code: def split(self, ix):
      assert hasattr(self, "Xmn"), "Run set_preprocess_params() first."
  
      first = type(self)(
          self.path_data,
          self.format,
          self.D,
          N=len(ix),
          classification=self.classification,
          preprocess=self.preprocess,
          n_to_estimate=None,
          MAXMEMGB=self.MAXMEMGB,
          set_params=False,
      )
      second = type(self)(
          self.path_data,
          self.format,
          self.D,
          N=self.N - len(ix),
          classification=self.classification,
          preprocess=self.preprocess,
          n_to_estimate=None,
          MAXMEMGB=self.MAXMEMGB,
          set_params=False,
      )
  
      first.storage_level = self.storage_level
      second.storage_level = self.storage_level
  
      # copy preprocess params
      if not self.classification:
          first.ymn = self.ymn
          second.ymn = self.ymn
          first.ysd = self.ysd
          second.ysd = self.ysd
  
      first.Xmn = self.Xmn
      second.Xmn = self.Xmn
      first.sv1 = self.sv1
      second.sv1 = self.sv1
  
      if self.storage_level == constants.StorageLevel.DISK:
          if self.format == constants.DataFormat.SVM:
              first.Xsd = self.Xsd
              second.Xsd = self.Xsd
          else:
              raise NotImplementedError
  
      # initialize data structures
      if self.storage_level == constants.StorageLevel.DISK:
          if self.format == constants.DataFormat.SVM:
              raise NotImplementedError
          raise NotImplementedError
      elif self.storage_level in [
          constants.StorageLevel.SPARSE,
          constants.StorageLevel.DENSE,
      ]:
          first.X, first.y = self.X[ix], self.y[ix]
          ixsec = list(set(range(self.N)).difference(set(ix)))
          second.X, second.y = self.X[ixsec], self.y[ixsec]
  
      return first, second
  
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 10 for calling the PrepareData obj's underlying member method code with example usage: obj.split(ix) and returns: first,  second