code: def test_l1l2_multi_task_squared_hinge_loss(mult_dense_train_data):
      mult_dense, mult_target = mult_dense_train_data
      Y = LabelBinarizer(neg_label=-1).fit_transform(mult_target)
      clf = CDClassifier(
          penalty="l1/l2",
          loss="squared_hinge",
          multiclass=False,
          max_iter=20,
          C=5.0,
          random_state=0,
      )
      clf.fit(mult_dense, mult_target)
      df = clf.decision_function(mult_dense)
      np.testing.assert_array_almost_equal(clf.errors_.T, 1 - Y * df)
      np.testing.assert_almost_equal(
          clf.score(mult_dense, mult_target), 0.8633, 3
      )
      nz = np.sum(clf.coef_ != 0)
      assert nz == 300
  
      clf = CDClassifier(
          penalty="l1/l2",
          loss="squared_hinge",
          multiclass=False,
          max_iter=20,
          C=0.05,
          random_state=0,
      )
      clf.fit(mult_dense, mult_target)
      np.testing.assert_almost_equal(
          clf.score(mult_dense, mult_target), 0.8266, 3
      )
      nz = np.sum(clf.coef_ != 0)
      assert nz == 231
  
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 25 for calling a global function with example usage: test_l1l2_multi_task_squared_hinge_loss(mult_dense_train_data) without return types