code: def test_condition_pass(diabetes_split_dataset_and_model):
      train, _, clf = diabetes_split_dataset_and_model
      train = Dataset(
          train.data.copy(), label="target", cat_features=["sex"]
      )
  
      # Arrange
      check = SpecialCharacters(
          n_top_columns=3
      ).add_condition_ratio_of_special_characters_less_or_equal()
      # Act
      results = check.conditions_decision(check.run(train))
      # Assert
      assert_that(
          results,
          has_items(
              equal_condition_result(
                  is_pass=True,
                  details="Passed for 11 relevant columns",
                  name="Ratio of samples containing solely special character is less or equal to 0.1%",
              )
          ),
      )
  
imports: import numpy
  import pandas
  from hamcrest import assert_that, calling, equal_to, has_items, has_length, raises
  from deepchecks.core import ConditionCategory
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.tabular.checks.data_integrity.special_chars import SpecialCharacters
  from deepchecks.tabular.dataset import Dataset
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 14 for calling a global function with example usage: test_condition_pass(diabetes_split_dataset_and_model) without return types