code: def test_plot_conformal_prediction_asymmetric(plotting_backend):
      log.info(
          f"testing: Plotting with asymmetric 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 = (0.03, 0.07)
      method = "cqr"
      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()
  
imports: import logging
  import os
  import pathlib
  import pandas
  import pytest
  from neuralprophet import NeuralProphet
label_desc: Use image node_id 16 for calling a global function with example usage: test_plot_conformal_prediction_asymmetric(plotting_backend) without return types