code: def test_uncertainty_estimation_multiple_quantiles():
      log.info("testing: Uncertainty Estimation Air Travel")
      df = pd.read_csv(AIR_FILE)
      multi_quantiles = [
          [0.5],  # forecast shows only yhat1, no duplicate yhat1 50.0%
          [0.8],  # forecast yhat1 and yhat1 80.0%
          [0.3, 0.6, 0.9],
          [0.05, 0.25, 0.75, 0.95],
      ]
      for quantiles in multi_quantiles:
          m = NeuralProphet(
              seasonality_mode="multiplicative",
              loss_func="MSE",
              quantiles=quantiles,
              epochs=EPOCHS,
              batch_size=BATCH_SIZE,
              learning_rate=LR,
          )
          m.fit(df, freq="MS")
          future = m.make_future_dataframe(
              df, periods=50, n_historic_predictions=len(df)
          )
          m.predict(future)
  
file_path: neural_prophet/tests/test_uncertainty.py
imports: import logging
  import os
  import pathlib
  import pandas
  import pytest
  from neuralprophet import NeuralProphet, uncertainty_evaluate
label_desc: Use image node_id 4 for calling a global function with example usage: test_uncertainty_estimation_multiple_quantiles() without return types