class_docstr: Takes a Dictionary/Vocabulary, assign ids to each
  token using the dictionary encode_line function.
code: def __init__(
      self,
      dict: Dictionary,
      tokenize: tp.Callable[[str], tp.List[str]] = tokenize_line,
      append_eos: bool = True,
      reverse_order: bool = False,
      already_numberized: bool = False,
  ) -> None:
      self.dict = dict
      self.tokenize = tokenize
      self.append_eos = append_eos
      self.reverse_order = reverse_order
      self.already_numberized = already_numberized
      super().__init__()
  
imports: import logging
  import os
  import typing
  from abc import ABC, abstractmethod
  from collections import Counter
  from dataclasses import dataclass
  from multiprocessing import Pool
  import torch
  from fairseq.data import Dictionary, indexed_dataset
  from fairseq.file_chunker_utils import Chunker, find_offsets
  from fairseq.file_io import PathManager
  from fairseq.tokenizer import tokenize_line
label_desc: Use image node_id 1 to create a new VocabularyDatasetBinarizer object from inherited base classes: Binarizer with example: obj = VocabularyDatasetBinarizer(dict, tokenize, append_eos, reverse_order, already_numberized)