code: def test_sklearn_multioutput_regressor(self):
      for n_targets in [2, 3, 4]:
          for model_class in [
              DecisionTreeRegressor,
              ExtraTreesRegressor,
              RandomForestRegressor,
              LinearRegression,
          ]:
              seed = random.randint(0, 2**32 - 1)
              if model_class != LinearRegression:
                  model = MultiOutputRegressor(
                      model_class(random_state=seed)
                  )
              else:
                  model = MultiOutputRegressor(model_class())
              X, y = datasets.make_regression(
                  n_samples=50,
                  n_features=10,
                  n_informative=5,
                  n_targets=n_targets,
                  random_state=seed,
              )
              X = X.astype("float32")
              y = y.astype("float32")
              model.fit(X, y)
  
              torch_model = hummingbird.ml.convert(
                  model,
                  "torch",
                  extra_config={
                      constants.TREE_OP_PRECISION_DTYPE: "float64"
                  },
              )
              self.assertTrue(torch_model is not None)
              np.testing.assert_allclose(
                  model.predict(X),
                  torch_model.predict(X),
                  rtol=1e-5,
                  atol=1e-4,
                  err_msg="{}/{}/{}".format(
                      n_targets, model_class, seed
                  ),
              )
  
file_path: hummingbird/tests/test_sklearn_multioutput_regression.py
imports: import unittest
  import warnings
  import sys
  import numpy
  import torch
  import sklearn
  from sklearn import datasets
  from sklearn.ensemble import ExtraTreesClassifier, ExtraTreesRegressor, RandomForestClassifier, RandomForestRegressor
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.linear_model import LinearRegression
  from sklearn.svm import LinearSVR
  from sklearn.multioutput import MultiOutputRegressor, RegressorChain
  import hummingbird.ml
  from hummingbird.ml import constants
  import random
label_desc: Use image node_id 1 for calling the TestSklearnMultioutputRegressor obj's underlying member method code with example usage: obj.test_sklearn_multioutput_regressor() without return types