class_docstr: Tokenizer that takes a list of strings and converts to a list of Tokens.
  Useful in cases where tokenizer is run before-hand
class_docstr_tok: ["Tokenizer","that","takes","a","list","of","strings","and","converts","to","a","list","of","Tokens",".","Useful","in","cases","where","tokenizer","is","run","before-hand"]
code: def torchscriptify(self):
      return ScriptDoNothingTokenizer()
  
imports: import copy
  import json
  import re
  from collections import OrderedDict
  from typing import List, NamedTuple, Optional, Union
  import torch
  from fairseq.data.encoders.gpt2_bpe import get_encoder
  from fairseq.data.encoders.gpt2_bpe_utils import Encoder
  from pytext.config import ConfigBase
  from pytext.config.component import Component, ComponentType, create_component
  from pytext.data.pickleable_gpt2bpe_encoder import PickleableGPT2BPEEncoder
  from pytext.torchscript.tokenizer import ScriptDoNothingTokenizer, ScriptWordTokenizer
  from pytext.utils.file_io import PathManager
  from pytext.utils.usage import log_class_usage
  from transformers.tokenization_bert import BasicTokenizer, WordpieceTokenizer
label_desc: Use image node_id 4 for calling the DoNothingTokenizer obj's underlying member method code with example usage: obj.torchscriptify() and returns: ScriptDoNothingTokenizer