code: def get_dense_data(self, ix_cols=None, ix_rows=None):
      if ix_cols is None:
          ix_cols = range(self.n_features)
  
      X = [np.zeros((0, len(ix_cols)))]
      y = [np.zeros((0, 1))]
      Bnp = self.return_np
  
      def f_Xy(Xb, yb, n):
          X[-1] = np.concatenate((X[-1], Xb), axis=0)
          y[-1] = np.concatenate((y[-1], yb), axis=0)
  
      self.apply(f_Xy=f_Xy, ix_rows=ix_rows, ix_cols=ix_cols)
      self.set_return_np(Bnp)
  
      return X[-1], y[-1]
  
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 17 for calling the PrepareData obj's underlying member method code with example usage: obj.get_dense_data(ix_cols, ix_rows) and returns: X,  y