code: def test_with_conflicting_labels(dataset_name, request):
      # Arrange
      dataset = request.getfixturevalue(dataset_name)
      expected_ratio = dataset.ambiguous_samples_ratio
      expected_ambiguous_samples = dataset.ambiguous_samples
      dataset = dataset.dataset
      check = (
          ConflictingLabels().add_condition_ratio_of_conflicting_labels_less_or_equal()
      )
  
      # Act
      result = check.run(dataset=dataset)
      conditions_decisions = check.conditions_decision(result)
  
      # Assert
      assert_that(
          result.value,
          has_entries(
              {
                  "percent_of_conflicting_samples": equal_to(
                      expected_ratio
                  ),
                  "conflicting_samples": instance_of(pd.DataFrame),
              }
          ),
      )
  
      assert_that(
          conditions_decisions[0],
          equal_condition_result(
              is_pass=False,
              details=f"Ratio of samples with conflicting labels: {format_percent(expected_ratio)}",
              name="Ambiguous sample ratio is less or equal to 0%",
          ),
      )  # type: ignore
  
      ambiguous_samples = result.value["conflicting_samples"]
      assert_result_dataframe(
          ambiguous_samples, expected_ambiguous_samples
      )
      assert_display_table(result.display)
  
imports: import typing
  import pandas
  import pytest
  from hamcrest import *
  from deepchecks.nlp.checks import ConflictingLabels
  from deepchecks.nlp.text_data import TextData
  from deepchecks.utils.strings import format_percent
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 6 for calling a global function with example usage: test_with_conflicting_labels(dataset_name, request) without return types