code: def test_anomaly_learner():
      df_train_binary = pd.DataFrame(
          {
              "id": ["id1", "id2", "id3", "id4"],
              "x1": [10.0, 13.0, 100.0, 13.0],
              "x2": [0, 1, 100, 0],
              "w": [2, 1, 2, 0.5],
              "y": [0, 1, 0, 1],
          }
      )
  
      df_test_binary = pd.DataFrame(
          {
              "id": ["id4", "id4", "id5", "id6"],
              "x1": [1200.0, 19000.0, -400.0, 0.0],
              "x2": [1, 101111, 111110, 1],
              "w": [1, 2, 0, 0.5],
              "y": [1, 0, 0, 1],
          }
      )
  
      # Standard Behavior
      predict_fn, pred_train, log = isolation_forest_learner(
          df_train_binary, features=["x1", "x2"]
      )
  
      pred_test = predict_fn(df_test_binary)
  
      expected_col_train = df_train_binary.columns.tolist() + [
          "prediction"
      ]
      expected_col_test = df_test_binary.columns.tolist() + [
          "prediction"
      ]
  
      assert Counter(expected_col_train) == Counter(
          pred_train.columns.tolist()
      )
      assert Counter(expected_col_test) == Counter(
          pred_test.columns.tolist()
      )
      assert (pred_test.columns == pred_train.columns).all()
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_anomaly_learner() without return types