code: def generate_pipeline() -> Pipeline:
      node_scaling = PipelineNode("scaling")
      node_lasso = PipelineNode("lasso", nodes_from=[node_scaling])
      node_ridge = PipelineNode("ridge", nodes_from=[node_scaling])
      node_root = PipelineNode(
          "linear", nodes_from=[node_lasso, node_ridge]
      )
      pipeline = Pipeline(node_root)
      return pipeline
  
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 3 for calling a global function with example usage: generate_pipeline() and returns: pipeline