code: def test_fit_reg_squared_multiple_outputs(mult_dense_train_data):
      mult_dense, mult_target = mult_dense_train_data
      reg = CDRegressor(
          C=0.05,
          random_state=0,
          penalty="l1/l2",
          loss="squared",
          max_iter=100,
      )
      lb = LabelBinarizer()
      Y = lb.fit_transform(mult_target)
      reg.fit(mult_dense, Y)
      y_pred = lb.inverse_transform(reg.predict(mult_dense))
      np.testing.assert_almost_equal(
          np.mean(y_pred == mult_target), 0.797, 3
      )
      np.testing.assert_almost_equal(
          reg.n_nonzero(percentage=True), 0.5
      )
  
imports: import numpy
  import pytest
  from sklearn.datasets import load_digits
  from sklearn.preprocessing import LabelBinarizer
  from lightning.impl.primal_cd import CDClassifier, CDRegressor
  from lightning.impl.tests.utils import check_predict_proba
label_desc: Use image node_id 31 for calling a global function with example usage: test_fit_reg_squared_multiple_outputs(mult_dense_train_data) without return types