code: def test_condition_misclassified_samples_lower_than_raises_error(
      tweet_emotion_train_test_textdata,
      tweet_emotion_train_test_predictions,
  ):
      # Arrange
      _, test_ds = tweet_emotion_train_test_textdata
      _, test_preds = tweet_emotion_train_test_predictions
  
      check = (
          ConfusionMatrixReport()
          .add_condition_misclassified_samples_lower_than_condition(
              misclassified_samples_threshold=-0.1
          )
          .add_condition_misclassified_samples_lower_than_condition(
              misclassified_samples_threshold=1.1
          )
      )
  
      # Act
      result = check.run(test_ds, predictions=test_preds)
  
      # Assert
      assert_that(
          result.conditions_results[0],
          equal_condition_result(
              is_pass=False,
              name=f"Misclassified cell size lower than {format_number(-0.1 * 100)}% of the total samples",
              details="Exception in condition: DeepchecksValueError: Condition requires the parameter "
              '"misclassified_samples_threshold" to be between 0 and 1 inclusive but got -0.1',
              category=ConditionCategory.ERROR,
          ),
      )
  
      assert_that(
          result.conditions_results[1],
          equal_condition_result(
              is_pass=False,
              name=f"Misclassified cell size lower than {format_number(1.1 * 100)}% of the total samples",
              details="Exception in condition: DeepchecksValueError: Condition requires the parameter "
              '"misclassified_samples_threshold" to be between 0 and 1 inclusive but got 1.1',
              category=ConditionCategory.ERROR,
          ),
      )
  
file_path: deepchecks/tests/nlp/checks/model_evaluation/confusion_matrix_test.py
imports: import numpy
  from hamcrest import assert_that, close_to, equal_to
  from deepchecks.core.condition import ConditionCategory
  from deepchecks.nlp.checks.model_evaluation import ConfusionMatrixReport
  from deepchecks.utils.strings import format_number, format_percent
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 5 for calling a global function with example usage: test_condition_misclassified_samples_lower_than_raises_error(tweet_emotion_train_test_textdata, tweet_emotion_train_test_predictions) without return types