatchBeamSearch
              logging.info(
                  "BatchBeamSearch implementation is selected."
              )
          else:
              logging.warning(
                  f"As non-batch scorers {non_batch} are found, "
                  f"fall back to non-batch implementation."
              )
  
          beam_search.to(
              device=device, dtype=getattr(torch, dtype)
          ).eval()
          for scorer in scorers.values():
              if isinstance(scorer, torch.nn.Module):
                  scorer.to(
                      device=device, dtype=getattr(torch, dtype)
                  ).eval()
          logging.info(f"Beam_search: {beam_search}")
          logging.info(f"Decoding device={device}, dtype={dtype}")
  
      # 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
      if token_type is None:
          token_type = s2t_train_args.token_type
      if bpemodel is None:
          bpemodel = s2t_train_args.bpemodel
  
      if token_type is None:
          tokenizer = None
      elif (
          token_type == "bpe"
          or token_type == "hugging_face"
          or "whisper" in token_type
      ):
          if bpemodel is not None:
              tokenizer = build_tokenizer(
                  token_type=token_type, bpemodel=bpemodel
              )
          else:
              tokenizer = None
      else:
          tokenizer = build_tokenizer(token_type=token_type)
  
      if bpemodel not in ["whisper_en", "whisper_multilingual"]:
          converter = TokenIDConverter(token_list=token_list)
      else:
          converter = OpenAIWhisperTokenIDConverter(model_type=bpemodel)
          beam_search.set_hyp_primer(
              list(
                  converter.tokenizer.sot_sequence_including_notimestamps
              )
          )
      logging.info(f"Text tokenizer: {tokenizer}")
  
      self.s2t_model = s2t_model
      self.s2t_train_args = s2t_train_args
      self.converter = converter
      self.tokenizer = tokenizer
      self.beam_search = beam_search
      self.device = device
      self.dtype = dtype
      self.nbest = nbest
  
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.nn.functional
  import torch.quantization
  from typeguard import check_argument_types, check_return_type
  from espnet2.asr.decoder.s4_decoder import S4Decoder
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.tasks.s2t import S2TTask
  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.pytorch_backend.transformer.subsampling import TooShortUttError
  from espnet.nets.scorer_interface import BatchScorerInterface
  from espnet.utils.cli_utils import get_commandline_args
label_desc: Use image node_id 1 to create a new Speech2Text object  with example: obj = Speech2Text(s2t_train_config, s2t_model_file, token_type, bpemodel, device, batch_size, dtype, nbest, normalize_length, quantize_s2t_model, quantize_modules, quantize_dtype)