class_docstr: Wrap a dataset so that, whenever a new item is returned, it is saved to memory.
class_docstr_tok: ["Wrap","a","dataset","so","that",",","whenever","a","new","item","is","returned",",","it","is","saved","to","memory","."]
code: def __len__(self):
      return len(self.dataset)
  
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 3 for calling the MapMemoryCache obj's underlying member method code with example usage: obj.__len__() and returns: len