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(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,
      )
  
      # exclusive spans are converted to inclusive spans for use with SelfAttentiveSpanExtractor
      span = ExclusiveSpan(
          start=self.span[0] + unpadded_inputs.cls_offset,
          end=self.span[1] + unpadded_inputs.cls_offset,
      ).to_inclusive()
  
      binary_label_ids = np.zeros((self.label_num,), dtype=int)
      for label_id in self.label_ids:
          binary_label_ids[label_id] = 1
  
      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([span]),
          label_ids=binary_label_ids,
          tokens=unpadded_inputs.unpadded_tokens,
          span_text=self.span_text,
      )
  
imports: from abc import ABC
  import numpy
  import torch
  from dataclasses import dataclass
  from typing import List, Tuple
  from jiant.tasks.core import BaseExample, BaseTokenizedExample, BaseDataRow, BatchMixin, Task, TaskTypes
  from jiant.tasks.lib.templates.shared import create_input_set_from_tokens_and_segments, add_cls_token
  from jiant.tasks.utils import ExclusiveSpan, truncate_sequences
  from jiant.utils import retokenize
  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