code: def test_make_regression_multitarget():
      X, y, c = make_regression(
          n_samples=100,
          n_features=10,
          n_informative=3,
          n_targets=3,
          coef=True,
          noise=1.0,
          random_state=0,
      )
      X, y, c = mt.ExecutableTuple((X, y, c)).execute().fetch()
  
      assert X.shape == (100, 10), "X shape mismatch"
      assert y.shape == (100, 3), "y shape mismatch"
      assert c.shape == (10, 3), "coef shape mismatch"
      np.testing.assert_array_equal(
          sum(c != 0.0), 3, "Unexpected number of informative features"
      )
  
      # Test that y ~= np.dot(X, c) + bias + N(0, 1.0)
      assert_almost_equal(np.std(y - np.dot(X, c)), 1.0, decimal=1)
  
file_path: mars/mars/learn/datasets/tests/test_samples_generator.py
imports: from functools import partial
  from collections import defaultdict
  import numpy
  from sklearn.utils._testing import assert_array_almost_equal, assert_raises, assert_almost_equal, assert_raise_message
  from ....None import tensor
  from ....tensor.linalg import svd
  from ..samples_generator import make_low_rank_matrix, make_classification, make_regression, make_blobs
label_desc: Use image node_id 4 for calling a global function with example usage: test_make_regression_multitarget() without return types