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","."]
docstr: This function contains logic for converting tokens into ids based on
  the specified vocab. It also outputs, for each instance, the vectors
  needed to run the actual model.
code: def numberize(self, row: Dict) -> Tuple[Any, ...]:
      """
      This function contains logic for converting tokens into ids based on
      the specified vocab. It also outputs, for each instance, the vectors
      needed to run the actual model.
      """
      per_sentence_tokens = [
          self.tokenizer.tokenize(row[column])
          for column in self.columns
      ]
      return self.tensorizer_script_impl.numberize(per_sentence_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 4 for calling the BERTTensorizerBase obj's underlying member method code with example usage: obj.numberize(row) and returns: self