docstr: Wire up tokenize(), numberize() and tensorize() functions for data
  processing.
  When export to TorchScript, the wrapper module should choose to use
  texts or pre_tokenized based on the TorchScript tokenizer
  implementation (e.g use external tokenizer such as Yoda or not).
code: def forward(
      self, inputs: ScriptBatchInput
  ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
      """
      Wire up tokenize(), numberize() and tensorize() functions for data
      processing.
      When export to TorchScript, the wrapper module should choose to use
      texts or pre_tokenized based on the TorchScript tokenizer
      implementation (e.g use external tokenizer such as Yoda or not).
      """
      tokens_2d: List[List[int]] = []
      segment_labels_2d: List[List[int]] = []
      seq_lens_1d: List[int] = []
      positions_2d: List[List[int]] = []
  
      for idx in range(self.batch_size(inputs)):
          tokens: List[List[Tuple[str, int, int]]] = self.tokenize(
              self.get_texts_by_index(inputs.texts, idx),
              self.get_tokens_by_index(inputs.tokens, idx),
          )
  
          numberized: Tuple[
              List[int], List[int], int, List[int]
          ] = self.numberize(tokens)
          tokens_2d.append(numberized[0])
          segment_labels_2d.append(numberized[1])
          seq_lens_1d.append(numberized[2])
          positions_2d.append(numberized[3])
  
      return self.tensorize(
          tokens_2d, segment_labels_2d, seq_lens_1d, positions_2d
      )
  
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 7 for calling the BERTTensorizerBaseScriptImpl obj's underlying member method code with example usage: obj.forward(inputs) and returns: self