code: def todense(self):
      assert hasattr(self, "Xmn"), "Set preprocess params first."
      assert (
          len(self) <= self.max_batch_size()
      ), "N must be <= max_batch_size()."
  
      with torch.no_grad():
          dense, _ = self.split(range(len(self)))
          Braw = self.return_raw
          Bnp = self.return_np
          self.set_return_raw(True)
          self.set_return_np(True)
          dense.X, dense.y = [], []
  
          def f_Xy(X, y):
              dense.X.append(X)
              dense.y.append(y)
  
          self.apply(f_Xy=f_Xy)
          dense.X = dense.X[-1]
          dense.y = dense.y[-1]
          self.set_return_raw(Braw)
          self.set_return_np(Bnp)
          dense.storage_level = constants.StorageLevel.DENSE
  
          return dense
  
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 9 for calling the PrepareData obj's underlying member method code with example usage: obj.todense() and returns: dense