code: def calculate_validation_metric(
      predicted: OutputData, dataset_to_validate: InputData
  ) -> float:
      # the quality assessment for the simulation results
      roc_auc_value = roc_auc(
          y_true=dataset_to_validate.target,
          y_score=predicted.predict,
          multi_class="ovo",
      )
      return roc_auc_value
  
file_path: FEDOT/examples/simple/classification/image_classification_problem.py
imports: from golem.utilities.requirements_notificator import warn_requirement
  from sklearn.metrics import roc_auc_score
  from examples.simple.classification.classification_pipelines import cnn_composite_pipeline
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import set_random_seed
label_desc: Use image node_id 1 for calling a global function with example usage: calculate_validation_metric(predicted, dataset_to_validate) and returns: roc_auc_value