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 = 6  # CLS, SEP-SEP, SEP-SEP, SEP
      else:
          maybe_extra_sep = []
          maybe_extra_sep_segment_id = []
          special_tokens_count = 4  # CLS, SEP, SEP, SEP
  
      input_obs1_a, input_hyp1_a, input_obs2_a = truncate_sequences(
          tokens_ls=[self.input_obs1, self.input_hyp1, self.input_obs2],
          max_length=feat_spec.max_seq_length
          - special_tokens_count
          - 1,
          # -1 for self.question
      )
      input_obs1_b, input_hyp2_b, input_obs2_b = truncate_sequences(
          tokens_ls=[self.input_obs1, self.input_hyp2, self.input_obs2],
          max_length=feat_spec.max_seq_length
          - special_tokens_count
          - 1,
          # -1 for self.question
      )
  
      unpadded_inputs_1 = add_cls_token(
          unpadded_tokens=(
              input_obs1_a
              + [tokenizer.sep_token]
              + maybe_extra_sep
              + input_hyp1_a
              + [tokenizer.sep_token]
              + maybe_extra_sep
              + input_obs2_a
              + [tokenizer.sep_token]
          ),
          unpadded_segment_ids=(
              # question + sep(s)
              [feat_spec.sequence_a_segment_id]
              * (len(input_obs1_a) + 1)
              + maybe_extra_sep_segment_id
              # premise + sep(s)
              + [feat_spec.sequence_a_segment_id]
              * (len(input_hyp1_a) + 1)
              + maybe_extra_sep_segment_id
              # choice + sep
              + [feat_spec.sequence_b_segment_id]
              * (len(input_obs2_a) + 1)
          ),
          tokenizer=tokenizer,
          feat_spec=feat_spec,
      )
  
      unpadded_inputs_2 = add_cls_token(
          unpadded_tokens=(
              input_obs1_b
              + [tokenizer.sep_token]
              + maybe_extra_sep
              + input_hyp2_b
              + [tokenizer.sep_token]
              + maybe_extra_sep
              + input_obs2_b
              + [tokenizer.sep_token]
          ),
          unpadded_segment_ids=(
              # question + sep(s)
              [feat_spec.sequence_a_segment_id]
              * (len(input_obs1_b) + 1)
              + maybe_extra_sep_segment_id
              # premise + sep(s)
              + [feat_spec.sequence_a_segment_id]
              * (len(input_hyp2_b) + 1)
              + maybe_extra_sep_segment_id
              # choice + sep
              + [feat_spec.sequence_b_segment_id]
              * (len(input_obs2_b) + 1)
          ),
          tokenizer=tokenizer,
          feat_spec=feat_spec,
      )
  
      input_set1 = create_input_set_from_tokens_and_segments(
          unpadded_tokens=unpadded_inputs_1.unpadded_tokens,
          unpadded_segment_ids=unpadded_inputs_1.unpadded_segment_ids,
          tokenizer=tokenizer,
          feat_spec=feat_spec,
      )
      input_set2 = create_input_set_from_tokens_and_segments(
          unpadded_tokens=unpadded_inputs_2.unpadded_tokens,
          unpadded_segment_ids=unpadded_inputs_2.unpadded_segment_ids,
          tokenizer=tokenizer,
          feat_spec=feat_spec,
      )
      return DataRow(
          guid=self.guid,
          input_ids=np.stack(
              [input_set1.input_ids, input_set2.input_ids]
          ),
          input_mask=np.stack(
              [input_set1.input_mask, input_set2.input_mask]
          ),
          segment_ids=np.stack(
              [input_set1.segment_ids, input_set2.segment_ids]
          ),
          label_id=self.label_id,
          tokens1=unpadded_inputs_1.unpadded_tokens,
          tokens2=unpadded_inputs_2.unpadded_tokens,
      )
  
imports: import numpy
  import torch
  from dataclasses import dataclass
  from typing import List
  from jiant.tasks.core import BaseExample, BaseTokenizedExample, BaseDataRow, BatchMixin, 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
  from jiant.utils.python.io import read_json_lines
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