code: def _mock_tokenizer(self):
      class MockTokenizer(ScriptTokenizerBase):
          def __init__(self, tokens: List[Tuple[str, int, int]]):
              super().__init__()
              self.tokens = torch.jit.Attribute(
                  tokens, List[Tuple[str, int, int]]
              )
  
          @torch.jit.script_method
          def tokenize(self, text: str) -> List[Tuple[str, int, int]]:
              return self.tokens
  
      rand_tokens = [
          (str(random.randint(100, 200)), -1, -1) for i in range(20)
      ]
      return MockTokenizer(rand_tokens), rand_tokens
  
imports: import random
  import unittest
  from typing import List, Tuple
  import torch
  from pytext.torchscript.tensorizer import ScriptBERTTensorizer, ScriptRoBERTaTensorizer, ScriptRoBERTaTensorizerWithIndices, ScriptXLMTensorizer
  from pytext.torchscript.tensorizer.tensorizer import VocabLookup
  from pytext.torchscript.tokenizer import ScriptDoNothingTokenizer
  from pytext.torchscript.tokenizer.tokenizer import ScriptTokenizerBase
  from pytext.torchscript.utils import squeeze_1d, squeeze_2d
  from pytext.torchscript.vocab import ScriptVocabulary
label_desc: Use image node_id 2 for calling the TensorizerTest obj's underlying member method code with example usage: obj._mock_tokenizer() and returns: MockTokenizer,  rand_tokens, self