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","."]
docstr: Return the length of the dataset i.e. the number of sequences in the index
  
  Returns:
      int: The length of the dataset
docstr_tok: ["Return","the","length","of","the","dataset","i.e",".","the","number","of","sequences","in","the","index","Returns",":","int",":","The","length","of","the","dataset"]
code: def __len__(self) -> int:
      """Return the length of the dataset i.e. the number of sequences in the index
  
      Returns:
          int: The length of the dataset
      """
      return len(self.index)
  
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 6 for calling the MMapIndexedDataset obj's underlying member method code with example usage: obj.__len__() and returns: len