code: def test_non_numeric_values_for_properties():
      # Arrange
      raw_text = ["This is an example.", "Another example here."] * 6
      labels = ["positive", "negative"] * 6
      task_type = "text_classification"
      text_data = TextData(
          raw_text=raw_text, label=labels, task_type=task_type
      )
      text_data.calculate_builtin_properties(
          include_properties=[
              "Sentences Count",
              "Average Word Length",
              "Text Length",
          ]
      )
      text_data.properties["Sentences Count"].iloc[9] = "as"
      text_data.properties["Average Word Length"].iloc[9] = 19
      text_data.properties["Average Word Length"].iloc[8] = "90"
      text_data.properties["Text Length"].iloc[8] = ["90", "asdh", None]
  
      # Act
      check = TextPropertyOutliers()
      result = check.run(text_data)
  
      # Assert
      assert_that(
          result.value["Sentences Count"],
          equal_to("Numeric property contains non-numeric values."),
      )
      assert_that(
          len(result.value["Average Word Length"]["indices"]),
          equal_to(2),
      )
      assert_that(
          result.value["Average Word Length"]["lower_limit"],
          equal_to(3.75),
      )
      assert_that(
          result.value["Average Word Length"]["upper_limit"],
          equal_to(10.5),
      )
      assert_that(
          result.value["Text Length"],
          equal_to("Numeric property contains non-numeric values."),
      )
  
file_path: deepchecks/tests/nlp/checks/data_integrity/text_property_outliers_test.py
imports: import numpy
  import pandas
  from hamcrest import assert_that, close_to, equal_to
  from deepchecks.nlp.checks import TextPropertyOutliers
  from deepchecks.nlp.text_data import TextData
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 5 for calling a global function with example usage: test_non_numeric_values_for_properties() without return types