code: def test_elasticnet_regression_learner():
      df_train = pd.DataFrame(
          {
              "id": ["id1", "id2", "id3", "id4"],
              "x1": [10.0, 13.0, 10.0, 13.0],
              "x2": [0, 1, 1, 0],
              "w": [2, 1, 2, 0.5],
              "y": [2.3, 4.0, 100.0, -3.9],
          }
      )
  
      df_test = pd.DataFrame(
          {
              "id": ["id4", "id4", "id5", "id6"],
              "x1": [12.0, 1000.0, -4.0, 0.0],
              "x2": [1, 1, 0, 1],
              "w": [1, 2, 0, 0.5],
              "y": [1.3, -4.0, 0.0, 49],
          }
      )
  
      learner = elasticnet_regression_learner(
          features=["x1", "x2"],
          target="y",
          params=None,
          prediction_column="prediction",
          weight_column="w",
      )
  
      predict_fn, pred_train, log = learner(df_train)
  
      pred_test = predict_fn(df_test)
  
      expected_col_train = df_train.columns.tolist() + ["prediction"]
      expected_col_test = df_test.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()
      assert "prediction" in pred_test.columns
  
file_path: fklearn/tests/training/test_regression.py
imports: from collections import Counter
  import numpy
  import pandas
  from fklearn.training.regression import linear_regression_learner, gp_regression_learner, xgb_regression_learner, lgbm_regression_learner, catboost_regressor_learner, custom_supervised_model_learner, elasticnet_regression_learner
label_desc: Use image node_id 1 for calling a global function with example usage: test_elasticnet_regression_learner() without return types