code: def featurize(self, tokenizer, feat_spec):
      if feat_spec.sep_token_extra:
          maybe_extra_sep = [tokenizer.sep_token]
          maybe_extra_sep_segment_id = [feat_spec.sequence_a_segment_id]
          special_tokens_count = 4  # CLS, SEP-SEP, SEP
      else:
          maybe_extra_sep = []
          maybe_extra_sep_segment_id = []
          special_tokens_count = 3  # CLS, SEP, SEP
  
      passage, question = truncate_sequences(
          tokens_ls=[self.passage, self.question],
          max_length=feat_spec.max_seq_length - special_tokens_count,
      )
      assert (
          len(passage) >= self.answer_token_span[1]
      ), f"Answer span {self.answer_token_span} truncated, please raise max_seq_length."
      unpadded_inputs = add_cls_token(
          unpadded_tokens=(
              passage
              + [tokenizer.sep_token]
              + maybe_extra_sep
              + question
              + [tokenizer.sep_token]
          ),
          unpadded_segment_ids=(
              [feat_spec.sequence_a_segment_id] * (len(passage) + 1)
              + maybe_extra_sep_segment_id
              + [feat_spec.sequence_b_segment_id] * (len(question) + 1)
          ),
          tokenizer=tokenizer,
          feat_spec=feat_spec,
      )
      gt_span_idxs = list(
          map(
              lambda x: x + unpadded_inputs.cls_offset,
              self.answer_token_span,
          )
      )
      input_set = create_input_set_from_tokens_and_segments(
          unpadded_tokens=unpadded_inputs.unpadded_tokens,
          unpadded_segment_ids=unpadded_inputs.unpadded_segment_ids,
          tokenizer=tokenizer,
          feat_spec=feat_spec,
      )
      pred_span_mask = pad_to_max_seq_length(
          ls=[0] * unpadded_inputs.cls_offset + [1] * len(passage),
          max_seq_length=feat_spec.max_seq_length,
          pad_idx=0,
          pad_right=not feat_spec.pad_on_left,
      )
      token_idx_to_char_idx_start = pad_to_max_seq_length(
          ls=[-1] * unpadded_inputs.cls_offset
          + (self.token_idx_to_char_idx_map > 0)
          .argmax(axis=1)
          .tolist()[: len(passage)],
          max_seq_length=feat_spec.max_seq_length,
          pad_idx=-1,
          pad_right=not feat_spec.pad_on_left,
      )
      token_idx_to_char_idx_end = pad_to_max_seq_length(
          ls=[-1] * unpadded_inputs.cls_offset
          + self.token_idx_to_char_idx_map.cumsum(axis=1)
          .argmax(axis=1)
          .tolist()[: len(passage)],
          max_seq_length=feat_spec.max_seq_length,
          pad_idx=-1,
          pad_right=not feat_spec.pad_on_left,
      )
      # When there are multiple greatest elements, argmax will return the index of the first one.
      # So, (x > 0).argmax() will return the index of the first non-zero element in an array,
      # token_idx_to_char_idx_start is computed in this way to map each token index to the
      # beginning char index of that token. On the other side, x.cumsum().argmax() will return
      # the index of the last non-zero element in an array, token_idx_to_char_idx_end is
      # computed in this way to map each token index to ending char index.
      # Once the model predict a span over the token index, these to mapping will help to project
      # the span back to char index, and slice the predicted answer string from the input text.
  
      return DataRow(
          guid=self.guid,
          input_ids=np.array(input_set.input_ids),
          input_mask=np.array(input_set.input_mask),
          segment_ids=np.array(input_set.segment_ids),
          gt_span_str=self.answer_str,
          gt_span_idxs=np.array(gt_span_idxs),
          selection_str=self.passage_str,
          selection_token_mask=np.array(pred_span_mask),
          token_idx_to_char_idx_start=np.array(
              token_idx_to_char_idx_start
          ),
          token_idx_to_char_idx_end=np.array(token_idx_to_char_idx_end),
      )
  
imports: from abc import ABC
  import numpy
  import torch
  from dataclasses import dataclass
  from typing import List, Tuple
  import jiant.shared.model_resolution
  from jiant.tasks.core import Task, TaskTypes, BaseExample, BaseTokenizedExample, BaseDataRow, BatchMixin
  from jiant.tasks.lib.templates.shared import create_input_set_from_tokens_and_segments, add_cls_token
  from jiant.tasks.utils import truncate_sequences, pad_to_max_seq_length
  from jiant.utils.retokenize import TokenAligner
label_desc: Use image node_id 1 for calling the TokenizedExample obj's underlying member method code with example usage: obj.featurize(tokenizer, feat_spec) and returns: DataRow