class_docstr: Test for SlowBytelevelBPE.
  
  Essentially, test whether using a stand-in GPT2 tokenizer for a dict originally
  built with HF's tokenizer produces the same results.
class_docstr_tok: ["Test","for","SlowBytelevelBPE",".","Essentially",",","test","whether","using","a","stand-in","GPT2","tokenizer","for","a","dict","originally","built","with","HF","'s","tokenizer","produces","the","same","results","."]
code: def _get_dict_opt(self, tokenizer: str):
      parser = ParlaiParser()
      parser.set_params(
          dict_tokenizer=tokenizer,
          bpe_vocab=DEFAULT_BYTELEVEL_BPE_VOCAB,
          bpe_merge=DEFAULT_BYTELEVEL_BPE_MERGE,
          bpe_add_prefix_space=False,
          dict_loaded=True,
      )
      opt = parser.parse_args([])
      return opt
  
imports: from parlai.core.build_data import modelzoo_path
  from parlai.core.dict import find_ngrams
  from parlai.core.params import ParlaiParser
  from parlai.core.dict import DictionaryAgent, TokenizationMode
  from parlai.core.opt import Opt
  import parlai.scripts.build_dict
  from parlai.utils.io import PathManager
  import parlai.utils.testing
  import os
  import shutil
  import unittest
  import pytest
label_desc: Use image node_id 1 for calling the TestGpt2HFInterop obj's underlying member method code with example usage: obj._get_dict_opt(tokenizer) and returns: opt