code: def test_calculate_data_quantiles():
      """Test that we calculate quantiles correctly."""
      y = load_airline()
  
      series_names = ["s1", "s2", "s3"]
      fh = [1, 2]
      alpha = [0, 0.5, 1]
  
      index = pd.MultiIndex.from_product(
          [series_names, fh], names=["time_series", "time"]
      )
      df = pd.DataFrame(data=[1, 10, 2, 11, 3, 12], index=index)
  
      quantiles_column_index = pd.MultiIndex.from_product(
          [[y.name], alpha]
      )
  
      output_df = pd.DataFrame(
          data=[[1.0, 2.0, 3.0], [10.0, 11.0, 12.0]],
          columns=quantiles_column_index,
          index=pd.Index(data=fh, name="time"),
      )
  
      f = BaggingForecaster.create_test_instance()
      f.fit(y)
  
      calc_output = f._calculate_data_quantiles(df, alpha)
      pd.testing.assert_frame_equal(calc_output, output_df)
  
file_path: sktime/sktime/forecasting/compose/tests/test_bagging.py
imports: import pandas
  import pytest
  from sktime.datasets import load_airline
  from sktime.forecasting.compose import BaggingForecaster
  from sktime.forecasting.naive import NaiveForecaster
  from sktime.tests.test_switch import run_test_for_class
  from sktime.transformations.bootstrap import STLBootstrapTransformer
  from sktime.transformations.series.boxcox import LogTransformer
label_desc: Use image node_id 3 for calling a global function with example usage: test_calculate_data_quantiles() without return types