code: def torchscriptify(self):
      # tokenizer will only be used in Inference, so we hold its torchscriptify
      # by end of the training.
      if not isinstance(self.tokenizer, torch.jit.ScriptModule):
          self.tokenizer = self.tokenizer.torchscriptify()
      return super().torchscriptify()
  
imports: from typing import Any, Dict, List, Optional, Tuple
  import torch
  from fairseq.data.dictionary import Dictionary
  from fairseq.data.legacy.masked_lm_dictionary import BertDictionary
  from pytext import resources
  from pytext.common.constants import Token
  from pytext.config.component import ComponentType, create_component
  from pytext.data.tensorizers import Tensorizer, TensorizerScriptImpl
  from pytext.data.tokenizers import Tokenizer, WordPieceTokenizer
  from pytext.data.utils import BOS, EOS, MASK, PAD, UNK, Vocabulary
  from pytext.torchscript.tensorizer.tensorizer import VocabLookup
  from pytext.torchscript.utils import pad_2d_mask, ScriptBatchInput
  from pytext.torchscript.vocab import ScriptVocabulary
  from pytext.utils.file_io import PathManager
  from pytext.utils.lazy import lazy_property
  from pytorch.text.fb.utils.transform_utils import pad_2d
label_desc: Use image node_id 8 for calling the BERTTensorizerBaseScriptImpl obj's underlying member method code with example usage: obj.torchscriptify() and returns: super