code: def collate_fn(self, batch):
      # get the batch class_ids
      labels = []
      feats = []
      for item in batch:
          utter_path = item["wav_file_path"]
          class_name = item["class_name"]
  
          # get classid
          class_id = self.classname_to_classid[class_name]
          # load wav file
          wav = self.load_wav(utter_path)
          offset = random.randint(0, wav.shape[0] - self.seq_len)
          wav = wav[offset : offset + self.seq_len]
  
          if self.augmentator is not None and self.data_augmentation_p:
              if random.random() < self.data_augmentation_p:
                  wav = self.augmentator.apply_one(wav)
  
          if not self.use_torch_spec:
              mel = self.ap.melspectrogram(wav)
              feats.append(torch.FloatTensor(mel))
          else:
              feats.append(torch.FloatTensor(wav))
  
          labels.append(class_id)
  
      feats = torch.stack(feats)
      labels = torch.LongTensor(labels)
  
      return feats, labels
  
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 10 for calling the EncoderDataset obj's underlying member method code with example usage: obj.collate_fn(batch) and returns: feats,  labels