code: def test_drift_with_nulls_not_enough_samples_in_numerical_columns(
      drifted_data_with_nulls,
  ):
      # Arrange
      train, test = drifted_data_with_nulls
  
      # Cramer's V with ignore_na=True:
  
      # Act
      check = FeatureDrift(min_samples=999, ignore_na=False)
      result = check.run(train, test)
  
      assert_that(
          result.value["numeric_with_drift"]["Drift score"],
          equal_to(None),
      )
      assert_that(
          result.display[3],
          equal_to(
              "<span>The following columns do not have enough samples to calculate drift score: "
              "['numeric_without_drift', 'numeric_with_drift']</span>"
          ),
      )
  
imports: import numpy
  import pandas
  from hamcrest import assert_that, calling, close_to, equal_to, greater_than, has_entries, has_item, has_length, is_not, raises
  from deepchecks.core.errors import NotEnoughSamplesError
  from deepchecks.tabular.checks import FeatureDrift
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 7 for calling a global function with example usage: test_drift_with_nulls_not_enough_samples_in_numerical_columns(drifted_data_with_nulls) without return types