code: def test_pipe_summary():
      """
      Test summary
      """
  
      reg_ab = LinearRegression()
      reg_ac = LinearRegression()
      my_pipe = Pipenet(
          {
              "A-B-regression-ad": {
                  "model": detector.RegressionAD(
                      regressor=reg_ab, target="B"
                  ),
                  "input": "original",
                  "subset": ["A", "B"],
              },
              "A-C-regression-error": {
                  "model": transformer.RegressionResidual(
                      regressor=reg_ac, target="C"
                  ),
                  "input": "original",
                  "subset": ["A", "C"],
              },
              "A-C-regression-ad": {
                  "model": detector.InterQuartileRangeAD(),
                  "input": "A-C-regression-error",
                  "subset": "all",
              },
              "ABC-ad": {
                  "model": aggregator.OrAggregator(),
                  "input": ["A-B-regression-ad", "A-C-regression-ad"],
              },
              "D-ad": {
                  "model": detector.QuantileAD(high=0.9, low=0.1),
                  "input": "original",
                  "subset": ["D"],
              },
              "ABCD-ad": {
                  "model": aggregator.OrAggregator(),
                  "input": ["ABC-ad", "D-ad"],
              },
          }
      )
      my_pipe.summary()
  
file_path: adtk/tests/test_pipe.py
imports: import numpy
  import pandas
  import pytest
  from sklearn.linear_model import LinearRegression
  import adtk.aggregator
  import adtk.detector
  import adtk.transformer
  from adtk.pipe import Pipeline, Pipenet
label_desc: Use image node_id 9 for calling a global function with example usage: test_pipe_summary() without return types