code: def run_refinement_scoring_example(
      train_path, test_path, with_tuning=False
  ):
      """Function launch example with error modeling for classification task
  
      :param train_path: path to the csv file with training sample
      :param test_path: path to the csv file with test sample
      :param with_tuning: is it need to tune pipelines or not
      """
  
      task = Task(TaskTypesEnum.classification)
      train_dataset = InputData.from_csv(train_path, task=task)
      test_dataset = InputData.from_csv(test_path, task=task)
  
      # Get and fit pipelines
      no_decompose_c = get_non_refinement_pipeline()
      decompose_c = get_refinement_pipeline()
  
      no_decompose_c.fit(train_dataset)
      decompose_c.fit(train_dataset)
  
      # Check metrics for both pipelines
      display_roc_auc(
          no_decompose_c, test_dataset, "Non decomposition pipeline"
      )
      display_roc_auc(
          decompose_c, test_dataset, "With decomposition pipeline"
      )
  
      if with_tuning:
          tuner = (
              TunerBuilder(task)
              .with_tuner(SimultaneousTuner)
              .with_metric(ClassificationMetricsEnum.ROCAUC)
              .with_iterations(30)
              .build(train_dataset)
          )
          no_decompose_c = tuner.tune(no_decompose_c)
          decompose_c = tuner.tune(decompose_c)
  
          no_decompose_c.fit(test_dataset)
          decompose_c.fit(test_dataset)
  
          display_roc_auc(
              no_decompose_c,
              test_dataset,
              "Non decomposition pipeline after tuning",
          )
          display_roc_auc(
              decompose_c,
              test_dataset,
              "With decomposition pipeline after tuning",
          )
  
file_path: FEDOT/examples/advanced/decompose/classification_refinement_example.py
imports: from golem.core.tuning.simultaneous import SimultaneousTuner
  from cases.credit_scoring.credit_scoring_problem import get_scoring_data, calculate_validation_metric
  from fedot.core.data.data import InputData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
  from fedot.core.repository.metrics_repository import ClassificationMetricsEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import set_random_seed
label_desc: Use image node_id 4 for calling a global function with example usage: run_refinement_scoring_example(train_path, test_path, with_tuning) without return types