code: def test_classification_deepchecks_scorers(
      iris_split_dataset_and_model,
  ):
      # Arrange
      train, test, model = iris_split_dataset_and_model
      check = TrainTestPerformance(
          scorers=[
              "fpr_per_class",
              "fnr_per_class",
              "specificity_per_class",
              "fnr_macro",
              "roc_auc_per_class",
          ]
      )
      # Act
      check_result = check.run(train, test, model)
      check_value = check_result.value
  
      per_class_df = check_value[check_value["Metric"] == "roc_auc"]
      assert_that(
          per_class_df.loc[
              per_class_df.Dataset == "Train", "Class"
          ].values[1],
          equal_to(1.0),
      )
      assert_that(
          per_class_df.loc[
              per_class_df.Dataset == "Train", "Value"
          ].values[1],
          close_to(0.997, 0.001),
      )
  
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 24 for calling a global function with example usage: test_classification_deepchecks_scorers(iris_split_dataset_and_model) without return types