code: def test_linear_regression():
      dataset = load_diabetes()
      X, y = dataset.data, dataset.target
      feature_names = dataset.feature_names
  
      sk_lr = SKLinear()
      our_lr = LinearRegression(feature_names=feature_names)
  
      sk_lr.fit(X, y)
      our_lr.fit(X, y)
  
      sk_pred = sk_lr.predict(X)
      our_pred = our_lr.predict(X)
      assert np.allclose(sk_pred, our_pred)
  
      # With labels
      local_expl = our_lr.explain_local(X, y)
      local_viz = local_expl.visualize(0)
      assert local_viz is not None
  
      # Without labels
      local_expl = our_lr.explain_local(X)
      local_viz = local_expl.visualize(0)
      assert local_viz is not None
  
      global_expl = our_lr.explain_global()
      global_viz = global_expl.visualize()
      assert global_viz is not None
  
file_path: interpret/python/interpret-core/tests/glassbox/test_linear.py
imports: from interpret.glassbox import LogisticRegression, LinearRegression
  from sklearn.datasets import load_breast_cancer, load_diabetes
  from sklearn.linear_model import LogisticRegression
  from sklearn.linear_model import LinearRegression
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: test_linear_regression() without return types