code: def featurize(self, tokenizer, feat_spec):
      unpadded_inputs = construct_single_input_tokens_and_segment_ids(
          input_tokens=self.text,
          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,
      )
  
      # Replicate padding / additional tokens for the label ids and mask
      if feat_spec.sep_token_extra:
          label_suffix = [None, None]
          mask_suffix = [0, 0]
          special_tokens_count = 3  # CLS, SEP-SEP
      else:
          label_suffix = [None]
          mask_suffix = [0]
          special_tokens_count = 2  # CLS, SEP
      unpadded_labels = (
          [None]
          + self.labels[
              : feat_spec.max_seq_length - special_tokens_count
          ]
          + label_suffix
      )
      unpadded_labels = [
          i if i is not None else -1 for i in unpadded_labels
      ]
      unpadded_label_mask = (
          [0]
          + self.label_mask[
              : feat_spec.max_seq_length - special_tokens_count
          ]
          + mask_suffix
      )
  
      padded_labels = pad_single_with_feat_spec(
          ls=unpadded_labels,
          feat_spec=feat_spec,
          pad_idx=-1,
      )
      padded_label_mask = pad_single_with_feat_spec(
          ls=unpadded_label_mask,
          feat_spec=feat_spec,
          pad_idx=0,
      )
  
      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_ids=np.array(padded_labels),
          label_mask=np.array(padded_label_mask),
          tokens=unpadded_inputs.unpadded_tokens,
      )
  
imports: import numpy
  import torch
  from dataclasses import dataclass
  from typing import List, Union
  from jiant.tasks.core import BaseExample, BaseTokenizedExample, BaseDataRow, BatchMixin, Task, TaskTypes
  from jiant.tasks.lib.templates.shared import labels_to_bimap, create_input_set_from_tokens_and_segments, construct_single_input_tokens_and_segment_ids, pad_single_with_feat_spec
  from jiant.tasks.lib.templates import hacky_tokenization_matching
  from jiant.utils.python.io import read_json
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