code: def test_just_dance_small_drift(
      just_dance_train_test_textdata_sampled,
  ):
      # Arrange
      train, test = just_dance_train_test_textdata_sampled
      check = PredictionDrift().add_condition_drift_score_less_than(0.1)
  
      # Act
      result = check.run(
          train,
          test,
          train_predictions=np.asarray(train.label),
          test_predictions=np.asarray(test.label),
      )
      condition_result = check.conditions_decision(result)
  
      # Assert
      assert_that(
          condition_result,
          has_items(
              equal_condition_result(
                  is_pass=True,
                  details="Found model prediction Cramer's V drift score of 0.07",
                  name="Prediction drift score < 0.1",
              )
          ),
      )
  
      assert_that(result.value["Drift score"], close_to(0.07, 0.01))
  
file_path: deepchecks/tests/nlp/checks/model_evaluation/prediction_drift_test.py
imports: import numpy
  import pytest
  from hamcrest import assert_that, close_to, equal_to, has_items, has_length
  from deepchecks.nlp import TextData
  from deepchecks.nlp.checks import PredictionDrift
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 4 for calling a global function with example usage: test_just_dance_small_drift(just_dance_train_test_textdata_sampled) without return types