code: def test_binary_classification(binary_mock_dataset_and_probabilities):
      # Arrange
      (
          text_data,
          proba_train,
          proba_test,
      ) = binary_mock_dataset_and_probabilities
      model_classes = {0: "negative", 1: "positive"}
      preds_train = [
          model_classes[x] for x in np.argmax(proba_train, axis=1)
      ]
      preds_test = [
          model_classes[x] for x in np.argmax(proba_test, axis=1)
      ]
      check = PredictionDrift()
  
      # Act
      result = check.run(
          text_data,
          text_data,
          train_predictions=preds_train,
          test_predictions=preds_test,
      )
  
      # Assert
      assert_that(result.value["Drift score"], close_to(0.327, 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 9 for calling a global function with example usage: test_binary_classification(binary_mock_dataset_and_probabilities) without return types