code: def test_plot_uncertainty(plotting_backend):
      log.info(
          f"testing: Plotting with uncertainty estimation with {plotting_backend}"
      )
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
  
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          quantiles=[0.25, 0.75],
      )
      m.fit(df, freq="D")
      future = m.make_future_dataframe(
          df, periods=30, n_historic_predictions=100
      )
      forecast = m.predict(future)
      fig1 = m.plot(forecast, plotting_backend=plotting_backend)
      fig2 = m.plot_components(
          forecast, plotting_backend=plotting_backend
      )
      fig3 = m.plot_parameters(
          quantile=0.75, plotting_backend=plotting_backend
      )
  
      log.info(
          f"testing: Plotting with uncertainty estimation for highlighted forecaste step with {plotting_backend}"
      )
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          quantiles=[0.25, 0.75],
          n_forecasts=7,
          n_lags=14,
      )
      m.fit(df, freq="D")
  
      m.highlight_nth_step_ahead_of_each_forecast(m.n_forecasts)
      future = m.make_future_dataframe(
          df, periods=30, n_historic_predictions=100
      )
      forecast = m.predict(future)
      fig4 = m.plot(forecast, plotting_backend=plotting_backend)
      fig5 = m.plot_latest_forecast(
          forecast,
          include_previous_forecasts=10,
          plotting_backend=plotting_backend,
      )
      fig6 = m.plot_components(
          forecast, plotting_backend=plotting_backend
      )
      fig7 = m.plot_parameters(
          quantile=0.75, plotting_backend=plotting_backend
      )
  
      log.info(
          f"Plot forecast parameters with wrong quantile with {plotting_backend} - Raise ValueError"
      )
      with pytest.raises(ValueError):
          m.plot_parameters(
              quantile=0.8, plotting_backend=plotting_backend
          )
      with pytest.raises(ValueError):
          m.plot_parameters(
              quantile=1.1, plotting_backend=plotting_backend
          )
  
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          quantiles=[0.25, 0.75],
          n_forecasts=3,
          n_lags=0,
      )
      m.fit(df, freq="D")
  
      m.highlight_nth_step_ahead_of_each_forecast(None)
      future = m.make_future_dataframe(
          df, periods=30, n_historic_predictions=100
      )
      forecast = m.predict(future)
      log.info(
          "Plot multi-steps ahead forecast without autoregression - Raise ValueError"
      )
      with pytest.raises(ValueError):
          m.plot(
              forecast,
              plotting_backend=plotting_backend,
              forecast_in_focus=4,
          )
          m.plot_components(
              forecast,
              plotting_backend=plotting_backend,
              forecast_in_focus=4,
          )
          m.plot_components(
              forecast,
              plotting_backend=plotting_backend,
              forecast_in_focus=None,
          )
          m.plot_parameters(
              quantile=0.75,
              plotting_backend=plotting_backend,
              forecast_in_focus=4,
          )
  
      if PLOT:
          fig1.show()
          fig2.show()
          fig3.show()
          fig4.show()
          fig5.show()
          fig6.show()
          fig7.show()
  
imports: import logging
  import os
  import pathlib
  import pandas
  import pytest
  from neuralprophet import NeuralProphet
label_desc: Use image node_id 13 for calling a global function with example usage: test_plot_uncertainty(plotting_backend) without return types