code: def test_tokenize_context(self):
      encoded_output = EncodedOutput(
          token_ids=[
              [40478, 40481],
              [1180, 40482],
              [535, 40483],
              [808, 40484],
              [289, 40485],
              [17164, 40486],
              [40480, 40487],
          ],
          tokens=[
              40478,
              "everything</w>",
              "'s</w>",
              "only</w>",
              "$</w>",
              "80</w>",
              40480,
          ],
          token_ends=[-1, 10, 12, 17, 19, 21, -1],
          token_starts=[-1, 0, 10, 13, 18, 19, -1],
      )
      context = [
          {
              "text": "everything's",
              "start": 0,
              "end": 12,
              "left": 10,
              "bold": False,
          },
          {
              "text": "only",
              "start": 13,
              "end": 17,
              "left": 20,
              "bold": False,
          },
          {
              "text": "$80",
              "start": 18,
              "end": 21,
              "left": 30,
              "bold": True,
          },
      ]
      config = get_config(
          **{"default_context": {"left": 0, "bold": False}}
      )
      expanded_context = tokenize_context(
          context, encoded_output, config
      )
      expected = [
          [False, 0],
          [False, 10],
          [False, 10],
          [False, 20],
          [True, 30],
          [True, 30],
          [False, 0],
      ]
      np.testing.assert_array_equal(expected, expanded_context)
  
imports: import os
  import tensorflow
  import pandas
  import numpy
  import warnings
  import unittest
  import spacy
  import random
  import json
  import copy
  import shutil
  from sklearn.metrics import accuracy_score, recall_score
  from sklearn.model_selection import train_test_split
  from finetune import Classifier, SequenceLabeler, Comparison, ComparisonRegressor, MultipleChoice
  from finetune.base_models import TextCNN, BERTModelCased, GPT2Model, GPTModel, RoBERTa, GPT
  from finetune.config import get_config
  from finetune.util.metrics import sequence_labeling_token_precision, sequence_labeling_token_recall
  from finetune.datasets.reuters import Reuters
  from finetune.encoding.input_encoder import tokenize_context, EncodedOutput
label_desc: Use image node_id 1 for calling the TestAuxiliaryTokenization obj's underlying member method code with example usage: obj.test_tokenize_context() without return types