class_docstr: keep an index of the offsets in the huffman binary file.
  First a header, then the list of sizes (num tokens) for each instance and finally
  the addresses of each instance.
class_docstr_tok: ["keep","an","index","of","the","offsets","in","the","huffman","binary","file",".","First","a","header",",","then","the","list","of","sizes","(","num","tokens",")","for","each","instance","and","finally","the","addresses","of","each","instance","."]
code: def __init__(self, path):
      with open(path, "rb") as stream:
          # read headers
          magic_test = stream.read(9)
          assert self._HDR_MAGIC == magic_test, (
              "Index file doesn't match expected format. "
              "Make sure that --dataset-impl is configured properly."
          )
          (version,) = struct.unpack("<Q", stream.read(8))
          assert (
              self._VERSION == version
          ), f"Unexpected file version{version} != code version {self._VERSION}"
  
          # read length of data file
          (self._data_len,) = struct.unpack("<Q", stream.read(8))
          # read number of items in data file/index
          (self._len,) = struct.unpack("<Q", stream.read(8))
          offset = stream.tell()
  
      indexed_dataset._warmup_mmap_file(path)
  
      self._bin_buffer_mmap = np.memmap(path, mode="r", order="C")
      self._bin_buffer = memoryview(self._bin_buffer_mmap)
      self._sizes = np.frombuffer(
          self._bin_buffer,
          dtype=np.int32,
          count=self._len,
          offset=offset,
      )
      self._pointers = np.frombuffer(
          self._bin_buffer,
          dtype=np.int64,
          count=self._len,
          offset=offset + self._sizes.nbytes,
      )
  
imports: import mmap
  import os
  import shutil
  import struct
  import typing
  from functools import lru_cache
  import numpy
  import torch
  from fairseq.data import indexed_dataset
  from fairseq.data.huffman import HuffmanCoder
  from fairseq.file_io import PathManager
label_desc: Use image node_id 2 to create a new HuffmanMMapIndex object  with example: obj = HuffmanMMapIndex(path)