code: def test_advanced_conformal_prediction_plots():
      log.info(
          "testing: Plotting with quantile regression and conformal prediction intervals"
      )
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      m = NeuralProphet(
          n_forecasts=7,
          quantiles=[0.05, 0.95],
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      train_df, test_df = m.split_df(df, freq="D", valid_p=0.2)
      train_df, cal_df = m.split_df(train_df, freq="D", valid_p=0.15)
      m.fit(train_df, freq="D")
      alpha = 0.1
      for method in ["naive", "cqr"]:  # Naive and CQR SCP methods
          future = m.make_future_dataframe(
              test_df, periods=m.n_forecasts, n_historic_predictions=10
          )
          forecast = m.conformal_predict(
              future,
              calibration_df=cal_df,
              alpha=alpha,
              method=method,
              show_all_PI=True,
          )
          fig0 = m.conformal_plot(forecast)
          if PLOT:
              fig0.show()
  
imports: import logging
  import os
  import pathlib
  import pandas
  import pytest
  from neuralprophet import NeuralProphet
label_desc: Use image node_id 15 for calling a global function with example usage: test_advanced_conformal_prediction_plots() without return types