class_docstr: The low-level interface dataset class
  
  Args:
      path_prefix (str): The index (.idx) and data (.bin) prefix
  
      multimodal (bool, optional): Whether the dataset is multimodal. Defaults to False.
class_docstr_tok: ["The","low-level","interface","dataset","class","Args",":","path_prefix","(","str",")",":","The","index","(",".idx",")","and","data","(",".bin",")","prefix","multimodal","(","bool",",","optional",")",":","Whether","the","dataset","is","multimodal",".","Defaults","to","False","."]
code: def __init__(
      self, path_prefix: str, multimodal: bool = False
  ) -> None:
      super().__init__()
      self.path_prefix = None
      self.multimodal = None
  
      self.index = None
      self.bin_buffer = None
      self.bin_buffer_mmap = None
  
      self.initialize(path_prefix, multimodal)
  
imports: import logging
  import os
  import shutil
  import struct
  import time
  from enum import Enum
  from functools import lru_cache
  from itertools import accumulate
  from types import TracebackType
  from typing import List, Optional, Tuple, Type, Union
  import numpy
  import torch
  from megatron.core.datasets.utils import log_single_rank
label_desc: Use image node_id 1 to create a new MMapIndexedDataset object from inherited base classes: torch.utils.data with example: obj = MMapIndexedDataset(path_prefix, multimodal)