class_docstr: A class that regroups important test to make sure that we properly handle the special tokens.
class_docstr_tok: ["A","class","that","regroups","important","test","to","make","sure","that","we","properly","handle","the","special","tokens","."]
code: def test_add_dummy_prefix(self):
      # make sure `'▁'` is prepended, and outputs match sp_model's
      # `sentencepiece.NormalizerSpec.add_dummy_prefix` attribute
      input_ids = self.tokenizer.encode(
          ". Hello", add_special_tokens=False
      )
      self.assertEqual(input_ids, [37, 86, 20])
      self.assertEqual(input_ids, [37, 86, 20])
      tokens = self.tokenizer.tokenize(". Hello")
      self.assertEqual(tokens, ["▁he", "ll", "o"])
  
      tokens = self.tokenizer.tokenize("")
      self.assertEqual(tokens, [])
      self.assertEqual(
          tokens, self.tokenizer.sp_model.encode("", out_type=str)
      )
  
      tokens = self.tokenizer.tokenize(" ")
      self.assertEqual(tokens, [])
      self.assertEqual(
          tokens, self.tokenizer.sp_model.encode(" ", out_type=str)
      )
  
      tokens = self.tokenizer.tokenize("▁")
      self.assertEqual(tokens, [])
      self.assertEqual(
          tokens, self.tokenizer.sp_model.encode("▁", out_type=str)
      )
  
imports: import json
  import os
  import tempfile
  import unittest
  from transformers import SPIECE_UNDERLINE, AddedToken, BatchEncoding, SiglipTokenizer
  from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
  from transformers.utils import cached_property, is_tf_available, is_torch_available
  from ...test_tokenization_common import TokenizerTesterMixin
label_desc: Use image node_id 2 for calling the CommonSpmIntegrationTests obj's underlying member method code with example usage: obj.test_add_dummy_prefix() without return types