code: def test_pipeline_for_side_task_predict():
      """Checks whether the pipeline for the side task gives correct predictions"""
  
      pipeline = generate_pipeline_with_decomposition(
          "scaling", "logit"
      )
  
      train_data, test_data = data_with_complicated_types()
      pipeline.fit_from_scratch(train_data)
      predicted_labels = pipeline.predict(test_data)
      preds = predicted_labels.predict
  
      reg_pipeline = pipeline.pipeline_for_side_task(
          task_type=TaskTypesEnum.regression
      )
      reg_predicted_labels = reg_pipeline.predict(test_data)
      reg_preds = reg_predicted_labels.predict
  
      assert reg_predicted_labels is not None
      assert not (preds == reg_preds).all()
  
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 6 for calling a global function with example usage: test_pipeline_for_side_task_predict() without return types