code: def test_multitask_pipeline_predict_correctly():
      """Test pipeline fit predict correctness for classification and regression task in one pipeline"""
      train_multimodal, test_multimodal = prepare_multitask_data()
  
      logit_node = PipelineNode("logit")
      data_source_node = PipelineNode("data_source_table/regression")
      final_node = PipelineNode(
          "rfr", nodes_from=[logit_node, data_source_node]
      )
      final_node.parameters = {"n_estimators": 5}
      multitask_pipeline = Pipeline(final_node)
  
      multitask_pipeline.fit(train_multimodal)
      side_pipeline = multitask_pipeline.pipeline_for_side_task(
          task_type=TaskTypesEnum.classification
      )
  
      # Replace the name of main "data source" in preprocessor
      side_pipeline.preprocessor.main_target_source_name = "logit"
      side_predict = np.ravel(
          side_pipeline.predict(
              test_multimodal, output_mode="labels"
          ).predict
      )
      main_predict = multitask_pipeline.predict(test_multimodal).predict
  
      assert np.array_equal(
          side_predict,
          np.array(
              ["a_category", "a_category", "b_category", "b_category"]
          ),
      )
      # Two source features and predicted class label as third
      assert (
          multitask_pipeline.root_node.fitted_operation.n_features_in_
          == 3
      )
  
imports: import numpy
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.tasks import TaskTypesEnum
  from examples.advanced.multitask_classification_regression import prepare_multitask_data
label_desc: Use image node_id 1 for calling a global function with example usage: test_multitask_pipeline_predict_correctly() without return types