code: def validate_model_quality(model: Pipeline, data_path: str):
      dataset_to_validate = InputData.from_csv(data_path)
      predicted_labels = model.predict(dataset_to_validate).predict
  
      roc_auc_valid = round(
          roc_auc(
              y_true=test_data.target,
              y_score=predicted_labels,
              multi_class="ovo",
              average="macro",
          ),
          3,
      )
      return roc_auc_valid
  
file_path: FEDOT/examples/simple/classification/multiclass_prediction.py
imports: import datetime
  import os
  import pandas
  from golem.utilities.requirements_notificator import warn_requirement
  from datetime import timedelta
  from fedot.core.composer.composer_builder import ComposerBuilder
  from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
  from fedot.core.data.data import InputData
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.operation_types_repository import OperationTypesRepository
  from fedot.core.repository.metrics_repository import ClassificationMetricsEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import ensure_directory_exists, fedot_project_root, get_split_data_paths, probs_to_labels, save_file_to_csv, split_data
  from sklearn.metrics import roc_auc_score
  from fedot.core.utils import set_random_seed
  from pathlib import Path
label_desc: Use image node_id 4 for calling a global function with example usage: validate_model_quality(model, data_path) and returns: roc_auc_valid