code: def test_pipeline_pca_rf(self):
      X, y = make_regression(
          n_samples=1000,
          n_features=8,
          n_informative=5,
          n_targets=1,
          random_state=0,
          shuffle=True,
      )
      pca = PCA(n_components=8, svd_solver="randomized", whiten=True)
      clf = make_pipeline(
          StandardScaler(),
          pca,
          RandomForestRegressor(
              n_estimators=10, max_depth=30, random_state=0
          ),
      )
      clf.fit(X, y)
  
      model = hummingbird.ml.convert(clf, "pytorch")
  
      prediction_sk = clf.predict(
          [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]
      )
  
      prediction_hb = model.predict(
          [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]
      )
  
      np.testing.assert_allclose(
          prediction_sk, prediction_hb, rtol=1e-06, atol=1e-06
      )
  
imports: import unittest
  import numpy
  from sklearn import datasets
  from sklearn.compose import ColumnTransformer
  from sklearn.datasets import load_iris, load_diabetes
  from sklearn.svm import LinearSVC, LinearSVR
  from sklearn.datasets import make_regression
  from sklearn.decomposition import PCA
  from sklearn.linear_model import LogisticRegression, RidgeCV
  from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline, FeatureUnion, make_pipeline
  from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler
  import hummingbird.ml
  from hummingbird.ml._utils import pandas_installed, onnx_runtime_installed
  from hummingbird.ml import constants
  from onnxconverter_common.data_types import FloatTensorType, Int64TensorType, StringTensorType
label_desc: Use image node_id 17 for calling the TestSklearnPipeline obj's underlying member method code with example usage: obj.test_pipeline_pca_rf() without return types