code: def test_plot_seasonality(plotting_backend):
      log.info(
          f"testing: Plotting with additive seasonality with {plotting_backend}"
      )
      df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          yearly_seasonality=8,
          weekly_seasonality=4,
          daily_seasonality=30,
          seasonality_mode="additive",
          seasonality_reg=1,
      )
      m.fit(df, freq="D")
      future = m.make_future_dataframe(
          df, n_historic_predictions=365, periods=365
      )
      forecast = m.predict(df=future)
  
      fig1 = m.plot_components(
          forecast, plotting_backend=plotting_backend
      )
      fig2 = m.plot(forecast, plotting_backend=plotting_backend)
      fig3 = m.plot_parameters(plotting_backend=plotting_backend)
  
      log.info(
          f"testing: Plotting with additive custom seasonality with {plotting_backend}"
      )
      other_seasons = False
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          yearly_seasonality=other_seasons,
          weekly_seasonality=other_seasons,
          daily_seasonality=other_seasons,
          seasonality_mode="additive",
          seasonality_reg=1,
      )
      m = m.add_seasonality(
          name="quarterly", period=90, fourier_order=5
      )
      m.fit(df, freq="D")
      future = m.make_future_dataframe(
          df, n_historic_predictions=365, periods=365
      )
      forecast = m.predict(df=future)
  
      fig4 = m.plot_parameters(plotting_backend=plotting_backend)
  
      log.info("testing: Seasonality Plotting with Business Day freq")
      m = NeuralProphet(
          yearly_seasonality=8,
          weekly_seasonality=4,
          daily_seasonality=30,
          seasonality_mode="additive",
          seasonality_reg=1,
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
      df["ds"] = pd.to_datetime(df["ds"])
      # create a range of business days over that period
      bdays = pd.bdate_range(start=df["ds"].min(), end=df["ds"].max())
      # Filter the series to just those days contained in the business day range.
      df = df[df["ds"].isin(bdays)]
      m.fit(df, freq="B")
      forecast = m.predict(df)
      fig5 = m.plot_components(
          forecast, plotting_backend=plotting_backend
      )
      fig6 = m.plot_parameters(plotting_backend=plotting_backend)
  
      if PLOT:
          fig1.show()
          fig2.show()
          fig3.show()
          fig4.show()
          fig5.show()
          fig6.show()
  
imports: import logging
  import os
  import pathlib
  import pandas
  import pytest
  from neuralprophet import NeuralProphet
label_desc: Use image node_id 9 for calling a global function with example usage: test_plot_seasonality(plotting_backend) without return types