code: def load_data(self, idx):
      item = self.items[idx]
  
      if len(item) == 4:
          text, wav_file, speaker_name, attn_file = item
      else:
          text, wav_file, speaker_name = item
          attn = None
  
      wav = np.asarray(self.load_wav(wav_file), dtype=np.float32)
  
      # apply noise for augmentation
      if self.use_noise_augment:
          wav = wav + (1.0 / 32768.0) * np.random.rand(*wav.shape)
  
      if not self.input_seq_computed:
          if self.use_phonemes:
              text = self._load_or_generate_phoneme_sequence(
                  wav_file,
                  text,
                  self.phoneme_cache_path,
                  self.enable_eos_bos,
                  self.cleaners,
                  self.phoneme_language,
                  self.tp,
                  self.add_blank,
              )
  
          else:
              text = np.asarray(
                  text_to_sequence(
                      text,
                      [self.cleaners],
                      tp=self.tp,
                      add_blank=self.add_blank,
                  ),
                  dtype=np.int32,
              )
  
      assert text.size > 0, self.items[idx][1]
      assert wav.size > 0, self.items[idx][1]
  
      if "attn_file" in locals():
          attn = np.load(attn_file)
  
      if len(text) > self.max_seq_len:
          # return a different sample if the phonemized
          # text is longer than the threshold
          # TODO: find a better fix
          return self.load_data(100)
  
      sample = {
          "text": text,
          "wav": wav,
          "attn": attn,
          "item_idx": self.items[idx][1],
          "speaker_name": speaker_name,
          "wav_file_name": os.path.basename(wav_file),
      }
      return sample
  
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 6 for calling the MyDataset obj's underlying member method code with example usage: obj.load_data(idx) and returns: sample, self