code: def prefetch(self, indices):
      if all(i in self.cache_index for i in indices):
          return
      if not self.data_file:
          self.read_data(self.path)
      indices = sorted(set(indices))
      total_size = 0
      for i in indices:
          total_size += self.data_offsets[i + 1] - self.data_offsets[i]
      self.cache = np.empty(total_size, dtype=self.dtype)
      ptx = 0
      self.cache_index.clear()
      for i in indices:
          self.cache_index[i] = ptx
          size = self.data_offsets[i + 1] - self.data_offsets[i]
          a = self.cache[ptx : ptx + size]
          self.data_file.seek(self.data_offsets[i] * self.element_size)
          self.data_file.readinto(a)
          ptx += size
      if self.data_file:
          # close and delete data file after prefetch so we can pickle
          self.data_file.close()
          self.data_file = None
  
imports: import os
  import shutil
  import struct
  from functools import lru_cache
  from itertools import accumulate
  import numpy
  import torch
label_desc: Use image node_id 3 for calling the IndexedCachedDataset obj's underlying member method code with example usage: obj.prefetch(indices) without return types