code: def get_buffer_chunked_indices(self):
      if self.explicit_subset is not None:
          indices = np.array(self.explicit_subset).astype(int)
      else:
          indices = np.arange(self.length).astype(int)
      if self.shuffle:
          np.random.shuffle(indices)
      if self.subset_num:
          indices = indices[: self.subset_num]
      buffer_chunked_indices = convert_to_chunks(
          indices, chunk_size=self.buffer_size
      )
      return buffer_chunked_indices
  
imports: import math
  import numpy
  import os
  from typing import Generator, Union, Sequence
  import torch
  import torch.utils.data.dataset
label_desc: Use image node_id 3 for calling the ChunkedFilesIterableDataset obj's underlying member method code with example usage: obj.get_buffer_chunked_indices() and returns: buffer_chunked_indices