code: def test_isotonic_calibration_learner():
      df_train = pd.DataFrame(
          {
              "id": ["id1", "id2", "id3", "id4"],
              "pred": [0.25, 0.64, 0.12, 0.9],
              "y": [0, 1, 0, 1],
          }
      )
  
      df_test = pd.DataFrame(
          {
              "id": ["id4", "id4", "id5", "id6", "id7"],
              "pred": [0.55, 0.12, 0.13, 0.9, 0.95],
              "y": [1, 0, 0, 1, 1],
          }
      )
  
      learner = isotonic_calibration_learner(
          prediction_column="pred", target_column="y"
      )
  
      predict_fn, pred_train, log = learner(df_train)
  
      pred_test = predict_fn(df_test)
  
      assert "calibrated_prediction" in pred_train.columns.values
      assert "calibrated_prediction" in pred_test.columns.values
      assert pred_test.calibrated_prediction.max() <= 1
      assert pred_test.calibrated_prediction.min() >= 0
      assert pred_test.calibrated_prediction.isnull().max() == 0
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_isotonic_calibration_learner() without return types