code: def featurize(self, tokenizer, feat_spec):
      special_tokens_count = 2  # CLS, SEP
  
      (tokens,) = truncate_sequences(
          tokens_ls=[self.tokens],
          max_length=feat_spec.max_seq_length - special_tokens_count,
      )
  
      unpadded_tokens = tokens + [tokenizer.sep_token]
      unpadded_segment_ids = [feat_spec.sequence_a_segment_id] * (
          len(self.tokens) + 1
      )
  
      unpadded_inputs = add_cls_token(
          unpadded_tokens=unpadded_tokens,
          unpadded_segment_ids=unpadded_segment_ids,
          tokenizer=tokenizer,
          feat_spec=feat_spec,
      )
  
      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,
      )
      span1_span = ExclusiveSpan(
          start=self.span1_span[0] + unpadded_inputs.cls_offset,
          end=self.span1_span[1] + unpadded_inputs.cls_offset,
      ).to_inclusive()
      span2_span = ExclusiveSpan(
          start=self.span2_span[0] + unpadded_inputs.cls_offset,
          end=self.span2_span[1] + unpadded_inputs.cls_offset,
      ).to_inclusive()
  
      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),
          spans=np.array([span1_span, span2_span]),
          label_id=self.label_id,
          tokens=unpadded_inputs.unpadded_tokens,
          span1_text=self.span1_text,
          span2_text=self.span2_text,
      )
  
imports: import numpy
  import torch
  from dataclasses import dataclass
  from typing import List
  from jiant.tasks.core import BaseExample, BaseTokenizedExample, BaseDataRow, BatchMixin, SuperGlueMixin, Task, TaskTypes
  from jiant.tasks.lib.templates.shared import labels_to_bimap, add_cls_token, create_input_set_from_tokens_and_segments
  from jiant.tasks.utils import truncate_sequences, ExclusiveSpan
  from jiant.utils import retokenize
  from jiant.utils.python.io import read_json_lines
  from jiant.utils.tokenization_normalization import normalize_tokenizations
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