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.
docstr: Tokenize, look up in vocabulary, return tokenized_texts in raw text
  
  Similarly to the above function, tokenization is done with the original
  and not the torchscriptified tokenizer.
code: def prepare_input(self, row):
      """
      Tokenize, look up in vocabulary, return tokenized_texts in raw text
  
      Similarly to the above function, tokenization is done with the original
      and not the torchscriptified tokenizer.
      """
      tokenized_texts, start_idx, end_idx = self._tokenize(
          row[self.text_column]
      )
      token_ranges = list(zip(start_idx, end_idx))
      return list(tokenized_texts), len(tokenized_texts), token_ranges
  
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 10 for calling the ScriptBasedTokenTensorizer obj's underlying member method code with example usage: obj.prepare_input(row) and returns: list,  len,  token_ranges