code: def __getitem__(self, idx):
      with torch.no_grad():
          X, y = self.getXy(idx)
          X = X.toarray() if scipy.sparse.issparse(X) else X
  
          X = torch.as_tensor(X, dtype=torch.get_default_dtype()).to(
              self.device
          )
          y = torch.as_tensor(y, dtype=torch.get_default_dtype()).to(
              self.device
          )
  
          if not self.return_raw:
              X, y = self.apply_preprocess(X, y)
  
          if self.classification and (
              self.n_classes is None or self.n_classes == 2
          ):
              y[y == 0] = -1
  
          if self.return_np:
              if constants.Device.CPU not in self.device:
                  X = X.cpu()
                  y = y.cpu()
              X = X.numpy()
              y = y.numpy()
              return X, y
  
          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 20 for calling the PrepareData obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: X,  y, X,  y