code: def test_plot_daily_seasonality(plotting_backend):
      log.info(
          f"testing: Plotting with daily seasonality with {plotting_backend}"
      )
      df = pd.read_csv(YOS_FILE, nrows=NROWS)
      m = NeuralProphet(
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
          changepoints_range=0.95,
          n_changepoints=50,
          trend_reg=1,
          weekly_seasonality=False,
          daily_seasonality=10,
      )
  
      m.fit(df, freq="5min")
      future = m.make_future_dataframe(
          df, periods=60 // 5 * 24 * 7, n_historic_predictions=True
      )
      forecast = m.predict(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)
  
      if PLOT:
          fig1.show()
          fig2.show()
          fig3.show()
  
imports: import logging
  import os
  import pathlib
  import pandas
  import pytest
  from neuralprophet import NeuralProphet
label_desc: Use image node_id 10 for calling a global function with example usage: test_plot_daily_seasonality(plotting_backend) without return types