code: def test_calculate_average_sentence_length_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=["Average Words Per Sentence"]
      )[0]
      result_none_text = calculate_builtin_properties(
          [None], include_properties=["Average Words Per Sentence"]
      )[0]
  
      # Assert
      assert_that(
          result["Average Words Per Sentence"][0:10],
          equal_to(
              [5.667, 7.0, 11.0, 12.0, 8.0, 19.0, 3.0, 9.0, 11.5, 7.333]
          ),
      )
      assert_that(
          result_none_text["Average Words Per Sentence"],
          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 10 for calling a global function with example usage: test_calculate_average_sentence_length_property(tweet_emotion_train_test_textdata) without return types