class_docstr: Produces inputs and answer spans for Squad.
class_docstr_tok: ["Produces","inputs","and","answer","spans","for","Squad","."]
code: def __init__(
      self,
      doc_tensorizer: TokenTensorizer,
      ques_tensorizer: TokenTensorizer,
      doc_column: str = Config.doc_column,
      ques_column: str = Config.ques_column,
      answers_column: str = Config.answers_column,
      answer_starts_column: str = Config.answer_starts_column,
      **kwargs,
  ):
      super().__init__(text_column=None, **kwargs)
      self.ques_tensorizer = ques_tensorizer
      self.doc_tensorizer = doc_tensorizer
      self.doc_column = doc_column
      self.ques_column = ques_column
      self.answers_column = answers_column
      self.answer_starts_column = answer_starts_column
  
imports: from typing import List
  import torch
  from pytext.common.constants import SpecialTokens
  from pytext.config.component import ComponentType, create_component
  from pytext.data.tensorizers import TokenTensorizer
  from pytext.data.tokenizers import Tokenizer, WordPieceTokenizer
  from pytext.data.utils import pad_and_tensorize, VocabBuilder, Vocabulary
label_desc: Use image node_id 2 to create a new SquadTensorizer object from inherited base classes: TokenTensorizer with example: obj = SquadTensorizer(doc_tensorizer, ques_tensorizer, doc_column, ques_column, answers_column, answer_starts_column)