code: def test_transformer_pipe():
      s = pd.Series(
          [0, 1, 2, 3, 2, 1] * 10,
          index=pd.date_range(start="2017-1-1", periods=60, freq="D"),
      )
      my_pipe = Pipenet(
          {
              "deseasonal_residual": {
                  "model": transformer.ClassicSeasonalDecomposition(
                      freq=6
                  ),
                  "input": "original",
              },
              "abs_residual": {
                  "model": transformer.CustomizedTransformer1D(
                      transform_func=abs
                  ),
                  "input": "deseasonal_residual",
              },
          }
      )
      with pytest.raises(RuntimeError, match="`fit_transform`"):
          my_pipe.fit_detect(s)
      my_pipe.fit(s)
      my_pipe.transform(s)
      my_pipe.fit_transform(s)
  
      my_pipe = Pipeline(
          [
              (
                  "deseasonal_residual",
                  transformer.ClassicSeasonalDecomposition(freq=6),
              ),
              (
                  "abs_residual",
                  transformer.CustomizedTransformer1D(
                      transform_func=abs
                  ),
              ),
          ]
      )
      with pytest.raises(RuntimeError, match="`fit_transform`"):
          my_pipe.fit_detect(s)
      my_pipe.fit(s)
      my_pipe.transform(s)
      my_pipe.fit_transform(s)
  
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 7 for calling a global function with example usage: test_transformer_pipe() without return types