code: def test_linear_regression_multiple_outcome(setup, random_state=0):
      # Test multiple-outcome linear regressions
      X, y = make_regression(random_state=random_state)
  
      Y = np.vstack((y, y)).T
      n_features = X.shape[1]
  
      reg = LinearRegression()
      reg.fit((X), Y)
      assert reg.coef_.shape == (2, n_features)
      Y_pred = reg.predict(X)
      reg.fit(X, y)
      y_pred = reg.predict(X)
      assert_array_almost_equal(
          np.vstack((y_pred, y_pred)).T, Y_pred, decimal=3
      )
  
imports: import re
  import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal, assert_array_equal, assert_almost_equal, assert_allclose
  from scipy import sparse
  from scipy import linalg
  from sklearn.datasets import make_sparse_uncorrelated, make_regression, load_iris
  from sklearn.linear_model import LinearRegression
  from sklearn.linear_model._base import make_dataset
  from sklearn.utils import check_random_state
  from ..None import LinearRegression
  from .._base import _preprocess_data, _rescale_data
label_desc: Use image node_id 8 for calling a global function with example usage: test_linear_regression_multiple_outcome(setup, random_state) without return types