code: def featurize(self, tokenizer, feat_spec):
      # Label not label_id, otherwise we can use double_sentence_featurize
      unpadded_inputs = construct_double_input_tokens_and_segment_ids(
          input_tokens_a=self.text_a,
          input_tokens_b=self.text_b,
          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,
      )
      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),
          label=self.label,
          tokens=unpadded_inputs.unpadded_tokens,
      )
  
imports: import numpy
  import torch
  from dataclasses import dataclass
  from typing import List
  from jiant.tasks.core import BaseExample, BaseTokenizedExample, BaseDataRow, BatchMixin, GlueMixin, Task, TaskTypes
  from jiant.tasks.lib.templates.shared import construct_double_input_tokens_and_segment_ids, create_input_set_from_tokens_and_segments
  from jiant.utils.python.io import read_jsonl
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