code: def double_sentence_featurize(
      guid: str,
      input_tokens_a: List[str],
      input_tokens_b: List[str],
      label_id: int,
      tokenizer,
      feat_spec: FeaturizationSpec,
      data_row_class,
  ):
      """Featurize an example for a two-input/two-sentence task, and return the example as a DataRow.
  
      Args:
          guid (str): human-readable identifier for interpretability and debugging.
          input_tokens_a (List[str]): sequence of tokens in segment a.
          input_tokens_b (List[str]): sequence of tokens in segment b.
          label_id (int): int representing the label for the task.
          tokenizer:
          feat_spec (FeaturizationSpec): Tokenization-related metadata.
          data_row_class (DataRow): DataRow class used in the task.
  
      Returns:
          DataRow representing an example.
  
      """
      unpadded_inputs = construct_double_input_tokens_and_segment_ids(
          input_tokens_a=input_tokens_a,
          input_tokens_b=input_tokens_b,
          tokenizer=tokenizer,
          feat_spec=feat_spec,
      )
  
      return create_generic_data_row_from_tokens_and_segments(
          guid=guid,
          unpadded_tokens=unpadded_inputs.unpadded_tokens,
          unpadded_segment_ids=unpadded_inputs.unpadded_segment_ids,
          label_id=label_id,
          tokenizer=tokenizer,
          feat_spec=feat_spec,
          data_row_class=data_row_class,
      )
  
imports: import numpy
  from dataclasses import dataclass
  from typing import List, NamedTuple
  from jiant.tasks.core import FeaturizationSpec
  from jiant.tasks.utils import truncate_sequences, pad_to_max_seq_length
  from jiant.utils.python.datastructures import BiMap
label_desc: Use image node_id 2 for calling a global function with example usage: double_sentence_featurize(guid, input_tokens_a, input_tokens_b, label_id, tokenizer, feat_spec, data_row_class) and returns: create_generic_data_row_from_tokens_and_segments