code: def __init__(
      self,
      buffer_size,
      shuffle,
      chunked_file_data_cache: ChunkedFilesDataCache,
      subset_num: Union[int, None] = None,
      explicit_subset: Union[Sequence, None] = None,
      verbose=False,
  ):
      self.buffer_size = buffer_size
      self.shuffle = shuffle
      self.subset_num = subset_num
      self.chunked_file_data_cache = chunked_file_data_cache
      self.explicit_subset = explicit_subset
      self.verbose = verbose
  
      if self.explicit_subset is not None:
          assert self.subset_num is None
          self.length = len(self.explicit_subset)
      else:
          self.length = self.chunked_file_data_cache.length
          if self.subset_num:
              self.length = min(self.subset_num, self.length)
  
      if self.buffer_size is None:
          self.buffer_size = self.length
  
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 1 to create a new ChunkedFilesIterableDataset object from inherited base classes: torch.utils.data.dataset with example: obj = ChunkedFilesIterableDataset(buffer_size, shuffle, chunked_file_data_cache, subset_num, explicit_subset, verbose)