code: def create_four_depth_pipeline():
      knn_node = PipelineNode("knn")
      lda_node = PipelineNode("lda")
      rf_node = PipelineNode("rf")
      logit_node = PipelineNode("logit")
  
      logit_node_second = PipelineNode(
          "logit", nodes_from=[knn_node, lda_node]
      )
      rf_node_second = PipelineNode("rf", nodes_from=[logit_node])
  
      qda_node_third = PipelineNode("qda", nodes_from=[rf_node_second])
      knn_node_third = PipelineNode(
          "knn", nodes_from=[logit_node_second, rf_node]
      )
  
      knn_root = PipelineNode(
          "knn", nodes_from=[qda_node_third, knn_node_third]
      )
  
      pipeline = Pipeline(knn_root)
  
      return pipeline
  
imports: import json
  import os
  import shutil
  import numpy
  import pytest
  from sklearn.metrics import mean_absolute_error
  from fedot import Fedot
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.template import PipelineTemplate, extract_subtree_root
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import fedot_project_root
  from test.integration.api.test_main_api import get_dataset
  from test.unit.data_operations.test_data_operations_implementations import get_mixed_data
  from test.unit.multimodal.data_generators import get_single_task_multimodal_tabular_data
  from test.unit.pipelines.test_decompose_pipelines import get_classification_data
  from test.unit.preprocessing.test_preprocessing_through_api import data_with_spaces_and_nans_in_features
  from test.unit.tasks.test_forecasting import get_multiscale_pipeline, get_simple_ts_pipeline, get_ts_data
label_desc: Use image node_id 8 for calling a global function with example usage: create_four_depth_pipeline() and returns: pipeline