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","."]
docstr: Tests gpt2standin can handle prefix space.
code: def _run_prefix_space_test(self, agent):
      """
      Tests gpt2standin can handle prefix space.
      """
      self.assertEqual(
          # grinning face emoji
          agent.bytelevelbpe_tokenize("Hello, ParlAI! \U0001f600"),
          ["\\xc4\\xa0"] + slow_bytelevel_bpe_RESULT,
      )
      self.assertEqual(
          agent.vec2txt(
              [
                  agent.tok2ind[w]
                  for w in ["\\xc4\\xa0"] + slow_bytelevel_bpe_RESULT
              ]
          ),
          # grinning face emoji
          "Hello, ParlAI! \U0001f600",
      )
      self.assertEqual(
          agent.txt2vec("Hello, ParlAI! \U0001f600"),
          [
              agent.tok2ind[w]
              for w in ["\\xc4\\xa0"] + slow_bytelevel_bpe_RESULT
          ],
      )
  
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 4 for calling the TestGpt2HFInterop obj's underlying member method code with example usage: obj._run_prefix_space_test(agent) without return types