class_docstr: Base Tensorizer class for all BERT style models including XLM,
  RoBERTa and XLM-R.
class_docstr_tok: ["Base","Tensorizer","class","for","all","BERT","style","models","including","XLM",",","RoBERTa","and","XLM-R","."]
code: def tensorizer_script_impl(self):
      return self.__TENSORIZER_SCRIPT_IMPL__(
          tokenizer=self.tokenizer,
          vocab=self.vocab,
          max_seq_len=self.max_seq_len,
      )
  
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 BERTTensorizerBase obj's underlying member method code with example usage: obj.tensorizer_script_impl() and returns: self