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],
      train_config: Optional[str],
      model_file: Optional[str],
      threshold: float,
      minlenratio: float,
      maxlenratio: float,
      st_subtask_minlenratio: float,
      st_subtask_maxlenratio: float,
      use_teacher_forcing: bool,
      use_att_constraint: bool,
      backward_window: int,
      forward_window: int,
      always_fix_seed: bool,
      nbest: int,
      normalize_length: bool,
      beam_size: int,
      penalty: float,
      st_subtask_nbest: int,
      st_subtask_beam_size: int,
      st_subtask_penalty: float,
      st_subtask_token_type: Optional[str],
      st_subtask_bpemodel: Optional[str],
      allow_variable_data_keys: bool,
      vocoder_config: Optional[str],
      vocoder_file: Optional[str],
      vocoder_tag: Optional[str],
  ):
      """Run text-to-speech inference."""
      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 model
      speech2speech_kwargs = dict(
          train_config=train_config,
          model_file=model_file,
          threshold=threshold,
          maxlenratio=maxlenratio,
          minlenratio=minlenratio,
          st_subtask_maxlenratio=st_subtask_maxlenratio,
          st_subtask_minlenratio=st_subtask_minlenratio,
          use_teacher_forcing=use_teacher_forcing,
          use_att_constraint=use_att_constraint,
          backward_window=backward_window,
          forward_window=forward_window,
          nbest=nbest,
          normalize_length=normalize_length,
          beam_size=beam_size,
          penalty=penalty,
          st_subtask_nbest=st_subtask_nbest,
          st_subtask_beam_size=st_subtask_beam_size,
          st_subtask_penalty=st_subtask_penalty,
          st_subtask_token_type=st_subtask_token_type,
          st_subtask_bpemodel=st_subtask_bpemodel,
          vocoder_config=vocoder_config,
          vocoder_file=vocoder_file,
          dtype=dtype,
          device=device,
          seed=seed,
          always_fix_seed=always_fix_seed,
      )
      speech2speech = Speech2Speech.from_pretrained(
          vocoder_tag=vocoder_tag,
          **speech2speech_kwargs,
      )
  
      # 3. Build data-iterator
      loader = S2STTask.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=S2STTask.build_preprocess_fn(
              speech2speech.train_args, False
          ),
          collate_fn=S2STTask.build_collate_fn(
              speech2speech.train_args, False
          ),
          allow_variable_data_keys=allow_variable_data_keys,
          inference=True,
      )
  
      # 6. Start for-loop
      output_dir = Path(output_dir)
      (output_dir / "norm").mkdir(parents=True, exist_ok=True)
      (output_dir / "denorm").mkdir(parents=True, exist_ok=True)
      (output_dir / "speech_shape").mkdir(parents=True, exist_ok=True)
      (output_dir / "wav").mkdir(parents=True, exist_ok=True)
      (output_dir / "att_ws").mkdir(parents=True, exist_ok=True)
      (output_dir / "probs").mkdir(parents=True, exist_ok=True)
      (output_dir / "focus_rates").mkdir(parents=True, exist_ok=True)
  
      # Lazy load to avoid the backend error
      import matplotlib
  
      matplotlib.use("Agg")
      import matplotlib.pyplot as plt
      from matplotlib.ticker import MaxNLocator
  
      with NpyScpWriter(
          output_dir / "norm",
          output_dir / "norm/feats.scp",
      ) as norm_writer, NpyScpWriter(
          output_dir / "denorm", output_dir / "denorm/feats.scp"
      ) as denorm_writer, open(
          output_dir / "speech_shape/speech_shape", "w"
      ) as shape_writer, open(
          output_dir / "focus_rates/focus_rates", "w"
      ) as focus_rate_writer, DatadirWriter(
          output_dir / "st_subtask"
      ) as st_subtask_wrtier:
          for idx, (keys, batch) in enumerate(loader, 1):
              assert isinstance(batch, dict), type(batch)
              assert all(isinstance(s, str) for s in keys), keys
              _bs = len(next(iter(batch.values())))
              assert _bs == 1, _bs
  
              # # Change to single sequence and remove *_length
              # # because inference() requires 1-seq, not mini-batch.
              # batch = {k: v for k, v in batch.items() if not k.endswith("_lengths")}
  
              start_time = time.perf_counter()
              output_dict = speech2speech(**batch)
  
              key = keys[0]
              insize = next(iter(batch.values())).size(0) + 1
              # standard speech2mel model case
              feat_gen = output_dict["feat_gen"]
              logging.info(
                  "inference speed = {:.1f} frames / sec.".format(
                      int(feat_gen.size(0))
                      / (time.perf_counter() - start_time)
                  )
              )
              logging.info(f"{key} (size:{insize}->{feat_gen.size(0)})")
              if feat_gen.size(0) == insize * maxlenratio:
                  logging.warning(
                      f"output length reaches maximum length ({key})."
                  )
  
              norm_writer[key] = output_dict["feat_gen"].cpu().numpy()
              shape_writer.write(
                  f"{key} "
                  + ",".join(map(str, output_dict["feat_gen"].shape))
                  + "\n"
              )
              if output_dict.get("feat_gen_denorm") is not None:
                  denorm_writer[key] = (
                      output_dict["feat_gen_denorm"].cpu().numpy()
                  )
  
