code: def test_linear_regression_sparse_multiple_outcome(
      global_random_seed, coo_container
  ):
      # Test multiple-outcome linear regressions with sparse data
      rng = np.random.RandomState(global_random_seed)
      X, y = make_sparse_uncorrelated(random_state=rng)
      X = coo_container(X)
      Y = np.vstack((y, y)).T
      n_features = X.shape[1]
  
      ols = LinearRegression()
      ols.fit(X, Y)
      assert ols.coef_.shape == (2, n_features)
      Y_pred = ols.predict(X)
      ols.fit(X, y.ravel())
      y_pred = ols.predict(X)
      assert_array_almost_equal(
          np.vstack((y_pred, y_pred)).T, Y_pred, decimal=3
      )
  
imports: import warnings
  import numpy
  import pytest
  from scipy import linalg, sparse
  from sklearn.datasets import load_iris, make_regression, make_sparse_uncorrelated
  from sklearn.linear_model import LinearRegression
  from sklearn.linear_model._base import _preprocess_data, _rescale_data, make_dataset
  from sklearn.preprocessing import add_dummy_feature
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS, LIL_CONTAINERS
label_desc: Use image node_id 9 for calling a global function with example usage: test_linear_regression_sparse_multiple_outcome(global_random_seed, coo_container) without return types