code: def test_tweet_emotion(
      tweet_emotion_train_test_textdata,
      tweet_emotion_train_test_predictions,
  ):
      # Arrange
      train, test = tweet_emotion_train_test_textdata
      train_preds, test_preds = tweet_emotion_train_test_predictions
      check = PredictionDrift().add_condition_drift_score_less_than(
          0.01
      )
      # Act
      result = check.run(
          train,
          test,
          train_predictions=train_preds,
          test_predictions=test_preds,
      )
      condition_result = check.conditions_decision(result)
  
      # Assert
      assert_that(
          condition_result,
          has_items(
              equal_condition_result(
                  is_pass=False,
                  details="Found model prediction Cramer's V drift score of 0.04",
                  name="Prediction drift score < 0.01",
              )
          ),
      )
  
      assert_that(result.value["Drift score"], close_to(0.04, 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 1 for calling a global function with example usage: test_tweet_emotion(tweet_emotion_train_test_textdata, tweet_emotion_train_test_predictions) without return types