docstr: If a class has a non-standard way of generating the final numberized text
  (eg: BERT) then a class specific version of wrap_numberized_text function
  should be implemented. This allows us to share the numberize
  function across classes without having to copy paste code. The default
  implementation doesnt do anything.
code: def _wrap_numberized_tokens(
      self, numberized_tokens: List[int], idx: int
  ) -> List[int]:
      """
      If a class has a non-standard way of generating the final numberized text
      (eg: BERT) then a class specific version of wrap_numberized_text function
      should be implemented. This allows us to share the numberize
      function across classes without having to copy paste code. The default
      implementation doesnt do anything.
      """
      return numberized_tokens
  
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 3 for calling the BERTTensorizerBaseScriptImpl obj's underlying member method code with example usage: obj._wrap_numberized_tokens(numberized_tokens, idx) and returns: numberized_tokens