class_docstr: An Implementation of TokenTensorizer that uses a TorchScript module in the
  background and is hence torchscriptifiable.
  
  Note that unlike the original TokenTensorizer, this version cannot deal with
  arbitrarily nested lists of tokens.
code: def tensorizer_script_impl(self):
      return self.__TENSORIZER_SCRIPT_IMPL__(
          add_bos_token=self.add_bos_token,
          add_eos_token=self.add_eos_token,
          use_eos_token_for_bos=self.use_eos_token_for_bos,
          max_seq_len=self.max_seq_len,
          vocab=self.vocab,
          tokenizer=self.tokenizer,
      )
  
imports: from typing import List, Optional, Tuple
  import torch
  from pytext.config.component import ComponentType, create_component
  from pytext.data.tensorizers import TensorizerScriptImpl
  from pytext.data.tokenizers import Tokenizer
  from pytext.torchscript.tensorizer.tensorizer import VocabLookup
  from pytext.torchscript.tokenizer import ScriptDoNothingTokenizer
  from pytext.torchscript.utils import ScriptBatchInput
  from pytext.torchscript.vocab import ScriptVocabulary
  from pytext.utils import cuda
  from pytext.utils.file_io import PathManager
  from pytext.utils.lazy import lazy_property
  from pytorch.text.fb.utils.transform_utils import pad_2d
  from .tensorizers import Tensorizer, tokenize, VocabConfig
  from .utils import VocabBuilder, Vocabulary
label_desc: Use image node_id 8 for calling the ScriptBasedTokenTensorizer obj's underlying member method code with example usage: obj.tensorizer_script_impl() and returns: self