code: def test_ts_forecasting_decomposition():
      """The function checks whether, after the decompose operation, the pipeline
      actually models the original target (not decomposed) for the time series
      forecasting task
      """
      # Generate synthetic data for time series forecasting
      train_data, _ = get_ts_data(forecast_length=5)
      # Distort original values
      train_data.features = train_data.features + 150
      train_data.target = train_data.target + 150
  
      _, pipeline_decompose_finish, pipeline = get_refinement_pipeline(
          lagged=10
      )
  
      pipeline.fit(train_data)
      pipeline_decompose_finish.fit(train_data)
  
      full_output = pipeline.predict(train_data)
      decompose_output = pipeline_decompose_finish.predict(train_data)
  
      full_level = np.mean(full_output.predict)
      decompose_level = np.mean(decompose_output.predict)
  
      assert full_level > (decompose_level + 100)
  
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 11 for calling a global function with example usage: test_ts_forecasting_decomposition() without return types