code: def test_cascade_classification_decomposition():
      # Generate synthetic dataset for multiclass classification task
      train_input, predict_input = get_classification_data(
          classes_amount=4
      )
  
      # Distort labels targets
      train_input.target = np.array(train_input.target) + 3
      predict_input.target = np.array(predict_input.target) + 3
  
      # Get pipeline
      pipeline = generate_cascade_decompose_pipeline()
      pipeline.fit(train_input)
      pipeline.predict(predict_input)
  
      is_pipeline_worked_correctly = True
      return is_pipeline_worked_correctly
  
imports: from itertools import product
  import numpy
  from examples.simple.classification.classification_with_tuning import get_classification_dataset
  from examples.advanced.decompose.refinement_forecast_example import get_refinement_pipeline
  from fedot.core.data.data import InputData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.preprocessing.preprocessing import DataPreprocessor
  from test.unit.tasks.test_classification import get_iris_data
  from test.unit.tasks.test_forecasting import get_ts_data
  from test.unit.preprocessing.test_preprocessors import data_with_complicated_types
label_desc: Use image node_id 10 for calling a global function with example usage: test_cascade_classification_decomposition() and returns: is_pipeline_worked_correctly