code: def numberize(self, row):
      self.total += 1
      numberized_row_tuple = super().numberize(row)
      try:
          tup = numberized_row_tuple + (
              self._get_token_logits(
                  row[self.start_logits_column],
                  row[self.pad_mask_column],
              ),
              self._get_token_logits(
                  row[self.end_logits_column], row[self.pad_mask_column]
              ),
              row[self.has_answer_logits_column],
          )
      except KeyError:
          # Logits for KD Tensorizer not provided, using padding.
          tup = numberized_row_tuple + (
              [self.vocab.get_pad_index()]
              * len(numberized_row_tuple[0]),
              [self.vocab.get_pad_index()]
              * len(numberized_row_tuple[0]),
              [self.vocab.get_pad_index()] * 2,
          )
  
      try:
          assert len(tup[0]) == len(tup[6])
      except AssertionError:
          self.mismatches += 1
          print(
              f"len(tup[0]) = {len(tup[0])} and len(tup[6]) = {len(tup[6])}",
              flush=True,
          )
          raise
      return tup
  
imports: import itertools
  from typing import List
  import torch
  from pytext.data.bert_tensorizer import BERTTensorizer
  from pytext.data.roberta_tensorizer import RoBERTaTensorizer
  from pytext.data.tensorizers import lookup_tokens
  from pytext.data.utils import pad_and_tensorize
  from pytext.torchscript.tensorizer import ScriptRoBERTaTensorizerWithIndices
  from pytext.torchscript.vocab import ScriptVocabulary
label_desc: Use image node_id 4 for calling the SquadForBERTTensorizerForKD obj's underlying member method code with example usage: obj.numberize(row) and returns: tup