code: def test_regression_pipeline_with_data_operation_fit_predict_correct():
      data = get_synthetic_regression_data()
      train_data, test_data = train_test_data_setup(data)
  
      #           linear
      #       /           \
      #     ridge          |
      #       |            |
      # ransac_lin_reg   lasso
      #        \         /
      #          scaling
      node_scaling = PipelineNode("scaling")
      node_ransac = PipelineNode(
          "ransac_lin_reg", nodes_from=[node_scaling]
      )
      node_lasso = PipelineNode("lasso", nodes_from=[node_scaling])
      node_ridge = PipelineNode("ridge", nodes_from=[node_ransac])
      node_root = PipelineNode(
          "linear", nodes_from=[node_lasso, node_ridge]
      )
      pipeline = Pipeline(node_root)
  
      pipeline.fit(train_data)
      results = pipeline.predict(test_data)
  
      assert results.predict.shape == test_data.target.shape
      assert check_predict_correct(pipeline, train_data)
  
imports: import numpy
  import pytest
  from sklearn.datasets import make_regression
  from sklearn.metrics import mean_squared_error
  from fedot import Fedot
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  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 test.unit.common_tests import is_predict_ignores_target
  from test.unit.composer.test_metrics import data_setup
label_desc: Use image node_id 7 for calling a global function with example usage: test_regression_pipeline_with_data_operation_fit_predict_correct() without return types