code: def test_train_test_simple_mix_with_categorical_data(iris_clean):
      # Arrange
      x, y = iris_clean.data, iris_clean.target
  
      x["cat_column"] = x["sepal length (cm)"].astype("category")
      x["cat_column"][::2] = np.nan
  
      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 + ["cat_column"],
          label="target",
      )
      test_dataset = Dataset(
          pd.concat([x_test, y_test], axis=1),
          features=iris_clean.feature_names + ["cat_column"],
          label="target",
      )
      # Run
      TrainTestSamplesMix().run(
          test_dataset=test_dataset, train_dataset=train_dataset
      )
  
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 9 for calling a global function with example usage: test_train_test_simple_mix_with_categorical_data(iris_clean) without return types