code: def test_evaluate_alpha_positive(metric, sample_data):
      """Tests output when required quantile is present."""
      # 0.5 in test quantile data don't raise error.
  
      y_true, y_pred = sample_data
  
      Loss = metric.create_test_instance().set_params(
          alpha=0.5, score_average=False
      )
      res = Loss(y_true=y_true, y_pred=y_pred)
      assert len(res) == 1
  
      if all(
          x in y_pred.columns.get_level_values(1) for x in [0.5, 0.95]
      ):
          Loss = metric.create_test_instance().set_params(
              alpha=[0.5, 0.95], score_average=False
          )
          res = Loss(y_true=y_true, y_pred=y_pred)
          assert len(res) == 2
  
file_path: sktime/sktime/performance_metrics/forecasting/probabilistic/tests/test_probabilistic_metrics.py
imports: import warnings
  import numpy
  import pandas
  import pytest
  from sktime.performance_metrics.forecasting.probabilistic import ConstraintViolation, EmpiricalCoverage, PinballLoss
  from sktime.split import temporal_train_test_split
  from sktime.utils._testing.series import _make_series
label_desc: Use image node_id 6 for calling a global function with example usage: test_evaluate_alpha_positive(metric, sample_data) without return types