code: def test_correlation_feature_selection():
      train_set = pd.DataFrame(
          {
              "id": ["id1", "id2", "id3", "id3"],
              "date": pd.to_datetime(
                  [
                      "2016-01-01",
                      "2016-02-01",
                      "2016-03-01",
                      "2016-04-01",
                  ]
              ),
              "x": [0.2, 0.9, 0.3, 0.3],
              "y": [0.2, 0.91, 0.3, 0.3],
              "z": [0.4, 0.4, 0.4, 0.4001],
              "a": [4, 2, 3, 1],
              "b": [-4, -2.1, -3.1, -1.1],
              "target": [0, 1, 0, 1],
          }
      )
  
      features = ["x", "y", "z", "a", "b"]
  
      result = correlation_feature_selection(
          train_set, features, threshold=0.99
      )
  
      assert set(result["features_to_drop"]) == {"x", "a"}
      assert set(result["final_features"]) == {"b", "z", "y"}
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_correlation_feature_selection() without return types