code: def test_check_execution(train_test_datasets: TrainTestSplit):
      # Arrange
      train = train_test_datasets.train
      test = train_test_datasets.test
      check = TrainTestSamplesMix().add_condition_duplicates_ratio_less_or_equal(
          0
      )
  
      # Act
      result = check.run(train_dataset=train, test_dataset=test)
      conditions_decisions = check.conditions_decision(result)
  
      # Assert
      assert_that(
          result.value,
          has_entries(
              {
                  "ratio": 0.0,
                  "duplicates": instance_of(pd.DataFrame),
              }
          ),
      )
  
      assert_that(
          conditions_decisions[0],
          equal_condition_result(
              is_pass=True,
              details="No samples mix found",
              name=(
                  "Percentage of test data samples that appear "
                  "in train data is less or equal to 0%"
              ),
          ),  # type: ignore
      )
  
      duplicates = result.value["duplicates"]
      assert_that(len(duplicates), equal_to(0))
      assert_result_dataframe(duplicates)
  
imports: import typing
  import pandas
  import pytest
  from hamcrest import *
  from deepchecks.nlp.checks import TrainTestSamplesMix
  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 3 for calling a global function with example usage: test_check_execution(train_test_datasets) without return types