code: def getXy(self, idx):
      if self.storage_level == constants.StorageLevel.DENSE:
          X, y = self.X[idx], self.y[idx]
      elif self.storage_level == constants.StorageLevel.SPARSE:
          # assume subset can fit into memory even if whole matrix cant
          X, y = self.X[idx].toarray(), self.y[idx]
      else:
          raise NotImplementedError
  
      return X, y
  
imports: import os
  import pickle
  import sys
  import time
  import numpy
  import scipy.sparse
  from sklearn.datasets import load_svmlight_file
  import torch
  from torch.utils.data import DataLoader, Dataset
  from torch.utils.data.dataloader import _SingleProcessDataLoaderIter, _MultiProcessingDataLoaderIter, _utils
  from .None import constants
  from .None import syssettings
label_desc: Use image node_id 19 for calling the PrepareData obj's underlying member method code with example usage: obj.getXy(idx) and returns: X,  y