code: def process_datapoint(self, item):
      specgram = self.transforms(item[0])
      return item[0].squeeze(0), specgram
  
file_path: audio/examples/pipeline_wavernn/datasets.py
imports: import random
  import torch
  from processing import bits_to_normalized_waveform, normalized_waveform_to_bits
  from torch.utils.data.dataset import random_split
  from torchaudio.datasets import LIBRITTS, LJSPEECH
  from torchaudio.transforms import MuLawEncoding
label_desc: Use image node_id 4 for calling the Processed obj's underlying member method code with example usage: obj.process_datapoint(item) and returns: item,  specgram