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
  
      input_set_ls = []
      unpadded_inputs_ls = []
      for choice in self.choice_list:
          prompt, choice = truncate_sequences(
              tokens_ls=[self.prompt, choice],
              max_length=feat_spec.max_seq_length
              - special_tokens_count,
          )
          unpadded_inputs = add_cls_token(
              unpadded_tokens=(
                  # choice
                  choice
                  + [tokenizer.sep_token]
                  + maybe_extra_sep
                  # prompt
                  + prompt
                  + [tokenizer.sep_token]
              ),
              unpadded_segment_ids=(
                  # choice
                  [feat_spec.sequence_a_segment_id] * (len(choice) + 1)
                  + maybe_extra_sep_segment_id
                  # prompt + sep
                  + [feat_spec.sequence_b_segment_id]
                  * (len(prompt) + 1)
              ),
              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,
          )
          input_set_ls.append(input_set)
          unpadded_inputs_ls.append(unpadded_inputs)
  
      return DataRow(
          guid=self.guid,
          input_ids=np.stack(
              [input_set.input_ids for input_set in input_set_ls]
          ),
          input_mask=np.stack(
              [input_set.input_mask for input_set in input_set_ls]
          ),
          segment_ids=np.stack(
              [input_set.segment_ids for input_set in input_set_ls]
          ),
          label_id=self.label_id,
          tokens_list=[
              unpadded_inputs.unpadded_tokens
              for unpadded_inputs in unpadded_inputs_ls
          ],
      )
  
imports: from dataclasses import dataclass
  import numpy
  from jiant.tasks.lib.templates.shared import labels_to_bimap
  from jiant.tasks.lib.templates import multiple_choice
  from jiant.utils.python.io import read_json_lines
  from jiant.tasks.lib.templates.shared import create_input_set_from_tokens_and_segments, add_cls_token
  from jiant.tasks.utils import truncate_sequences
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