code: def test_tuner_cv_classification_correct():
      folds = 5
      dataset = get_iris_data()
  
      simple_pipeline = pipeline_simple()
      tuner = (
          TunerBuilder(dataset.task)
          .with_tuner(SimultaneousTuner)
          .with_metric(ClassificationMetricsEnum.ROCAUC)
          .with_cv_folds(folds)
          .with_iterations(1)
          .with_timeout(timedelta(minutes=1))
          .build(dataset)
      )
      tuned = tuner.tune(simple_pipeline)
      assert tuned
  
imports: import logging
  from datetime import timedelta
  import pytest
  from golem.core.tuning.simultaneous import SimultaneousTuner
  from fedot import Fedot
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.optimisers.objective import PipelineObjectiveEvaluate
  from fedot.core.optimisers.objective.data_source_splitter import DataSourceSplitter
  from fedot.core.optimisers.objective.metrics_objective import MetricsObjective
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
  from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
  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 fedot_project_root
  from test.integration.models.test_model import classification_dataset
  from test.unit.tasks.test_classification import get_iris_data, pipeline_simple
label_desc: Use image node_id 5 for calling a global function with example usage: test_tuner_cv_classification_correct() without return types