code: def apply(self, ix_rows=None, ix_cols=None, f_Xy=None):
      if f_Xy is None:
          return
  
      if ix_rows is None:
          ix_rows = range(self.N)
  
      if ix_cols is None:
          ix_cols = range(self.n_features)
  
      f_Xy(
          (
              self.X[ix_rows, ix_cols]
              if not self.storage_level == constants.StorageLevel.SPARSE
              else self.X[ix_rows, ix_cols].toarray()
          ),
          self.y[ix_rows],
      )
  
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 16 for calling the PrepareData obj's underlying member method code with example usage: obj.apply(ix_rows, ix_cols, f_Xy) without return types