class_docstr: GAN Dataset searchs for all the wav files under root path
  and converts them to acoustic features on the fly and returns
  random segments of (audio, feature) couples.
docstr: Return different items for Generator and Discriminator and
  cache acoustic features 
docstr_tok: ["Return","different","items","for","Generator","and","Discriminator","and","cache","acoustic","features"]
code: def __getitem__(self, idx):
      """Return different items for Generator and Discriminator and
      cache acoustic features"""
      if self.return_segments:
          idx2 = self.G_to_D_mappings[idx]
          item1 = self.load_item(idx)
          item2 = self.load_item(idx2)
          return item1, item2
      item1 = self.load_item(idx)
      return item1
  
file_path: TTS/TTS/vocoder/datasets/gan_dataset.py
imports: import os
  import glob
  import torch
  import random
  import numpy
  from torch.utils.data import Dataset
  from multiprocessing import Manager
label_desc: Use image node_id 5 for calling the GANDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: item1, item1,  item2