code: def test_condition_misclassified_samples_lower_than_passes(
      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
  
      threshold = 0.1
  
      check = ConfusionMatrixReport().add_condition_misclassified_samples_lower_than_condition(
          misclassified_samples_threshold=threshold
      )
  
      # Act
      result = check.run(test_ds, predictions=test_preds)
  
      # Assert
      assert_that(
          result.conditions_results[0],
          equal_condition_result(
              is_pass=True,
              name=f"Misclassified cell size lower than {format_percent(threshold)} of the total samples",
              details="All misclassified confusion matrix cells contain less than "
              f"{format_percent(threshold)} of the data.",
          ),
      )
  
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 6 for calling a global function with example usage: test_condition_misclassified_samples_lower_than_passes(tweet_emotion_train_test_textdata, tweet_emotion_train_test_predictions) without return types