class_docstr: Takes a Dictionary/Vocabulary, assign ids to each
  token using the dictionary encode_line function.
code: def binarize_line(
      self,
      line: str,
      summary: BinarizeSummary,
  ):
      if summary.replaced is None:
          summary.replaced = Counter()
  
      def replaced_consumer(word, idx):
          if idx == self.dict.unk_index and word != self.dict.unk_word:
              summary.replaced.update([word])
  
      if self.already_numberized:
          id_strings = line.strip().split()
          id_list = [int(id_string) for id_string in id_strings]
          if self.reverse_order:
              id_list.reverse()
          if self.append_eos:
              id_list.append(self.dict.eos())
          ids = torch.IntTensor(id_list)
      else:
          ids = self.dict.encode_line(
              line=line,
              line_tokenizer=self.tokenize,
              add_if_not_exist=False,
              consumer=replaced_consumer,
              append_eos=self.append_eos,
              reverse_order=self.reverse_order,
          )
  
      summary.num_seq += 1
      summary.num_tok += len(ids)
      return ids
  
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 2 for calling the VocabularyDatasetBinarizer obj's underlying member method code with example usage: obj.binarize_line(line, summary) and returns: ids