code: def test_multioutput_linear_regression(self):
      for n_targets in [1, 2, 7]:
          model = LinearRegression()
          X, y = datasets.make_regression(
              n_samples=100,
              n_features=10,
              n_informative=5,
              n_targets=n_targets,
              random_state=2021,
          )
          model.fit(X, y)
  
          torch_model = hummingbird.ml.convert(model, "torch")
          self.assertTrue(torch_model is not None)
          np.testing.assert_allclose(
              model.predict(X),
              torch_model.predict(X),
              rtol=1e-5,
              atol=1e-5,
          )
  
imports: import unittest
  import warnings
  from packaging.version import Version, parse
  import numpy
  import torch
  from sklearn.linear_model import LinearRegression, LogisticRegression, SGDClassifier, LogisticRegressionCV, RidgeCV, Lasso, ElasticNet, Ridge, TweedieRegressor, PoissonRegressor, GammaRegressor
  from sklearn import datasets
  import hummingbird.ml
  from hummingbird.ml._utils import tvm_installed, pandas_installed
  from hummingbird.ml import constants
label_desc: Use image node_id 49 for calling the TestSklearnLinearClassifiers obj's underlying member method code with example usage: obj.test_multioutput_linear_regression() without return types