code: def test_plot_conformal_prediction(plotting_backend):
      log.info(
          f"testing: Plotting with conformal prediction with {plotting_backend}"
      )
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      # Without auto-regression enabled
      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
          )
          m.highlight_nth_step_ahead_of_each_forecast(m.n_forecasts)
          fig0 = m.plot(forecast, plotting_backend=plotting_backend)
          fig1 = m.plot_components(
              forecast, plotting_backend=plotting_backend
          )
          fig2 = m.plot_parameters(plotting_backend=plotting_backend)
          if PLOT:
              fig0.show()
              fig1.show()
              fig2.show()
      # With auto-regression enabled
      m = NeuralProphet(
          n_forecasts=7,
          n_lags=14,
          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(
              df, periods=m.n_forecasts, n_historic_predictions=10
          )
          forecast = m.conformal_predict(
              future, calibration_df=cal_df, alpha=alpha, method=method
          )
          m.highlight_nth_step_ahead_of_each_forecast(m.n_forecasts)
          fig0 = m.plot(forecast)
          fig1 = m.plot_latest_forecast(
              forecast,
              include_previous_forecasts=10,
              plotting_backend=plotting_backend,
          )
          fig2 = m.plot_latest_forecast(
              forecast,
              include_previous_forecasts=10,
              plot_history_data=True,
              plotting_backend=plotting_backend,
          )
          fig3 = m.plot_latest_forecast(
              forecast,
              include_previous_forecasts=10,
              plot_history_data=False,
              plotting_backend=plotting_backend,
          )
          fig4 = m.plot_components(
              forecast, plotting_backend=plotting_backend
          )
          fig5 = m.plot_parameters(plotting_backend=plotting_backend)
          if PLOT:
              fig0.show()
              fig1.show()
              fig2.show()
              fig3.show()
              fig4.show()
              fig5.show()
  
imports: import logging
  import os
  import pathlib
  import pandas
  import pytest
  from neuralprophet import NeuralProphet
label_desc: Use image node_id 14 for calling a global function with example usage: test_plot_conformal_prediction(plotting_backend) without return types