code: def test_composer_with_cv_optimization_correct():
      task = Task(task_type=TaskTypesEnum.classification)
      dataset_to_compose, dataset_to_validate = train_test_data_setup(
          get_classification_data()
      )
  
      models_repo = OperationTypesRepository()
      available_model_types = models_repo.suitable_operation(
          task_type=task.task_type, tags=["linear"]
      )
  
      metric_function = [
          ClassificationMetricsEnum.ROCAUC_penalty,
          ClassificationMetricsEnum.accuracy,
          ClassificationMetricsEnum.logloss,
      ]
  
      composer_requirements = PipelineComposerRequirements(
          primary=available_model_types,
          secondary=available_model_types,
          timeout=timedelta(minutes=0.2),
          num_of_generations=2,
          cv_folds=5,
          show_progress=False,
      )
  
      builder = (
          ComposerBuilder(task)
          .with_requirements(composer_requirements)
          .with_metrics(metric_function)
      )
      composer = builder.build()
  
      pipeline_evo_composed = composer.compose_pipeline(
          data=dataset_to_compose
      )[0]
  
      assert isinstance(pipeline_evo_composed, Pipeline)
  
      pipeline_evo_composed.fit(input_data=dataset_to_compose)
      predicted = pipeline_evo_composed.predict(dataset_to_validate)
      roc_on_valid_evo_composed = roc_auc(
          y_score=predicted.predict, y_true=dataset_to_validate.target
      )
  
      assert roc_on_valid_evo_composed > 0
  
file_path: FEDOT/test/integration/validation/test_table_cv.py
imports: from datetime import timedelta
  from sklearn.metrics import roc_auc_score
  from fedot.core.composer.composer_builder import ComposerBuilder
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
  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 test.unit.validation.test_table_cv import get_classification_data
label_desc: Use image node_id 1 for calling a global function with example usage: test_composer_with_cv_optimization_correct() without return types