code: def test_pipe_classification():
      # no context data, single time series
      X = [np.random.rand(1000, 10)]
      y = [5]
  
      pipe = Pype(
          [
              ("seg", SegmentX()),
              ("ftr", FeatureRep()),
              ("rf", RandomForestClassifier(n_estimators=10)),
          ]
      )
  
      classifier_test(pipe, X, y)
  
      # context data, single time seres
      Xt = [np.random.rand(1000, 10)]
      Xc = [np.random.rand(3)]
      X = TS_Data(Xt, Xc)
      y = [5]
      classifier_test(pipe, X, y)
  
      X = pd.DataFrame(Xc)
      X["ts_data"] = Xt
      X = TS_Data.from_df(X)
      classifier_test(pipe, X, y)
  
      # multiple time series
      Xt = [
          np.random.rand(1000, 10),
          np.random.rand(100, 10),
          np.random.rand(500, 10),
      ]
      Xc = np.random.rand(3, 3)
      X = TS_Data(Xt, Xc)
      y = [1, 2, 3]
      classifier_test(pipe, X, y)
  
      X = pd.DataFrame(Xc)
      X["ts_data"] = Xt
      X = TS_Data.from_df(X)
      classifier_test(pipe, X, y)
  
      # univariate data
      Xt = [
          np.random.rand(1000),
          np.random.rand(100),
          np.random.rand(500),
      ]
      Xc = np.random.rand(3)
      X = TS_Data(Xt, Xc)
      y = [1, 2, 3]
      classifier_test(pipe, X, y)
  
      X = pd.DataFrame(Xc)
      X["ts_data"] = Xt
      X = TS_Data.from_df(X)
      classifier_test(pipe, X, y)
  
file_path: seglearn/seglearn/tests/test_pipe.py
imports: import numpy
  import pandas
  import pytest
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import Ridge
  from sklearn.model_selection import cross_validate
  from sklearn.preprocessing import StandardScaler
  from seglearn.base import TS_Data
  from seglearn.pipe import Pype
  from seglearn.transform import FeatureRep, SegmentX, SegmentXY, SegmentXYForecast, PadTrunc
label_desc: Use image node_id 5 for calling a global function with example usage: test_pipe_classification() without return types