class_docstr: Tensorizer for BERT tasks.  Works for single sentence, sentence pair, triples etc.
class_docstr_tok: ["Tensorizer","for","BERT","tasks",".","Works","for","single","sentence",",","sentence","pair",",","triples","etc","."]
code: def __init__(
      self,
      columns: List[str] = Config.columns,
      vocab: Vocabulary = None,
      tokenizer: Tokenizer = None,
      max_seq_len: int = Config.max_seq_len,
      **kwargs,
  ) -> None:
      super().__init__(
          columns=columns,
          vocab=vocab,
          tokenizer=tokenizer,
          max_seq_len=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 2 to create a new BERTTensorizer object from inherited base classes: BERTTensorizerBase with example: obj = BERTTensorizer(columns, vocab, tokenizer, max_seq_len)