code: def classification_rf_complex_pipeline():
      """
      Returns pipeline with the following structure:
  
      .. image:: img_classification_pipelines/complex_rf_pipeline.png
        :width: 55%
  
      Where lda - discriminant analysis, logit - logistic regression, rf - random forest classifier,
      knn - K nearest neighbors classifier
      """
      pipeline = Pipeline()
  
      root_of_tree, root_child_first, root_child_second = [
          PipelineNode(model) for model in ("rf", "rf", "knn")
      ]
  
      for root_node_child in (root_child_first, root_child_second):
          for requirement_model in ("logit", "lda"):
              new_node = PipelineNode(requirement_model)
              root_node_child.nodes_from.append(new_node)
              pipeline.add_node(new_node)
          pipeline.add_node(root_node_child)
          root_of_tree.nodes_from.append(root_node_child)
  
      pipeline.add_node(root_of_tree)
      return pipeline
  
file_path: FEDOT/examples/simple/classification/classification_pipelines.py
imports: from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
label_desc: Use image node_id 9 for calling a global function with example usage: classification_rf_complex_pipeline() and returns: pipeline