code: def test_calculate_readability_score_property(
      tweet_emotion_train_test_textdata,
  ):
      # Arrange
      _, test = tweet_emotion_train_test_textdata
      test_text = test.text
  
      # Act
      result = calculate_builtin_properties(
          test_text, include_properties=["Reading Ease"]
      )[0]
      result_none_text = calculate_builtin_properties(
          [None], include_properties=["Reading Ease"]
      )[0]
  
      # Assert
      assert_that(
          result["Reading Ease"][0:10],
          equal_to(
              [
                  96.577,
                  97.001,
                  80.306,
                  67.755,
                  77.103,
                  71.782,
                  np.nan,
                  75.5,
                  70.102,
                  95.564,
              ]
          ),
      )
      assert_that(result_none_text["Reading Ease"], equal_to([np.nan]))
  
imports: import os
  import pathlib
  import timeit
  import uuid
  import numpy
  import pytest
  from hamcrest import *
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.nlp.utils.text_properties import TOXICITY_MODEL_NAME_ONNX, _sample_for_property, calculate_builtin_properties, english_text
  from deepchecks.nlp.utils.text_properties_models import MODELS_STORAGE, _get_transformer_model_and_tokenizer
label_desc: Use image node_id 12 for calling a global function with example usage: test_calculate_readability_score_property(tweet_emotion_train_test_textdata) without return types