code: def test_tweet_dataset(tweet_emotion_train_test_textdata_sampled):
      # Arrange
      _, text_data = tweet_emotion_train_test_textdata_sampled
      check = (
          SpecialCharacters().add_condition_samples_ratio_w_special_characters_less_or_equal()
      )
  
      # Act
      result = check.run(dataset=text_data)
      conditions_decision = check.conditions_decision(result)
  
      # Assert
      assert_that(
          result.value,
          has_entries(
              {
                  "samples_per_special_char": has_entries(
                      {"😍": [71, 614, 1813, 1901]}
                  ),
                  "percent_of_samples_with_special_chars": equal_to(
                      0.168
                  ),
                  "percent_special_chars_per_sample": has_length(500),
              }
          ),
      )
  
      assert_that(result.display, has_length(3))
  
      assert_that(
          conditions_decision[0],
          equal_condition_result(
              is_pass=True,
              details="Found 1 samples with special char ratio above threshold",
              name="Ratio of samples containing more than 20% special characters is below 5%",
          ),
      )
  
file_path: deepchecks/tests/nlp/checks/data_integrity/special_characters_test.py
imports: import pytest
  from hamcrest import *
  from deepchecks.nlp.checks.data_integrity.special_characters import SpecialCharacters
  from deepchecks.nlp.text_data import TextData
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 6 for calling a global function with example usage: test_tweet_dataset(tweet_emotion_train_test_textdata_sampled) without return types