code: def test_leakage_without_display(iris_clean):
      x = iris_clean.data
      y = iris_clean.target
      x_train, x_test, y_train, y_test = train_test_split(
          x, y, test_size=0.3, random_state=55
      )
      train_dataset = Dataset(
          pd.concat([x_train, y_train], axis=1),
          features=iris_clean.feature_names,
          label="target",
      )
  
      test_df = pd.concat([x_test, y_test], axis=1)
      bad_test = test_df.append(
          train_dataset.data.iloc[[0, 1, 2, 3, 4]], ignore_index=True
      )
  
      test_dataset = Dataset(
          bad_test, features=iris_clean.feature_names, label="target"
      )
      # Arrange
      check = TrainTestSamplesMix()
      # Act X
      result = check.run(
          test_dataset=test_dataset,
          train_dataset=train_dataset,
          with_display=False,
      )
      # Assert
      assert_that(result.value, has_entry("ratio", 0.1))
      assert_that(result.display, has_length(0))
  
file_path: deepchecks/tests/tabular/checks/train_test_validation/train_test_samples_mix_test.py
imports: import numpy
  import pandas
  from hamcrest import assert_that, calling, equal_to, greater_than, has_entry, has_items, has_length, raises
  from sklearn.model_selection import train_test_split
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.tabular.checks.train_test_validation import TrainTestSamplesMix
  from deepchecks.tabular.dataset import Dataset
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 5 for calling a global function with example usage: test_leakage_without_display(iris_clean) without return types