code: def __parse_items(self):
      class_to_utters = {}
      for item in self.items:
          path_ = item["audio_file"]
          class_name = item[self.config.class_name_key]
          if class_name in class_to_utters.keys():
              class_to_utters[class_name].append(path_)
          else:
              class_to_utters[class_name] = [
                  path_,
              ]
  
      # skip classes with number of samples >= self.num_utter_per_class
      class_to_utters = {
          k: v
          for (k, v) in class_to_utters.items()
          if len(v) >= self.num_utter_per_class
      }
  
      classes = list(class_to_utters.keys())
      classes.sort()
  
      new_items = []
      for item in self.items:
          path_ = item["audio_file"]
          class_name = (
              item["emotion_name"]
              if self.config.model == "emotion_encoder"
              else item["speaker_name"]
          )
          # ignore filtered classes
          if class_name not in classes:
              continue
          # ignore small audios
          if self.load_wav(path_).shape[0] - self.seq_len <= 0:
              continue
  
          new_items.append(
              {"wav_file_path": path_, "class_name": class_name}
          )
  
      return classes, new_items
  
file_path: TTS/TTS/encoder/dataset.py
imports: import random
  import torch
  from torch.utils.data import Dataset
  from TTS.encoder.utils.generic_utils import AugmentWAV
label_desc: Use image node_id 3 for calling the EncoderDataset obj's underlying member method code with example usage: obj.__parse_items() and returns: classes,  new_items