code: def test_area_under_the_cumulative_gain_curve():
      df = pd.DataFrame(
          dict(
              t=[1, 1, 1, 2, 2, 2, 3, 3, 3],
              x=[1, 2, 3, 1, 2, 3, 1, 2, 3],
              y=[1, 1, 1, 2, 3, 4, 3, 5, 7],
          )
      )
  
      result = area_under_the_cumulative_gain_curve(
          df,
          prediction="x",
          outcome="y",
          treatment="t",
          min_rows=3,
          steps=df.shape[0],
          effect_fn=linear_effect,
      )
      assert round(result, 3) == 1.529
  
imports: import pandas
  from fklearn.causal.effects import linear_effect
  from fklearn.causal.validation.auc import area_under_the_cumulative_gain_curve, area_under_the_cumulative_effect_curve, area_under_the_relative_cumulative_gain_curve
label_desc: Use image node_id 2 for calling a global function with example usage: test_area_under_the_cumulative_gain_curve() without return types