code: def test_classification_nan_labels(
      iris_labeled_dataset, iris_adaboost
  ):
      # Arrange
      check = TrainTestPerformance()
      data_with_nan = iris_labeled_dataset.data.copy()
      data_with_nan[iris_labeled_dataset.label_name].iloc[0] = float(
          "nan"
      )
      iris_labeled_dataset = Dataset(
          data_with_nan, label=iris_labeled_dataset.label_name
      )
      # Act
      result = check.run(
          iris_labeled_dataset, iris_labeled_dataset, iris_adaboost
      ).value
      # Assert
      assert_classification_result(result, iris_labeled_dataset)
  
imports: import re
  from typing import List
  import numpy
  from hamcrest import assert_that, calling, close_to, equal_to, greater_than, has_items, has_length, instance_of, raises
  from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
  from sklearn.metrics import jaccard_score, make_scorer
  from sklearn.model_selection import train_test_split
  from deepchecks.core import ConditionResult
  from deepchecks.core.errors import DatasetValidationError, DeepchecksNotSupportedError, DeepchecksValueError, ModelValidationError
  from deepchecks.tabular.checks.model_evaluation import TrainTestPerformance
  from deepchecks.tabular.dataset import Dataset
  from deepchecks.tabular.metric_utils.scorers import DEFAULT_REGRESSION_SCORERS, MULTICLASS_SCORERS_NON_AVERAGE
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 11 for calling a global function with example usage: test_classification_nan_labels(iris_labeled_dataset, iris_adaboost) without return types