              if output_dict.get("focus_rate") is not None:
                  focus_rate_writer.write(
                      f"{key} {float(output_dict['focus_rate']):.5f}\n"
                  )
  
              if output_dict.get("att_w") is not None:
                  # Plot attention weight
                  att_w = output_dict["att_w"].cpu().numpy()
  
                  if att_w.ndim == 3:
                      logging.warning(
                          "Cannot plot attn due to dim mismatch (for multihead)"
                      )
                      output_dict["att_w"] = None
                  else:
                      if att_w.ndim == 2:
                          att_w = att_w[None][None]
                      elif att_w.ndim != 4:
                          raise RuntimeError(
                              f"Must be 2 or 4 dimension: {att_w.ndim}"
                          )
  
                      w, h = plt.figaspect(
                          att_w.shape[0] / att_w.shape[1]
                      )
                      fig = plt.Figure(
                          figsize=(
                              w * 1.3 * min(att_w.shape[0], 2.5),
                              h * 1.3 * min(att_w.shape[1], 2.5),
                          )
                      )
                      fig.suptitle(f"{key}")
                      axes = fig.subplots(
                          att_w.shape[0], att_w.shape[1]
                      )
                      if len(att_w) == 1:
                          axes = [[axes]]
                      for ax, att_w in zip(axes, att_w):
                          for ax_, att_w_ in zip(ax, att_w):
                              ax_.imshow(
                                  att_w_.astype(np.float32),
                                  aspect="auto",
                              )
                              ax_.set_xlabel("Input")
                              ax_.set_ylabel("Output")
                              ax_.xaxis.set_major_locator(
                                  MaxNLocator(integer=True)
                              )
                              ax_.yaxis.set_major_locator(
                                  MaxNLocator(integer=True)
                              )
  
                      fig.set_tight_layout({"rect": [0, 0.03, 1, 0.95]})
                      fig.savefig(output_dir / f"att_ws/{key}.png")
                      fig.clf()
  
              if output_dict.get("prob") is not None:
                  # Plot stop token prediction
                  prob = output_dict["prob"].cpu().numpy()
  
                  fig = plt.Figure()
                  ax = fig.add_subplot(1, 1, 1)
                  ax.plot(prob)
                  ax.set_title(f"{key}")
                  ax.set_xlabel("Output")
                  ax.set_ylabel("Stop probability")
                  ax.set_ylim(0, 1)
                  ax.grid(which="both")
  
                  fig.set_tight_layout(True)
                  fig.savefig(output_dir / f"probs/{key}.png")
                  fig.clf()
  
              if output_dict.get("wav") is not None:
                  # TODO(kamo): Write scp
                  logging.info(
                      "wav {}".format(output_dict["wav"].size())
                  )
                  sf.write(
                      f"{output_dir}/wav/{key}.wav",
                      output_dict["wav"].cpu().numpy(),
                      speech2speech.fs,
                      "PCM_16",
                  )
  
              if output_dict.get("st_subtask_token") is not None:
                  writer["token"][key] = " ".join(
                      output_dict["st_subtask_token"]
                  )
                  writer["token_int"][key] == " ".join(
                      map(str, output_dict["st_subtask_token_int"])
                  )
                  if output_dict.get("st_subtask_text") is not None:
                      writer["text"][key] = output_dict[
                          "st_subtask_text"
                      ]
  
      # remove files if those are not included in output dict
      if output_dict.get("feat_gen") is None:
          shutil.rmtree(output_dir / "norm")
      if output_dict.get("feat_gen_denorm") is None:
          shutil.rmtree(output_dir / "denorm")
      if output_dict.get("att_w") is None:
          shutil.rmtree(output_dir / "att_ws")
      if output_dict.get("focus_rate") is None:
          shutil.rmtree(output_dir / "focus_rates")
      if output_dict.get("prob") is None:
          shutil.rmtree(output_dir / "probs")
      if output_dict.get("wav") is None:
          shutil.rmtree(output_dir / "wav")
      if output_dict.get("st_subtask_token") is not None:
          shutil.rmtree(output_dict / "st_subtask")
  
imports: import argparse
  import logging
  import shutil
  import sys
  import time
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import soundfile
  import torch
  from packaging.version import parse
  from typeguard import check_argument_types
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.fileio.npy_scp import NpyScpWriter
  from espnet2.tasks.s2st import S2STTask
  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 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.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, batch_size, dtype, ngpu, seed, num_workers, log_level, data_path_and_name_and_type, key_file, train_config, model_file, threshold, minlenratio, maxlenratio, st_subtask_minlenratio, st_subtask_maxlenratio, use_teacher_forcing, use_att_constraint, backward_window, forward_window, always_fix_seed, nbest, normalize_length, beam_size, penalty, st_subtask_nbest, st_subtask_beam_size, st_subtask_penalty, st_subtask_token_type, st_subtask_bpemodel, allow_variable_data_keys, vocoder_config, vocoder_file, vocoder_tag) without return types