code: def _load_or_generate_phoneme_sequence(
      wav_file,
      text,
      phoneme_cache_path,
      enable_eos_bos,
      cleaners,
      language,
      tp,
      add_blank,
  ):
      file_name = os.path.splitext(os.path.basename(wav_file))[0]
  
      # different names for normal phonemes and with blank chars.
      file_name_ext = (
          "_blanked_phoneme.npy" if add_blank else "_phoneme.npy"
      )
      cache_path = os.path.join(
          phoneme_cache_path, file_name + file_name_ext
      )
      try:
          phonemes = np.load(cache_path)
      except FileNotFoundError:
          phonemes = MyDataset._generate_and_cache_phoneme_sequence(
              text, cache_path, cleaners, language, tp, add_blank
          )
      except (ValueError, IOError):
          print(
              " [!] failed loading phonemes for {}. "
              "Recomputing.".format(wav_file)
          )
          phonemes = MyDataset._generate_and_cache_phoneme_sequence(
              text, cache_path, cleaners, language, tp, add_blank
          )
      if enable_eos_bos:
          phonemes = pad_with_eos_bos(phonemes, tp=tp)
          phonemes = np.asarray(phonemes, dtype=np.int32)
      return phonemes
  
imports: import collections
  import os
  import random
  from multiprocessing import Manager, Pool
  import numpy
  import torch
  import tqdm
  from torch.utils.data import Dataset
  from TTS.tts.utils.data import prepare_data, prepare_stop_target, prepare_tensor
  from TTS.tts.utils.text import pad_with_eos_bos, phoneme_to_sequence, text_to_sequence
label_desc: Use image node_id 5 for calling the MyDataset obj's underlying member method code with example usage: obj._load_or_generate_phoneme_sequence(wav_file, text, phoneme_cache_path, enable_eos_bos, cleaners, language, tp, add_blank) and returns: phonemes